diff --git a/.circleci/config.yml b/.circleci/config.yml deleted file mode 100644 index e7ae4a3..0000000 --- a/.circleci/config.yml +++ /dev/null @@ -1,38 +0,0 @@ -version: 2 - -jobs: - build: - docker: - - image: circleci/python:3.10.0a7 - working_directory: ~/repo - steps: - - checkout - - run: - name: install dependencies - command: | - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh - chmod +x miniconda.sh && ./miniconda.sh -b -p ~/miniconda - export PATH="~/miniconda/bin:$PATH" - conda update --yes --quiet conda - conda create -n testenv --yes --quiet python=3 - source activate testenv - conda install --yes pip numpy scipy scikit-learn matplotlib sphinx sphinx_rtd_theme numpydoc pillow - pip install sphinx-gallery - pip install . - cd doc - make html - - store_artifacts: - path: doc/_build/html/ - destination: doc - - store_artifacts: - path: ~/log.txt - - run: ls -ltrh doc/_build/html - filters: - branches: - ignore: gh-pages - -workflows: - version: 2 - workflow: - jobs: - - build diff --git a/.env b/.env deleted file mode 100644 index 4e4e306..0000000 --- a/.env +++ /dev/null @@ -1,8 +0,0 @@ -score=accuracy -platform=iMac27 -n_folds=5 -stratified=1 -model=ODTE -source_data=Arff -seeds=[271, 314, 171] -discretize=1 diff --git a/.readthedocs.yml b/.readthedocs.yml deleted file mode 100644 index 228fc8a..0000000 --- a/.readthedocs.yml +++ /dev/null @@ -1,8 +0,0 @@ -formats: - - none -requirements_file: requirements.txt -python: - pip_install: true - extra_requirements: - - tests - - docs diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 91121d4..0000000 --- a/.travis.yml +++ /dev/null @@ -1,50 +0,0 @@ -dist: trusty -sudo: false - -language: python - -cache: - directories: - - $HOME/.cache/pip - -matrix: - include: - - env: PYTHON_VERSION="3.5" NUMPY_VERSION="1.13.1" SCIPY_VERSION="0.19.1" - SKLEARN_VERSION="0.19.1" - - env: PYTHON_VERSION="3.6" NUMPY_VERSION="1.13.1" SCIPY_VERSION="0.19.1" - SKLEARN_VERSION="0.20.3" - - env: PYTHON_VERSION="3.7" NUMPY_VERSION="*" SCIPY_VERSION="*" - SKLEARN_VERSION="*" - - env: PYTHON_VERSION="3.7" NUMPY_VERSION="*" SCIPY_VERSION="*" - SKLEARN_VERSION="nightly" - -install: - # install miniconda - - deactivate - - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh - - MINICONDA_PATH=/home/travis/miniconda - - chmod +x miniconda.sh && ./miniconda.sh -b -p $MINICONDA_PATH - - export PATH=$MINICONDA_PATH/bin:$PATH - - conda update --yes conda - # create the testing environment - - conda create -n testenv --yes python=$PYTHON_VERSION pip - - source activate testenv - - | - if [ $SKLEARN_VERSION = "nightly" ]; then - conda install --yes -c conda-forge numpy==$NUMPY_VERSION scipy==$SCIPY_VERSION - # install nightly wheels - pip install --pre -f https://sklearn-nightly.scdn8.secure.raxcdn.com scikit-learn - else - conda install --yes -c conda-forge numpy==$NUMPY_VERSION scipy==$SCIPY_VERSION scikit-learn==$SKLEARN_VERSION - fi - - pip install codecov cython nose pytest pytest-cov - - pip install . - -script: - - pytest -v doc/*rst - - mkdir for_test - - cd for_test - - pytest -v --cov=bayesclass --pyargs bayesclass - -after_success: - - codecov diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index f9bd145..0000000 --- a/MANIFEST.in +++ /dev/null @@ -1 +0,0 @@ -include requirements.txt diff --git a/Makefile b/Makefile index 8471acd..69c1059 100644 --- a/Makefile +++ b/Makefile @@ -20,7 +20,7 @@ push: ## Push code with tags git push && git push --tags test: ## Run tests - python -m doctest bayesclass/bayesclass.py + python -m doctest bayesclass.clfs.py pytest doc: ## Update documentation diff --git a/README.rst b/README.rst deleted file mode 100644 index 0c104e1..0000000 --- a/README.rst +++ /dev/null @@ -1,38 +0,0 @@ -.. -*- mode: rst -*- - -|Travis|_ |AppVeyor|_ |Codecov|_ |CircleCI|_ |ReadTheDocs|_ - -.. |Travis| image:: https://travis-ci.org/scikit-learn-contrib/project-template.svg?branch=master -.. _Travis: https://travis-ci.org/scikit-learn-contrib/project-template - -.. |AppVeyor| image:: https://ci.appveyor.com/api/projects/status/coy2qqaqr1rnnt5y/branch/master?svg=true -.. _AppVeyor: https://ci.appveyor.com/project/glemaitre/project-template - -.. |Codecov| image:: https://codecov.io/gh/scikit-learn-contrib/project-template/branch/master/graph/badge.svg -.. _Codecov: https://codecov.io/gh/scikit-learn-contrib/project-template - -.. |CircleCI| image:: https://circleci.com/gh/scikit-learn-contrib/project-template.svg?style=shield&circle-token=:circle-token -.. _CircleCI: https://circleci.com/gh/scikit-learn-contrib/project-template/tree/master - -.. |ReadTheDocs| image:: https://readthedocs.org/projects/sklearn-template/badge/?version=latest -.. _ReadTheDocs: https://sklearn-template.readthedocs.io/en/latest/?badge=latest - -project-template - A template for scikit-learn contributions -============================================================ - -.. _scikit-learn: https://scikit-learn.org - -**project-template** is a template project for scikit-learn_ compatible -extensions. - -It aids development of estimators that can be used in scikit-learn pipelines -and (hyper)parameter search, while facilitating testing (including some API -compliance), documentation, open source development, packaging, and continuous -integration. - -.. _documentation: https://sklearn-template.readthedocs.io/en/latest/quick_start.html - -Refer to the documentation_ to modify the template for your own scikit-learn -contribution. - -*Thank you for cleanly contributing to the scikit-learn ecosystem!* diff --git a/appveyor.yml b/appveyor.yml deleted file mode 100644 index cd43aaf..0000000 --- a/appveyor.yml +++ /dev/null @@ -1,37 +0,0 @@ -build: false - -environment: - matrix: - - PYTHON: "C:\\Miniconda3-x64" - PYTHON_VERSION: "3.5.x" - PYTHON_ARCH: "32" - NUMPY_VERSION: "1.13.1" - SCIPY_VERSION: "0.19.1" - SKLEARN_VERSION: "0.19.1" - - - PYTHON: "C:\\Miniconda3-x64" - PYTHON_VERSION: "3.6.x" - PYTHON_ARCH: "64" - NUMPY_VERSION: "*" - SCIPY_VERSION: "*" - SKLEARN_VERSION: "*" - -install: - # Prepend miniconda installed Python to the PATH of this build - # Add Library/bin directory to fix issue - # https://github.com/conda/conda/issues/1753 - - "SET PATH=%PYTHON%;%PYTHON%\\Scripts;%PYTHON%\\Library\\bin;%PATH%" - # install the dependencies - - "conda install --yes -c conda-forge pip numpy==%NUMPY_VERSION% scipy==%SCIPY_VERSION% scikit-learn==%SKLEARN_VERSION%" - - pip install codecov nose pytest pytest-cov - - pip install . - -test_script: - - mkdir for_test - - cd for_test - - pytest -v --cov=bayesclass --pyargs bayesclass - -after_test: - - cp .coverage %APPVEYOR_BUILD_FOLDER% - - cd %APPVEYOR_BUILD_FOLDER% - - codecov diff --git a/bayesclass/__init__.py b/bayesclass/__init__.py index 78a0c35..fb89952 100644 --- a/bayesclass/__init__.py +++ b/bayesclass/__init__.py @@ -1,9 +1,19 @@ -# from .bayesclass import TAN, KDB, AODE -from bayesclass._version import __version__ +from ._version import __version__ __author__ = "Ricardo Montañana Gómez" __copyright__ = "Copyright 2020-2023, Ricardo Montañana Gómez" __license__ = "MIT License" __author_email__ = "ricardo.montanana@alu.uclm.es" +__status__ = "Development" -__all__ = ["TAN", "KDB", "AODE", "__version__"] +__all__ = [ + "__version__", + "__author__", + "__copyright__", + "__license__", + "__author_email__", + "__status__", + "TAN", + "KDB", + "AODE", +] diff --git a/bayesclass/bayesclass.py b/bayesclass/clfs.py similarity index 98% rename from bayesclass/bayesclass.py rename to bayesclass/clfs.py index 7b8853e..b00099a 100644 --- a/bayesclass/bayesclass.py +++ b/bayesclass/clfs.py @@ -1,6 +1,3 @@ -""" -This is a module to be used as a reference for building other modules -""" import random import numpy as np import pandas as pd @@ -86,7 +83,7 @@ class BayesBase(BaseEstimator, ClassifierMixin): -------- >>> import numpy as np >>> import pandas as pd - >>> from bayesclass.bayesclass import TAN + >>> from bayesclass.clfs import TAN >>> features = ['A', 'B', 'C', 'D', 'E'] >>> np.random.seed(17) >>> values = pd.DataFrame(np.random.randint(low=0, high=2, @@ -142,7 +139,7 @@ class BayesBase(BaseEstimator, ClassifierMixin): -------- >>> import numpy as np >>> import pandas as pd - >>> from bayesclass.bayesclass import TAN + >>> from bayesclass.clfs import TAN >>> features = ['A', 'B', 'C', 'D', 'E'] >>> np.random.seed(17) >>> values = pd.DataFrame(np.random.randint(low=0, high=2, diff --git a/bayesclass/tests/test_AODE.py b/bayesclass/tests/test_AODE.py index 16f6c58..8c9a347 100644 --- a/bayesclass/tests/test_AODE.py +++ b/bayesclass/tests/test_AODE.py @@ -6,7 +6,7 @@ from matplotlib.testing.decorators import image_comparison from matplotlib.testing.conftest import mpl_test_settings -from bayesclass.bayesclass import AODE +from bayesclass.clfs import AODE from .._version import __version__ diff --git a/bayesclass/tests/test_KDB.py b/bayesclass/tests/test_KDB.py index bb97b2c..c304747 100644 --- a/bayesclass/tests/test_KDB.py +++ b/bayesclass/tests/test_KDB.py @@ -6,7 +6,7 @@ from matplotlib.testing.decorators import image_comparison from matplotlib.testing.conftest import mpl_test_settings -from bayesclass.bayesclass import KDB +from bayesclass.clfs import KDB from .._version import __version__ diff --git a/bayesclass/tests/test_TAN.py b/bayesclass/tests/test_TAN.py index 7c2bc9e..355ccd8 100644 --- a/bayesclass/tests/test_TAN.py +++ b/bayesclass/tests/test_TAN.py @@ -6,7 +6,7 @@ from matplotlib.testing.decorators import image_comparison from matplotlib.testing.conftest import mpl_test_settings -from bayesclass.bayesclass import TAN +from bayesclass.clfs import TAN from .._version import __version__ diff --git a/bayesclass/tests/test_common.py b/bayesclass/tests/test_common.py index 4c9089e..917ad76 100644 --- a/bayesclass/tests/test_common.py +++ b/bayesclass/tests/test_common.py @@ -2,7 +2,7 @@ import pytest from sklearn.utils.estimator_checks import check_estimator -from bayesclass.bayesclass import TAN, KDB, AODE +from bayesclass.clfs import TAN, KDB, AODE @pytest.mark.parametrize("estimator", [TAN(), KDB(k=2), AODE()]) diff --git a/datasets/all.txt b/datasets/all.txt deleted file mode 100644 index 1d1b855..0000000 --- a/datasets/all.txt +++ /dev/null @@ -1,26 +0,0 @@ -balance-scale,class -breast-w,Class -diabetes,class -ecoli,class -glass,Type -hayes-roth,class -heart-statlog,class -ionosphere,class -iris,class -kdd_JapaneseVowels,speaker -letter,class -liver-disorders,selector -mfeat-factors,class -mfeat-fourier,class -mfeat-karhunen,class -mfeat-morphological,class -mfeat-zernike,class -optdigits,class -page-blocks,class -pendigits,class -segment,class -sonar,Class -spambase,class -vehicle,Class -waveform-5000,class -wine,class \ No newline at end of file diff --git a/datasets/balance-scale.arff b/datasets/balance-scale.arff deleted file mode 100755 index 560c6d4..0000000 --- a/datasets/balance-scale.arff +++ /dev/null @@ -1,694 +0,0 @@ -%1. Title: Balance Scale Weight & Distance Database -% -%2. Source Information: -% (a) Source: Generated to model psychological experiments reported -% by Siegler, R. S. (1976). Three Aspects of Cognitive -% Development. Cognitive Psychology, 8, 481-520. -% (b) Donor: Tim Hume (hume@ics.uci.edu) -% (c) Date: 22 April 1994 -% -%3. Past Usage: (possibly different formats of this data) -% - Publications -% 1. Klahr, D., & Siegler, R.S. (1978). The Representation of -% Children's Knowledge. In H. W. Reese & L. P. Lipsitt (Eds.), -% Advances in Child Development and Behavior, pp. 61-116. New -% York: Academic Press -% 2. Langley,P. (1987). A General Theory of Discrimination -% Learning. In D. Klahr, P. Langley, & R. Neches (Eds.), -% Production System Models of Learning and Development, pp. -% 99-161. Cambridge, MA: MIT Press -% 3. Newell, A. (1990). Unified Theories of Cognition. -% Cambridge, MA: Harvard University Press -% 4. McClelland, J.L. (1988). Parallel Distibuted Processing: -% Implications for Cognition and Development. Technical -% Report AIP-47, Department of Psychology, Carnegie-Mellon -% University -% 5. Shultz, T., Mareschal, D., & Schmidt, W. (1994). Modeling -% Cognitive Development on Balance Scale Phenomena. Machine -% Learning, Vol. 16, pp. 59-88. -% -%4. Relevant Information: -% This data set was generated to model psychological -% experimental results. Each example is classified as having the -% balance scale tip to the right, tip to the left, or be -% balanced. The attributes are the left weight, the left -% distance, the right weight, and the right distance. The -% correct way to find the class is the greater of -% (left-distance * left-weight) and (right-distance * -% right-weight). If they are equal, it is balanced. -% -%5. Number of Instances: 625 (49 balanced, 288 left, 288 right) -% -%6. Number of Attributes: 4 (numeric) + class name = 5 -% -%7. Attribute Information: -% 1. Class Name: 3 (L, B, R) -% 2. Left-Weight: 5 (1, 2, 3, 4, 5) -% 3. Left-Distance: 5 (1, 2, 3, 4, 5) -% 4. Right-Weight: 5 (1, 2, 3, 4, 5) -% 5. Right-Distance: 5 (1, 2, 3, 4, 5) -% -%8. Missing Attribute Values: -% none -% -%9. Class Distribution: -% 1. 46.08 percent are L -% 2. 07.84 percent are B -% 3. 46.08 percent are R -% - -@relation balance-scale -@attribute left-weight real -@attribute left-distance real -@attribute right-weight real -@attribute right-distance real -@attribute class { L, B, R} -@data -1,1,1,1,B -1,1,1,2,R -1,1,1,3,R -1,1,1,4,R -1,1,1,5,R -1,1,2,1,R -1,1,2,2,R -1,1,2,3,R -1,1,2,4,R -1,1,2,5,R -1,1,3,1,R -1,1,3,2,R -1,1,3,3,R -1,1,3,4,R -1,1,3,5,R -1,1,4,1,R -1,1,4,2,R -1,1,4,3,R -1,1,4,4,R -1,1,4,5,R -1,1,5,1,R -1,1,5,2,R -1,1,5,3,R -1,1,5,4,R -1,1,5,5,R -1,2,1,1,L -1,2,1,2,B -1,2,1,3,R -1,2,1,4,R -1,2,1,5,R -1,2,2,1,B -1,2,2,2,R -1,2,2,3,R -1,2,2,4,R -1,2,2,5,R -1,2,3,1,R -1,2,3,2,R -1,2,3,3,R -1,2,3,4,R -1,2,3,5,R -1,2,4,1,R -1,2,4,2,R -1,2,4,3,R -1,2,4,4,R -1,2,4,5,R -1,2,5,1,R -1,2,5,2,R -1,2,5,3,R -1,2,5,4,R -1,2,5,5,R -1,3,1,1,L -1,3,1,2,L -1,3,1,3,B -1,3,1,4,R -1,3,1,5,R -1,3,2,1,L -1,3,2,2,R -1,3,2,3,R -1,3,2,4,R -1,3,2,5,R -1,3,3,1,B -1,3,3,2,R -1,3,3,3,R -1,3,3,4,R -1,3,3,5,R 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-5,5,3,2,L -5,5,3,3,L -5,5,3,4,L -5,5,3,5,L -5,5,4,1,L -5,5,4,2,L -5,5,4,3,L -5,5,4,4,L -5,5,4,5,L -5,5,5,1,L -5,5,5,2,L -5,5,5,3,L -5,5,5,4,L -5,5,5,5,B -% -% -% diff --git a/datasets/breast-w.arff b/datasets/breast-w.arff deleted file mode 100755 index ab842bc..0000000 --- a/datasets/breast-w.arff +++ /dev/null @@ -1,711 +0,0 @@ -@relation wisconsin-breast-cancer -@attribute Clump_Thickness integer [1,10] -@attribute Cell_Size_Uniformity integer [1,10] -@attribute Cell_Shape_Uniformity integer [1,10] -@attribute Marginal_Adhesion integer [1,10] -@attribute Single_Epi_Cell_Size integer [1,10] -@attribute Bare_Nuclei integer [1,10] -@attribute Bland_Chromatin integer [1,10] -@attribute Normal_Nucleoli integer [1,10] -@attribute Mitoses integer [1,10] -@attribute Class { benign, malignant} -@data -5,1,1,1,2,1,3,1,1,benign -5,4,4,5,7,10,3,2,1,benign -3,1,1,1,2,2,3,1,1,benign -6,8,8,1,3,4,3,7,1,benign -4,1,1,3,2,1,3,1,1,benign -8,10,10,8,7,10,9,7,1,malignant -1,1,1,1,2,10,3,1,1,benign -2,1,2,1,2,1,3,1,1,benign -2,1,1,1,2,1,1,1,5,benign -4,2,1,1,2,1,2,1,1,benign -1,1,1,1,1,1,3,1,1,benign -2,1,1,1,2,1,2,1,1,benign -5,3,3,3,2,3,4,4,1,malignant -1,1,1,1,2,3,3,1,1,benign -8,7,5,10,7,9,5,5,4,malignant -7,4,6,4,6,1,4,3,1,malignant -4,1,1,1,2,1,2,1,1,benign -4,1,1,1,2,1,3,1,1,benign -10,7,7,6,4,10,4,1,2,malignant -6,1,1,1,2,1,3,1,1,benign -7,3,2,10,5,10,5,4,4,malignant -10,5,5,3,6,7,7,10,1,malignant -3,1,1,1,2,1,2,1,1,benign -8,4,5,1,2,?,7,3,1,malignant -1,1,1,1,2,1,3,1,1,benign -5,2,3,4,2,7,3,6,1,malignant -3,2,1,1,1,1,2,1,1,benign -5,1,1,1,2,1,2,1,1,benign -2,1,1,1,2,1,2,1,1,benign -1,1,3,1,2,1,1,1,1,benign -3,1,1,1,1,1,2,1,1,benign -2,1,1,1,2,1,3,1,1,benign -10,7,7,3,8,5,7,4,3,malignant -2,1,1,2,2,1,3,1,1,benign -3,1,2,1,2,1,2,1,1,benign -2,1,1,1,2,1,2,1,1,benign -10,10,10,8,6,1,8,9,1,malignant -6,2,1,1,1,1,7,1,1,benign -5,4,4,9,2,10,5,6,1,malignant -2,5,3,3,6,7,7,5,1,malignant -6,6,6,9,6,?,7,8,1,benign -10,4,3,1,3,3,6,5,2,malignant -6,10,10,2,8,10,7,3,3,malignant -5,6,5,6,10,1,3,1,1,malignant -10,10,10,4,8,1,8,10,1,malignant -1,1,1,1,2,1,2,1,2,benign -3,7,7,4,4,9,4,8,1,malignant -1,1,1,1,2,1,2,1,1,benign -4,1,1,3,2,1,3,1,1,benign -7,8,7,2,4,8,3,8,2,malignant -9,5,8,1,2,3,2,1,5,malignant -5,3,3,4,2,4,3,4,1,malignant -10,3,6,2,3,5,4,10,2,malignant -5,5,5,8,10,8,7,3,7,malignant -10,5,5,6,8,8,7,1,1,malignant -10,6,6,3,4,5,3,6,1,malignant -8,10,10,1,3,6,3,9,1,malignant -8,2,4,1,5,1,5,4,4,malignant -5,2,3,1,6,10,5,1,1,malignant -9,5,5,2,2,2,5,1,1,malignant -5,3,5,5,3,3,4,10,1,malignant -1,1,1,1,2,2,2,1,1,benign -9,10,10,1,10,8,3,3,1,malignant -6,3,4,1,5,2,3,9,1,malignant -1,1,1,1,2,1,2,1,1,benign -10,4,2,1,3,2,4,3,10,malignant -4,1,1,1,2,1,3,1,1,benign -5,3,4,1,8,10,4,9,1,malignant -8,3,8,3,4,9,8,9,8,malignant -1,1,1,1,2,1,3,2,1,benign -5,1,3,1,2,1,2,1,1,benign -6,10,2,8,10,2,7,8,10,malignant -1,3,3,2,2,1,7,2,1,benign -9,4,5,10,6,10,4,8,1,malignant -10,6,4,1,3,4,3,2,3,malignant -1,1,2,1,2,2,4,2,1,benign -1,1,4,1,2,1,2,1,1,benign 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Title: Pima Indians Diabetes Database -% -% 2. Sources: -% (a) Original owners: National Institute of Diabetes and Digestive and -% Kidney Diseases -% (b) Donor of database: Vincent Sigillito (vgs@aplcen.apl.jhu.edu) -% Research Center, RMI Group Leader -% Applied Physics Laboratory -% The Johns Hopkins University -% Johns Hopkins Road -% Laurel, MD 20707 -% (301) 953-6231 -% (c) Date received: 9 May 1990 -% -% 3. Past Usage: -% 1. Smith,~J.~W., Everhart,~J.~E., Dickson,~W.~C., Knowler,~W.~C., \& -% Johannes,~R.~S. (1988). Using the ADAP learning algorithm to forecast -% the onset of diabetes mellitus. In {\it Proceedings of the Symposium -% on Computer Applications and Medical Care} (pp. 261--265). IEEE -% Computer Society Press. -% -% The diagnostic, binary-valued variable investigated is whether the -% patient shows signs of diabetes according to World Health Organization -% criteria (i.e., if the 2 hour post-load plasma glucose was at least -% 200 mg/dl at any survey examination or if found during routine medical -% care). The population lives near Phoenix, Arizona, USA. -% -% Results: Their ADAP algorithm makes a real-valued prediction between -% 0 and 1. This was transformed into a binary decision using a cutoff of -% 0.448. Using 576 training instances, the sensitivity and specificity -% of their algorithm was 76% on the remaining 192 instances. -% -% 4. Relevant Information: -% Several constraints were placed on the selection of these instances from -% a larger database. In particular, all patients here are females at -% least 21 years old of Pima Indian heritage. ADAP is an adaptive learning -% routine that generates and executes digital analogs of perceptron-like -% devices. It is a unique algorithm; see the paper for details. -% -% 5. Number of Instances: 768 -% -% 6. Number of Attributes: 8 plus class -% -% 7. For Each Attribute: (all numeric-valued) -% 1. Number of times pregnant -% 2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test -% 3. Diastolic blood pressure (mm Hg) -% 4. Triceps skin fold thickness (mm) -% 5. 2-Hour serum insulin (mu U/ml) -% 6. Body mass index (weight in kg/(height in m)^2) -% 7. Diabetes pedigree function -% 8. Age (years) -% 9. Class variable (0 or 1) -% -% 8. Missing Attribute Values: None -% -% 9. Class Distribution: (class value 1 is interpreted as "tested positive for -% diabetes") -% -% Class Value Number of instances -% 0 500 -% 1 268 -% -% 10. Brief statistical analysis: -% -% Attribute number: Mean: Standard Deviation: -% 1. 3.8 3.4 -% 2. 120.9 32.0 -% 3. 69.1 19.4 -% 4. 20.5 16.0 -% 5. 79.8 115.2 -% 6. 32.0 7.9 -% 7. 0.5 0.3 -% 8. 33.2 11.8 -% -% -% -% -% -% -% Relabeled values in attribute 'class' -% From: 0 To: tested_negative -% From: 1 To: tested_positive -% -@relation pima_diabetes -@attribute 'preg' real -@attribute 'plas' real -@attribute 'pres' real -@attribute 'skin' real -@attribute 'insu' real -@attribute 'mass' real -@attribute 'pedi' real -@attribute 'age' real -@attribute 'class' { tested_negative, tested_positive} -@data -6,148,72,35,0,33.6,0.627,50,tested_positive -1,85,66,29,0,26.6,0.351,31,tested_negative -8,183,64,0,0,23.3,0.672,32,tested_positive -1,89,66,23,94,28.1,0.167,21,tested_negative -0,137,40,35,168,43.1,2.288,33,tested_positive -5,116,74,0,0,25.6,0.201,30,tested_negative -3,78,50,32,88,31,0.248,26,tested_positive -10,115,0,0,0,35.3,0.134,29,tested_negative -2,197,70,45,543,30.5,0.158,53,tested_positive -8,125,96,0,0,0,0.232,54,tested_positive -4,110,92,0,0,37.6,0.191,30,tested_negative -10,168,74,0,0,38,0.537,34,tested_positive -10,139,80,0,0,27.1,1.441,57,tested_negative -1,189,60,23,846,30.1,0.398,59,tested_positive -5,166,72,19,175,25.8,0.587,51,tested_positive -7,100,0,0,0,30,0.484,32,tested_positive -0,118,84,47,230,45.8,0.551,31,tested_positive -7,107,74,0,0,29.6,0.254,31,tested_positive -1,103,30,38,83,43.3,0.183,33,tested_negative -1,115,70,30,96,34.6,0.529,32,tested_positive -3,126,88,41,235,39.3,0.704,27,tested_negative -8,99,84,0,0,35.4,0.388,50,tested_negative -7,196,90,0,0,39.8,0.451,41,tested_positive -9,119,80,35,0,29,0.263,29,tested_positive -11,143,94,33,146,36.6,0.254,51,tested_positive -10,125,70,26,115,31.1,0.205,41,tested_positive -7,147,76,0,0,39.4,0.257,43,tested_positive -1,97,66,15,140,23.2,0.487,22,tested_negative -13,145,82,19,110,22.2,0.245,57,tested_negative -5,117,92,0,0,34.1,0.337,38,tested_negative -5,109,75,26,0,36,0.546,60,tested_negative -3,158,76,36,245,31.6,0.851,28,tested_positive -3,88,58,11,54,24.8,0.267,22,tested_negative -6,92,92,0,0,19.9,0.188,28,tested_negative -10,122,78,31,0,27.6,0.512,45,tested_negative -4,103,60,33,192,24,0.966,33,tested_negative -11,138,76,0,0,33.2,0.42,35,tested_negative -9,102,76,37,0,32.9,0.665,46,tested_positive -2,90,68,42,0,38.2,0.503,27,tested_positive -4,111,72,47,207,37.1,1.39,56,tested_positive -3,180,64,25,70,34,0.271,26,tested_negative -7,133,84,0,0,40.2,0.696,37,tested_negative -7,106,92,18,0,22.7,0.235,48,tested_negative -9,171,110,24,240,45.4,0.721,54,tested_positive -7,159,64,0,0,27.4,0.294,40,tested_negative -0,180,66,39,0,42,1.893,25,tested_positive -1,146,56,0,0,29.7,0.564,29,tested_negative -2,71,70,27,0,28,0.586,22,tested_negative -7,103,66,32,0,39.1,0.344,31,tested_positive -7,105,0,0,0,0,0.305,24,tested_negative -1,103,80,11,82,19.4,0.491,22,tested_negative -1,101,50,15,36,24.2,0.526,26,tested_negative -5,88,66,21,23,24.4,0.342,30,tested_negative -8,176,90,34,300,33.7,0.467,58,tested_positive -7,150,66,42,342,34.7,0.718,42,tested_negative -1,73,50,10,0,23,0.248,21,tested_negative -7,187,68,39,304,37.7,0.254,41,tested_positive -0,100,88,60,110,46.8,0.962,31,tested_negative -0,146,82,0,0,40.5,1.781,44,tested_negative -0,105,64,41,142,41.5,0.173,22,tested_negative -2,84,0,0,0,0,0.304,21,tested_negative -8,133,72,0,0,32.9,0.27,39,tested_positive -5,44,62,0,0,25,0.587,36,tested_negative -2,141,58,34,128,25.4,0.699,24,tested_negative -7,114,66,0,0,32.8,0.258,42,tested_positive -5,99,74,27,0,29,0.203,32,tested_negative -0,109,88,30,0,32.5,0.855,38,tested_positive -2,109,92,0,0,42.7,0.845,54,tested_negative -1,95,66,13,38,19.6,0.334,25,tested_negative -4,146,85,27,100,28.9,0.189,27,tested_negative -2,100,66,20,90,32.9,0.867,28,tested_positive -5,139,64,35,140,28.6,0.411,26,tested_negative -13,126,90,0,0,43.4,0.583,42,tested_positive -4,129,86,20,270,35.1,0.231,23,tested_negative -1,79,75,30,0,32,0.396,22,tested_negative -1,0,48,20,0,24.7,0.14,22,tested_negative -7,62,78,0,0,32.6,0.391,41,tested_negative -5,95,72,33,0,37.7,0.37,27,tested_negative -0,131,0,0,0,43.2,0.27,26,tested_positive -2,112,66,22,0,25,0.307,24,tested_negative -3,113,44,13,0,22.4,0.14,22,tested_negative -2,74,0,0,0,0,0.102,22,tested_negative -7,83,78,26,71,29.3,0.767,36,tested_negative -0,101,65,28,0,24.6,0.237,22,tested_negative -5,137,108,0,0,48.8,0.227,37,tested_positive -2,110,74,29,125,32.4,0.698,27,tested_negative -13,106,72,54,0,36.6,0.178,45,tested_negative -2,100,68,25,71,38.5,0.324,26,tested_negative -15,136,70,32,110,37.1,0.153,43,tested_positive -1,107,68,19,0,26.5,0.165,24,tested_negative -1,80,55,0,0,19.1,0.258,21,tested_negative -4,123,80,15,176,32,0.443,34,tested_negative -7,81,78,40,48,46.7,0.261,42,tested_negative -4,134,72,0,0,23.8,0.277,60,tested_positive -2,142,82,18,64,24.7,0.761,21,tested_negative -6,144,72,27,228,33.9,0.255,40,tested_negative -2,92,62,28,0,31.6,0.13,24,tested_negative -1,71,48,18,76,20.4,0.323,22,tested_negative -6,93,50,30,64,28.7,0.356,23,tested_negative -1,122,90,51,220,49.7,0.325,31,tested_positive -1,163,72,0,0,39,1.222,33,tested_positive -1,151,60,0,0,26.1,0.179,22,tested_negative -0,125,96,0,0,22.5,0.262,21,tested_negative -1,81,72,18,40,26.6,0.283,24,tested_negative -2,85,65,0,0,39.6,0.93,27,tested_negative -1,126,56,29,152,28.7,0.801,21,tested_negative -1,96,122,0,0,22.4,0.207,27,tested_negative -4,144,58,28,140,29.5,0.287,37,tested_negative -3,83,58,31,18,34.3,0.336,25,tested_negative -0,95,85,25,36,37.4,0.247,24,tested_positive -3,171,72,33,135,33.3,0.199,24,tested_positive -8,155,62,26,495,34,0.543,46,tested_positive -1,89,76,34,37,31.2,0.192,23,tested_negative -4,76,62,0,0,34,0.391,25,tested_negative -7,160,54,32,175,30.5,0.588,39,tested_positive -4,146,92,0,0,31.2,0.539,61,tested_positive -5,124,74,0,0,34,0.22,38,tested_positive -5,78,48,0,0,33.7,0.654,25,tested_negative -4,97,60,23,0,28.2,0.443,22,tested_negative -4,99,76,15,51,23.2,0.223,21,tested_negative -0,162,76,56,100,53.2,0.759,25,tested_positive -6,111,64,39,0,34.2,0.26,24,tested_negative -2,107,74,30,100,33.6,0.404,23,tested_negative -5,132,80,0,0,26.8,0.186,69,tested_negative -0,113,76,0,0,33.3,0.278,23,tested_positive -1,88,30,42,99,55,0.496,26,tested_positive -3,120,70,30,135,42.9,0.452,30,tested_negative -1,118,58,36,94,33.3,0.261,23,tested_negative -1,117,88,24,145,34.5,0.403,40,tested_positive -0,105,84,0,0,27.9,0.741,62,tested_positive -4,173,70,14,168,29.7,0.361,33,tested_positive -9,122,56,0,0,33.3,1.114,33,tested_positive -3,170,64,37,225,34.5,0.356,30,tested_positive -8,84,74,31,0,38.3,0.457,39,tested_negative -2,96,68,13,49,21.1,0.647,26,tested_negative -2,125,60,20,140,33.8,0.088,31,tested_negative -0,100,70,26,50,30.8,0.597,21,tested_negative -0,93,60,25,92,28.7,0.532,22,tested_negative -0,129,80,0,0,31.2,0.703,29,tested_negative -5,105,72,29,325,36.9,0.159,28,tested_negative -3,128,78,0,0,21.1,0.268,55,tested_negative -5,106,82,30,0,39.5,0.286,38,tested_negative -2,108,52,26,63,32.5,0.318,22,tested_negative -10,108,66,0,0,32.4,0.272,42,tested_positive -4,154,62,31,284,32.8,0.237,23,tested_negative -0,102,75,23,0,0,0.572,21,tested_negative -9,57,80,37,0,32.8,0.096,41,tested_negative -2,106,64,35,119,30.5,1.4,34,tested_negative -5,147,78,0,0,33.7,0.218,65,tested_negative -2,90,70,17,0,27.3,0.085,22,tested_negative -1,136,74,50,204,37.4,0.399,24,tested_negative -4,114,65,0,0,21.9,0.432,37,tested_negative -9,156,86,28,155,34.3,1.189,42,tested_positive -1,153,82,42,485,40.6,0.687,23,tested_negative -8,188,78,0,0,47.9,0.137,43,tested_positive -7,152,88,44,0,50,0.337,36,tested_positive -2,99,52,15,94,24.6,0.637,21,tested_negative -1,109,56,21,135,25.2,0.833,23,tested_negative -2,88,74,19,53,29,0.229,22,tested_negative -17,163,72,41,114,40.9,0.817,47,tested_positive -4,151,90,38,0,29.7,0.294,36,tested_negative -7,102,74,40,105,37.2,0.204,45,tested_negative -0,114,80,34,285,44.2,0.167,27,tested_negative -2,100,64,23,0,29.7,0.368,21,tested_negative -0,131,88,0,0,31.6,0.743,32,tested_positive -6,104,74,18,156,29.9,0.722,41,tested_positive -3,148,66,25,0,32.5,0.256,22,tested_negative -4,120,68,0,0,29.6,0.709,34,tested_negative -4,110,66,0,0,31.9,0.471,29,tested_negative -3,111,90,12,78,28.4,0.495,29,tested_negative -6,102,82,0,0,30.8,0.18,36,tested_positive -6,134,70,23,130,35.4,0.542,29,tested_positive -2,87,0,23,0,28.9,0.773,25,tested_negative -1,79,60,42,48,43.5,0.678,23,tested_negative -2,75,64,24,55,29.7,0.37,33,tested_negative -8,179,72,42,130,32.7,0.719,36,tested_positive -6,85,78,0,0,31.2,0.382,42,tested_negative -0,129,110,46,130,67.1,0.319,26,tested_positive -5,143,78,0,0,45,0.19,47,tested_negative -5,130,82,0,0,39.1,0.956,37,tested_positive -6,87,80,0,0,23.2,0.084,32,tested_negative -0,119,64,18,92,34.9,0.725,23,tested_negative -1,0,74,20,23,27.7,0.299,21,tested_negative -5,73,60,0,0,26.8,0.268,27,tested_negative -4,141,74,0,0,27.6,0.244,40,tested_negative -7,194,68,28,0,35.9,0.745,41,tested_positive -8,181,68,36,495,30.1,0.615,60,tested_positive -1,128,98,41,58,32,1.321,33,tested_positive -8,109,76,39,114,27.9,0.64,31,tested_positive -5,139,80,35,160,31.6,0.361,25,tested_positive -3,111,62,0,0,22.6,0.142,21,tested_negative -9,123,70,44,94,33.1,0.374,40,tested_negative -7,159,66,0,0,30.4,0.383,36,tested_positive -11,135,0,0,0,52.3,0.578,40,tested_positive -8,85,55,20,0,24.4,0.136,42,tested_negative -5,158,84,41,210,39.4,0.395,29,tested_positive -1,105,58,0,0,24.3,0.187,21,tested_negative -3,107,62,13,48,22.9,0.678,23,tested_positive -4,109,64,44,99,34.8,0.905,26,tested_positive -4,148,60,27,318,30.9,0.15,29,tested_positive -0,113,80,16,0,31,0.874,21,tested_negative -1,138,82,0,0,40.1,0.236,28,tested_negative -0,108,68,20,0,27.3,0.787,32,tested_negative -2,99,70,16,44,20.4,0.235,27,tested_negative -6,103,72,32,190,37.7,0.324,55,tested_negative -5,111,72,28,0,23.9,0.407,27,tested_negative -8,196,76,29,280,37.5,0.605,57,tested_positive -5,162,104,0,0,37.7,0.151,52,tested_positive -1,96,64,27,87,33.2,0.289,21,tested_negative -7,184,84,33,0,35.5,0.355,41,tested_positive -2,81,60,22,0,27.7,0.29,25,tested_negative -0,147,85,54,0,42.8,0.375,24,tested_negative -7,179,95,31,0,34.2,0.164,60,tested_negative -0,140,65,26,130,42.6,0.431,24,tested_positive -9,112,82,32,175,34.2,0.26,36,tested_positive -12,151,70,40,271,41.8,0.742,38,tested_positive -5,109,62,41,129,35.8,0.514,25,tested_positive -6,125,68,30,120,30,0.464,32,tested_negative -5,85,74,22,0,29,1.224,32,tested_positive -5,112,66,0,0,37.8,0.261,41,tested_positive -0,177,60,29,478,34.6,1.072,21,tested_positive -2,158,90,0,0,31.6,0.805,66,tested_positive -7,119,0,0,0,25.2,0.209,37,tested_negative -7,142,60,33,190,28.8,0.687,61,tested_negative -1,100,66,15,56,23.6,0.666,26,tested_negative -1,87,78,27,32,34.6,0.101,22,tested_negative -0,101,76,0,0,35.7,0.198,26,tested_negative -3,162,52,38,0,37.2,0.652,24,tested_positive -4,197,70,39,744,36.7,2.329,31,tested_negative -0,117,80,31,53,45.2,0.089,24,tested_negative -4,142,86,0,0,44,0.645,22,tested_positive -6,134,80,37,370,46.2,0.238,46,tested_positive -1,79,80,25,37,25.4,0.583,22,tested_negative -4,122,68,0,0,35,0.394,29,tested_negative -3,74,68,28,45,29.7,0.293,23,tested_negative -4,171,72,0,0,43.6,0.479,26,tested_positive -7,181,84,21,192,35.9,0.586,51,tested_positive -0,179,90,27,0,44.1,0.686,23,tested_positive -9,164,84,21,0,30.8,0.831,32,tested_positive -0,104,76,0,0,18.4,0.582,27,tested_negative -1,91,64,24,0,29.2,0.192,21,tested_negative -4,91,70,32,88,33.1,0.446,22,tested_negative -3,139,54,0,0,25.6,0.402,22,tested_positive -6,119,50,22,176,27.1,1.318,33,tested_positive -2,146,76,35,194,38.2,0.329,29,tested_negative -9,184,85,15,0,30,1.213,49,tested_positive -10,122,68,0,0,31.2,0.258,41,tested_negative -0,165,90,33,680,52.3,0.427,23,tested_negative -9,124,70,33,402,35.4,0.282,34,tested_negative -1,111,86,19,0,30.1,0.143,23,tested_negative 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-2,197,70,99,0,34.7,0.575,62,tested_positive -0,151,90,46,0,42.1,0.371,21,tested_positive -6,109,60,27,0,25,0.206,27,tested_negative -12,121,78,17,0,26.5,0.259,62,tested_negative -8,100,76,0,0,38.7,0.19,42,tested_negative -8,124,76,24,600,28.7,0.687,52,tested_positive -1,93,56,11,0,22.5,0.417,22,tested_negative -8,143,66,0,0,34.9,0.129,41,tested_positive -6,103,66,0,0,24.3,0.249,29,tested_negative -3,176,86,27,156,33.3,1.154,52,tested_positive -0,73,0,0,0,21.1,0.342,25,tested_negative -11,111,84,40,0,46.8,0.925,45,tested_positive -2,112,78,50,140,39.4,0.175,24,tested_negative -3,132,80,0,0,34.4,0.402,44,tested_positive -2,82,52,22,115,28.5,1.699,25,tested_negative -6,123,72,45,230,33.6,0.733,34,tested_negative -0,188,82,14,185,32,0.682,22,tested_positive -0,67,76,0,0,45.3,0.194,46,tested_negative -1,89,24,19,25,27.8,0.559,21,tested_negative -1,173,74,0,0,36.8,0.088,38,tested_positive -1,109,38,18,120,23.1,0.407,26,tested_negative -1,108,88,19,0,27.1,0.4,24,tested_negative -6,96,0,0,0,23.7,0.19,28,tested_negative -1,124,74,36,0,27.8,0.1,30,tested_negative -7,150,78,29,126,35.2,0.692,54,tested_positive -4,183,0,0,0,28.4,0.212,36,tested_positive -1,124,60,32,0,35.8,0.514,21,tested_negative -1,181,78,42,293,40,1.258,22,tested_positive -1,92,62,25,41,19.5,0.482,25,tested_negative -0,152,82,39,272,41.5,0.27,27,tested_negative -1,111,62,13,182,24,0.138,23,tested_negative -3,106,54,21,158,30.9,0.292,24,tested_negative -3,174,58,22,194,32.9,0.593,36,tested_positive -7,168,88,42,321,38.2,0.787,40,tested_positive -6,105,80,28,0,32.5,0.878,26,tested_negative -11,138,74,26,144,36.1,0.557,50,tested_positive -3,106,72,0,0,25.8,0.207,27,tested_negative -6,117,96,0,0,28.7,0.157,30,tested_negative -2,68,62,13,15,20.1,0.257,23,tested_negative -9,112,82,24,0,28.2,1.282,50,tested_positive -0,119,0,0,0,32.4,0.141,24,tested_positive -2,112,86,42,160,38.4,0.246,28,tested_negative -2,92,76,20,0,24.2,1.698,28,tested_negative -6,183,94,0,0,40.8,1.461,45,tested_negative -0,94,70,27,115,43.5,0.347,21,tested_negative -2,108,64,0,0,30.8,0.158,21,tested_negative -4,90,88,47,54,37.7,0.362,29,tested_negative -0,125,68,0,0,24.7,0.206,21,tested_negative -0,132,78,0,0,32.4,0.393,21,tested_negative -5,128,80,0,0,34.6,0.144,45,tested_negative -4,94,65,22,0,24.7,0.148,21,tested_negative -7,114,64,0,0,27.4,0.732,34,tested_positive -0,102,78,40,90,34.5,0.238,24,tested_negative -2,111,60,0,0,26.2,0.343,23,tested_negative -1,128,82,17,183,27.5,0.115,22,tested_negative -10,92,62,0,0,25.9,0.167,31,tested_negative -13,104,72,0,0,31.2,0.465,38,tested_positive -5,104,74,0,0,28.8,0.153,48,tested_negative -2,94,76,18,66,31.6,0.649,23,tested_negative -7,97,76,32,91,40.9,0.871,32,tested_positive -1,100,74,12,46,19.5,0.149,28,tested_negative -0,102,86,17,105,29.3,0.695,27,tested_negative -4,128,70,0,0,34.3,0.303,24,tested_negative -6,147,80,0,0,29.5,0.178,50,tested_positive -4,90,0,0,0,28,0.61,31,tested_negative -3,103,72,30,152,27.6,0.73,27,tested_negative -2,157,74,35,440,39.4,0.134,30,tested_negative -1,167,74,17,144,23.4,0.447,33,tested_positive -0,179,50,36,159,37.8,0.455,22,tested_positive -11,136,84,35,130,28.3,0.26,42,tested_positive -0,107,60,25,0,26.4,0.133,23,tested_negative -1,91,54,25,100,25.2,0.234,23,tested_negative -1,117,60,23,106,33.8,0.466,27,tested_negative -5,123,74,40,77,34.1,0.269,28,tested_negative -2,120,54,0,0,26.8,0.455,27,tested_negative -1,106,70,28,135,34.2,0.142,22,tested_negative -2,155,52,27,540,38.7,0.24,25,tested_positive -2,101,58,35,90,21.8,0.155,22,tested_negative -1,120,80,48,200,38.9,1.162,41,tested_negative -11,127,106,0,0,39,0.19,51,tested_negative -3,80,82,31,70,34.2,1.292,27,tested_positive -10,162,84,0,0,27.7,0.182,54,tested_negative -1,199,76,43,0,42.9,1.394,22,tested_positive -8,167,106,46,231,37.6,0.165,43,tested_positive -9,145,80,46,130,37.9,0.637,40,tested_positive -6,115,60,39,0,33.7,0.245,40,tested_positive -1,112,80,45,132,34.8,0.217,24,tested_negative -4,145,82,18,0,32.5,0.235,70,tested_positive -10,111,70,27,0,27.5,0.141,40,tested_positive -6,98,58,33,190,34,0.43,43,tested_negative -9,154,78,30,100,30.9,0.164,45,tested_negative -6,165,68,26,168,33.6,0.631,49,tested_negative -1,99,58,10,0,25.4,0.551,21,tested_negative -10,68,106,23,49,35.5,0.285,47,tested_negative -3,123,100,35,240,57.3,0.88,22,tested_negative -8,91,82,0,0,35.6,0.587,68,tested_negative -6,195,70,0,0,30.9,0.328,31,tested_positive -9,156,86,0,0,24.8,0.23,53,tested_positive -0,93,60,0,0,35.3,0.263,25,tested_negative -3,121,52,0,0,36,0.127,25,tested_positive -2,101,58,17,265,24.2,0.614,23,tested_negative -2,56,56,28,45,24.2,0.332,22,tested_negative -0,162,76,36,0,49.6,0.364,26,tested_positive -0,95,64,39,105,44.6,0.366,22,tested_negative -4,125,80,0,0,32.3,0.536,27,tested_positive -5,136,82,0,0,0,0.64,69,tested_negative -2,129,74,26,205,33.2,0.591,25,tested_negative -3,130,64,0,0,23.1,0.314,22,tested_negative -1,107,50,19,0,28.3,0.181,29,tested_negative -1,140,74,26,180,24.1,0.828,23,tested_negative -1,144,82,46,180,46.1,0.335,46,tested_positive -8,107,80,0,0,24.6,0.856,34,tested_negative -13,158,114,0,0,42.3,0.257,44,tested_positive -2,121,70,32,95,39.1,0.886,23,tested_negative -7,129,68,49,125,38.5,0.439,43,tested_positive -2,90,60,0,0,23.5,0.191,25,tested_negative -7,142,90,24,480,30.4,0.128,43,tested_positive -3,169,74,19,125,29.9,0.268,31,tested_positive -0,99,0,0,0,25,0.253,22,tested_negative -4,127,88,11,155,34.5,0.598,28,tested_negative -4,118,70,0,0,44.5,0.904,26,tested_negative -2,122,76,27,200,35.9,0.483,26,tested_negative -6,125,78,31,0,27.6,0.565,49,tested_positive -1,168,88,29,0,35,0.905,52,tested_positive -2,129,0,0,0,38.5,0.304,41,tested_negative -4,110,76,20,100,28.4,0.118,27,tested_negative -6,80,80,36,0,39.8,0.177,28,tested_negative -10,115,0,0,0,0,0.261,30,tested_positive -2,127,46,21,335,34.4,0.176,22,tested_negative -9,164,78,0,0,32.8,0.148,45,tested_positive -2,93,64,32,160,38,0.674,23,tested_positive -3,158,64,13,387,31.2,0.295,24,tested_negative -5,126,78,27,22,29.6,0.439,40,tested_negative -10,129,62,36,0,41.2,0.441,38,tested_positive -0,134,58,20,291,26.4,0.352,21,tested_negative -3,102,74,0,0,29.5,0.121,32,tested_negative -7,187,50,33,392,33.9,0.826,34,tested_positive -3,173,78,39,185,33.8,0.97,31,tested_positive -10,94,72,18,0,23.1,0.595,56,tested_negative -1,108,60,46,178,35.5,0.415,24,tested_negative -5,97,76,27,0,35.6,0.378,52,tested_positive -4,83,86,19,0,29.3,0.317,34,tested_negative -1,114,66,36,200,38.1,0.289,21,tested_negative -1,149,68,29,127,29.3,0.349,42,tested_positive -5,117,86,30,105,39.1,0.251,42,tested_negative -1,111,94,0,0,32.8,0.265,45,tested_negative -4,112,78,40,0,39.4,0.236,38,tested_negative -1,116,78,29,180,36.1,0.496,25,tested_negative -0,141,84,26,0,32.4,0.433,22,tested_negative -2,175,88,0,0,22.9,0.326,22,tested_negative -2,92,52,0,0,30.1,0.141,22,tested_negative -3,130,78,23,79,28.4,0.323,34,tested_positive -8,120,86,0,0,28.4,0.259,22,tested_positive -2,174,88,37,120,44.5,0.646,24,tested_positive -2,106,56,27,165,29,0.426,22,tested_negative -2,105,75,0,0,23.3,0.56,53,tested_negative -4,95,60,32,0,35.4,0.284,28,tested_negative -0,126,86,27,120,27.4,0.515,21,tested_negative -8,65,72,23,0,32,0.6,42,tested_negative -2,99,60,17,160,36.6,0.453,21,tested_negative -1,102,74,0,0,39.5,0.293,42,tested_positive -11,120,80,37,150,42.3,0.785,48,tested_positive -3,102,44,20,94,30.8,0.4,26,tested_negative -1,109,58,18,116,28.5,0.219,22,tested_negative -9,140,94,0,0,32.7,0.734,45,tested_positive -13,153,88,37,140,40.6,1.174,39,tested_negative -12,100,84,33,105,30,0.488,46,tested_negative -1,147,94,41,0,49.3,0.358,27,tested_positive -1,81,74,41,57,46.3,1.096,32,tested_negative -3,187,70,22,200,36.4,0.408,36,tested_positive -6,162,62,0,0,24.3,0.178,50,tested_positive -4,136,70,0,0,31.2,1.182,22,tested_positive -1,121,78,39,74,39,0.261,28,tested_negative -3,108,62,24,0,26,0.223,25,tested_negative -0,181,88,44,510,43.3,0.222,26,tested_positive -8,154,78,32,0,32.4,0.443,45,tested_positive -1,128,88,39,110,36.5,1.057,37,tested_positive -7,137,90,41,0,32,0.391,39,tested_negative -0,123,72,0,0,36.3,0.258,52,tested_positive -1,106,76,0,0,37.5,0.197,26,tested_negative -6,190,92,0,0,35.5,0.278,66,tested_positive -2,88,58,26,16,28.4,0.766,22,tested_negative -9,170,74,31,0,44,0.403,43,tested_positive -9,89,62,0,0,22.5,0.142,33,tested_negative -10,101,76,48,180,32.9,0.171,63,tested_negative -2,122,70,27,0,36.8,0.34,27,tested_negative -5,121,72,23,112,26.2,0.245,30,tested_negative -1,126,60,0,0,30.1,0.349,47,tested_positive -1,93,70,31,0,30.4,0.315,23,tested_negative diff --git a/datasets/ecoli.arff b/datasets/ecoli.arff deleted file mode 100755 index 6008975..0000000 --- a/datasets/ecoli.arff +++ /dev/null @@ -1,428 +0,0 @@ -% -% 1. Title: Protein Localization Sites -% -% -% 2. Creator and Maintainer: -% Kenta Nakai -% Institue of Molecular and Cellular Biology -% Osaka, University -% 1-3 Yamada-oka, Suita 565 Japan -% nakai@imcb.osaka-u.ac.jp -% http://www.imcb.osaka-u.ac.jp/nakai/psort.html -% Donor: Paul Horton (paulh@cs.berkeley.edu) -% Date: September, 1996 -% See also: yeast database -% -% 3. Past Usage. -% Reference: "A Probablistic Classification System for Predicting the Cellular -% Localization Sites of Proteins", Paul Horton & Kenta Nakai, -% Intelligent Systems in Molecular Biology, 109-115. -% St. Louis, USA 1996. -% Results: 81% for E.coli with an ad hoc structured -% probability model. Also similar accuracy for Binary Decision Tree and -% Bayesian Classifier methods applied by the same authors in -% unpublished results. -% -% Predicted Attribute: Localization site of protein. ( non-numeric ). -% -% -% 4. The references below describe a predecessor to this dataset and its -% development. They also give results (not cross-validated) for classification -% by a rule-based expert system with that version of the dataset. -% -% Reference: "Expert Sytem for Predicting Protein Localization Sites in -% Gram-Negative Bacteria", Kenta Nakai & Minoru Kanehisa, -% PROTEINS: Structure, Function, and Genetics 11:95-110, 1991. -% -% Reference: "A Knowledge Base for Predicting Protein Localization Sites in -% Eukaryotic Cells", Kenta Nakai & Minoru Kanehisa, -% Genomics 14:897-911, 1992. -% -% -% 5. Number of Instances: 336 for the E.coli dataset and -% -% -% 6. Number of Attributes. -% for E.coli dataset: 8 ( 7 predictive, 1 name ) -% -% 7. Attribute Information. -% -% 1. Sequence Name: Accession number for the SWISS-PROT database -% 2. mcg: McGeoch's method for signal sequence recognition. -% 3. gvh: von Heijne's method for signal sequence recognition. -% 4. lip: von Heijne's Signal Peptidase II consensus sequence score. -% Binary attribute. -% 5. chg: Presence of charge on N-terminus of predicted lipoproteins. -% Binary attribute. -% 6. aac: score of discriminant analysis of the amino acid content of -% outer membrane and periplasmic proteins. -% 7. alm1: score of the ALOM membrane spanning region prediction program. -% 8. alm2: score of ALOM program after excluding putative cleavable signal -% regions from the sequence. -% -% NOTE - the sequence name has been removed -% -% 8. Missing Attribute Values: None. -% -% -% 9. Class Distribution. The class is the localization site. Please see Nakai & -% Kanehisa referenced above for more details. -% -% cp (cytoplasm) 143 -% im (inner membrane without signal sequence) 77 -% pp (perisplasm) 52 -% imU (inner membrane, uncleavable signal sequence) 35 -% om (outer membrane) 20 -% omL (outer membrane lipoprotein) 5 -% imL (inner membrane lipoprotein) 2 -% imS (inner membrane, cleavable signal sequence) 2 - -@relation ecoli - -@attribute mcg numeric -@attribute gvh numeric -@attribute lip numeric -@attribute chg numeric -@attribute aac numeric -@attribute alm1 numeric -@attribute alm2 numeric -@attribute class {cp,im,pp,imU,om,omL,imL,imS} - -@data - -0.49,0.29,0.48,0.5,0.56,0.24,0.35,cp -0.07,0.4,0.48,0.5,0.54,0.35,0.44,cp -0.56,0.4,0.48,0.5,0.49,0.37,0.46,cp -0.59,0.49,0.48,0.5,0.52,0.45,0.36,cp -0.23,0.32,0.48,0.5,0.55,0.25,0.35,cp -0.67,0.39,0.48,0.5,0.36,0.38,0.46,cp -0.29,0.28,0.48,0.5,0.44,0.23,0.34,cp -0.21,0.34,0.48,0.5,0.51,0.28,0.39,cp -0.2,0.44,0.48,0.5,0.46,0.51,0.57,cp -0.42,0.4,0.48,0.5,0.56,0.18,0.3,cp -0.42,0.24,0.48,0.5,0.57,0.27,0.37,cp -0.25,0.48,0.48,0.5,0.44,0.17,0.29,cp -0.39,0.32,0.48,0.5,0.46,0.24,0.35,cp -0.51,0.5,0.48,0.5,0.46,0.32,0.35,cp -0.22,0.43,0.48,0.5,0.48,0.16,0.28,cp -0.25,0.4,0.48,0.5,0.46,0.44,0.52,cp -0.34,0.45,0.48,0.5,0.38,0.24,0.35,cp -0.44,0.27,0.48,0.5,0.55,0.52,0.58,cp -0.23,0.4,0.48,0.5,0.39,0.28,0.38,cp -0.41,0.57,0.48,0.5,0.39,0.21,0.32,cp -0.4,0.45,0.48,0.5,0.38,0.22,0,cp -0.31,0.23,0.48,0.5,0.73,0.05,0.14,cp -0.51,0.54,0.48,0.5,0.41,0.34,0.43,cp -0.3,0.16,0.48,0.5,0.56,0.11,0.23,cp -0.36,0.39,0.48,0.5,0.48,0.22,0.23,cp -0.29,0.37,0.48,0.5,0.48,0.44,0.52,cp -0.25,0.4,0.48,0.5,0.47,0.33,0.42,cp -0.21,0.51,0.48,0.5,0.5,0.32,0.41,cp -0.43,0.37,0.48,0.5,0.53,0.35,0.44,cp -0.43,0.39,0.48,0.5,0.47,0.31,0.41,cp -0.53,0.38,0.48,0.5,0.44,0.26,0.36,cp -0.34,0.33,0.48,0.5,0.38,0.35,0.44,cp -0.56,0.51,0.48,0.5,0.34,0.37,0.46,cp -0.4,0.29,0.48,0.5,0.42,0.35,0.44,cp -0.24,0.35,0.48,0.5,0.31,0.19,0.31,cp -0.36,0.54,0.48,0.5,0.41,0.38,0.46,cp 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-0.5,0.51,0.48,0.5,0.27,0.23,0.34,cp -0.53,0.42,0.48,0.5,0.16,0.29,0.39,cp -0.34,0.46,0.48,0.5,0.52,0.35,0.44,cp -0.4,0.42,0.48,0.5,0.37,0.27,0.27,cp -0.41,0.43,0.48,0.5,0.5,0.24,0.25,cp -0.3,0.45,0.48,0.5,0.36,0.21,0.32,cp -0.31,0.47,0.48,0.5,0.29,0.28,0.39,cp -0.64,0.76,0.48,0.5,0.45,0.35,0.38,cp -0.35,0.37,0.48,0.5,0.3,0.34,0.43,cp -0.57,0.54,0.48,0.5,0.37,0.28,0.33,cp -0.65,0.55,0.48,0.5,0.34,0.37,0.28,cp -0.51,0.46,0.48,0.5,0.58,0.31,0.41,cp -0.38,0.4,0.48,0.5,0.63,0.25,0.35,cp -0.24,0.57,0.48,0.5,0.63,0.34,0.43,cp -0.38,0.26,0.48,0.5,0.54,0.16,0.28,cp -0.33,0.47,0.48,0.5,0.53,0.18,0.29,cp -0.24,0.34,0.48,0.5,0.38,0.3,0.4,cp -0.26,0.5,0.48,0.5,0.44,0.32,0.41,cp -0.44,0.49,0.48,0.5,0.39,0.38,0.4,cp -0.43,0.32,0.48,0.5,0.33,0.45,0.52,cp -0.49,0.43,0.48,0.5,0.49,0.3,0.4,cp -0.47,0.28,0.48,0.5,0.56,0.2,0.25,cp -0.32,0.33,0.48,0.5,0.6,0.06,0.2,cp -0.34,0.35,0.48,0.5,0.51,0.49,0.56,cp -0.35,0.34,0.48,0.5,0.46,0.3,0.27,cp -0.38,0.3,0.48,0.5,0.43,0.29,0.39,cp 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Title: Glass Identification Database -% -% 2. Sources: -% (a) Creator: B. German -% -- Central Research Establishment -% Home Office Forensic Science Service -% Aldermaston, Reading, Berkshire RG7 4PN -% (b) Donor: Vina Spiehler, Ph.D., DABFT -% Diagnostic Products Corporation -% (213) 776-0180 (ext 3014) -% (c) Date: September, 1987 -% -% 3. Past Usage: -% -- Rule Induction in Forensic Science -% -- Ian W. Evett and Ernest J. Spiehler -% -- Central Research Establishment -% Home Office Forensic Science Service -% Aldermaston, Reading, Berkshire RG7 4PN -% -- Unknown technical note number (sorry, not listed here) -% -- General Results: nearest neighbor held its own with respect to the -% rule-based system -% -% 4. Relevant Information:n -% Vina conducted a comparison test of her rule-based system, BEAGLE, the -% nearest-neighbor algorithm, and discriminant analysis. BEAGLE is -% a product available through VRS Consulting, Inc.; 4676 Admiralty Way, -% Suite 206; Marina Del Ray, CA 90292 (213) 827-7890 and FAX: -3189. -% In determining whether the glass was a type of "float" glass or not, -% the following results were obtained (# incorrect answers): -% -% Type of Sample Beagle NN DA -% Windows that were float processed (87) 10 12 21 -% Windows that were not: (76) 19 16 22 -% -% The study of classification of types of glass was motivated by -% criminological investigation. At the scene of the crime, the glass left -% can be used as evidence...if it is correctly identified! -% -% 5. Number of Instances: 214 -% -% 6. Number of Attributes: 10 (including an Id#) plus the class attribute -% -- all attributes are continuously valued -% -% 7. Attribute Information: -% 1. Id number: 1 to 214 -% 2. RI: refractive index -% 3. Na: Sodium (unit measurement: weight percent in corresponding oxide, as -% are attributes 4-10) -% 4. Mg: Magnesium -% 5. Al: Aluminum -% 6. Si: Silicon -% 7. K: Potassium -% 8. Ca: Calcium -% 9. Ba: Barium -% 10. Fe: Iron -% 11. Type of glass: (class attribute) -% -- 1 building_windows_float_processed -% -- 2 building_windows_non_float_processed -% -- 3 vehicle_windows_float_processed -% -- 4 vehicle_windows_non_float_processed (none in this database) -% -- 5 containers -% -- 6 tableware -% -- 7 headlamps -% -% 8. Missing Attribute Values: None -% -% Summary Statistics: -% Attribute: Min Max Mean SD Correlation with class -% 2. RI: 1.5112 1.5339 1.5184 0.0030 -0.1642 -% 3. Na: 10.73 17.38 13.4079 0.8166 0.5030 -% 4. Mg: 0 4.49 2.6845 1.4424 -0.7447 -% 5. Al: 0.29 3.5 1.4449 0.4993 0.5988 -% 6. Si: 69.81 75.41 72.6509 0.7745 0.1515 -% 7. K: 0 6.21 0.4971 0.6522 -0.0100 -% 8. Ca: 5.43 16.19 8.9570 1.4232 0.0007 -% 9. Ba: 0 3.15 0.1750 0.4972 0.5751 -% 10. Fe: 0 0.51 0.0570 0.0974 -0.1879 -% -% 9. Class Distribution: (out of 214 total instances) -% -- 163 Window glass (building windows and vehicle windows) -% -- 87 float processed -% -- 70 building windows -% -- 17 vehicle windows -% -- 76 non-float processed -% -- 76 building windows -% -- 0 vehicle windows -% -- 51 Non-window glass -% -- 13 containers -% -- 9 tableware -% -- 29 headlamps -% -% -% -% -% -% -% -% Relabeled values in attribute 'Type' -% From: '1' To: 'build wind float' -% From: '2' To: 'build wind non-float' -% From: '3' To: 'vehic wind float' -% From: '4' To: 'vehic wind non-float' -% From: '5' To: containers -% From: '6' To: tableware -% From: '7' To: headlamps -% -@relation Glass -@attribute 'RI' real -@attribute 'Na' real -@attribute 'Mg' real -@attribute 'Al' real -@attribute 'Si' real -@attribute 'K' real -@attribute 'Ca' real -@attribute 'Ba' real -@attribute 'Fe' real -@attribute 'Type' { 'build wind float', 'build wind non-float', 'vehic wind float', 'vehic wind non-float', containers, tableware, headlamps} -@data -1.51793,12.79,3.5,1.12,73.03,0.64,8.77,0,0,'build wind float' -1.51643,12.16,3.52,1.35,72.89,0.57,8.53,0,0,'vehic wind float' -1.51793,13.21,3.48,1.41,72.64,0.59,8.43,0,0,'build wind float' -1.51299,14.4,1.74,1.54,74.55,0,7.59,0,0,tableware -1.53393,12.3,0,1,70.16,0.12,16.19,0,0.24,'build wind non-float' -1.51655,12.75,2.85,1.44,73.27,0.57,8.79,0.11,0.22,'build wind non-float' -1.51779,13.64,3.65,0.65,73,0.06,8.93,0,0,'vehic wind float' -1.51837,13.14,2.84,1.28,72.85,0.55,9.07,0,0,'build wind float' -1.51545,14.14,0,2.68,73.39,0.08,9.07,0.61,0.05,headlamps -1.51789,13.19,3.9,1.3,72.33,0.55,8.44,0,0.28,'build wind non-float' -1.51625,13.36,3.58,1.49,72.72,0.45,8.21,0,0,'build wind non-float' -1.51743,12.2,3.25,1.16,73.55,0.62,8.9,0,0.24,'build wind non-float' -1.52223,13.21,3.77,0.79,71.99,0.13,10.02,0,0,'build wind float' -1.52121,14.03,3.76,0.58,71.79,0.11,9.65,0,0,'vehic wind float' -1.51665,13.14,3.45,1.76,72.48,0.6,8.38,0,0.17,'vehic wind float' 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+0,0 @@ -% 1. Title: Hayes-Roth & Hayes-Roth (1977) Database -% -% 2. Source Information: -% (a) Creators: Barbara and Frederick Hayes-Roth -% (b) Donor: David W. Aha (aha@ics.uci.edu) (714) 856-8779 -% (c) Date: March, 1989 -% -% 3. Past Usage: -% 1. Hayes-Roth, B., & Hayes-Roth, F. (1977). Concept learning and the -% recognition and classification of exemplars. Journal of Verbal Learning -% and Verbal Behavior, 16, 321-338. -% -- Results: -% -- Human subjects classification and recognition performance: -% 1. decreases with distance from the prototype, -% 2. is better on unseen prototypes than old instances, and -% 3. improves with presentation frequency during learning. -% 2. Anderson, J.R., & Kline, P.J. (1979). A learning system and its -% psychological implications. In Proceedings of the Sixth International -% Joint Conference on Artificial Intelligence (pp. 16-21). Tokyo, Japan: -% Morgan Kaufmann. -% -- Partitioned the results into 4 classes: -% 1. prototypes -% 2. near-prototypes with high presentation frequency during learning -% 3. near-prototypes with low presentation frequency during learning -% 4. instances that are far from protoypes -% -- Described evidence that ACT's classification confidence and -% recognition behaviors closely simulated human subjects' behaviors. -% 3. Aha, D.W. (1989). Incremental learning of independent, overlapping, and -% graded concept descriptions with an instance-based process framework. -% Manuscript submitted for publication. -% -- Used same partition as Anderson & Kline -% -- Described evidence that Bloom's classification confidence behavior -% is similar to the human subjects' behavior. Bloom fitted the data -% more closely than did ACT. -% -% 4. Relevant Information: -% This database contains 5 numeric-valued attributes. Only a subset of -% 3 are used during testing (the latter 3). Furthermore, only 2 of the -% 3 concepts are "used" during testing (i.e., those with the prototypes -% 000 and 111). I've mapped all values to their zero-indexing equivalents. -% -% Some instances could be placed in either category 0 or 1. I've followed -% the authors' suggestion, placing them in each category with equal -% probability. -% -% I've replaced the actual values of the attributes (i.e., hobby has values -% chess, sports and stamps) with numeric values. I think this is how -% the authors' did this when testing the categorization models described -% in the paper. I find this unfair. While the subjects were able to bring -% background knowledge to bear on the attribute values and their -% relationships, the algorithms were provided with no such knowledge. I'm -% uncertain whether the 2 distractor attributes (name and hobby) are -% presented to the authors' algorithms during testing. However, it is clear -% that only the age, educational status, and marital status attributes are -% given during the human subjects' transfer tests. -% -% 5. Number of Instances: 132 training instances, 28 test instances -% -% 6. Number of Attributes: 5 plus the class membership attribute. 3 concepts. -% -% 7. Attribute Information: -% -- 1. name: distinct for each instance and represented numerically -% -- 2. hobby: nominal values ranging between 1 and 3 -% -- 3. age: nominal values ranging between 1 and 4 -% -- 4. educational level: nominal values ranging between 1 and 4 -% -- 5. marital status: nominal values ranging between 1 and 4 -% -- 6. class: nominal value between 1 and 3 -% -% 9. Missing Attribute Values: none -% -% 10. Class Distribution: see below -% -% 11. Detailed description of the experiment: -% 1. 3 categories (1, 2, and neither -- which I call 3) -% -- some of the instances could be classified in either class 1 or 2, and -% they have been evenly distributed between the two classes -% 2. 5 Attributes -% -- A. name (a randomly-generated number between 1 and 132) -% -- B. hobby (a randomly-generated number between 1 and 3) -% -- C. age (a number between 1 and 4) -% -- D. education level (a number between 1 and 4) -% -- E. marital status (a number between 1 and 4) -% 3. Classification: -% -- only attributes C-E are diagnostic; values for A and B are ignored -% -- Class Neither: if a 4 occurs for any attribute C-E -% -- Class 1: Otherwise, if (# of 1's)>(# of 2's) for attributes C-E -% -- Class 2: Otherwise, if (# of 2's)>(# of 1's) for attributes C-E -% -- Either 1 or 2: Otherwise, if (# of 2's)=(# of 1's) for attributes C-E -% 4. Prototypes: -% -- Class 1: 111 -% -- Class 2: 222 -% -- Class Either: 333 -% -- Class Neither: 444 -% 5. Number of training instances: 132 -% -- Each instance presented 0, 1, or 10 times -% -- None of the prototypes seen during training -% -- 3 instances from each of categories 1, 2, and either are repeated -% 10 times each -% -- 3 additional instances from the Either category are shown during -% learning -% 5. Number of test instances: 28 -% -- All 9 class 1 -% -- All 9 class 2 -% -- All 6 class Either -% -- All 4 prototypes -% -------------------- -% -- 28 total -% -% Observations of interest: -% 1. Relative classification confidence of -% -- prototypes for classes 1 and 2 (2 instances) -% (Anderson calls these Class 1 instances) -% -- instances of class 1 with frequency 10 during training and -% instances of class 2 with frequency 10 during training that -% are 1 value away from their respective prototypes (6 instances) -% (Anderson calls these Class 2 instances) -% -- instances of class 1 with frequency 1 during training and -% instances of class 2 with frequency 1 during training that -% are 1 value away from their respective prototypes (6 instances) -% (Anderson calls these Class 3 instances) -% -- instances of class 1 with frequency 1 during training and -% instances of class 2 with frequency 1 during training that -% are 2 values away from their respective prototypes (6 instances) -% (Anderson calls these Class 4 instances) -% 2. Relative classification recognition of them also -% -% Some Expected results: -% Both frequency and distance from prototype will effect the classification -% accuracy of instances. Greater the frequency, higher the classification -% confidence. Closer to prototype, higher the classification confidence. -% -% Information about the dataset -% CLASSTYPE: nominal -% CLASSINDEX: last -% - -@relation hayes-roth - -@attribute hobby INTEGER -@attribute age INTEGER -@attribute educational_level INTEGER -@attribute marital_status INTEGER -@attribute class {1,2,3,4} - -@data -2,1,1,2,1 -2,1,3,2,2 -3,1,4,1,3 -2,4,2,2,3 -1,1,3,4,3 -1,1,3,2,2 -3,1,3,2,2 -3,4,2,4,3 -2,2,1,1,1 -3,2,1,1,1 -1,2,1,1,1 -2,2,3,4,3 -1,1,2,1,1 -2,1,2,2,2 -2,4,1,4,3 -1,1,3,3,1 -3,2,1,2,2 -1,2,1,1,1 -3,3,2,1,1 -3,1,3,2,1 -1,2,2,1,2 -3,2,1,3,1 -2,1,2,1,1 -3,2,1,3,1 -2,3,2,1,1 -3,2,2,1,2 -3,2,1,3,2 -2,1,2,2,2 -1,1,3,2,1 -3,2,1,1,1 -1,4,1,1,3 -2,2,1,3,1 -1,2,1,3,2 -1,1,1,2,1 -2,4,3,1,3 -3,1,2,2,2 -1,1,2,2,2 -3,2,2,1,2 -1,2,1,2,2 -3,4,3,2,3 -2,2,2,1,2 -2,2,1,2,2 -3,2,1,3,2 -3,2,1,1,1 -3,1,2,1,1 -1,2,1,3,2 -2,1,1,2,1 -1,1,1,2,1 -1,2,2,3,2 -3,3,1,1,1 -3,3,3,1,1 -3,2,1,2,2 -3,2,1,2,2 -3,1,2,1,1 -1,1,1,2,1 -2,1,3,2,1 -2,2,2,1,2 -2,1,2,1,1 -2,2,1,3,1 -2,1,2,2,2 -1,2,4,2,3 -2,2,1,2,2 -1,1,2,4,3 -1,3,2,1,1 -2,4,4,2,3 -2,3,2,1,1 -3,1,2,2,2 -1,1,2,2,2 -1,3,2,4,3 -1,1,2,2,2 -3,1,4,2,3 -2,1,3,2,2 -1,1,3,2,2 -3,1,3,2,1 -1,2,4,4,3 -1,4,2,1,3 -2,1,2,1,1 -3,4,1,2,3 -2,2,1,1,1 -1,1,2,1,1 -2,2,4,3,3 -3,1,2,2,2 -1,1,3,2,1 -1,2,1,3,1 -1,4,4,1,3 -3,3,3,2,2 -2,2,1,3,2 -3,3,2,1,2 -1,1,1,3,1 -2,2,1,2,2 -2,2,2,1,2 -2,3,2,3,2 -1,3,2,1,2 -2,2,1,2,2 -1,1,1,2,1 -3,2,2,1,2 -3,2,1,1,1 -1,1,2,1,1 -3,1,4,4,3 -3,3,2,1,2 -2,3,2,1,2 -2,1,3,1,1 -1,2,1,2,2 -3,1,1,2,1 -2,2,4,1,3 -1,2,2,1,2 -2,3,2,1,2 -2,2,1,4,3 -1,4,2,3,3 -2,2,1,1,1 -1,2,1,1,1 -2,2,3,2,2 -1,3,2,1,1 -3,1,2,1,1 -3,1,1,2,1 -3,3,1,4,3 -2,3,4,1,3 -1,2,3,3,2 -3,3,2,2,2 -3,3,4,2,3 -1,2,2,1,2 -2,1,1,4,3 -3,1,2,2,2 -3,2,2,4,3 -2,3,1,3,1 -2,1,1,2,1 -3,4,1,3,3 -1,1,4,3,3 -2,1,2,1,1 -1,2,1,2,2 -1,2,2,1,2 -3,1,1,2,1 -1,1,1,2,1 -1,1,2,1,1 -1,2,1,1,1 -1,1,1,3,1 -1,1,3,1,1 -1,3,1,1,1 -1,1,3,3,1 -1,3,1,3,1 -1,3,3,1,1 -1,2,2,1,2 -1,2,1,2,2 -1,1,2,2,2 -1,2,2,3,2 -1,2,3,2,2 -1,3,2,2,2 -1,2,3,3,2 -1,3,2,3,2 -1,3,3,2,2 -1,1,3,2,1 -1,3,2,1,2 -1,2,1,3,1 -1,2,3,1,2 -1,1,2,3,1 -1,3,1,2,2 -1,1,1,1,1 -1,2,2,2,2 -1,3,3,3,1 -1,4,4,4,3 \ No newline at end of file diff --git a/datasets/hayes-roth_test.arff b/datasets/hayes-roth_test.arff deleted file mode 100755 index 22e486e..0000000 --- a/datasets/hayes-roth_test.arff +++ /dev/null @@ -1,173 +0,0 @@ -% 1. Title: Hayes-Roth & Hayes-Roth (1977) Database -% -% 2. Source Information: -% (a) Creators: Barbara and Frederick Hayes-Roth -% (b) Donor: David W. Aha (aha@ics.uci.edu) (714) 856-8779 -% (c) Date: March, 1989 -% -% 3. Past Usage: -% 1. Hayes-Roth, B., & Hayes-Roth, F. (1977). Concept learning and the -% recognition and classification of exemplars. Journal of Verbal Learning -% and Verbal Behavior, 16, 321-338. -% -- Results: -% -- Human subjects classification and recognition performance: -% 1. decreases with distance from the prototype, -% 2. is better on unseen prototypes than old instances, and -% 3. improves with presentation frequency during learning. -% 2. Anderson, J.R., & Kline, P.J. (1979). A learning system and its -% psychological implications. In Proceedings of the Sixth International -% Joint Conference on Artificial Intelligence (pp. 16-21). Tokyo, Japan: -% Morgan Kaufmann. -% -- Partitioned the results into 4 classes: -% 1. prototypes -% 2. near-prototypes with high presentation frequency during learning -% 3. near-prototypes with low presentation frequency during learning -% 4. instances that are far from protoypes -% -- Described evidence that ACT's classification confidence and -% recognition behaviors closely simulated human subjects' behaviors. -% 3. Aha, D.W. (1989). Incremental learning of independent, overlapping, and -% graded concept descriptions with an instance-based process framework. -% Manuscript submitted for publication. -% -- Used same partition as Anderson & Kline -% -- Described evidence that Bloom's classification confidence behavior -% is similar to the human subjects' behavior. Bloom fitted the data -% more closely than did ACT. -% -% 4. Relevant Information: -% This database contains 5 numeric-valued attributes. Only a subset of -% 3 are used during testing (the latter 3). Furthermore, only 2 of the -% 3 concepts are "used" during testing (i.e., those with the prototypes -% 000 and 111). I've mapped all values to their zero-indexing equivalents. -% -% Some instances could be placed in either category 0 or 1. I've followed -% the authors' suggestion, placing them in each category with equal -% probability. -% -% I've replaced the actual values of the attributes (i.e., hobby has values -% chess, sports and stamps) with numeric values. I think this is how -% the authors' did this when testing the categorization models described -% in the paper. I find this unfair. While the subjects were able to bring -% background knowledge to bear on the attribute values and their -% relationships, the algorithms were provided with no such knowledge. I'm -% uncertain whether the 2 distractor attributes (name and hobby) are -% presented to the authors' algorithms during testing. However, it is clear -% that only the age, educational status, and marital status attributes are -% given during the human subjects' transfer tests. -% -% 5. Number of Instances: 132 training instances, 28 test instances -% -% 6. Number of Attributes: 5 plus the class membership attribute. 3 concepts. -% -% 7. Attribute Information: -% -- 1. name: distinct for each instance and represented numerically -% -- 2. hobby: nominal values ranging between 1 and 3 -% -- 3. age: nominal values ranging between 1 and 4 -% -- 4. educational level: nominal values ranging between 1 and 4 -% -- 5. marital status: nominal values ranging between 1 and 4 -% -- 6. class: nominal value between 1 and 3 -% -% 9. Missing Attribute Values: none -% -% 10. Class Distribution: see below -% -% 11. Detailed description of the experiment: -% 1. 3 categories (1, 2, and neither -- which I call 3) -% -- some of the instances could be classified in either class 1 or 2, and -% they have been evenly distributed between the two classes -% 2. 5 Attributes -% -- A. name (a randomly-generated number between 1 and 132) -% -- B. hobby (a randomly-generated number between 1 and 3) -% -- C. age (a number between 1 and 4) -% -- D. education level (a number between 1 and 4) -% -- E. marital status (a number between 1 and 4) -% 3. Classification: -% -- only attributes C-E are diagnostic; values for A and B are ignored -% -- Class Neither: if a 4 occurs for any attribute C-E -% -- Class 1: Otherwise, if (# of 1's)>(# of 2's) for attributes C-E -% -- Class 2: Otherwise, if (# of 2's)>(# of 1's) for attributes C-E -% -- Either 1 or 2: Otherwise, if (# of 2's)=(# of 1's) for attributes C-E -% 4. Prototypes: -% -- Class 1: 111 -% -- Class 2: 222 -% -- Class Either: 333 -% -- Class Neither: 444 -% 5. Number of training instances: 132 -% -- Each instance presented 0, 1, or 10 times -% -- None of the prototypes seen during training -% -- 3 instances from each of categories 1, 2, and either are repeated -% 10 times each -% -- 3 additional instances from the Either category are shown during -% learning -% 5. Number of test instances: 28 -% -- All 9 class 1 -% -- All 9 class 2 -% -- All 6 class Either -% -- All 4 prototypes -% -------------------- -% -- 28 total -% -% Observations of interest: -% 1. Relative classification confidence of -% -- prototypes for classes 1 and 2 (2 instances) -% (Anderson calls these Class 1 instances) -% -- instances of class 1 with frequency 10 during training and -% instances of class 2 with frequency 10 during training that -% are 1 value away from their respective prototypes (6 instances) -% (Anderson calls these Class 2 instances) -% -- instances of class 1 with frequency 1 during training and -% instances of class 2 with frequency 1 during training that -% are 1 value away from their respective prototypes (6 instances) -% (Anderson calls these Class 3 instances) -% -- instances of class 1 with frequency 1 during training and -% instances of class 2 with frequency 1 during training that -% are 2 values away from their respective prototypes (6 instances) -% (Anderson calls these Class 4 instances) -% 2. Relative classification recognition of them also -% -% Some Expected results: -% Both frequency and distance from prototype will effect the classification -% accuracy of instances. Greater the frequency, higher the classification -% confidence. Closer to prototype, higher the classification confidence. -% -% Information about the dataset -% CLASSTYPE: nominal -% CLASSINDEX: last -% - -@relation hayes-roth - -@attribute hobby INTEGER -@attribute age INTEGER -@attribute educational_level INTEGER -@attribute marital_status INTEGER -@attribute class {1,2,3,4} - -@data -1,1,1,2,1 -1,1,2,1,1 -1,2,1,1,1 -1,1,1,3,1 -1,1,3,1,1 -1,3,1,1,1 -1,1,3,3,1 -1,3,1,3,1 -1,3,3,1,1 -1,2,2,1,2 -1,2,1,2,2 -1,1,2,2,2 -1,2,2,3,2 -1,2,3,2,2 -1,3,2,2,2 -1,2,3,3,2 -1,3,2,3,2 -1,3,3,2,2 -1,1,3,2,1 -1,3,2,1,2 -1,2,1,3,1 -1,2,3,1,2 -1,1,2,3,1 -1,3,1,2,2 -1,1,1,1,1 -1,2,2,2,2 -1,3,3,3,1 -1,4,4,4,3 diff --git a/datasets/hayes-roth_train.arff b/datasets/hayes-roth_train.arff deleted file mode 100755 index f83c9cc..0000000 --- a/datasets/hayes-roth_train.arff +++ /dev/null @@ -1,277 +0,0 @@ -% 1. Title: Hayes-Roth & Hayes-Roth (1977) Database -% -% 2. Source Information: -% (a) Creators: Barbara and Frederick Hayes-Roth -% (b) Donor: David W. Aha (aha@ics.uci.edu) (714) 856-8779 -% (c) Date: March, 1989 -% -% 3. Past Usage: -% 1. Hayes-Roth, B., & Hayes-Roth, F. (1977). Concept learning and the -% recognition and classification of exemplars. Journal of Verbal Learning -% and Verbal Behavior, 16, 321-338. -% -- Results: -% -- Human subjects classification and recognition performance: -% 1. decreases with distance from the prototype, -% 2. is better on unseen prototypes than old instances, and -% 3. improves with presentation frequency during learning. -% 2. Anderson, J.R., & Kline, P.J. (1979). A learning system and its -% psychological implications. In Proceedings of the Sixth International -% Joint Conference on Artificial Intelligence (pp. 16-21). Tokyo, Japan: -% Morgan Kaufmann. -% -- Partitioned the results into 4 classes: -% 1. prototypes -% 2. near-prototypes with high presentation frequency during learning -% 3. near-prototypes with low presentation frequency during learning -% 4. instances that are far from protoypes -% -- Described evidence that ACT's classification confidence and -% recognition behaviors closely simulated human subjects' behaviors. -% 3. Aha, D.W. (1989). Incremental learning of independent, overlapping, and -% graded concept descriptions with an instance-based process framework. -% Manuscript submitted for publication. -% -- Used same partition as Anderson & Kline -% -- Described evidence that Bloom's classification confidence behavior -% is similar to the human subjects' behavior. Bloom fitted the data -% more closely than did ACT. -% -% 4. Relevant Information: -% This database contains 5 numeric-valued attributes. Only a subset of -% 3 are used during testing (the latter 3). Furthermore, only 2 of the -% 3 concepts are "used" during testing (i.e., those with the prototypes -% 000 and 111). I've mapped all values to their zero-indexing equivalents. -% -% Some instances could be placed in either category 0 or 1. I've followed -% the authors' suggestion, placing them in each category with equal -% probability. -% -% I've replaced the actual values of the attributes (i.e., hobby has values -% chess, sports and stamps) with numeric values. I think this is how -% the authors' did this when testing the categorization models described -% in the paper. I find this unfair. While the subjects were able to bring -% background knowledge to bear on the attribute values and their -% relationships, the algorithms were provided with no such knowledge. I'm -% uncertain whether the 2 distractor attributes (name and hobby) are -% presented to the authors' algorithms during testing. However, it is clear -% that only the age, educational status, and marital status attributes are -% given during the human subjects' transfer tests. -% -% 5. Number of Instances: 132 training instances, 28 test instances -% -% 6. Number of Attributes: 5 plus the class membership attribute. 3 concepts. -% -% 7. Attribute Information: -% -- 1. name: distinct for each instance and represented numerically -% -- 2. hobby: nominal values ranging between 1 and 3 -% -- 3. age: nominal values ranging between 1 and 4 -% -- 4. educational level: nominal values ranging between 1 and 4 -% -- 5. marital status: nominal values ranging between 1 and 4 -% -- 6. class: nominal value between 1 and 3 -% -% 9. Missing Attribute Values: none -% -% 10. Class Distribution: see below -% -% 11. Detailed description of the experiment: -% 1. 3 categories (1, 2, and neither -- which I call 3) -% -- some of the instances could be classified in either class 1 or 2, and -% they have been evenly distributed between the two classes -% 2. 5 Attributes -% -- A. name (a randomly-generated number between 1 and 132) -% -- B. hobby (a randomly-generated number between 1 and 3) -% -- C. age (a number between 1 and 4) -% -- D. education level (a number between 1 and 4) -% -- E. marital status (a number between 1 and 4) -% 3. Classification: -% -- only attributes C-E are diagnostic; values for A and B are ignored -% -- Class Neither: if a 4 occurs for any attribute C-E -% -- Class 1: Otherwise, if (# of 1's)>(# of 2's) for attributes C-E -% -- Class 2: Otherwise, if (# of 2's)>(# of 1's) for attributes C-E -% -- Either 1 or 2: Otherwise, if (# of 2's)=(# of 1's) for attributes C-E -% 4. Prototypes: -% -- Class 1: 111 -% -- Class 2: 222 -% -- Class Either: 333 -% -- Class Neither: 444 -% 5. Number of training instances: 132 -% -- Each instance presented 0, 1, or 10 times -% -- None of the prototypes seen during training -% -- 3 instances from each of categories 1, 2, and either are repeated -% 10 times each -% -- 3 additional instances from the Either category are shown during -% learning -% 5. Number of test instances: 28 -% -- All 9 class 1 -% -- All 9 class 2 -% -- All 6 class Either -% -- All 4 prototypes -% -------------------- -% -- 28 total -% -% Observations of interest: -% 1. Relative classification confidence of -% -- prototypes for classes 1 and 2 (2 instances) -% (Anderson calls these Class 1 instances) -% -- instances of class 1 with frequency 10 during training and -% instances of class 2 with frequency 10 during training that -% are 1 value away from their respective prototypes (6 instances) -% (Anderson calls these Class 2 instances) -% -- instances of class 1 with frequency 1 during training and -% instances of class 2 with frequency 1 during training that -% are 1 value away from their respective prototypes (6 instances) -% (Anderson calls these Class 3 instances) -% -- instances of class 1 with frequency 1 during training and -% instances of class 2 with frequency 1 during training that -% are 2 values away from their respective prototypes (6 instances) -% (Anderson calls these Class 4 instances) -% 2. Relative classification recognition of them also -% -% Some Expected results: -% Both frequency and distance from prototype will effect the classification -% accuracy of instances. Greater the frequency, higher the classification -% confidence. Closer to prototype, higher the classification confidence. -% -% Information about the dataset -% CLASSTYPE: nominal -% CLASSINDEX: last -% - -@relation hayes-roth - -@attribute hobby INTEGER -@attribute age INTEGER -@attribute educational_level INTEGER -@attribute marital_status INTEGER -@attribute class {1,2,3,4} - -@data -2,1,1,2,1 -2,1,3,2,2 -3,1,4,1,3 -2,4,2,2,3 -1,1,3,4,3 -1,1,3,2,2 -3,1,3,2,2 -3,4,2,4,3 -2,2,1,1,1 -3,2,1,1,1 -1,2,1,1,1 -2,2,3,4,3 -1,1,2,1,1 -2,1,2,2,2 -2,4,1,4,3 -1,1,3,3,1 -3,2,1,2,2 -1,2,1,1,1 -3,3,2,1,1 -3,1,3,2,1 -1,2,2,1,2 -3,2,1,3,1 -2,1,2,1,1 -3,2,1,3,1 -2,3,2,1,1 -3,2,2,1,2 -3,2,1,3,2 -2,1,2,2,2 -1,1,3,2,1 -3,2,1,1,1 -1,4,1,1,3 -2,2,1,3,1 -1,2,1,3,2 -1,1,1,2,1 -2,4,3,1,3 -3,1,2,2,2 -1,1,2,2,2 -3,2,2,1,2 -1,2,1,2,2 -3,4,3,2,3 -2,2,2,1,2 -2,2,1,2,2 -3,2,1,3,2 -3,2,1,1,1 -3,1,2,1,1 -1,2,1,3,2 -2,1,1,2,1 -1,1,1,2,1 -1,2,2,3,2 -3,3,1,1,1 -3,3,3,1,1 -3,2,1,2,2 -3,2,1,2,2 -3,1,2,1,1 -1,1,1,2,1 -2,1,3,2,1 -2,2,2,1,2 -2,1,2,1,1 -2,2,1,3,1 -2,1,2,2,2 -1,2,4,2,3 -2,2,1,2,2 -1,1,2,4,3 -1,3,2,1,1 -2,4,4,2,3 -2,3,2,1,1 -3,1,2,2,2 -1,1,2,2,2 -1,3,2,4,3 -1,1,2,2,2 -3,1,4,2,3 -2,1,3,2,2 -1,1,3,2,2 -3,1,3,2,1 -1,2,4,4,3 -1,4,2,1,3 -2,1,2,1,1 -3,4,1,2,3 -2,2,1,1,1 -1,1,2,1,1 -2,2,4,3,3 -3,1,2,2,2 -1,1,3,2,1 -1,2,1,3,1 -1,4,4,1,3 -3,3,3,2,2 -2,2,1,3,2 -3,3,2,1,2 -1,1,1,3,1 -2,2,1,2,2 -2,2,2,1,2 -2,3,2,3,2 -1,3,2,1,2 -2,2,1,2,2 -1,1,1,2,1 -3,2,2,1,2 -3,2,1,1,1 -1,1,2,1,1 -3,1,4,4,3 -3,3,2,1,2 -2,3,2,1,2 -2,1,3,1,1 -1,2,1,2,2 -3,1,1,2,1 -2,2,4,1,3 -1,2,2,1,2 -2,3,2,1,2 -2,2,1,4,3 -1,4,2,3,3 -2,2,1,1,1 -1,2,1,1,1 -2,2,3,2,2 -1,3,2,1,1 -3,1,2,1,1 -3,1,1,2,1 -3,3,1,4,3 -2,3,4,1,3 -1,2,3,3,2 -3,3,2,2,2 -3,3,4,2,3 -1,2,2,1,2 -2,1,1,4,3 -3,1,2,2,2 -3,2,2,4,3 -2,3,1,3,1 -2,1,1,2,1 -3,4,1,3,3 -1,1,4,3,3 -2,1,2,1,1 -1,2,1,2,2 -1,2,2,1,2 -3,1,1,2,1 diff --git a/datasets/heart-statlog.arff b/datasets/heart-statlog.arff deleted file mode 100755 index 04aebc0..0000000 --- a/datasets/heart-statlog.arff +++ /dev/null @@ -1,338 +0,0 @@ -% This database contains 13 attributes (which have been extracted from -% a larger set of 75) -% -% -% -% Attribute Information: -% ------------------------ -% -- 1. age -% -- 2. sex -% -- 3. chest pain type (4 values) -% -- 4. resting blood pressure -% -- 5. serum cholestoral in mg/dl -% -- 6. fasting blood sugar > 120 mg/dl -% -- 7. resting electrocardiographic results (values 0,1,2) -% -- 8. maximum heart rate achieved -% -- 9. exercise induced angina -% -- 10. oldpeak = ST depression induced by exercise relative to rest -% -- 11. the slope of the peak exercise ST segment -% -- 12. number of major vessels (0-3) colored by flourosopy -% -- 13. thal: 3 = normal; 6 = fixed defect; 7 = reversable defect -% -% Attributes types -% ----------------- -% -% Real: 1,4,5,8,10,12 -% Ordered:11, -% Binary: 2,6,9 -% Nominal:7,3,13 -% -% Variable to be predicted -% ------------------------ -% Absence (1) or presence (2) of heart disease -% -% Cost Matrix -% -% abse pres -% absence 0 1 -% presence 5 0 -% -% where the rows represent the true values and the columns the predicted. -% -% No missing values. -% -% 270 observations -% -% -% -% -% Relabeled values in attribute class -% From: 1 To: absent -% From: 2 To: present -% -@relation heart-statlog -@attribute age real -@attribute sex real -@attribute chest real -@attribute resting_blood_pressure real -@attribute serum_cholestoral real -@attribute fasting_blood_sugar real -@attribute resting_electrocardiographic_results real -@attribute maximum_heart_rate_achieved real -@attribute exercise_induced_angina real -@attribute oldpeak real -@attribute slope real -@attribute number_of_major_vessels real -@attribute thal real -@attribute class { absent, present} -@data -70,1,4,130,322,0,2,109,0,2.4,2,3,3,present -67,0,3,115,564,0,2,160,0,1.6,2,0,7,absent -57,1,2,124,261,0,0,141,0,0.3,1,0,7,present -64,1,4,128,263,0,0,105,1,0.2,2,1,7,absent -74,0,2,120,269,0,2,121,1,0.2,1,1,3,absent -65,1,4,120,177,0,0,140,0,0.4,1,0,7,absent -56,1,3,130,256,1,2,142,1,0.6,2,1,6,present -59,1,4,110,239,0,2,142,1,1.2,2,1,7,present -60,1,4,140,293,0,2,170,0,1.2,2,2,7,present -63,0,4,150,407,0,2,154,0,4,2,3,7,present -59,1,4,135,234,0,0,161,0,0.5,2,0,7,absent -53,1,4,142,226,0,2,111,1,0,1,0,7,absent -44,1,3,140,235,0,2,180,0,0,1,0,3,absent -61,1,1,134,234,0,0,145,0,2.6,2,2,3,present -57,0,4,128,303,0,2,159,0,0,1,1,3,absent -71,0,4,112,149,0,0,125,0,1.6,2,0,3,absent -46,1,4,140,311,0,0,120,1,1.8,2,2,7,present -53,1,4,140,203,1,2,155,1,3.1,3,0,7,present -64,1,1,110,211,0,2,144,1,1.8,2,0,3,absent -40,1,1,140,199,0,0,178,1,1.4,1,0,7,absent -67,1,4,120,229,0,2,129,1,2.6,2,2,7,present -48,1,2,130,245,0,2,180,0,0.2,2,0,3,absent -43,1,4,115,303,0,0,181,0,1.2,2,0,3,absent -47,1,4,112,204,0,0,143,0,0.1,1,0,3,absent -54,0,2,132,288,1,2,159,1,0,1,1,3,absent -48,0,3,130,275,0,0,139,0,0.2,1,0,3,absent -46,0,4,138,243,0,2,152,1,0,2,0,3,absent -51,0,3,120,295,0,2,157,0,0.6,1,0,3,absent -58,1,3,112,230,0,2,165,0,2.5,2,1,7,present -71,0,3,110,265,1,2,130,0,0,1,1,3,absent -57,1,3,128,229,0,2,150,0,0.4,2,1,7,present -66,1,4,160,228,0,2,138,0,2.3,1,0,6,absent -37,0,3,120,215,0,0,170,0,0,1,0,3,absent -59,1,4,170,326,0,2,140,1,3.4,3,0,7,present -50,1,4,144,200,0,2,126,1,0.9,2,0,7,present -48,1,4,130,256,1,2,150,1,0,1,2,7,present -61,1,4,140,207,0,2,138,1,1.9,1,1,7,present -59,1,1,160,273,0,2,125,0,0,1,0,3,present -42,1,3,130,180,0,0,150,0,0,1,0,3,absent -48,1,4,122,222,0,2,186,0,0,1,0,3,absent -40,1,4,152,223,0,0,181,0,0,1,0,7,present -62,0,4,124,209,0,0,163,0,0,1,0,3,absent -44,1,3,130,233,0,0,179,1,0.4,1,0,3,absent -46,1,2,101,197,1,0,156,0,0,1,0,7,absent -59,1,3,126,218,1,0,134,0,2.2,2,1,6,present -58,1,3,140,211,1,2,165,0,0,1,0,3,absent -49,1,3,118,149,0,2,126,0,0.8,1,3,3,present -44,1,4,110,197,0,2,177,0,0,1,1,3,present -66,1,2,160,246,0,0,120,1,0,2,3,6,present -65,0,4,150,225,0,2,114,0,1,2,3,7,present -42,1,4,136,315,0,0,125,1,1.8,2,0,6,present -52,1,2,128,205,1,0,184,0,0,1,0,3,absent -65,0,3,140,417,1,2,157,0,0.8,1,1,3,absent -63,0,2,140,195,0,0,179,0,0,1,2,3,absent -45,0,2,130,234,0,2,175,0,0.6,2,0,3,absent -41,0,2,105,198,0,0,168,0,0,1,1,3,absent -61,1,4,138,166,0,2,125,1,3.6,2,1,3,present -60,0,3,120,178,1,0,96,0,0,1,0,3,absent -59,0,4,174,249,0,0,143,1,0,2,0,3,present -62,1,2,120,281,0,2,103,0,1.4,2,1,7,present -57,1,3,150,126,1,0,173,0,0.2,1,1,7,absent -51,0,4,130,305,0,0,142,1,1.2,2,0,7,present -44,1,3,120,226,0,0,169,0,0,1,0,3,absent -60,0,1,150,240,0,0,171,0,0.9,1,0,3,absent -63,1,1,145,233,1,2,150,0,2.3,3,0,6,absent -57,1,4,150,276,0,2,112,1,0.6,2,1,6,present -51,1,4,140,261,0,2,186,1,0,1,0,3,absent -58,0,2,136,319,1,2,152,0,0,1,2,3,present -44,0,3,118,242,0,0,149,0,0.3,2,1,3,absent -47,1,3,108,243,0,0,152,0,0,1,0,3,present -61,1,4,120,260,0,0,140,1,3.6,2,1,7,present -57,0,4,120,354,0,0,163,1,0.6,1,0,3,absent -70,1,2,156,245,0,2,143,0,0,1,0,3,absent -76,0,3,140,197,0,1,116,0,1.1,2,0,3,absent -67,0,4,106,223,0,0,142,0,0.3,1,2,3,absent -45,1,4,142,309,0,2,147,1,0,2,3,7,present -45,1,4,104,208,0,2,148,1,3,2,0,3,absent -39,0,3,94,199,0,0,179,0,0,1,0,3,absent -42,0,3,120,209,0,0,173,0,0,2,0,3,absent -56,1,2,120,236,0,0,178,0,0.8,1,0,3,absent -58,1,4,146,218,0,0,105,0,2,2,1,7,present -35,1,4,120,198,0,0,130,1,1.6,2,0,7,present -58,1,4,150,270,0,2,111,1,0.8,1,0,7,present -41,1,3,130,214,0,2,168,0,2,2,0,3,absent -57,1,4,110,201,0,0,126,1,1.5,2,0,6,absent -42,1,1,148,244,0,2,178,0,0.8,1,2,3,absent -62,1,2,128,208,1,2,140,0,0,1,0,3,absent -59,1,1,178,270,0,2,145,0,4.2,3,0,7,absent -41,0,2,126,306,0,0,163,0,0,1,0,3,absent -50,1,4,150,243,0,2,128,0,2.6,2,0,7,present -59,1,2,140,221,0,0,164,1,0,1,0,3,absent -61,0,4,130,330,0,2,169,0,0,1,0,3,present -54,1,4,124,266,0,2,109,1,2.2,2,1,7,present -54,1,4,110,206,0,2,108,1,0,2,1,3,present -52,1,4,125,212,0,0,168,0,1,1,2,7,present -47,1,4,110,275,0,2,118,1,1,2,1,3,present -66,1,4,120,302,0,2,151,0,0.4,2,0,3,absent -58,1,4,100,234,0,0,156,0,0.1,1,1,7,present -64,0,3,140,313,0,0,133,0,0.2,1,0,7,absent -50,0,2,120,244,0,0,162,0,1.1,1,0,3,absent -44,0,3,108,141,0,0,175,0,0.6,2,0,3,absent -67,1,4,120,237,0,0,71,0,1,2,0,3,present -49,0,4,130,269,0,0,163,0,0,1,0,3,absent -57,1,4,165,289,1,2,124,0,1,2,3,7,present -63,1,4,130,254,0,2,147,0,1.4,2,1,7,present -48,1,4,124,274,0,2,166,0,0.5,2,0,7,present -51,1,3,100,222,0,0,143,1,1.2,2,0,3,absent -60,0,4,150,258,0,2,157,0,2.6,2,2,7,present -59,1,4,140,177,0,0,162,1,0,1,1,7,present -45,0,2,112,160,0,0,138,0,0,2,0,3,absent -55,0,4,180,327,0,1,117,1,3.4,2,0,3,present -41,1,2,110,235,0,0,153,0,0,1,0,3,absent -60,0,4,158,305,0,2,161,0,0,1,0,3,present -54,0,3,135,304,1,0,170,0,0,1,0,3,absent -42,1,2,120,295,0,0,162,0,0,1,0,3,absent -49,0,2,134,271,0,0,162,0,0,2,0,3,absent -46,1,4,120,249,0,2,144,0,0.8,1,0,7,present -56,0,4,200,288,1,2,133,1,4,3,2,7,present -66,0,1,150,226,0,0,114,0,2.6,3,0,3,absent -56,1,4,130,283,1,2,103,1,1.6,3,0,7,present -49,1,3,120,188,0,0,139,0,2,2,3,7,present -54,1,4,122,286,0,2,116,1,3.2,2,2,3,present -57,1,4,152,274,0,0,88,1,1.2,2,1,7,present -65,0,3,160,360,0,2,151,0,0.8,1,0,3,absent -54,1,3,125,273,0,2,152,0,0.5,3,1,3,absent -54,0,3,160,201,0,0,163,0,0,1,1,3,absent -62,1,4,120,267,0,0,99,1,1.8,2,2,7,present -52,0,3,136,196,0,2,169,0,0.1,2,0,3,absent -52,1,2,134,201,0,0,158,0,0.8,1,1,3,absent -60,1,4,117,230,1,0,160,1,1.4,1,2,7,present -63,0,4,108,269,0,0,169,1,1.8,2,2,3,present -66,1,4,112,212,0,2,132,1,0.1,1,1,3,present -42,1,4,140,226,0,0,178,0,0,1,0,3,absent -64,1,4,120,246,0,2,96,1,2.2,3,1,3,present -54,1,3,150,232,0,2,165,0,1.6,1,0,7,absent -46,0,3,142,177,0,2,160,1,1.4,3,0,3,absent -67,0,3,152,277,0,0,172,0,0,1,1,3,absent -56,1,4,125,249,1,2,144,1,1.2,2,1,3,present -34,0,2,118,210,0,0,192,0,0.7,1,0,3,absent -57,1,4,132,207,0,0,168,1,0,1,0,7,absent -64,1,4,145,212,0,2,132,0,2,2,2,6,present -59,1,4,138,271,0,2,182,0,0,1,0,3,absent -50,1,3,140,233,0,0,163,0,0.6,2,1,7,present -51,1,1,125,213,0,2,125,1,1.4,1,1,3,absent -54,1,2,192,283,0,2,195,0,0,1,1,7,present -53,1,4,123,282,0,0,95,1,2,2,2,7,present -52,1,4,112,230,0,0,160,0,0,1,1,3,present -40,1,4,110,167,0,2,114,1,2,2,0,7,present -58,1,3,132,224,0,2,173,0,3.2,1,2,7,present -41,0,3,112,268,0,2,172,1,0,1,0,3,absent -41,1,3,112,250,0,0,179,0,0,1,0,3,absent -50,0,3,120,219,0,0,158,0,1.6,2,0,3,absent -54,0,3,108,267,0,2,167,0,0,1,0,3,absent -64,0,4,130,303,0,0,122,0,2,2,2,3,absent -51,0,3,130,256,0,2,149,0,0.5,1,0,3,absent -46,0,2,105,204,0,0,172,0,0,1,0,3,absent -55,1,4,140,217,0,0,111,1,5.6,3,0,7,present -45,1,2,128,308,0,2,170,0,0,1,0,3,absent -56,1,1,120,193,0,2,162,0,1.9,2,0,7,absent -66,0,4,178,228,1,0,165,1,1,2,2,7,present -38,1,1,120,231,0,0,182,1,3.8,2,0,7,present -62,0,4,150,244,0,0,154,1,1.4,2,0,3,present -55,1,2,130,262,0,0,155,0,0,1,0,3,absent -58,1,4,128,259,0,2,130,1,3,2,2,7,present -43,1,4,110,211,0,0,161,0,0,1,0,7,absent -64,0,4,180,325,0,0,154,1,0,1,0,3,absent -50,0,4,110,254,0,2,159,0,0,1,0,3,absent -53,1,3,130,197,1,2,152,0,1.2,3,0,3,absent -45,0,4,138,236,0,2,152,1,0.2,2,0,3,absent -65,1,1,138,282,1,2,174,0,1.4,2,1,3,present -69,1,1,160,234,1,2,131,0,0.1,2,1,3,absent -69,1,3,140,254,0,2,146,0,2,2,3,7,present -67,1,4,100,299,0,2,125,1,0.9,2,2,3,present -68,0,3,120,211,0,2,115,0,1.5,2,0,3,absent -34,1,1,118,182,0,2,174,0,0,1,0,3,absent -62,0,4,138,294,1,0,106,0,1.9,2,3,3,present -51,1,4,140,298,0,0,122,1,4.2,2,3,7,present -46,1,3,150,231,0,0,147,0,3.6,2,0,3,present -67,1,4,125,254,1,0,163,0,0.2,2,2,7,present -50,1,3,129,196,0,0,163,0,0,1,0,3,absent -42,1,3,120,240,1,0,194,0,0.8,3,0,7,absent -56,0,4,134,409,0,2,150,1,1.9,2,2,7,present -41,1,4,110,172,0,2,158,0,0,1,0,7,present -42,0,4,102,265,0,2,122,0,0.6,2,0,3,absent -53,1,3,130,246,1,2,173,0,0,1,3,3,absent -43,1,3,130,315,0,0,162,0,1.9,1,1,3,absent -56,1,4,132,184,0,2,105,1,2.1,2,1,6,present -52,1,4,108,233,1,0,147,0,0.1,1,3,7,absent -62,0,4,140,394,0,2,157,0,1.2,2,0,3,absent -70,1,3,160,269,0,0,112,1,2.9,2,1,7,present -54,1,4,140,239,0,0,160,0,1.2,1,0,3,absent -70,1,4,145,174,0,0,125,1,2.6,3,0,7,present -54,1,2,108,309,0,0,156,0,0,1,0,7,absent -35,1,4,126,282,0,2,156,1,0,1,0,7,present -48,1,3,124,255,1,0,175,0,0,1,2,3,absent -55,0,2,135,250,0,2,161,0,1.4,2,0,3,absent -58,0,4,100,248,0,2,122,0,1,2,0,3,absent -54,0,3,110,214,0,0,158,0,1.6,2,0,3,absent -69,0,1,140,239,0,0,151,0,1.8,1,2,3,absent -77,1,4,125,304,0,2,162,1,0,1,3,3,present -68,1,3,118,277,0,0,151,0,1,1,1,7,absent -58,1,4,125,300,0,2,171,0,0,1,2,7,present -60,1,4,125,258,0,2,141,1,2.8,2,1,7,present -51,1,4,140,299,0,0,173,1,1.6,1,0,7,present -55,1,4,160,289,0,2,145,1,0.8,2,1,7,present -52,1,1,152,298,1,0,178,0,1.2,2,0,7,absent -60,0,3,102,318,0,0,160,0,0,1,1,3,absent -58,1,3,105,240,0,2,154,1,0.6,2,0,7,absent -64,1,3,125,309,0,0,131,1,1.8,2,0,7,present -37,1,3,130,250,0,0,187,0,3.5,3,0,3,absent -59,1,1,170,288,0,2,159,0,0.2,2,0,7,present -51,1,3,125,245,1,2,166,0,2.4,2,0,3,absent -43,0,3,122,213,0,0,165,0,0.2,2,0,3,absent 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-53,0,4,138,234,0,2,160,0,0,1,0,3,absent -43,1,4,120,177,0,2,120,1,2.5,2,0,7,present -47,1,3,138,257,0,2,156,0,0,1,0,3,absent -52,1,2,120,325,0,0,172,0,0.2,1,0,3,absent -68,1,3,180,274,1,2,150,1,1.6,2,0,7,present -39,1,3,140,321,0,2,182,0,0,1,0,3,absent -53,0,4,130,264,0,2,143,0,0.4,2,0,3,absent -62,0,4,140,268,0,2,160,0,3.6,3,2,3,present -51,0,3,140,308,0,2,142,0,1.5,1,1,3,absent -60,1,4,130,253,0,0,144,1,1.4,1,1,7,present -65,1,4,110,248,0,2,158,0,0.6,1,2,6,present -65,0,3,155,269,0,0,148,0,0.8,1,0,3,absent -60,1,3,140,185,0,2,155,0,3,2,0,3,present -60,1,4,145,282,0,2,142,1,2.8,2,2,7,present -54,1,4,120,188,0,0,113,0,1.4,2,1,7,present -44,1,2,130,219,0,2,188,0,0,1,0,3,absent -44,1,4,112,290,0,2,153,0,0,1,1,3,present -51,1,3,110,175,0,0,123,0,0.6,1,0,3,absent -59,1,3,150,212,1,0,157,0,1.6,1,0,3,absent -71,0,2,160,302,0,0,162,0,0.4,1,2,3,absent -61,1,3,150,243,1,0,137,1,1,2,0,3,absent -55,1,4,132,353,0,0,132,1,1.2,2,1,7,present -64,1,3,140,335,0,0,158,0,0,1,0,3,present 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Title: Johns Hopkins University Ionosphere database -% -%2. Source Information: -% -- Donor: Vince Sigillito (vgs@aplcen.apl.jhu.edu) -% -- Date: 1989 -% -- Source: Space Physics Group -% Applied Physics Laboratory -% Johns Hopkins University -% Johns Hopkins Road -% Laurel, MD 20723 -% -%3. Past Usage: -% -- Sigillito, V. G., Wing, S. P., Hutton, L. V., \& Baker, K. B. (1989). -% Classification of radar returns from the ionosphere using neural -% networks. Johns Hopkins APL Technical Digest, 10, 262-266. -% -% They investigated using backprop and the perceptron training algorithm -% on this database. Using the first 200 instances for training, which -% were carefully split almost 50% positive and 50% negative, they found -% that a "linear" perceptron attained 90.7%, a "non-linear" perceptron -% attained 92%, and backprop an average of over 96% accuracy on the -% remaining 150 test instances, consisting of 123 "good" and only 24 "bad" -% instances. (There was a counting error or some mistake somewhere; there -% are a total of 351 rather than 350 instances in this domain.) Accuracy -% on "good" instances was much higher than for "bad" instances. Backprop -% was tested with several different numbers of hidden units (in [0,15]) -% and incremental results were also reported (corresponding to how well -% the different variants of backprop did after a periodic number of -% epochs). -% -% David Aha (aha@ics.uci.edu) briefly investigated this database. -% He found that nearest neighbor attains an accuracy of 92.1%, that -% Ross Quinlan's C4 algorithm attains 94.0% (no windowing), and that -% IB3 (Aha \& Kibler, IJCAI-1989) attained 96.7% (parameter settings: -% 70% and 80% for acceptance and dropping respectively). -% -%4. Relevant Information: -% This radar data was collected by a system in Goose Bay, Labrador. This -% system consists of a phased array of 16 high-frequency antennas with a -% total transmitted power on the order of 6.4 kilowatts. See the paper -% for more details. The targets were free electrons in the ionosphere. -% "Good" radar returns are those showing evidence of some type of structure -% in the ionosphere. "Bad" returns are those that do not; their signals pass -% through the ionosphere. -% -% Received signals were processed using an autocorrelation function whose -% arguments are the time of a pulse and the pulse number. There were 17 -% pulse numbers for the Goose Bay system. Instances in this databse are -% described by 2 attributes per pulse number, corresponding to the complex -% values returned by the function resulting from the complex electromagnetic -% signal. -% -%5. Number of Instances: 351 -% -%6. Number of Attributes: 34 plus the class attribute -% -- All 34 predictor attributes are continuous -% -%7. Attribute Information: -% -- All 34 are continuous, as described above -% -- The 35th attribute is either "good" or "bad" according to the definition -% summarized above. This is a binary classification task. -% -%8. Missing Values: None - -@relation ionosphere - -@attribute a01 real -@attribute a02 real -@attribute a03 real -@attribute a04 real -@attribute a05 real -@attribute a06 real -@attribute a07 real -@attribute a08 real -@attribute a09 real -@attribute a10 real -@attribute a11 real -@attribute a12 real -@attribute a13 real -@attribute a14 real -@attribute a15 real -@attribute a16 real -@attribute a17 real -@attribute a18 real -@attribute a19 real -@attribute a20 real -@attribute a21 real -@attribute a22 real -@attribute a23 real -@attribute a24 real -@attribute a25 real -@attribute a26 real -@attribute a27 real -@attribute a28 real -@attribute a29 real -@attribute a30 real -@attribute a31 real -@attribute a32 real -@attribute a33 real -@attribute a34 real -@attribute class {b, g} - -@data - 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Title: Iris Plants Database -% -% 2. Sources: -% (a) Creator: R.A. Fisher -% (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov) -% (c) Date: July, 1988 -% -% 3. Past Usage: -% - Publications: too many to mention!!! Here are a few. -% 1. Fisher,R.A. "The use of multiple measurements in taxonomic problems" -% Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions -% to Mathematical Statistics" (John Wiley, NY, 1950). -% 2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis. -% (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218. -% 3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System -% Structure and Classification Rule for Recognition in Partially Exposed -% Environments". IEEE Transactions on Pattern Analysis and Machine -% Intelligence, Vol. PAMI-2, No. 1, 67-71. -% -- Results: -% -- very low misclassification rates (0% for the setosa class) -% 4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE -% Transactions on Information Theory, May 1972, 431-433. -% -- Results: -% -- very low misclassification rates again -% 5. See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II -% conceptual clustering system finds 3 classes in the data. -% -% 4. Relevant Information: -% --- This is perhaps the best known database to be found in the pattern -% recognition literature. Fisher's paper is a classic in the field -% and is referenced frequently to this day. (See Duda & Hart, for -% example.) The data set contains 3 classes of 50 instances each, -% where each class refers to a type of iris plant. One class is -% linearly separable from the other 2; the latter are NOT linearly -% separable from each other. -% --- Predicted attribute: class of iris plant. -% --- This is an exceedingly simple domain. -% -% 5. Number of Instances: 150 (50 in each of three classes) -% -% 6. Number of Attributes: 4 numeric, predictive attributes and the class -% -% 7. Attribute Information: -% 1. sepal length in cm -% 2. sepal width in cm -% 3. petal length in cm -% 4. petal width in cm -% 5. class: -% -- Iris Setosa -% -- Iris Versicolour -% -- Iris Virginica -% -% 8. Missing Attribute Values: None -% -% Summary Statistics: -% Min Max Mean SD Class Correlation -% sepal length: 4.3 7.9 5.84 0.83 0.7826 -% sepal width: 2.0 4.4 3.05 0.43 -0.4194 -% petal length: 1.0 6.9 3.76 1.76 0.9490 (high!) -% petal width: 0.1 2.5 1.20 0.76 0.9565 (high!) -% -% 9. Class Distribution: 33.3% for each of 3 classes. - -@RELATION iris - -@ATTRIBUTE sepallength REAL -@ATTRIBUTE sepalwidth REAL -@ATTRIBUTE petallength REAL -@ATTRIBUTE petalwidth REAL -@ATTRIBUTE class {Iris-setosa,Iris-versicolor,Iris-virginica} - -@DATA -5.1,3.5,1.4,0.2,Iris-setosa -4.9,3.0,1.4,0.2,Iris-setosa -4.7,3.2,1.3,0.2,Iris-setosa -4.6,3.1,1.5,0.2,Iris-setosa -5.0,3.6,1.4,0.2,Iris-setosa -5.4,3.9,1.7,0.4,Iris-setosa -4.6,3.4,1.4,0.3,Iris-setosa -5.0,3.4,1.5,0.2,Iris-setosa -4.4,2.9,1.4,0.2,Iris-setosa -4.9,3.1,1.5,0.1,Iris-setosa -5.4,3.7,1.5,0.2,Iris-setosa -4.8,3.4,1.6,0.2,Iris-setosa -4.8,3.0,1.4,0.1,Iris-setosa -4.3,3.0,1.1,0.1,Iris-setosa -5.8,4.0,1.2,0.2,Iris-setosa -5.7,4.4,1.5,0.4,Iris-setosa -5.4,3.9,1.3,0.4,Iris-setosa -5.1,3.5,1.4,0.3,Iris-setosa -5.7,3.8,1.7,0.3,Iris-setosa -5.1,3.8,1.5,0.3,Iris-setosa -5.4,3.4,1.7,0.2,Iris-setosa -5.1,3.7,1.5,0.4,Iris-setosa -4.6,3.6,1.0,0.2,Iris-setosa -5.1,3.3,1.7,0.5,Iris-setosa -4.8,3.4,1.9,0.2,Iris-setosa -5.0,3.0,1.6,0.2,Iris-setosa -5.0,3.4,1.6,0.4,Iris-setosa -5.2,3.5,1.5,0.2,Iris-setosa -5.2,3.4,1.4,0.2,Iris-setosa -4.7,3.2,1.6,0.2,Iris-setosa -4.8,3.1,1.6,0.2,Iris-setosa -5.4,3.4,1.5,0.4,Iris-setosa -5.2,4.1,1.5,0.1,Iris-setosa -5.5,4.2,1.4,0.2,Iris-setosa -4.9,3.1,1.5,0.1,Iris-setosa -5.0,3.2,1.2,0.2,Iris-setosa -5.5,3.5,1.3,0.2,Iris-setosa -4.9,3.1,1.5,0.1,Iris-setosa -4.4,3.0,1.3,0.2,Iris-setosa -5.1,3.4,1.5,0.2,Iris-setosa -5.0,3.5,1.3,0.3,Iris-setosa -4.5,2.3,1.3,0.3,Iris-setosa -4.4,3.2,1.3,0.2,Iris-setosa -5.0,3.5,1.6,0.6,Iris-setosa -5.1,3.8,1.9,0.4,Iris-setosa -4.8,3.0,1.4,0.3,Iris-setosa -5.1,3.8,1.6,0.2,Iris-setosa -4.6,3.2,1.4,0.2,Iris-setosa -5.3,3.7,1.5,0.2,Iris-setosa -5.0,3.3,1.4,0.2,Iris-setosa -7.0,3.2,4.7,1.4,Iris-versicolor -6.4,3.2,4.5,1.5,Iris-versicolor -6.9,3.1,4.9,1.5,Iris-versicolor -5.5,2.3,4.0,1.3,Iris-versicolor -6.5,2.8,4.6,1.5,Iris-versicolor -5.7,2.8,4.5,1.3,Iris-versicolor -6.3,3.3,4.7,1.6,Iris-versicolor -4.9,2.4,3.3,1.0,Iris-versicolor -6.6,2.9,4.6,1.3,Iris-versicolor -5.2,2.7,3.9,1.4,Iris-versicolor -5.0,2.0,3.5,1.0,Iris-versicolor -5.9,3.0,4.2,1.5,Iris-versicolor -6.0,2.2,4.0,1.0,Iris-versicolor -6.1,2.9,4.7,1.4,Iris-versicolor -5.6,2.9,3.6,1.3,Iris-versicolor -6.7,3.1,4.4,1.4,Iris-versicolor -5.6,3.0,4.5,1.5,Iris-versicolor -5.8,2.7,4.1,1.0,Iris-versicolor -6.2,2.2,4.5,1.5,Iris-versicolor -5.6,2.5,3.9,1.1,Iris-versicolor -5.9,3.2,4.8,1.8,Iris-versicolor -6.1,2.8,4.0,1.3,Iris-versicolor -6.3,2.5,4.9,1.5,Iris-versicolor -6.1,2.8,4.7,1.2,Iris-versicolor -6.4,2.9,4.3,1.3,Iris-versicolor -6.6,3.0,4.4,1.4,Iris-versicolor -6.8,2.8,4.8,1.4,Iris-versicolor -6.7,3.0,5.0,1.7,Iris-versicolor -6.0,2.9,4.5,1.5,Iris-versicolor -5.7,2.6,3.5,1.0,Iris-versicolor -5.5,2.4,3.8,1.1,Iris-versicolor -5.5,2.4,3.7,1.0,Iris-versicolor -5.8,2.7,3.9,1.2,Iris-versicolor -6.0,2.7,5.1,1.6,Iris-versicolor -5.4,3.0,4.5,1.5,Iris-versicolor -6.0,3.4,4.5,1.6,Iris-versicolor -6.7,3.1,4.7,1.5,Iris-versicolor -6.3,2.3,4.4,1.3,Iris-versicolor -5.6,3.0,4.1,1.3,Iris-versicolor -5.5,2.5,4.0,1.3,Iris-versicolor -5.5,2.6,4.4,1.2,Iris-versicolor -6.1,3.0,4.6,1.4,Iris-versicolor -5.8,2.6,4.0,1.2,Iris-versicolor -5.0,2.3,3.3,1.0,Iris-versicolor -5.6,2.7,4.2,1.3,Iris-versicolor -5.7,3.0,4.2,1.2,Iris-versicolor -5.7,2.9,4.2,1.3,Iris-versicolor -6.2,2.9,4.3,1.3,Iris-versicolor -5.1,2.5,3.0,1.1,Iris-versicolor -5.7,2.8,4.1,1.3,Iris-versicolor -6.3,3.3,6.0,2.5,Iris-virginica -5.8,2.7,5.1,1.9,Iris-virginica -7.1,3.0,5.9,2.1,Iris-virginica -6.3,2.9,5.6,1.8,Iris-virginica -6.5,3.0,5.8,2.2,Iris-virginica -7.6,3.0,6.6,2.1,Iris-virginica -4.9,2.5,4.5,1.7,Iris-virginica -7.3,2.9,6.3,1.8,Iris-virginica -6.7,2.5,5.8,1.8,Iris-virginica -7.2,3.6,6.1,2.5,Iris-virginica -6.5,3.2,5.1,2.0,Iris-virginica -6.4,2.7,5.3,1.9,Iris-virginica -6.8,3.0,5.5,2.1,Iris-virginica -5.7,2.5,5.0,2.0,Iris-virginica -5.8,2.8,5.1,2.4,Iris-virginica -6.4,3.2,5.3,2.3,Iris-virginica -6.5,3.0,5.5,1.8,Iris-virginica -7.7,3.8,6.7,2.2,Iris-virginica -7.7,2.6,6.9,2.3,Iris-virginica -6.0,2.2,5.0,1.5,Iris-virginica -6.9,3.2,5.7,2.3,Iris-virginica -5.6,2.8,4.9,2.0,Iris-virginica -7.7,2.8,6.7,2.0,Iris-virginica -6.3,2.7,4.9,1.8,Iris-virginica -6.7,3.3,5.7,2.1,Iris-virginica -7.2,3.2,6.0,1.8,Iris-virginica -6.2,2.8,4.8,1.8,Iris-virginica -6.1,3.0,4.9,1.8,Iris-virginica -6.4,2.8,5.6,2.1,Iris-virginica -7.2,3.0,5.8,1.6,Iris-virginica -7.4,2.8,6.1,1.9,Iris-virginica -7.9,3.8,6.4,2.0,Iris-virginica -6.4,2.8,5.6,2.2,Iris-virginica -6.3,2.8,5.1,1.5,Iris-virginica -6.1,2.6,5.6,1.4,Iris-virginica -7.7,3.0,6.1,2.3,Iris-virginica -6.3,3.4,5.6,2.4,Iris-virginica -6.4,3.1,5.5,1.8,Iris-virginica -6.0,3.0,4.8,1.8,Iris-virginica -6.9,3.1,5.4,2.1,Iris-virginica -6.7,3.1,5.6,2.4,Iris-virginica -6.9,3.1,5.1,2.3,Iris-virginica -5.8,2.7,5.1,1.9,Iris-virginica -6.8,3.2,5.9,2.3,Iris-virginica -6.7,3.3,5.7,2.5,Iris-virginica -6.7,3.0,5.2,2.3,Iris-virginica -6.3,2.5,5.0,1.9,Iris-virginica -6.5,3.0,5.2,2.0,Iris-virginica -6.2,3.4,5.4,2.3,Iris-virginica -5.9,3.0,5.1,1.8,Iris-virginica -% -% -% diff --git a/datasets/kdd_JapaneseVowels.arff b/datasets/kdd_JapaneseVowels.arff deleted file mode 100755 index d9b546d..0000000 --- a/datasets/kdd_JapaneseVowels.arff +++ /dev/null @@ -1,10177 +0,0 @@ -%%%%%%%%%%%%%%%%%%%% -% Data-Description % -%%%%%%%%%%%%%%%%%%%% -% -% Japanese vowels -% -% Data Type -% -% multivariate time series. -% -% Abstract -% -% This dataset records 640 time series of 12 LPC cepstrum coefficients -% taken from nine male speakers. -% -% Sources -% -% Original Owner and Donor -% -% Mineichi Kudo, Jun Toyama, Masaru Shimbo -% [1]Information Processing Laboratory -% Division of Systems and Information Engineering -% Graduate School of Engineering -% Hokkaido University, Sapporo 060-8628, JAPAN -% {[2]mine,[3]jun,[4]shimbo}@main.eng.hokudai.ac.jp -% -% Date Donated: June 13, 2000 -% -% Data Characteristics -% -% The data was collected for examining our newly developed classifier -% for multidimensional curves (multidimensional time series). Nine male -% speakers uttered two Japanese vowels /ae/ successively. For each -% utterance, with the analysis parameters described below, we applied -% 12-degree linear prediction analysis to it to obtain a discrete-time -% series with 12 LPC cepstrum coefficients. This means that one -% utterance by a speaker forms a time series whose length is in the -% range 7-29 and each point of a time series is of 12 features (12 -% coefficients). -% -% The number of the time series is 640 in total. We used one set of 270 -% time series for training and the other set of 370 time series for -% testing. -% -% Number of Instances (Utterances) -% -% * Training: 270 (30 utterances by 9 speakers. See file -% 'size_ae.train'.) -% * Testing: 370 (24-88 utterances by the same 9 speakers in different -% opportunities. See file 'size_ae.test'.) -% -% Length of Time Series -% -% * 7 - 29 depending on utterances -% -% Number of Attributes -% -% * 12 real values -% -% Analysis parameters -% -% * Sampling rate : 10kHz -% * Frame length : 25.6 ms -% * Shift length : 6.4ms -% * Degree of LPC coefficients : 12 -% -% Data Format -% -% Files -% -% * Training file: ae.train -% * Testing file: ae.test -% -% Format -% -% Each line in ae.train or ae.test represents 12 LPC coefficients in the -% increasing order separated by spaces. This corresponds to one analysis -% frame. -% -% Lines are organized into blocks, which are a set of 7-29 lines -% separated by blank lines and corresponds to a single speech utterance -% of /ae/ with 7-29 frames. -% -% Each speaker is a set of consecutive blocks. In ae.train there are 30 -% blocks for each speaker. Blocks 1-30 represent speaker 1, blocks 31-60 -% represent speaker 2, and so on up to speaker 9. In ae.test, speakers 1 -% to 9 have the corresponding number of blocks: 31 35 88 44 29 24 40 50 -% 29. Thus, blocks 1-31 represent speaker 1 (31 utterances of /ae/), -% blocks 32-66 represent speaker 2 (35 utterances of /ae/), and so on. -% -% Past Usage -% -% M. Kudo, J. Toyama and M. Shimbo. (1999). "Multidimensional Curve -% Classification Using Passing-Through Regions". Pattern Recognition -% Letters, Vol. 20, No. 11--13, pages 1103--1111. -% -% Acknowledgements, Copyright Information, and Availability -% -% If you publish any work using the dataset, please inform the donor. -% Use for commercial purposes requires donor permission. -% -% References and Further Information -% -% Similar data are available for different utterances /ei/, /iu/, /uo/, -% /oa/ in addition to /ae/. Please contact the donor if you are -% interested in using this data. -% _________________________________________________________________ -% -% -% [5]The UCI KDD Archive -% [6]Information and Computer Science -% [7]University of California, Irvine -% Irvine, CA 92697-3425 -% -% Last modified: June 14, 2000 -% -% References -% -% 1. http://ips9.main.eng.hokudai.ac.jp/index_e.html -% 2. mailto:mine@main.eng.hokudai.ac.jp -% 3. mailto:jun@main.eng.hokudai.ac.jp -% 4. mailto:shimbo@main.eng.hokudai.ac.jp -% 5. http://kdd.ics.uci.edu/ -% 6. http://www.ics.uci.edu/ -% 7. http://www.uci.edu/ -%%%%%%%%%%%%%%%%%%%% -% Task-Description % -%%%%%%%%%%%%%%%%%%%% -% Japanese vowels -% -% Task Type -% -% classification, speaker identification -% -% Sources -% -% Original Owner and Donor -% -% Mineichi Kudo, Jun Toyama, Masaru Shimbo -% [1]Information Processing Laboratory -% Division of Systems and Information Engineering -% Graduate School of Engineering -% Hokkaido University, Sapporo 060-8628, JAPAN -% {[2]mine,[3]jun,[4]shimbo}@main.eng.hokudai.ac.jp -% -% Date Donated: June 13, 2000 -% -% Problem Description -% -% Distinguish nine male speakers by their utterances of two Japanese -% vowels /ae/. -% -% Other Relevant Information -% -% The training file 'ae.train' was used for constructing the classifier -% and the test file 'ae.test' was used for obtaining the generalization -% classification rate. -% -% Results -% -% Results for this dataset are reported in: -% -% M. Kudo, J. Toyama and M. Shimbo. (1999). "Multidimensional Curve -% Classification Using Passing-Through Regions". Pattern Recognition -% Letters, Vol. 20, No. 11--13, pages 1103--1111. -% -% The classifier proposed in the paper showed the classification rate of -% 94.1%, while, a 5-state continuous Hidden Markov Model attained up to -% 96.2%. However, the proposed classifier was shown in the paper to be -% able to handle a great variety of datasets. In addition, the -% classifier helps people to have some intuition about what the obtained -% classification rule is and how the rule will work. -% _________________________________________________________________ -% -% -% [5]The UCI KDD Archive -% [6]Information and Computer Science -% [7]University of California, Irvine -% Irvine, CA 92697-3425 -% -% Last modified: June 30, 1999 -% -% References -% -% 1. http://ips9.main.eng.hokudai.ac.jp/index_e.html -% 2. mailto:mine@main.eng.hokudai.ac.jp -% 3. mailto:jun@main.eng.hokudai.ac.jp -% 4. mailto:shimbo@main.eng.hokudai.ac.jp -% 5. http://kdd.ics.uci.edu/ -% 6. http://www.ics.uci.edu/ -% 7. http://www.uci.edu/ -% -% Information about the dataset -% CLASSTYPE: nominal -% CLASSINDEX: first -% - -@relation JapaneseVowels - -@attribute speaker {1,2,3,4,5,6,7,8,9} -@attribute utterance INTEGER -@attribute frame INTEGER -@attribute coefficient1 REAL -@attribute coefficient2 REAL -@attribute coefficient3 REAL -@attribute coefficient4 REAL -@attribute coefficient5 REAL -@attribute coefficient6 REAL -@attribute coefficient7 REAL -@attribute coefficient8 REAL -@attribute coefficient9 REAL -@attribute coefficient10 REAL -@attribute coefficient11 REAL -@attribute coefficient12 REAL - -@data -1,1,1,1.860936,-0.207383,0.261557,-0.214562,-0.171253,-0.118167,-0.277557,0.025668,0.126701,-0.306756,-0.213076,0.088728 -1,1,2,1.891651,-0.193249,0.235363,-0.249118,-0.112890,-0.112238,-0.311997,-0.027122,0.171457,-0.289431,-0.247722,0.093011 -1,1,3,1.939205,-0.239664,0.258561,-0.291458,-0.041053,-0.102034,-0.383300,0.019013,0.169510,-0.314894,-0.227908,0.074638 -1,1,4,1.717517,-0.218572,0.217119,-0.228186,-0.018608,-0.137624,-0.403318,-0.009643,0.164607,-0.323267,-0.210105,0.098098 -1,1,5,1.741191,-0.279891,0.196583,-0.236377,-0.032012,-0.090612,-0.363134,-0.012571,0.124298,-0.351171,-0.216545,0.113899 -1,1,6,1.684695,-0.311977,0.195453,-0.231970,-0.068670,-0.003822,-0.341940,-0.008826,0.085097,-0.364329,-0.204794,0.101838 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a/datasets/kdd_JapaneseVowels_test.arff b/datasets/kdd_JapaneseVowels_test.arff deleted file mode 100755 index 80ea090..0000000 --- a/datasets/kdd_JapaneseVowels_test.arff +++ /dev/null @@ -1,5903 +0,0 @@ -%%%%%%%%%%%%%%%%%%%% -% Data-Description % -%%%%%%%%%%%%%%%%%%%% -% -% Japanese vowels -% -% Data Type -% -% multivariate time series. -% -% Abstract -% -% This dataset records 640 time series of 12 LPC cepstrum coefficients -% taken from nine male speakers. -% -% Sources -% -% Original Owner and Donor -% -% Mineichi Kudo, Jun Toyama, Masaru Shimbo -% [1]Information Processing Laboratory -% Division of Systems and Information Engineering -% Graduate School of Engineering -% Hokkaido University, Sapporo 060-8628, JAPAN -% {[2]mine,[3]jun,[4]shimbo}@main.eng.hokudai.ac.jp -% -% Date Donated: June 13, 2000 -% -% Data Characteristics -% -% The data was collected for examining our newly developed classifier -% for multidimensional curves (multidimensional time series). Nine male -% speakers uttered two Japanese vowels /ae/ successively. For each -% utterance, with the analysis parameters described below, we applied -% 12-degree linear prediction analysis to it to obtain a discrete-time -% series with 12 LPC cepstrum coefficients. This means that one -% utterance by a speaker forms a time series whose length is in the -% range 7-29 and each point of a time series is of 12 features (12 -% coefficients). -% -% The number of the time series is 640 in total. We used one set of 270 -% time series for training and the other set of 370 time series for -% testing. -% -% Number of Instances (Utterances) -% -% * Training: 270 (30 utterances by 9 speakers. See file -% 'size_ae.train'.) -% * Testing: 370 (24-88 utterances by the same 9 speakers in different -% opportunities. See file 'size_ae.test'.) -% -% Length of Time Series -% -% * 7 - 29 depending on utterances -% -% Number of Attributes -% -% * 12 real values -% -% Analysis parameters -% -% * Sampling rate : 10kHz -% * Frame length : 25.6 ms -% * Shift length : 6.4ms -% * Degree of LPC coefficients : 12 -% -% Data Format -% -% Files -% -% * Training file: ae.train -% * Testing file: ae.test -% -% Format -% -% Each line in ae.train or ae.test represents 12 LPC coefficients in the -% increasing order separated by spaces. This corresponds to one analysis -% frame. -% -% Lines are organized into blocks, which are a set of 7-29 lines -% separated by blank lines and corresponds to a single speech utterance -% of /ae/ with 7-29 frames. -% -% Each speaker is a set of consecutive blocks. In ae.train there are 30 -% blocks for each speaker. Blocks 1-30 represent speaker 1, blocks 31-60 -% represent speaker 2, and so on up to speaker 9. In ae.test, speakers 1 -% to 9 have the corresponding number of blocks: 31 35 88 44 29 24 40 50 -% 29. Thus, blocks 1-31 represent speaker 1 (31 utterances of /ae/), -% blocks 32-66 represent speaker 2 (35 utterances of /ae/), and so on. -% -% Past Usage -% -% M. Kudo, J. Toyama and M. Shimbo. (1999). "Multidimensional Curve -% Classification Using Passing-Through Regions". Pattern Recognition -% Letters, Vol. 20, No. 11--13, pages 1103--1111. -% -% Acknowledgements, Copyright Information, and Availability -% -% If you publish any work using the dataset, please inform the donor. -% Use for commercial purposes requires donor permission. -% -% References and Further Information -% -% Similar data are available for different utterances /ei/, /iu/, /uo/, -% /oa/ in addition to /ae/. Please contact the donor if you are -% interested in using this data. -% _________________________________________________________________ -% -% -% [5]The UCI KDD Archive -% [6]Information and Computer Science -% [7]University of California, Irvine -% Irvine, CA 92697-3425 -% -% Last modified: June 14, 2000 -% -% References -% -% 1. http://ips9.main.eng.hokudai.ac.jp/index_e.html -% 2. mailto:mine@main.eng.hokudai.ac.jp -% 3. mailto:jun@main.eng.hokudai.ac.jp -% 4. mailto:shimbo@main.eng.hokudai.ac.jp -% 5. http://kdd.ics.uci.edu/ -% 6. http://www.ics.uci.edu/ -% 7. http://www.uci.edu/ -%%%%%%%%%%%%%%%%%%%% -% Task-Description % -%%%%%%%%%%%%%%%%%%%% -% Japanese vowels -% -% Task Type -% -% classification, speaker identification -% -% Sources -% -% Original Owner and Donor -% -% Mineichi Kudo, Jun Toyama, Masaru Shimbo -% [1]Information Processing Laboratory -% Division of Systems and Information Engineering -% Graduate School of Engineering -% Hokkaido University, Sapporo 060-8628, JAPAN -% {[2]mine,[3]jun,[4]shimbo}@main.eng.hokudai.ac.jp -% -% Date Donated: June 13, 2000 -% -% Problem Description -% -% Distinguish nine male speakers by their utterances of two Japanese -% vowels /ae/. -% -% Other Relevant Information -% -% The training file 'ae.train' was used for constructing the classifier -% and the test file 'ae.test' was used for obtaining the generalization -% classification rate. -% -% Results -% -% Results for this dataset are reported in: -% -% M. Kudo, J. Toyama and M. Shimbo. (1999). "Multidimensional Curve -% Classification Using Passing-Through Regions". Pattern Recognition -% Letters, Vol. 20, No. 11--13, pages 1103--1111. -% -% The classifier proposed in the paper showed the classification rate of -% 94.1%, while, a 5-state continuous Hidden Markov Model attained up to -% 96.2%. However, the proposed classifier was shown in the paper to be -% able to handle a great variety of datasets. In addition, the -% classifier helps people to have some intuition about what the obtained -% classification rule is and how the rule will work. -% _________________________________________________________________ -% -% -% [5]The UCI KDD Archive -% [6]Information and Computer Science -% [7]University of California, Irvine -% Irvine, CA 92697-3425 -% -% Last modified: June 30, 1999 -% -% References -% -% 1. http://ips9.main.eng.hokudai.ac.jp/index_e.html -% 2. mailto:mine@main.eng.hokudai.ac.jp -% 3. mailto:jun@main.eng.hokudai.ac.jp -% 4. mailto:shimbo@main.eng.hokudai.ac.jp -% 5. http://kdd.ics.uci.edu/ -% 6. http://www.ics.uci.edu/ -% 7. http://www.uci.edu/ -% -% Information about the dataset -% CLASSTYPE: nominal -% CLASSINDEX: first -% - -@relation JapaneseVowels-test - -@attribute speaker {1,2,3,4,5,6,7,8,9} -@attribute utterance INTEGER -@attribute frame INTEGER -@attribute coefficient1 REAL -@attribute coefficient2 REAL -@attribute coefficient3 REAL -@attribute coefficient4 REAL 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a/datasets/kdd_JapaneseVowels_train.arff b/datasets/kdd_JapaneseVowels_train.arff deleted file mode 100755 index 0240a3f..0000000 --- a/datasets/kdd_JapaneseVowels_train.arff +++ /dev/null @@ -1,4490 +0,0 @@ -%%%%%%%%%%%%%%%%%%%% -% Data-Description % -%%%%%%%%%%%%%%%%%%%% -% -% Japanese vowels -% -% Data Type -% -% multivariate time series. -% -% Abstract -% -% This dataset records 640 time series of 12 LPC cepstrum coefficients -% taken from nine male speakers. -% -% Sources -% -% Original Owner and Donor -% -% Mineichi Kudo, Jun Toyama, Masaru Shimbo -% [1]Information Processing Laboratory -% Division of Systems and Information Engineering -% Graduate School of Engineering -% Hokkaido University, Sapporo 060-8628, JAPAN -% {[2]mine,[3]jun,[4]shimbo}@main.eng.hokudai.ac.jp -% -% Date Donated: June 13, 2000 -% -% Data Characteristics -% -% The data was collected for examining our newly developed classifier -% for multidimensional curves (multidimensional time series). Nine male -% speakers uttered two Japanese vowels /ae/ successively. For each -% utterance, with the analysis parameters described below, we applied -% 12-degree linear prediction analysis to it to obtain a discrete-time -% series with 12 LPC cepstrum coefficients. This means that one -% utterance by a speaker forms a time series whose length is in the -% range 7-29 and each point of a time series is of 12 features (12 -% coefficients). -% -% The number of the time series is 640 in total. We used one set of 270 -% time series for training and the other set of 370 time series for -% testing. -% -% Number of Instances (Utterances) -% -% * Training: 270 (30 utterances by 9 speakers. See file -% 'size_ae.train'.) -% * Testing: 370 (24-88 utterances by the same 9 speakers in different -% opportunities. See file 'size_ae.test'.) -% -% Length of Time Series -% -% * 7 - 29 depending on utterances -% -% Number of Attributes -% -% * 12 real values -% -% Analysis parameters -% -% * Sampling rate : 10kHz -% * Frame length : 25.6 ms -% * Shift length : 6.4ms -% * Degree of LPC coefficients : 12 -% -% Data Format -% -% Files -% -% * Training file: ae.train -% * Testing file: ae.test -% -% Format -% -% Each line in ae.train or ae.test represents 12 LPC coefficients in the -% increasing order separated by spaces. This corresponds to one analysis -% frame. -% -% Lines are organized into blocks, which are a set of 7-29 lines -% separated by blank lines and corresponds to a single speech utterance -% of /ae/ with 7-29 frames. -% -% Each speaker is a set of consecutive blocks. In ae.train there are 30 -% blocks for each speaker. Blocks 1-30 represent speaker 1, blocks 31-60 -% represent speaker 2, and so on up to speaker 9. In ae.test, speakers 1 -% to 9 have the corresponding number of blocks: 31 35 88 44 29 24 40 50 -% 29. Thus, blocks 1-31 represent speaker 1 (31 utterances of /ae/), -% blocks 32-66 represent speaker 2 (35 utterances of /ae/), and so on. -% -% Past Usage -% -% M. Kudo, J. Toyama and M. Shimbo. (1999). "Multidimensional Curve -% Classification Using Passing-Through Regions". Pattern Recognition -% Letters, Vol. 20, No. 11--13, pages 1103--1111. -% -% Acknowledgements, Copyright Information, and Availability -% -% If you publish any work using the dataset, please inform the donor. -% Use for commercial purposes requires donor permission. -% -% References and Further Information -% -% Similar data are available for different utterances /ei/, /iu/, /uo/, -% /oa/ in addition to /ae/. Please contact the donor if you are -% interested in using this data. -% _________________________________________________________________ -% -% -% [5]The UCI KDD Archive -% [6]Information and Computer Science -% [7]University of California, Irvine -% Irvine, CA 92697-3425 -% -% Last modified: June 14, 2000 -% -% References -% -% 1. http://ips9.main.eng.hokudai.ac.jp/index_e.html -% 2. mailto:mine@main.eng.hokudai.ac.jp -% 3. mailto:jun@main.eng.hokudai.ac.jp -% 4. mailto:shimbo@main.eng.hokudai.ac.jp -% 5. http://kdd.ics.uci.edu/ -% 6. http://www.ics.uci.edu/ -% 7. http://www.uci.edu/ -%%%%%%%%%%%%%%%%%%%% -% Task-Description % -%%%%%%%%%%%%%%%%%%%% -% Japanese vowels -% -% Task Type -% -% classification, speaker identification -% -% Sources -% -% Original Owner and Donor -% -% Mineichi Kudo, Jun Toyama, Masaru Shimbo -% [1]Information Processing Laboratory -% Division of Systems and Information Engineering -% Graduate School of Engineering -% Hokkaido University, Sapporo 060-8628, JAPAN -% {[2]mine,[3]jun,[4]shimbo}@main.eng.hokudai.ac.jp -% -% Date Donated: June 13, 2000 -% -% Problem Description -% -% Distinguish nine male speakers by their utterances of two Japanese -% vowels /ae/. -% -% Other Relevant Information -% -% The training file 'ae.train' was used for constructing the classifier -% and the test file 'ae.test' was used for obtaining the generalization -% classification rate. -% -% Results -% -% Results for this dataset are reported in: -% -% M. Kudo, J. Toyama and M. Shimbo. (1999). "Multidimensional Curve -% Classification Using Passing-Through Regions". Pattern Recognition -% Letters, Vol. 20, No. 11--13, pages 1103--1111. -% -% The classifier proposed in the paper showed the classification rate of -% 94.1%, while, a 5-state continuous Hidden Markov Model attained up to -% 96.2%. However, the proposed classifier was shown in the paper to be -% able to handle a great variety of datasets. In addition, the -% classifier helps people to have some intuition about what the obtained -% classification rule is and how the rule will work. -% _________________________________________________________________ -% -% -% [5]The UCI KDD Archive -% [6]Information and Computer Science -% [7]University of California, Irvine -% Irvine, CA 92697-3425 -% -% Last modified: June 30, 1999 -% -% References -% -% 1. http://ips9.main.eng.hokudai.ac.jp/index_e.html -% 2. mailto:mine@main.eng.hokudai.ac.jp -% 3. mailto:jun@main.eng.hokudai.ac.jp -% 4. mailto:shimbo@main.eng.hokudai.ac.jp -% 5. http://kdd.ics.uci.edu/ -% 6. http://www.ics.uci.edu/ -% 7. http://www.uci.edu/ -% -% Information about the dataset -% CLASSTYPE: nominal -% CLASSINDEX: first -% - -@relation JapaneseVowels - -@attribute speaker {1,2,3,4,5,6,7,8,9} -@attribute utterance INTEGER -@attribute frame INTEGER -@attribute coefficient1 REAL -@attribute coefficient2 REAL -@attribute coefficient3 REAL -@attribute coefficient4 REAL 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-1,20191 +0,0 @@ -% 1. TITLE: -% Letter Image Recognition Data -% -% The objective is to identify each of a large number of black-and-white -% rectangular pixel displays as one of the 26 capital letters in the English -% alphabet. The character images were based on 20 different fonts and each -% letter within these 20 fonts was randomly distorted to produce a file of -% 20,000 unique stimuli. Each stimulus was converted into 16 primitive -% numerical attributes (statistical moments and edge counts) which were then -% scaled to fit into a range of integer values from 0 through 15. We -% typically train on the first 16000 items and then use the resulting model -% to predict the letter category for the remaining 4000. See the article -% cited above for more details. -% -% 2.USE IN STATLOG -% 2.1 Testing Mode -% Train and Test -% -% 2.2 Special PreProcessing -% No -% -% 2.3 Test Results -% Error Rate TIME -% Algorithm Train Test Train Test -% -------------------------------------------- -% Alloc80 0.065 0.064 39575 ? -% KNN 0 0.068 15 2135 -% LVQ 0.057 0.079 1487 48 -% QuaDisc 0.101 0.113 3736 1223 -% Cn2 0.021 0.115 40458 52 -% BayTree 0.015 0.124 276 7 -% NewId 0 0.128 1056 2 -% IndCart 0.010 0.130 1098 1020 -% C4.5 0.042 0.132 309 292 -% Dipol92 0.167 0.176 1303 80 -% Radial 0.220 0.233 ? ? -% LogDisc 0.234 0.234 5062 39 -% Ac2 0 0.245 2529 92 -% Castle 0.237 0.245 9455 2933 -% Kohonen 0.218 0.252 ? ? -% Cal5 0.158 0.253 1033 8 -% Smart 0.287 0.295 400919 184 -% Discrim 0.297 0.302 326 84 -% BackProp 0.323 0.327 277445 22 -% Bayes 0.516 0.529 75 18 -% Itrule 0.585 0.594 22325 69 -% Default 0.955 0.960 ? ? -% Cascade 1.0 -% Cart 1.000 -% -% 3. SOURCE Information and Paste Usage -% 3.1 Source -% -- Creator: David J. Slate -% -- Odesta Corporation; 1890 Maple Ave; Suite 115; Evanston, IL 60201 -% -- Donor: David J. Slate (dave@math.nwu.edu) (708) 491-3867 -% -- Date: January, 1991 -% -% 3.2 Past Usage: -% -- P. W. Frey and D. J. Slate (Machine Learning Vol 6 #2 March 91): -% "Letter Recognition Using Holland-style Adaptive Classifiers". -% -% The research for this article investigated the ability of several -% variations of Holland-style adaptive classifier systems to learn to -% correctly guess the letter categories associated with vectors of 16 -% integer attributes extracted from raster scan images of the letters. -% The best accuracy obtained was a little over 80%. It would be -% interesting to see how well other methods do with the same data. -% -% -% 4. DATASET DESCRIPTION -% Number of Instances: -% 20000 -% Train 15000 -% Test 5000 -% -% Number of Attributes: -% 16 (numeric features) -% -% NUMBER of CLASSES : 26 -% capital letter (26 values from A to Z) -% -% Class Distribution: -% 789 A 766 B 736 C 805 D 768 E 775 F 773 G -% 734 H 755 I 747 J 739 K 761 L 792 M 783 N -% 753 O 803 P 783 Q 758 R 748 S 796 T 813 U -% 764 V 752 W 787 X 786 Y 734 Z -% -% Attribute Information: -% -% 1. x-box horizontal position of box (integer) -% 2. y-box vertical position of box (integer) -% 3. width width of box (integer) -% 4. high height of box (integer) -% 5. onpix total # on pixels (integer) -% 6. x-bar mean x of on pixels in box (integer) -% 7. y-bar mean y of on pixels in box (integer) -% 8. x2bar mean x variance (integer) -% 9. y2bar mean y variance (integer) -% 10. xybar mean x y correlation (integer) -% 11. x2ybr mean of x * x * y (integer) -% 12. xy2br mean of x * y * y (integer) -% 13. x-ege mean edge count left to right (integer) -% 14. xegvy correlation of x-ege with y (integer) -% 15. y-ege mean edge count bottom to top (integer) -% 16. yegvx correlation of y-ege with x (integer) -% -% Missing Attribute Values: None -% -% CONTACTS -% statlog-adm@ncc.up.pt -% bob@stams.strathclyde.ac.uk -% -% -% ================================================================================ -% -% Num Instances: 20000 -% Num Attributes: 17 -% Num Continuous: 16 (Int 16 / Real 0) -% Num Nominal: 1 -% Missing values: 0 / 0.0% -% -% name type enum ints real missing distinct (1) -% 1 'x-box' Int 0% 100% 0% 0 / 0% 16 / 0% 0% -% 2 'y-box' Int 0% 100% 0% 0 / 0% 16 / 0% 0% -% 3 'width' Int 0% 100% 0% 0 / 0% 16 / 0% 0% -% 4 'high' Int 0% 100% 0% 0 / 0% 16 / 0% 0% -% 5 'onpix' Int 0% 100% 0% 0 / 0% 16 / 0% 0% -% 6 'x-bar' Int 0% 100% 0% 0 / 0% 16 / 0% 0% -% 7 'y-bar' Int 0% 100% 0% 0 / 0% 16 / 0% 0% -% 8 'x2bar' Int 0% 100% 0% 0 / 0% 16 / 0% 0% -% 9 'y2bar' Int 0% 100% 0% 0 / 0% 16 / 0% 0% -% 10 'xybar' Int 0% 100% 0% 0 / 0% 16 / 0% 0% -% 11 'x2ybr' Int 0% 100% 0% 0 / 0% 16 / 0% 0% -% 12 'xy2br' Int 0% 100% 0% 0 / 0% 16 / 0% 0% -% 13 'x-ege' Int 0% 100% 0% 0 / 0% 16 / 0% 0% -% 14 'xegvy' Int 0% 100% 0% 0 / 0% 16 / 0% 0% -% 15 'y-ege' Int 0% 100% 0% 0 / 0% 16 / 0% 0% -% 16 'yegvx' Int 0% 100% 0% 0 / 0% 16 / 0% 0% -% 17 'class' Enum 0% 100% 0% 0 / 0% 26 / 0% 0% -% -% -% -% -% -% Relabeled values in attribute 'class' -% From: 1 To: A -% From: 2 To: B -% From: 3 To: C -% From: 4 To: D -% From: 5 To: E -% From: 6 To: F -% From: 7 To: G -% From: 8 To: H -% From: 9 To: I -% From: 10 To: J -% From: 11 To: K -% From: 12 To: L -% From: 13 To: M -% From: 14 To: N -% From: 15 To: O -% From: 16 To: P -% From: 17 To: Q -% From: 18 To: R -% From: 19 To: S -% From: 20 To: T -% From: 21 To: U -% From: 22 To: V -% From: 23 To: W -% From: 24 To: X -% From: 25 To: Y -% From: 26 To: Z -% -@relation letter -@attribute 'x-box' integer -@attribute 'y-box' integer -@attribute 'width' integer -@attribute 'high' integer -@attribute 'onpix' integer -@attribute 'x-bar' integer -@attribute 'y-bar' integer -@attribute 'x2bar' integer -@attribute 'y2bar' integer -@attribute 'xybar' integer -@attribute 'x2ybr' integer -@attribute 'xy2br' integer -@attribute 'x-ege' integer -@attribute 'xegvy' integer -@attribute 'y-ege' integer -@attribute 'yegvx' integer -@attribute 'class' { A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V, W, X, Y, Z} -@data -2,4,4,3,2,7,8,2,9,11,7,7,1,8,5,6,Z -4,7,5,5,5,5,9,6,4,8,7,9,2,9,7,10,P -7,10,8,7,4,8,8,5,10,11,2,8,2,5,5,10,S -4,9,5,7,4,7,7,13,1,7,6,8,3,8,0,8,H -6,7,8,5,4,7,6,3,7,10,7,9,3,8,3,7,H -4,7,5,5,3,4,12,2,5,13,7,5,1,10,1,7,F -6,10,8,8,4,7,8,2,5,10,7,8,5,8,1,8,N -1,0,2,0,1,6,10,7,2,7,5,8,2,7,4,9,R -5,9,7,6,7,7,7,2,4,9,8,9,7,6,2,8,M -1,0,2,1,1,5,7,8,6,7,6,6,2,8,3,8,D -2,7,4,5,1,9,8,4,2,5,13,8,3,10,0,8,V -3,11,5,8,3,13,4,5,3,12,1,8,2,6,4,9,A -3,7,5,5,5,8,7,4,4,7,6,7,4,6,8,13,K -3,8,5,6,4,8,8,4,5,9,5,4,3,8,3,7,D -8,11,9,8,6,7,8,3,6,10,5,7,4,6,6,8,S -7,11,10,8,9,9,7,3,6,10,4,7,5,6,4,9,H -5,5,5,7,3,3,7,6,12,7,6,15,0,8,7,6,E -2,4,5,3,2,7,8,2,9,11,7,7,1,8,5,6,Z -4,7,6,5,4,8,9,8,4,7,7,7,3,8,3,8,O -4,6,4,7,5,8,8,6,2,7,7,11,3,9,5,8,Q -5,10,7,9,3,8,5,9,8,6,4,8,3,8,4,8,Q -3,7,3,5,1,1,1,5,5,0,1,6,0,8,0,8,L -1,1,2,1,0,7,9,4,2,7,13,8,2,10,0,8,V -2,1,3,2,1,8,7,3,8,6,6,8,2,8,5,8,X -8,9,10,8,12,7,6,5,5,7,6,8,7,10,6,9,V -5,10,8,7,11,9,6,2,2,8,4,8,10,5,3,6,M -5,6,5,4,3,4,9,1,7,10,10,6,1,10,2,4,Y -2,5,4,4,3,9,6,4,6,10,4,6,2,8,3,8,D -3,8,5,6,5,7,7,4,8,7,7,8,3,8,5,10,E -4,7,6,5,4,11,7,3,5,10,1,4,5,9,1,7,N -4,10,6,7,2,9,4,3,2,8,1,8,3,7,3,8,A -3,4,4,3,3,7,7,6,7,7,6,5,2,8,3,7,D -4,8,5,6,4,9,9,6,4,7,5,8,3,8,10,7,Z -3,5,5,3,2,5,9,3,4,10,8,8,5,8,0,7,N -4,11,5,8,2,7,7,4,4,7,6,8,3,8,4,8,X -4,9,6,7,7,6,8,1,4,10,8,7,6,11,4,5,F -6,9,5,4,2,6,9,4,5,4,4,10,5,10,2,8,N -3,4,4,7,1,0,1,6,6,0,0,6,0,8,0,8,L -4,5,6,4,3,7,8,2,5,10,6,6,5,9,1,7,N -1,3,3,2,1,7,8,2,9,11,6,8,1,8,5,7,Z -6,9,6,4,3,7,6,4,4,9,9,5,3,10,4,4,Y -0,0,0,1,0,7,7,4,4,7,6,8,0,8,0,8,I -8,11,8,8,9,4,10,2,3,9,8,7,9,13,3,5,W -2,1,3,2,2,7,9,5,6,7,9,9,3,9,1,8,U -6,10,4,5,2,8,7,6,5,9,4,7,5,9,5,9,O -3,4,4,3,2,5,12,3,6,11,9,4,1,11,1,5,T -4,5,6,4,4,7,6,5,5,8,5,8,3,7,5,6,O -4,7,6,10,9,8,9,4,0,8,7,6,5,11,5,7,P -2,3,2,2,1,4,9,5,6,12,9,10,1,9,2,7,C -3,7,4,5,2,6,6,6,6,10,8,11,2,10,4,9,G -4,8,6,6,8,8,7,5,3,7,7,7,6,10,8,9,B -3,4,4,3,3,5,7,4,7,7,6,11,3,8,5,9,K -8,8,8,6,5,3,10,3,4,11,10,8,8,10,2,6,W -2,6,4,4,2,9,11,3,4,4,11,8,2,10,1,8,V -4,6,6,4,5,7,11,2,2,7,8,8,6,11,1,8,W -5,11,7,8,7,6,6,6,5,6,5,8,6,6,7,10,H -4,9,4,7,4,7,8,3,12,9,6,8,0,8,7,7,Z -1,4,2,3,1,9,6,2,6,12,4,9,1,7,1,7,J -2,4,4,3,1,5,11,3,4,13,7,4,1,9,1,7,F -5,10,7,8,6,7,7,8,5,7,6,5,4,8,3,7,D -3,5,5,4,2,8,7,3,8,11,5,7,1,9,5,8,S -8,13,7,8,3,7,8,3,9,13,5,6,2,9,5,5,T -4,9,5,7,5,8,9,8,4,7,8,7,4,7,4,9,O -2,6,3,4,3,8,7,5,9,7,7,7,1,8,7,8,Z -3,9,4,7,3,6,7,10,6,7,6,7,2,8,9,10,B -3,2,4,3,2,6,10,4,5,10,9,5,2,9,3,6,F -6,9,10,7,11,10,5,3,2,9,4,8,11,6,4,7,M -2,6,4,4,1,8,15,1,5,6,11,8,0,8,0,8,T -7,11,5,6,3,6,7,5,4,7,4,7,5,8,5,7,O -2,4,3,3,2,5,7,4,6,6,6,11,3,8,4,10,K -1,4,3,3,1,7,7,2,9,11,6,8,1,8,5,8,Z -3,4,4,3,1,5,13,3,6,12,9,4,1,11,1,5,T -4,10,7,8,6,7,10,2,3,6,9,8,8,11,1,8,W -3,5,5,3,2,6,2,2,2,5,2,8,2,6,3,6,A -2,2,3,3,2,8,8,6,1,5,6,9,2,9,5,10,Q -9,10,7,14,6,8,7,4,2,12,6,8,3,9,13,6,Z -4,5,6,7,5,9,10,3,2,8,7,7,3,10,8,6,H -5,9,8,6,8,6,6,3,4,7,5,8,7,10,7,6,R -1,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8,I -2,4,4,3,3,7,8,4,4,9,5,8,2,6,3,10,R -5,10,7,7,5,7,9,4,6,5,8,10,6,10,1,7,U -4,7,4,5,3,7,6,12,4,7,12,8,3,9,0,8,U -7,10,5,5,2,6,8,6,8,11,7,11,2,8,5,9,C -5,9,6,7,4,8,7,8,5,7,6,8,3,8,3,8,O -7,13,5,7,3,6,8,6,8,10,6,10,1,8,8,8,E -5,10,7,7,5,10,6,4,7,11,3,6,4,6,4,8,D -5,7,6,5,3,4,8,5,7,9,8,9,3,9,2,5,U -3,4,5,3,3,9,7,3,5,11,3,6,2,7,3,9,R -2,4,3,3,2,7,7,5,7,7,6,9,2,8,5,10,E -6,10,6,8,4,6,7,6,7,10,8,11,2,9,5,9,G -5,9,7,6,6,10,6,2,6,11,3,8,3,8,4,12,B -4,10,7,7,2,8,7,3,0,7,0,8,3,7,2,8,A -1,1,3,2,1,6,10,1,6,8,11,8,1,11,2,8,Y -2,4,4,6,4,8,11,3,3,5,8,9,2,11,5,6,Y -5,10,6,8,5,8,6,9,4,7,5,8,3,8,3,8,O -7,10,7,7,5,7,6,7,8,11,6,11,2,11,5,9,G -5,9,7,7,5,8,3,1,2,6,2,7,3,5,4,7,A -3,4,4,3,1,5,9,5,7,12,9,10,1,10,3,7,C -4,5,6,7,6,7,8,4,1,8,6,6,4,10,8,5,H -4,11,6,8,8,7,7,3,4,7,6,8,7,10,8,6,R -4,7,6,5,4,8,6,5,5,8,7,7,3,7,4,6,J -4,7,5,5,3,8,7,12,1,6,9,8,8,6,0,8,M -3,9,5,7,3,7,11,2,3,6,11,9,2,10,1,8,V -2,3,4,2,2,8,8,3,5,9,4,7,2,6,3,10,R -3,8,5,6,3,7,12,4,6,7,11,8,2,12,1,8,T -5,8,7,9,7,8,9,4,5,7,7,8,5,7,10,10,J -3,5,5,3,2,8,7,3,8,11,5,7,1,9,4,8,S -6,11,8,9,9,9,7,5,2,6,6,10,8,8,5,10,G -2,3,3,2,2,9,6,4,6,10,4,6,2,8,2,9,D -2,0,2,1,1,8,6,6,5,6,5,9,2,8,5,10,G -5,8,7,10,7,10,5,4,4,8,3,6,3,5,8,8,Z -6,11,6,8,4,5,7,10,10,7,6,6,3,8,4,8,D -1,3,3,2,1,6,3,2,2,5,2,8,1,6,1,7,A -1,0,1,0,0,7,13,1,4,7,10,8,0,8,0,8,T -2,3,3,1,1,7,7,6,5,7,6,9,2,9,4,10,G -1,3,2,1,0,6,4,1,7,8,2,10,0,7,2,9,L -6,9,8,7,8,8,4,7,3,7,6,7,8,7,6,3,W -0,1,1,2,1,7,7,1,6,7,6,8,0,8,2,8,I -6,8,9,7,10,6,8,5,3,6,5,8,13,9,5,8,M -4,9,4,4,2,11,4,4,5,12,3,8,2,7,6,12,Q -2,3,3,4,2,8,7,5,8,4,6,8,0,8,8,8,S -9,14,8,8,5,7,4,4,8,9,5,9,3,8,9,9,Q -3,6,5,4,1,8,8,4,2,6,14,8,3,10,0,8,V -5,10,5,8,6,6,8,9,5,6,5,7,2,8,5,11,R 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-5,9,6,11,6,8,7,6,3,8,8,11,3,8,6,8,Q diff --git a/datasets/liver-disorders.arff b/datasets/liver-disorders.arff deleted file mode 100755 index fe967df..0000000 --- a/datasets/liver-disorders.arff +++ /dev/null @@ -1,399 +0,0 @@ -% 1. Title: BUPA liver disorders -% -% 2. Source information: -% -- Creators: BUPA Medical Research Ltd. -% -- Donor: Richard S. Forsyth -% 8 Grosvenor Avenue -% Mapperley Park -% Nottingham NG3 5DX -% 0602-621676 -% -- Date: 5/15/1990 -% -% 3. Past usage: -% -- None known other than what is shown in the PC/BEAGLE User's Guide -% (written by Richard S. Forsyth). -% -% 4. Relevant information: -% -- The first 5 variables are all blood tests which are thought -% to be sensitive to liver disorders that might arise from -% excessive alcohol consumption. Each line in the bupa.data file -% constitutes the record of a single male individual. -% -- It appears that drinks>5 is some sort of a selector on this database. -% See the PC/BEAGLE User's Guide for more information. -% -% 5. Number of instances: 345 -% -% 6. Number of attributes: 7 overall -% -% 7. Attribute information: -% 1. mcv mean corpuscular volume -% 2. alkphos alkaline phosphotase -% 3. sgpt alamine aminotransferase -% 4. sgot aspartate aminotransferase -% 5. gammagt gamma-glutamyl transpeptidase -% 6. drinks number of half-pint equivalents of alcoholic beverages -% drunk per day -% 7. selector field used to split data into two sets -% -% 8. Missing values: none% -% Information about the dataset -% CLASSTYPE: nominal -% CLASSINDEX: last -% - -@relation liver-disorders - -@attribute mcv INTEGER -@attribute alkphos INTEGER -@attribute sgpt INTEGER -@attribute sgot INTEGER -@attribute gammagt INTEGER -@attribute drinks REAL -@attribute selector {1,2} - -@data -85,92,45,27,31,0.0,1 -85,64,59,32,23,0.0,2 -86,54,33,16,54,0.0,2 -91,78,34,24,36,0.0,2 -87,70,12,28,10,0.0,2 -98,55,13,17,17,0.0,2 -88,62,20,17,9,0.5,1 -88,67,21,11,11,0.5,1 -92,54,22,20,7,0.5,1 -90,60,25,19,5,0.5,1 -89,52,13,24,15,0.5,1 -82,62,17,17,15,0.5,1 -90,64,61,32,13,0.5,1 -86,77,25,19,18,0.5,1 -96,67,29,20,11,0.5,1 -91,78,20,31,18,0.5,1 -89,67,23,16,10,0.5,1 -89,79,17,17,16,0.5,1 -91,107,20,20,56,0.5,1 -94,116,11,33,11,0.5,1 -92,59,35,13,19,0.5,1 -93,23,35,20,20,0.5,1 -90,60,23,27,5,0.5,1 -96,68,18,19,19,0.5,1 -84,80,47,33,97,0.5,1 -92,70,24,13,26,0.5,1 -90,47,28,15,18,0.5,1 -88,66,20,21,10,0.5,1 -91,102,17,13,19,0.5,1 -87,41,31,19,16,0.5,1 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-91,68,27,26,14,16.0,1 -98,99,57,45,65,20.0,1 diff --git a/datasets/mfeat-factors.arff b/datasets/mfeat-factors.arff deleted file mode 100755 index 6f74a29..0000000 --- a/datasets/mfeat-factors.arff +++ /dev/null @@ -1,2306 +0,0 @@ -% The multi-feature digit dataset -% ------------------------------- -% -% Oowned and donated by: -% ---------------------- -% -% Robert P.W. Duin -% Department of Applied Physics -% Delft University of Technology -% P.O. Box 5046, 2600 GA Delft -% The Netherlands -% -% email: duin@ph.tn.tudelft.nl -% http : //www.ph.tn.tudelft.nl/~duin -% tel +31 15 2786143 -% -% Usage -% ----- -% A slightly different version of the database is used in -% -% M. van Breukelen, R.P.W. Duin, D.M.J. Tax, and J.E. den Hartog, Handwritten -% digit recognition by combined classifiers, Kybernetika, vol. 34, no. 4, -% 1998, 381-386. -% -% M. van Breukelen and R.P.W. Duin, Neural Network Initialization by Combined -% Classifiers, in: A.K. Jain, S. Venkatesh, B.C. Lovell (eds.), ICPR'98, -% Proc. 14th Int. Conference on Pattern Recognition (Brisbane, Aug. 16-20), -% -% The database as it is is used in: -% -% A.K. Jain, R.P.W. Duin, J. Mao, Statisitcal Pattern Recognition: A Review, -% in preparation -% -% Description -% ----------- -% -% This dataset consists of features of handwritten numerals (`0'--`9') -% extracted from a collection of Dutch utility maps. 200 patterns per -% class (for a total of 2,000 patterns) have been digitized in binary -% images. These digits are represented in terms of the following six -% feature sets (files): -% -% 1. mfeat-fou: 76 Fourier coefficients of the character shapes; -% 2. mfeat-fac: 216 profile correlations; -% 3. mfeat-kar: 64 Karhunen-Love coefficients; -% 4. mfeat-pix: 240 pixel averages in 2 x 3 windows; -% 5. mfeat-zer: 47 Zernike moments; -% 6. mfeat-mor: 6 morphological features. -% -% In each file the 2000 patterns are stored in ASCI on 2000 lines. The -% first 200 patterns are of class `0', followed by sets of 200 patterns -% for each of the classes `1' - `9'. Corresponding patterns in different -% feature sets (files) correspond to the same original character. -% -% The source image dataset is lost. Using the pixel-dataset (mfeat-pix) -% sampled versions of the original images may be obtained (15 x 16 pixels). -% -% Total number of instances: -% -------------------------- -% 2000 (200 instances per class) -% -% Total number of attributes: -% --------------------------- -% 649 (distributed over 6 datasets,see above) -% -% no missing attributes -% -% Total number of classes: -% ------------------------ -% 10 -% -% Format: -% ------ -% 6 files, see above. -% Each file contains 2000 lines, one for each instance. -% Attributes are SPACE separated and can be loaded by Matlab as -% > load filename -% No missing attributes. Some are integer, others are real. -% -% -% Information about the dataset -% CLASSTYPE: nominal -% CLASSINDEX: last -% - -@relation mfeat - -@attribute att1 INTEGER -@attribute att2 INTEGER -@attribute att3 INTEGER -@attribute att4 INTEGER -@attribute att5 INTEGER -@attribute att6 INTEGER -@attribute att7 INTEGER -@attribute att8 INTEGER -@attribute att9 INTEGER -@attribute att10 INTEGER -@attribute att11 INTEGER -@attribute att12 INTEGER -@attribute att13 INTEGER -@attribute att14 INTEGER -@attribute att15 INTEGER -@attribute att16 INTEGER -@attribute att17 INTEGER -@attribute att18 INTEGER -@attribute att19 INTEGER -@attribute att20 INTEGER -@attribute att21 INTEGER -@attribute att22 INTEGER -@attribute att23 INTEGER -@attribute att24 INTEGER -@attribute att25 INTEGER -@attribute att26 INTEGER -@attribute att27 INTEGER -@attribute att28 INTEGER -@attribute att29 INTEGER -@attribute att30 INTEGER -@attribute att31 INTEGER -@attribute att32 INTEGER -@attribute att33 INTEGER -@attribute att34 INTEGER -@attribute att35 INTEGER -@attribute att36 INTEGER -@attribute att37 INTEGER -@attribute att38 INTEGER -@attribute att39 INTEGER -@attribute att40 INTEGER -@attribute att41 INTEGER -@attribute att42 INTEGER -@attribute att43 INTEGER -@attribute att44 INTEGER -@attribute att45 INTEGER -@attribute att46 INTEGER -@attribute att47 INTEGER -@attribute att48 INTEGER -@attribute att49 INTEGER -@attribute att50 INTEGER -@attribute att51 INTEGER -@attribute att52 INTEGER -@attribute att53 INTEGER -@attribute att54 INTEGER -@attribute att55 INTEGER -@attribute att56 INTEGER -@attribute att57 INTEGER -@attribute att58 INTEGER -@attribute att59 INTEGER -@attribute att60 INTEGER -@attribute att61 INTEGER -@attribute att62 INTEGER -@attribute att63 INTEGER -@attribute att64 INTEGER -@attribute att65 INTEGER -@attribute att66 INTEGER -@attribute att67 INTEGER -@attribute att68 INTEGER -@attribute att69 INTEGER -@attribute att70 INTEGER -@attribute att71 INTEGER -@attribute att72 INTEGER -@attribute att73 INTEGER -@attribute att74 INTEGER -@attribute att75 INTEGER -@attribute att76 INTEGER -@attribute att77 INTEGER -@attribute att78 INTEGER -@attribute att79 INTEGER -@attribute att80 INTEGER -@attribute att81 INTEGER -@attribute att82 INTEGER -@attribute att83 INTEGER -@attribute att84 INTEGER -@attribute att85 INTEGER -@attribute att86 INTEGER -@attribute att87 INTEGER -@attribute att88 INTEGER -@attribute att89 INTEGER -@attribute att90 INTEGER -@attribute att91 INTEGER -@attribute att92 INTEGER -@attribute att93 INTEGER -@attribute att94 INTEGER -@attribute att95 INTEGER -@attribute att96 INTEGER -@attribute att97 INTEGER -@attribute att98 INTEGER -@attribute att99 INTEGER -@attribute att100 INTEGER -@attribute att101 INTEGER -@attribute att102 INTEGER -@attribute att103 INTEGER -@attribute att104 INTEGER -@attribute att105 INTEGER -@attribute att106 INTEGER -@attribute att107 INTEGER -@attribute att108 INTEGER -@attribute att109 INTEGER -@attribute att110 INTEGER -@attribute att111 INTEGER -@attribute att112 INTEGER -@attribute att113 INTEGER -@attribute att114 INTEGER -@attribute att115 INTEGER -@attribute att116 INTEGER -@attribute att117 INTEGER -@attribute att118 INTEGER -@attribute att119 INTEGER -@attribute att120 INTEGER -@attribute att121 INTEGER -@attribute att122 INTEGER -@attribute att123 INTEGER -@attribute att124 INTEGER -@attribute att125 INTEGER -@attribute att126 INTEGER -@attribute att127 INTEGER -@attribute att128 INTEGER -@attribute att129 INTEGER -@attribute att130 INTEGER -@attribute att131 INTEGER -@attribute att132 INTEGER -@attribute att133 INTEGER -@attribute att134 INTEGER -@attribute att135 INTEGER -@attribute att136 INTEGER -@attribute att137 INTEGER -@attribute att138 INTEGER -@attribute att139 INTEGER -@attribute att140 INTEGER -@attribute att141 INTEGER -@attribute att142 INTEGER -@attribute att143 INTEGER -@attribute att144 INTEGER -@attribute att145 INTEGER -@attribute att146 INTEGER -@attribute att147 INTEGER -@attribute att148 INTEGER -@attribute att149 INTEGER -@attribute att150 INTEGER -@attribute att151 INTEGER -@attribute att152 INTEGER -@attribute att153 INTEGER -@attribute att154 INTEGER -@attribute att155 INTEGER -@attribute att156 INTEGER -@attribute att157 INTEGER -@attribute att158 INTEGER -@attribute att159 INTEGER -@attribute att160 INTEGER -@attribute att161 INTEGER -@attribute att162 INTEGER -@attribute att163 INTEGER -@attribute att164 INTEGER -@attribute att165 INTEGER -@attribute att166 INTEGER -@attribute att167 INTEGER -@attribute att168 INTEGER -@attribute att169 INTEGER -@attribute att170 INTEGER -@attribute att171 INTEGER -@attribute att172 INTEGER -@attribute att173 INTEGER -@attribute att174 INTEGER -@attribute att175 INTEGER -@attribute att176 INTEGER -@attribute att177 INTEGER -@attribute att178 INTEGER -@attribute att179 INTEGER -@attribute att180 INTEGER -@attribute att181 INTEGER -@attribute att182 INTEGER -@attribute att183 INTEGER -@attribute att184 INTEGER -@attribute att185 INTEGER -@attribute att186 INTEGER -@attribute att187 INTEGER -@attribute att188 INTEGER -@attribute att189 INTEGER -@attribute att190 INTEGER -@attribute att191 INTEGER -@attribute att192 INTEGER -@attribute att193 INTEGER -@attribute att194 INTEGER -@attribute att195 INTEGER -@attribute att196 INTEGER -@attribute att197 INTEGER -@attribute att198 INTEGER -@attribute att199 INTEGER -@attribute att200 INTEGER -@attribute att201 INTEGER -@attribute att202 INTEGER -@attribute att203 INTEGER -@attribute att204 INTEGER -@attribute att205 INTEGER -@attribute att206 INTEGER -@attribute att207 INTEGER -@attribute att208 INTEGER -@attribute att209 INTEGER -@attribute att210 INTEGER -@attribute att211 INTEGER -@attribute att212 INTEGER -@attribute att213 INTEGER -@attribute att214 INTEGER -@attribute att215 INTEGER -@attribute att216 INTEGER -@attribute class {1,2,3,4,5,6,7,8,9,10} - -@data 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-274,352,882,706,646,720,28,11,11,15,12,8,385,279,625,635,661,1043,31,30,20,6,14,12,391,199,736,572,994,794,22,20,10,2,16,5,241,211,870,747,835,995,4,20,20,5,8,14,212,226,816,761,935,813,9,20,10,6,9,7,286,210,737,729,1028,773,10,18,3,0,10,3,348,254,676,544,698,1093,35,34,20,11,12,9,394,382,637,511,686,955,34,28,13,2,9,15,321,291,698,663,790,1049,18,23,12,5,14,16,371,213,886,785,973,830,18,18,26,12,3,3,376,266,732,666,883,752,19,33,8,12,9,9,256,262,971,741,654,774,21,12,7,11,13,4,402,308,927,751,710,728,25,22,9,4,14,13,406,342,561,601,744,1001,30,31,5,10,16,6,354,336,736,594,936,815,27,17,2,13,15,3,322,360,980,875,1167,686,19,23,22,13,1,2,302,502,747,768,648,1237,34,22,12,7,11,8,393,293,839,611,687,753,18,28,22,12,10,16,4 -284,360,752,903,677,670,25,27,21,13,10,11,327,221,551,774,726,885,30,30,12,8,14,7,283,215,666,851,1081,678,29,10,34,8,10,14,349,185,842,974,860,715,15,12,28,11,2,3,278,212,796,1008,972,639,20,18,34,12,3,12,234,236,643,1024,1127,761,21,12,29,6,10,22,324,208,706,795,799,929,36,22,28,11,10,0,280,300,641,722,823,819,33,14,35,4,13,12,463,233,676,866,795,895,17,25,36,11,14,19,269,229,728,898,1074,650,25,20,8,12,3,8,264,326,574,955,992,824,24,15,28,6,9,14,242,262,813,868,775,724,22,28,21,5,9,11,390,274,775,954,825,760,26,30,21,10,12,10,368,268,551,860,851,963,31,23,33,12,10,13,246,278,678,801,1075,763,28,13,28,7,9,20,230,444,854,1088,1292,602,24,25,30,13,5,19,484,418,791,979,671,1061,23,38,36,13,17,15,341,241,683,756,642,523,17,4,12,8,14,13,4 -412,448,778,691,492,601,24,23,14,14,17,13,279,375,455,624,429,588,19,22,17,7,13,13,161,371,728,535,692,549,18,14,7,7,9,4,439,405,696,658,579,692,18,30,17,10,13,15,472,366,702,616,627,520,13,30,7,11,12,12,240,266,597,486,722,502,12,18,6,5,5,2,286,326,524,507,432,642,13,16,17,10,13,14,286,244,531,538,494,532,16,10,10,3,14,18,451,311,522,690,572,618,36,21,9,10,13,19,261,317,782,574,663,695,4,26,29,13,10,2,250,218,628,459,573,527,3,15,11,7,8,8,332,356,867,766,462,591,15,20,10,6,16,3,422,358,847,778,468,509,17,28,8,9,15,16,230,136,403,498,454,560,8,15,2,11,9,11,196,140,586,387,654,508,5,11,1,8,8,4,334,312,832,654,871,603,3,21,25,14,12,3,490,320,505,745,478,784,30,22,9,12,16,11,399,321,775,658,551,722,16,22,19,7,5,19,4 -196,444,838,697,664,746,33,25,16,16,15,14,257,305,669,646,695,1043,34,28,25,13,13,8,309,239,672,573,1072,814,17,16,15,3,5,1,277,215,846,756,889,1015,15,4,25,6,7,10,292,274,818,762,997,833,8,6,15,7,8,13,280,336,675,748,1106,783,9,12,24,1,5,3,286,376,784,553,766,1083,28,20,25,18,5,5,308,508,719,490,764,957,31,24,18,9,12,13,445,373,730,636,800,1055,19,37,17,6,7,14,387,295,836,814,1045,862,17,26,29,17,8,3,366,378,682,675,959,744,18,25,19,11,10,3,274,372,921,734,730,764,24,26,14,10,14,2,216,420,841,724,788,726,26,34,12,7,15,11,332,484,583,606,810,991,25,25,16,17,5,12,328,472,690,617,1014,827,26,19,19,12,4,5,348,436,916,892,1245,716,18,37,17,16,10,4,422,604,877,763,658,1231,35,36,17,14,10,10,281,383,787,588,681,787,27,14,13,11,11,14,4 -365,361,848,719,752,691,32,17,20,12,17,10,320,324,729,656,763,1060,33,40,19,11,15,12,302,144,634,529,1018,771,16,6,9,5,3,3,254,184,836,756,877,970,16,12,19,2,9,16,263,213,808,756,949,790,15,18,9,1,10,9,289,233,683,702,1056,770,16,6,12,7,3,1,233,295,790,557,826,1130,33,32,19,16,3,11,413,385,747,520,786,972,36,20,12,9,10,17,378,360,748,672,854,1054,14,29,11,2,9,18,466,130,852,772,1057,783,24,32,39,15,10,1,361,193,698,643,959,787,25,21,17,19,8,7,293,255,937,756,762,811,23,18,16,18,16,2,269,343,849,760,774,743,25,26,14,5,15,15,245,273,539,594,864,1040,30,33,4,15,3,8,353,239,708,533,1032,816,33,7,7,8,2,1,393,291,912,812,1215,643,25,25,23,10,12,0,279,515,819,781,746,1266,34,30,11,12,12,10,480,310,799,618,711,702,26,16,23,11,11,18,4 -196,290,768,686,625,665,29,13,10,13,7,9,297,365,653,655,638,1030,34,40,23,12,5,15,277,225,614,596,989,745,23,10,13,0,7,6,227,269,796,743,836,940,9,16,23,3,7,15,226,278,732,765,926,766,14,22,13,4,8,14,236,240,663,763,1029,744,15,8,8,2,7,4,264,344,730,546,693,1100,40,34,23,17,7,16,374,384,663,489,685,942,37,22,16,8,2,18,369,339,738,633,769,1024,13,25,15,3,7,13,389,183,772,847,968,753,23,28,27,16,10,4,336,210,622,694,876,763,24,23,13,14,4,10,192,236,857,721,649,781,26,14,4,13,6,5,266,298,821,721,711,719,30,24,4,6,5,18,300,258,499,611,735,1016,35,31,8,16,7,13,294,230,660,634,937,792,32,7,5,13,8,6,374,282,906,913,1164,617,24,21,19,13,10,5,332,512,767,750,623,1238,31,26,15,13,10,9,383,239,723,581,654,672,21,18,17,12,9,17,4 -287,259,963,596,645,707,34,18,6,11,6,3,304,384,792,573,686,1004,29,31,37,20,4,15,294,246,731,572,1083,775,14,19,27,2,8,12,330,310,923,675,878,976,10,23,37,1,10,9,225,317,877,713,994,794,7,23,27,2,11,8,261,239,820,743,1125,744,8,17,14,4,8,10,249,283,837,502,765,1044,33,25,37,19,10,12,407,369,780,429,781,918,30,21,30,16,5,10,386,356,753,535,777,1016,20,18,29,11,10,11,438,156,967,821,1048,823,16,23,23,14,11,10,325,213,813,668,968,705,17,26,9,16,5,16,169,203,1052,627,739,725,25,13,10,15,5,11,341,257,966,623,807,687,31,17,12,14,4,12,269,309,630,565,809,952,28,24,22,18,8,7,305,289,809,644,1033,788,25,16,19,11,9,8,363,281,1055,909,1260,677,17,18,5,11,13,9,357,489,842,666,637,1192,40,17,29,17,13,3,442,352,910,493,656,748,18,25,17,10,12,11,4 -375,475,995,764,635,788,38,14,21,15,8,10,340,292,670,665,656,865,33,31,12,10,12,12,238,84,819,696,1045,784,14,19,26,2,12,3,312,150,983,829,854,911,14,19,28,5,8,6,277,141,951,849,956,823,1,21,26,6,9,11,165,207,832,857,1087,743,2,17,23,0,10,3,309,287,797,642,727,817,27,31,28,15,12,5,297,433,776,607,743,799,30,27,31,6,11,17,466,350,687,725,775,907,26,22,32,5,18,18,234,228,997,881,1010,854,10,19,14,16,1,1,327,299,843,790,930,646,11,32,28,12,7,7,259,357,1082,773,701,702,29,15,15,11,9,2,399,449,1006,813,769,634,31,23,13,4,10,15,377,335,616,695,771,813,22,32,25,14,12,10,195,335,833,756,995,791,19,16,20,13,11,3,195,355,1075,1027,1222,772,11,22,30,15,3,0,467,543,774,838,631,1001,44,21,28,11,15,10,374,368,950,631,658,757,22,25,8,12,14,18,4 -240,360,950,647,720,674,32,11,12,14,7,11,357,323,627,594,757,1041,37,36,27,11,11,11,291,157,742,569,1062,754,20,14,17,1,7,2,179,191,904,712,853,951,8,14,27,4,11,17,260,240,878,736,947,775,9,22,17,5,12,10,222,228,775,740,1112,755,10,12,14,1,9,0,294,312,712,525,836,1111,35,36,27,16,9,12,368,440,699,464,848,953,38,26,20,7,10,16,411,339,610,594,822,1035,18,23,19,4,17,17,339,149,954,820,1077,764,18,24,29,17,6,0,366,274,800,669,995,778,19,29,15,13,6,6,198,282,1039,676,800,792,23,12,4,12,6,1,286,350,959,682,842,734,25,24,2,5,9,14,378,356,581,584,874,1027,30,37,12,15,7,9,270,332,768,635,1096,803,27,11,9,14,8,2,310,320,1014,900,1271,626,19,23,15,14,8,1,362,558,739,715,716,1247,38,22,19,12,14,9,421,327,905,536,671,683,24,24,11,13,13,17,4 -323,495,1022,734,619,810,36,22,21,13,12,13,308,298,693,637,616,807,31,33,10,8,10,9,208,156,846,636,981,798,16,17,14,0,6,0,310,162,1010,791,802,983,12,15,26,3,14,7,263,151,972,803,892,809,1,15,14,4,15,12,219,275,859,779,1023,739,0,15,13,2,8,2,297,401,826,606,663,811,25,29,20,13,12,2,337,513,801,577,679,751,28,33,19,4,13,14,450,362,740,693,773,875,28,34,20,3,16,15,320,218,1020,849,946,908,8,21,14,14,7,2,321,373,866,720,866,624,9,30,18,14,5,4,245,409,1105,751,637,742,27,23,17,13,11,1,339,497,1029,781,707,690,29,25,15,2,10,12,331,391,657,645,707,747,20,30,13,12,6,11,221,395,862,694,931,769,17,16,8,13,5,4,257,349,1102,967,1158,822,9,34,32,13,9,3,457,607,823,806,627,973,42,33,16,9,15,9,370,392,973,617,674,865,24,23,12,12,12,15,4 -265,445,830,832,636,671,32,28,19,15,12,12,290,200,565,697,661,888,37,29,12,6,16,8,244,178,742,776,1054,697,26,15,28,6,8,5,328,136,888,897,855,816,12,15,28,9,4,2,273,177,852,923,965,694,13,17,28,10,5,11,185,229,697,937,1096,738,14,17,23,4,8,13,287,235,744,722,736,924,35,21,28,11,12,1,259,339,695,671,752,818,38,19,33,2,15,13,442,236,716,795,776,912,18,26,34,9,16,14,268,238,816,897,1019,705,18,17,12,14,5,3,233,333,662,870,939,745,19,14,26,8,11,5,229,341,901,809,710,683,23,29,17,7,11,2,359,339,853,883,778,721,25,27,17,8,14,11,355,341,571,775,780,926,30,20,27,10,8,12,231,331,724,798,1004,752,27,18,22,9,7,13,237,455,924,1093,1231,631,19,26,30,15,7,10,477,455,795,908,632,1072,32,33,30,11,19,14,330,296,775,693,659,592,22,7,8,8,14,14,4 -316,328,813,634,671,679,27,24,15,14,13,13,311,441,748,587,702,1040,30,29,30,17,11,9,359,303,615,550,1079,759,15,15,20,1,1,0,335,291,805,701,898,950,21,11,30,4,13,13,354,352,777,707,1002,780,16,11,20,5,14,12,384,358,680,721,1115,758,17,11,23,1,7,2,352,446,831,504,773,1110,24,21,30,22,7,8,390,512,762,445,771,954,27,19,23,13,8,14,473,459,727,573,807,1034,11,26,22,8,13,15,523,251,813,783,1052,763,25,29,28,17,14,2,470,308,679,650,966,777,26,24,18,13,8,4,336,268,898,667,739,793,24,19,15,12,12,1,252,376,802,661,797,733,20,27,13,11,11,12,360,382,618,555,815,1030,29,22,17,21,1,11,444,360,695,610,1023,806,32,18,20,14,2,4,444,372,883,877,1252,631,26,26,12,14,16,3,434,636,912,704,665,1252,27,27,22,18,16,9,379,281,756,529,688,682,29,17,12,13,15,15,4 -234,368,764,748,624,706,25,23,20,14,11,4,303,323,593,641,665,1033,30,30,15,5,15,14,331,191,676,582,1062,780,23,16,11,1,9,9,259,173,828,795,857,915,7,16,21,2,3,14,198,258,768,795,973,799,14,12,11,3,4,9,286,262,663,747,1104,793,13,14,24,3,9,9,278,324,708,596,744,1099,36,22,15,10,9,11,336,426,629,569,760,963,33,24,14,5,14,9,309,341,748,721,756,1045,15,31,15,2,9,8,385,181,762,787,1027,774,21,22,29,11,4,9,358,290,616,682,947,812,22,25,27,15,10,13,272,254,847,767,718,814,24,24,14,14,10,8,322,338,821,809,786,770,28,26,12,1,13,11,332,344,515,635,788,1057,33,23,16,9,9,10,356,330,670,606,1012,835,30,13,19,12,8,11,354,376,888,883,1239,658,22,31,27,12,6,10,336,550,783,824,616,1229,29,30,11,6,14,0,339,303,717,639,635,651,17,16,15,11,15,8,4 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-301,375,985,720,736,667,38,14,17,13,9,11,304,370,666,625,785,998,35,31,16,12,11,11,280,108,763,620,1084,745,18,19,12,0,13,2,192,178,967,793,849,894,14,19,24,3,3,11,167,209,939,811,949,766,13,21,12,4,4,10,203,269,816,793,1136,754,14,17,23,2,9,0,221,341,785,592,864,1066,39,31,18,17,7,6,347,465,766,527,892,928,40,25,17,8,10,16,308,430,621,675,828,1006,20,20,18,3,15,17,306,162,987,825,1109,735,22,19,30,16,0,0,327,261,833,726,1025,777,23,32,24,14,6,6,219,305,1072,735,844,773,29,9,13,13,10,1,301,429,980,763,870,733,31,21,11,6,11,14,315,337,578,655,914,1022,34,32,15,16,13,9,311,323,817,668,1136,800,31,16,18,13,12,2,267,267,1059,937,1277,621,23,20,26,13,2,1,297,605,758,796,732,1226,40,19,14,13,14,9,442,382,932,587,677,616,22,25,10,12,11,17,4 -242,362,1024,671,752,637,36,18,8,15,8,13,313,367,701,590,771,992,33,27,23,12,12,9,245,119,794,639,1032,717,16,23,19,2,12,0,193,153,996,752,835,902,12,23,31,5,8,13,234,224,964,786,909,738,15,21,19,6,9,12,210,232,855,802,1086,722,16,21,14,0,10,2,228,324,788,583,854,1062,41,27,25,17,12,8,342,456,783,496,862,908,38,27,24,8,11,14,301,395,620,622,834,988,18,18,25,5,18,15,323,145,1028,850,1067,717,24,15,17,18,1,2,304,222,874,733,981,745,25,28,11,12,7,4,224,278,1113,686,814,747,27,11,12,11,9,1,294,376,1013,710,840,701,31,19,12,6,10,12,310,326,627,642,884,992,36,28,18,16,12,11,284,298,854,689,1100,768,33,20,15,13,11,4,348,300,1098,962,1209,589,25,20,19,15,3,3,278,578,771,747,744,1206,42,17,21,13,15,9,371,345,971,538,695,634,20,23,5,12,14,15,4 -351,433,948,766,641,696,36,17,17,13,9,12,330,250,627,639,660,893,31,32,14,8,11,10,216,88,760,670,1037,726,12,18,18,0,13,1,310,114,934,827,850,917,16,18,30,3,7,8,245,153,900,841,956,765,3,18,18,4,8,11,183,189,785,831,1079,711,4,16,17,2,9,1,285,255,764,640,725,921,25,28,24,13,11,3,315,401,729,589,735,821,28,28,23,4,10,15,436,308,644,729,783,923,28,25,24,3,17,16,264,200,950,855,1004,778,12,20,18,14,0,1,309,259,796,766,922,654,13,29,22,14,6,5,243,321,1035,759,693,630,27,18,13,13,10,0,373,395,951,817,761,642,29,24,11,2,11,13,337,315,615,683,769,869,20,29,17,12,13,10,211,293,786,714,987,743,17,15,12,13,12,3,231,375,1028,987,1214,670,13,25,28,13,2,2,439,515,763,842,639,1085,42,24,20,9,14,8,396,304,899,633,670,703,24,22,8,12,13,16,4 -301,375,846,657,672,724,34,22,21,13,12,11,398,330,643,624,685,1071,33,25,24,14,16,5,428,248,632,533,1024,804,26,21,14,6,8,12,214,202,824,714,883,1003,10,7,24,3,4,9,303,245,800,724,973,823,11,11,14,4,5,4,373,283,669,712,1058,793,10,17,15,6,8,10,341,387,724,515,738,1133,27,17,24,15,8,10,383,491,683,452,716,983,30,23,17,10,15,10,464,374,710,600,816,1071,26,30,16,11,10,13,376,204,850,786,1009,828,10,19,38,12,5,8,447,291,696,639,913,790,11,24,20,14,11,8,407,293,935,696,686,814,25,31,9,13,11,11,219,355,883,688,740,760,27,23,9,10,14,8,413,363,477,568,782,1041,22,24,9,14,8,5,433,339,676,569,966,833,19,24,10,7,7,8,315,359,902,846,1197,686,11,30,18,13,7,9,393,611,743,723,670,1273,32,31,16,13,15,11,390,278,795,570,701,749,24,15,18,6,14,11,4 -258,352,916,692,634,679,29,12,16,11,11,12,311,351,667,617,665,1004,32,37,21,10,15,10,291,127,692,570,1046,757,23,13,11,2,9,1,235,163,884,749,859,958,5,13,23,1,3,14,182,240,856,761,967,780,10,19,11,2,4,11,244,258,745,743,1088,740,11,11,18,4,9,1,242,334,724,552,736,1056,36,33,21,15,11,9,374,442,683,503,744,916,35,25,16,6,14,15,329,385,674,651,770,1010,17,24,17,1,15,16,377,143,920,813,1015,793,19,25,33,16,4,1,352,228,766,674,931,719,20,26,21,16,10,5,200,262,1005,715,702,737,22,13,8,15,10,0,298,364,935,739,770,699,26,25,6,4,13,13,282,328,525,601,780,968,31,34,10,14,9,10,310,300,740,616,996,776,28,10,13,11,8,3,328,298,986,887,1223,651,20,24,21,11,6,2,330,570,773,764,628,1200,35,23,13,11,18,8,413,313,867,579,651,716,19,21,13,10,15,16,4 -276,398,954,710,676,687,36,23,10,17,14,10,353,337,655,631,725,980,39,30,23,18,12,12,271,135,738,706,1080,745,20,18,33,4,8,3,257,165,936,801,859,856,12,18,39,7,10,6,258,192,902,851,971,762,11,12,33,8,9,11,176,214,791,887,1126,790,12,16,20,2,2,3,276,354,770,652,798,1044,37,22,39,23,10,5,338,466,747,547,822,918,40,28,38,14,9,17,417,373,658,655,794,998,18,27,39,9,10,18,323,195,956,861,1073,729,20,20,9,16,7,1,340,220,802,818,991,813,21,25,17,10,5,7,164,292,1041,721,774,777,27,18,20,9,13,2,332,380,949,743,824,769,29,24,20,12,12,15,368,314,563,711,850,1032,32,23,32,22,8,10,240,298,792,724,1074,818,29,15,27,15,7,3,250,322,1034,1003,1291,641,21,27,19,17,7,0,400,598,809,784,670,1202,38,26,35,19,11,10,433,313,901,563,641,580,24,16,11,12,6,18,4 -293,353,1035,637,764,641,37,12,9,16,10,10,346,358,728,588,787,992,32,31,26,13,12,12,286,126,789,607,1056,719,15,19,22,3,14,3,266,178,975,708,831,902,13,21,34,6,10,14,247,225,957,740,915,740,14,29,22,7,11,9,219,217,862,770,1110,726,15,17,15,1,8,1,267,299,755,537,884,1062,36,35,28,18,14,9,375,435,764,480,904,908,33,21,27,9,11,17,358,372,643,586,832,988,19,18,28,6,16,18,334,158,1039,838,1075,717,23,19,22,17,1,1,303,201,885,701,999,749,24,28,16,11,7,7,193,267,1124,662,860,749,28,7,7,10,11,2,355,345,1028,674,874,705,30,19,7,7,12,15,351,317,650,594,912,996,35,32,21,17,14,8,263,285,855,669,1126,772,32,16,16,12,13,1,297,305,1101,936,1197,593,24,20,16,16,1,0,325,555,786,707,752,1208,43,17,24,14,13,10,404,332,982,508,725,634,21,29,10,11,12,18,4 -216,224,855,640,655,652,20,12,11,11,1,5,291,399,776,597,686,1013,21,33,30,14,3,19,283,237,695,576,1005,732,16,17,20,2,15,10,257,307,875,705,814,923,18,21,30,1,15,11,204,346,813,735,914,753,23,21,20,2,14,12,240,302,754,749,1047,731,24,15,19,4,15,8,262,318,833,522,765,1083,27,33,30,19,15,14,374,366,760,451,763,927,24,25,23,10,8,14,367,377,791,589,763,1007,14,20,22,5,9,11,377,167,859,819,1008,736,32,21,28,14,8,8,334,192,711,676,926,750,31,28,14,16,10,14,218,166,944,673,729,766,19,9,9,15,2,9,292,276,890,677,765,706,23,19,7,8,3,16,288,258,640,587,803,1003,28,30,15,18,15,11,264,242,761,628,1011,779,25,14,14,11,16,8,368,262,977,897,1206,604,31,16,12,11,10,7,346,494,874,708,647,1225,26,19,22,15,10,5,387,307,804,539,638,655,20,25,10,10,17,13,4 -252,264,834,626,656,684,31,10,12,16,9,7,381,413,621,605,677,1031,36,37,29,9,13,15,359,229,626,558,1058,764,25,13,19,3,11,6,271,273,824,685,875,963,9,15,29,6,9,15,254,328,762,711,971,785,14,23,19,7,10,14,302,302,695,727,1100,755,15,11,12,1,11,4,344,352,644,502,742,1093,40,37,29,14,13,16,428,400,595,447,756,943,39,25,22,5,12,16,385,391,640,573,796,1031,13,24,21,6,19,13,415,179,838,811,1023,788,23,25,29,15,2,4,370,232,684,654,943,752,24,28,15,11,8,10,198,194,923,659,714,774,26,11,2,10,8,5,378,316,873,661,782,720,30,23,4,5,11,18,386,276,509,563,784,1003,35,36,14,13,11,13,342,256,688,618,1008,795,32,10,11,12,10,6,358,298,934,887,1235,646,24,22,13,16,4,5,346,528,713,690,652,1233,29,23,21,10,16,7,397,275,785,519,679,709,23,23,13,11,15,15,4 -315,407,731,865,670,682,28,33,22,12,12,8,360,220,554,730,711,897,31,26,13,9,16,10,280,238,659,807,1108,696,24,10,31,9,8,9,378,150,801,930,903,775,18,10,29,12,4,0,345,189,767,956,1019,665,19,12,31,13,5,9,231,271,604,976,1150,761,20,12,26,7,8,17,345,291,707,751,790,933,29,18,29,12,10,3,321,361,644,690,806,821,32,18,36,5,15,15,512,256,661,828,802,905,26,31,37,12,12,18,318,252,715,896,1073,688,22,16,13,11,5,3,283,353,563,907,993,796,21,19,29,5,11,9,229,315,800,842,764,720,19,34,16,4,11,6,407,311,742,916,832,766,21,32,16,11,14,13,401,315,552,810,834,949,24,19,30,13,8,10,259,301,637,777,1058,763,21,13,25,6,7,17,267,473,825,1070,1285,630,21,31,29,12,7,14,551,485,788,941,662,1063,26,40,33,14,17,12,390,252,670,720,681,583,16,2,7,9,14,16,4 -315,419,982,680,592,751,38,11,10,16,7,12,342,248,671,637,595,860,33,30,21,9,9,14,294,214,794,566,972,763,14,20,11,5,13,5,282,232,914,721,789,948,14,20,21,8,11,16,249,183,910,725,885,748,1,22,11,9,10,11,177,155,803,713,1014,672,2,18,2,3,13,3,271,273,694,522,656,874,27,36,21,14,13,15,353,323,713,503,670,786,30,30,14,5,10,19,412,236,692,633,738,900,26,23,13,8,11,20,306,270,986,789,937,881,10,18,21,15,6,3,293,261,832,650,857,585,11,35,7,9,8,9,217,365,1071,719,628,703,29,12,8,8,8,4,373,311,995,721,696,611,31,22,10,7,9,17,365,251,647,563,698,778,22,31,6,13,13,8,255,223,800,606,922,726,19,17,3,10,14,1,263,395,1034,885,1149,745,11,23,21,16,4,2,395,427,727,742,596,1030,44,22,13,10,8,12,450,250,939,583,651,848,24,30,19,9,15,20,4 -281,367,925,708,652,700,36,11,13,14,9,12,360,322,602,637,683,987,39,34,20,7,11,10,288,126,759,586,1064,772,22,16,12,1,13,1,232,164,899,753,877,959,12,16,24,4,7,12,217,219,869,763,985,799,7,20,12,5,8,11,207,215,756,753,1106,749,8,14,15,1,9,1,279,291,717,556,754,1025,33,34,20,12,11,7,373,435,692,529,762,905,36,26,17,3,10,15,390,344,607,671,788,1003,20,23,18,4,17,16,312,168,929,817,1033,812,16,22,28,13,0,1,345,225,775,682,949,710,17,29,20,13,6,5,183,273,1014,735,720,718,27,12,7,12,10,0,337,343,944,759,788,698,29,24,5,3,11,13,369,329,574,605,798,957,28,35,11,11,13,10,279,301,755,630,1014,783,25,13,10,14,12,3,275,319,999,901,1241,678,17,23,22,14,2,2,381,553,688,776,646,1185,36,22,14,8,14,8,444,312,884,589,669,729,28,24,10,13,13,16,4 -315,381,966,696,784,673,37,15,15,11,6,10,322,320,689,603,827,1030,34,32,22,8,6,12,248,112,730,570,1090,751,15,18,12,2,8,3,236,158,918,751,851,940,13,18,24,1,12,14,231,201,892,759,941,770,14,20,12,2,11,9,185,213,793,741,1146,756,15,16,19,4,8,1,213,279,748,556,914,1100,40,30,22,13,8,9,353,435,729,513,942,944,39,24,17,6,5,17,372,372,646,661,854,1024,19,19,18,1,12,18,350,176,970,799,1129,753,23,20,32,14,7,1,335,207,816,674,1045,775,24,31,20,16,3,7,225,267,1055,719,894,785,28,10,9,15,5,2,287,367,963,749,904,733,30,20,7,2,4,15,295,327,623,601,956,1024,35,31,11,12,8,8,255,301,788,616,1172,800,32,15,14,11,9,1,305,325,1034,887,1261,625,24,19,20,13,9,0,335,559,807,770,774,1240,43,18,14,9,9,10,468,334,915,583,711,672,21,24,12,10,10,18,4 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-332,350,650,963,663,660,9,13,25,10,9,6,219,163,757,852,656,1029,4,16,6,3,11,14,329,309,696,765,779,740,27,30,4,5,13,15,347,241,766,954,784,939,33,20,6,12,11,14,250,276,748,868,818,759,36,22,4,9,10,9,378,296,621,730,799,739,31,26,17,7,17,13,214,210,860,791,649,1099,12,24,6,0,9,13,308,162,799,808,577,941,9,28,1,7,6,11,221,163,872,960,749,1023,15,19,2,0,1,12,523,327,600,738,840,752,31,18,34,13,16,13,346,364,530,719,736,754,28,27,22,17,16,17,454,270,685,1016,545,780,18,18,21,16,10,14,272,260,679,1048,605,712,14,14,19,1,11,13,220,196,733,792,701,1007,17,17,9,1,13,10,438,222,676,621,785,783,22,31,12,8,14,13,506,488,726,684,982,612,28,17,36,8,14,12,218,296,1005,1035,653,1235,15,18,2,2,2,10,371,261,561,898,650,671,17,28,18,15,15,10,7 -320,346,697,886,660,654,9,8,22,10,13,5,167,185,756,769,635,1017,4,31,9,3,11,19,171,377,741,682,748,734,27,17,1,3,1,12,357,297,797,887,747,927,33,23,9,10,13,9,286,296,777,835,785,753,32,29,1,7,14,12,258,314,618,731,786,735,27,19,14,5,7,10,140,312,853,714,616,1087,8,33,9,0,7,12,280,204,794,725,616,929,5,27,2,7,8,14,263,169,881,877,732,1011,15,20,3,0,13,13,453,405,661,735,765,748,27,23,31,13,14,10,252,384,539,690,673,752,24,22,19,17,10,16,358,332,746,933,544,770,18,17,18,16,12,11,264,230,736,965,616,710,12,15,16,1,11,16,124,266,734,719,658,1003,13,26,6,1,1,11,272,278,681,560,754,779,18,18,9,8,2,8,458,516,779,733,943,614,24,14,33,8,16,9,294,222,1010,958,644,1225,15,19,1,2,16,5,337,373,620,815,681,669,17,27,15,13,15,13,7 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-308,316,658,869,672,655,11,21,18,11,14,11,229,231,713,784,607,1024,6,24,13,4,12,19,203,419,704,667,722,735,25,26,3,4,0,10,357,347,766,864,655,934,31,32,13,11,12,11,300,342,746,816,665,754,32,32,3,8,13,16,286,334,601,686,702,734,31,24,10,6,6,8,140,318,840,695,602,1094,12,18,13,1,6,16,330,194,769,710,636,936,9,18,6,8,7,20,287,163,862,862,698,1018,13,11,5,1,12,13,469,369,616,684,699,755,31,24,33,14,13,8,230,358,510,655,593,749,28,17,15,18,7,14,300,308,701,930,598,775,20,20,14,17,13,9,278,214,689,950,600,707,14,18,12,2,12,22,104,244,679,696,610,1002,17,17,2,0,0,13,308,256,656,517,692,778,22,23,5,7,1,6,510,424,738,712,859,611,28,19,29,9,15,7,266,180,953,937,648,1230,17,10,5,1,15,11,351,377,587,828,669,678,19,24,17,14,14,19,7 -341,345,760,829,638,669,14,10,22,11,17,12,224,208,789,736,631,1038,11,29,9,4,15,18,228,328,692,623,742,749,22,17,1,4,3,13,354,282,880,830,705,948,32,23,9,7,9,10,281,307,856,772,737,768,37,25,1,4,10,19,301,289,703,664,786,748,38,15,14,6,7,11,151,193,922,657,644,1108,19,29,9,3,3,15,345,135,845,666,584,950,16,21,2,8,10,21,268,192,842,818,706,1032,10,20,1,1,9,14,502,288,708,724,811,761,38,23,33,14,10,11,241,277,582,627,701,763,35,14,19,18,12,17,347,261,793,880,574,789,23,19,18,17,16,12,287,265,749,906,578,721,21,21,16,2,15,23,111,147,739,662,682,1016,24,22,6,2,3,16,341,177,774,541,794,792,29,14,9,9,2,9,501,411,838,716,949,621,35,14,33,9,14,10,239,255,1017,901,634,1244,20,19,1,1,12,12,364,274,655,774,605,680,22,25,17,10,13,20,7 -279,411,764,780,684,683,22,23,20,13,17,2,166,172,739,705,673,1052,19,30,11,2,15,16,282,274,714,554,790,763,14,12,1,0,3,13,372,158,884,805,751,962,24,4,11,3,9,10,343,223,854,779,783,782,29,10,1,4,10,9,367,347,685,701,834,762,30,8,12,2,3,11,255,273,872,612,668,1122,27,22,11,7,3,11,299,323,801,595,622,964,24,20,4,4,10,11,268,192,840,747,760,1046,4,33,3,3,9,8,552,328,714,761,837,775,38,26,33,10,10,11,355,435,574,644,735,777,39,21,17,14,8,17,413,365,799,815,606,803,31,24,16,13,16,12,245,311,769,835,640,735,29,32,14,2,15,13,237,325,685,633,714,1030,32,27,4,6,3,8,395,347,750,510,836,806,37,15,7,13,2,13,573,555,844,769,949,635,39,33,31,11,12,12,257,403,967,846,682,1258,28,34,3,5,12,4,334,280,671,711,693,694,26,10,17,12,11,10,7 -303,315,560,937,698,704,3,18,21,9,16,4,208,192,667,826,671,1071,8,15,10,2,14,16,322,482,656,737,746,784,39,29,0,8,2,11,352,330,690,930,761,981,27,23,10,15,10,12,351,379,664,876,843,803,40,23,0,12,11,9,407,427,545,730,784,785,39,25,13,10,12,13,277,383,780,761,648,1141,14,17,10,1,4,17,333,247,705,780,626,983,11,19,3,6,9,11,268,182,830,932,782,1065,21,14,2,1,10,6,588,476,498,736,787,794,31,21,36,12,13,13,401,479,440,713,701,802,30,22,18,16,17,15,437,373,583,992,554,822,12,17,17,15,15,12,219,245,603,1020,646,760,10,17,15,0,14,13,257,313,661,762,686,1053,19,10,5,2,2,8,443,349,600,561,720,829,22,28,8,9,3,15,597,605,638,698,941,656,30,20,32,9,19,14,259,273,927,1005,684,1277,3,13,2,3,13,4,354,348,483,878,723,713,19,23,20,16,14,10,7 -330,382,657,845,668,676,10,18,21,10,12,8,223,201,722,748,639,1045,9,27,10,1,10,14,327,323,667,629,718,756,26,19,0,1,2,11,415,207,785,860,739,955,34,15,10,8,14,18,378,274,761,818,755,775,41,15,0,5,15,13,422,380,622,716,768,755,36,15,13,3,8,13,284,270,851,675,618,1115,17,29,10,2,8,15,362,276,774,672,602,957,14,25,3,5,9,13,241,223,855,824,736,1039,14,30,2,2,14,6,609,357,595,742,771,768,36,23,32,11,15,13,392,454,503,673,665,772,33,28,18,15,9,9,444,332,680,886,562,796,19,19,17,14,11,12,286,288,670,912,614,728,17,27,15,1,10,15,250,284,686,688,658,1025,22,22,5,3,2,14,446,314,687,523,762,801,27,22,8,10,3,15,634,576,733,736,937,628,33,30,32,10,17,14,274,386,962,913,656,1251,16,29,2,4,17,4,349,217,558,772,653,687,18,23,16,11,16,12,7 -253,265,894,676,650,669,22,22,13,11,3,3,216,374,853,583,637,1014,19,27,20,12,3,17,228,246,778,568,816,749,14,23,12,4,11,10,326,310,1014,727,617,924,24,25,24,1,9,11,277,335,984,743,691,770,29,27,12,0,8,10,293,287,815,737,868,750,30,21,19,6,11,8,147,263,992,556,742,1084,27,23,20,17,11,14,289,295,927,509,792,938,24,15,17,8,4,12,232,348,868,641,672,1016,4,12,18,3,9,11,476,178,848,801,831,745,38,15,30,14,8,8,245,183,704,668,763,773,39,24,20,18,6,14,323,177,933,697,748,779,31,13,9,17,2,9,267,243,871,729,712,729,29,25,7,6,3,14,123,207,747,589,744,1022,32,24,11,16,11,9,331,197,882,626,924,798,37,20,14,9,12,8,523,253,984,895,973,621,39,22,22,9,10,7,227,425,1001,752,628,1238,28,13,14,13,10,3,298,304,791,565,615,656,26,17,10,10,13,11,7 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-297,307,563,936,670,662,2,20,18,9,10,8,188,286,652,849,599,1031,7,17,13,2,8,18,240,574,671,740,736,742,38,27,3,10,4,13,380,446,675,915,597,941,26,33,13,17,16,8,301,497,649,853,709,761,39,33,3,14,17,15,349,483,508,705,710,741,38,25,10,12,10,11,189,405,751,758,594,1101,13,19,13,1,10,13,345,215,684,783,630,943,10,19,6,6,11,17,258,244,825,935,718,1025,20,10,5,1,16,10,546,478,525,701,627,762,30,23,33,12,17,11,293,463,435,692,551,756,29,18,15,16,11,17,377,379,610,1009,574,782,11,19,14,15,9,12,271,253,626,1023,614,714,9,17,12,0,8,19,129,313,650,755,600,1009,18,12,2,2,4,14,351,341,563,550,618,785,21,24,5,9,5,9,557,497,653,649,805,618,29,18,29,9,19,10,261,141,920,1000,648,1237,4,9,5,3,19,8,360,392,518,907,719,685,18,25,17,16,16,16,7 -313,245,666,847,611,655,6,15,20,11,10,7,224,238,733,752,646,1024,3,20,15,4,10,19,342,412,690,655,959,735,30,26,7,8,12,10,354,370,786,832,768,934,30,24,15,15,6,13,217,379,766,764,868,754,35,26,7,12,5,14,365,315,621,640,1009,734,30,22,20,10,12,10,233,285,848,675,731,1094,11,26,15,1,10,18,337,155,771,698,729,936,8,26,8,8,7,16,256,214,836,850,709,1018,18,19,9,1,0,9,506,328,610,646,964,747,30,18,33,14,17,10,383,281,504,617,876,749,27,25,23,18,11,14,423,233,695,910,689,775,15,18,12,17,11,9,257,169,685,938,715,707,13,14,10,2,10,18,231,169,713,672,765,1002,16,19,12,0,12,13,445,193,690,561,973,778,21,29,15,7,11,12,485,415,740,650,1172,607,27,13,27,9,15,11,265,263,985,919,607,1230,12,18,7,1,3,7,376,264,567,802,558,666,14,28,17,14,16,15,7 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Oowned and donated by: -% ---------------------- -% -% Robert P.W. Duin -% Department of Applied Physics -% Delft University of Technology -% P.O. Box 5046, 2600 GA Delft -% The Netherlands -% -% email: duin@ph.tn.tudelft.nl -% http : //www.ph.tn.tudelft.nl/~duin -% tel +31 15 2786143 -% -% Usage -% ----- -% A slightly different version of the database is used in -% -% M. van Breukelen, R.P.W. Duin, D.M.J. Tax, and J.E. den Hartog, Handwritten -% digit recognition by combined classifiers, Kybernetika, vol. 34, no. 4, -% 1998, 381-386. -% -% M. van Breukelen and R.P.W. Duin, Neural Network Initialization by Combined -% Classifiers, in: A.K. Jain, S. Venkatesh, B.C. Lovell (eds.), ICPR'98, -% Proc. 14th Int. Conference on Pattern Recognition (Brisbane, Aug. 16-20), -% -% The database as it is is used in: -% -% A.K. Jain, R.P.W. Duin, J. Mao, Statisitcal Pattern Recognition: A Review, -% in preparation -% -% Description -% ----------- -% -% This dataset consists of features of handwritten numerals (`0'--`9') -% extracted from a collection of Dutch utility maps. 200 patterns per -% class (for a total of 2,000 patterns) have been digitized in binary -% images. These digits are represented in terms of the following six -% feature sets (files): -% -% 1. mfeat-fou: 76 Fourier coefficients of the character shapes; -% 2. mfeat-fac: 216 profile correlations; -% 3. mfeat-kar: 64 Karhunen-Love coefficients; -% 4. mfeat-pix: 240 pixel averages in 2 x 3 windows; -% 5. mfeat-zer: 47 Zernike moments; -% 6. mfeat-mor: 6 morphological features. -% -% In each file the 2000 patterns are stored in ASCI on 2000 lines. The -% first 200 patterns are of class `0', followed by sets of 200 patterns -% for each of the classes `1' - `9'. Corresponding patterns in different -% feature sets (files) correspond to the same original character. -% -% The source image dataset is lost. Using the pixel-dataset (mfeat-pix) -% sampled versions of the original images may be obtained (15 x 16 pixels). -% -% Total number of instances: -% -------------------------- -% 2000 (200 instances per class) -% -% Total number of attributes: -% --------------------------- -% 649 (distributed over 6 datasets,see above) -% -% no missing attributes -% -% Total number of classes: -% ------------------------ -% 10 -% -% Format: -% ------ -% 6 files, see above. -% Each file contains 2000 lines, one for each instance. -% Attributes are SPACE separated and can be loaded by Matlab as -% > load filename -% No missing attributes. Some are integer, others are real. -% -% -% Information about the dataset -% CLASSTYPE: nominal -% CLASSINDEX: last -% - -@relation mfeat - -@attribute att1 REAL -@attribute att2 REAL -@attribute att3 REAL -@attribute att4 REAL -@attribute att5 REAL -@attribute att6 REAL -@attribute att7 REAL -@attribute att8 REAL -@attribute att9 REAL -@attribute att10 REAL -@attribute att11 REAL -@attribute att12 REAL -@attribute att13 REAL -@attribute att14 REAL -@attribute att15 REAL -@attribute att16 REAL -@attribute att17 REAL -@attribute att18 REAL -@attribute att19 REAL -@attribute att20 REAL -@attribute att21 REAL -@attribute att22 REAL -@attribute att23 REAL -@attribute att24 REAL -@attribute att25 REAL -@attribute att26 REAL -@attribute att27 REAL -@attribute att28 REAL -@attribute att29 REAL -@attribute att30 REAL -@attribute att31 REAL -@attribute att32 REAL -@attribute att33 REAL -@attribute att34 REAL -@attribute att35 REAL -@attribute att36 REAL -@attribute att37 REAL -@attribute att38 REAL -@attribute att39 REAL -@attribute att40 REAL -@attribute att41 REAL -@attribute att42 REAL -@attribute att43 REAL -@attribute att44 REAL -@attribute att45 REAL -@attribute att46 REAL -@attribute att47 REAL -@attribute att48 REAL -@attribute att49 REAL -@attribute att50 REAL -@attribute att51 REAL -@attribute att52 REAL -@attribute att53 REAL -@attribute att54 REAL -@attribute att55 REAL -@attribute att56 REAL -@attribute att57 REAL -@attribute att58 REAL -@attribute att59 REAL -@attribute att60 REAL -@attribute att61 REAL -@attribute att62 REAL -@attribute att63 REAL -@attribute att64 REAL -@attribute att65 REAL -@attribute att66 REAL -@attribute att67 REAL -@attribute att68 REAL -@attribute att69 REAL -@attribute att70 REAL -@attribute att71 REAL -@attribute att72 REAL -@attribute att73 REAL -@attribute att74 REAL -@attribute att75 REAL -@attribute att76 REAL -@attribute class {1,2,3,4,5,6,7,8,9,10} - -@data 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/dev/null @@ -1,2154 +0,0 @@ -% The multi-feature digit dataset -% ------------------------------- -% -% Oowned and donated by: -% ---------------------- -% -% Robert P.W. Duin -% Department of Applied Physics -% Delft University of Technology -% P.O. Box 5046, 2600 GA Delft -% The Netherlands -% -% email: duin@ph.tn.tudelft.nl -% http : //www.ph.tn.tudelft.nl/~duin -% tel +31 15 2786143 -% -% Usage -% ----- -% A slightly different version of the database is used in -% -% M. van Breukelen, R.P.W. Duin, D.M.J. Tax, and J.E. den Hartog, Handwritten -% digit recognition by combined classifiers, Kybernetika, vol. 34, no. 4, -% 1998, 381-386. -% -% M. van Breukelen and R.P.W. Duin, Neural Network Initialization by Combined -% Classifiers, in: A.K. Jain, S. Venkatesh, B.C. Lovell (eds.), ICPR'98, -% Proc. 14th Int. Conference on Pattern Recognition (Brisbane, Aug. 16-20), -% -% The database as it is is used in: -% -% A.K. Jain, R.P.W. Duin, J. Mao, Statisitcal Pattern Recognition: A Review, -% in preparation -% -% Description -% ----------- -% -% This dataset consists of features of handwritten numerals (`0'--`9') -% extracted from a collection of Dutch utility maps. 200 patterns per -% class (for a total of 2,000 patterns) have been digitized in binary -% images. These digits are represented in terms of the following six -% feature sets (files): -% -% 1. mfeat-fou: 76 Fourier coefficients of the character shapes; -% 2. mfeat-fac: 216 profile correlations; -% 3. mfeat-kar: 64 Karhunen-Love coefficients; -% 4. mfeat-pix: 240 pixel averages in 2 x 3 windows; -% 5. mfeat-zer: 47 Zernike moments; -% 6. mfeat-mor: 6 morphological features. -% -% In each file the 2000 patterns are stored in ASCI on 2000 lines. The -% first 200 patterns are of class `0', followed by sets of 200 patterns -% for each of the classes `1' - `9'. Corresponding patterns in different -% feature sets (files) correspond to the same original character. -% -% The source image dataset is lost. Using the pixel-dataset (mfeat-pix) -% sampled versions of the original images may be obtained (15 x 16 pixels). -% -% Total number of instances: -% -------------------------- -% 2000 (200 instances per class) -% -% Total number of attributes: -% --------------------------- -% 649 (distributed over 6 datasets,see above) -% -% no missing attributes -% -% Total number of classes: -% ------------------------ -% 10 -% -% Format: -% ------ -% 6 files, see above. -% Each file contains 2000 lines, one for each instance. -% Attributes are SPACE separated and can be loaded by Matlab as -% > load filename -% No missing attributes. Some are integer, others are real. -% -% -% Information about the dataset -% CLASSTYPE: nominal -% CLASSINDEX: last -% - -@relation mfeat - -@attribute att1 REAL -@attribute att2 REAL -@attribute att3 REAL -@attribute att4 REAL -@attribute att5 REAL -@attribute att6 REAL -@attribute att7 REAL -@attribute att8 REAL -@attribute att9 REAL -@attribute att10 REAL -@attribute att11 REAL -@attribute att12 REAL -@attribute att13 REAL -@attribute att14 REAL -@attribute att15 REAL -@attribute att16 REAL -@attribute att17 REAL -@attribute att18 REAL -@attribute att19 REAL -@attribute att20 REAL -@attribute att21 REAL -@attribute att22 REAL -@attribute att23 REAL -@attribute att24 REAL -@attribute att25 REAL -@attribute att26 REAL -@attribute att27 REAL -@attribute att28 REAL -@attribute att29 REAL -@attribute att30 REAL -@attribute att31 REAL -@attribute att32 REAL -@attribute att33 REAL -@attribute att34 REAL -@attribute att35 REAL -@attribute att36 REAL -@attribute att37 REAL -@attribute att38 REAL -@attribute att39 REAL -@attribute att40 REAL -@attribute att41 REAL -@attribute att42 REAL -@attribute att43 REAL -@attribute att44 REAL -@attribute att45 REAL -@attribute att46 REAL -@attribute att47 REAL -@attribute att48 REAL -@attribute att49 REAL -@attribute att50 REAL -@attribute att51 REAL -@attribute att52 REAL -@attribute att53 REAL -@attribute att54 REAL -@attribute att55 REAL -@attribute att56 REAL -@attribute att57 REAL -@attribute att58 REAL -@attribute att59 REAL -@attribute att60 REAL -@attribute att61 REAL -@attribute att62 REAL -@attribute att63 REAL -@attribute att64 REAL -@attribute class {1,2,3,4,5,6,7,8,9,10} - -@data 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--2.41524792,-6.61980629,5.05353785,6.66230011,12.13667297,-1.44784164,-2.32187271,4.04216862,-2.98180628,-0.10678527,0.02133965,1.24912238,-2.65800619,2.57817960,0.41813564,-2.63228893,0.00028908,0.90037572,-3.22290134,-1.54482090,-2.62241840,-1.37017679,-0.13465855,0.08077836,3.61350298,-0.96991217,2.91444969,1.21133304,-2.54653883,1.71368349,-0.56633627,-1.81025529,-0.06941637,-3.99154711,0.53669405,-0.52859700,-1.18754721,1.21767807,-1.92698109,1.91144156,-1.01806664,-0.12504967,-0.43688589,-1.90169179,0.15814376,0.99983811,1.03113806,0.89885831,-0.41890606,-0.25876561,-0.05727406,-0.74716163,1.40577459,0.75476533,-1.43835545,-0.71428460,0.01705098,0.46057200,-0.95176315,0.24190116,-0.39905143,-0.30485660,-0.06841105,-1.04905212,10 -5.89268351,-8.18587494,1.81930494,6.87126303,1.02133250,-0.86937547,-6.75973797,-3.89199328,-4.78135157,3.35565615,0.02136922,4.33147621,4.49066067,0.09125943,1.31369710,-1.76589346,0.08865798,0.95824957,-0.41319886,-2.97842765,0.76792830,-5.34973764,3.88349915,2.42931175,-3.78013325,-2.69649506,-0.72855157,-1.79733515,-2.62240934,-1.00271952,-1.01607692,0.70518756,-0.26890373,0.63305289,1.54465890,-0.89622056,-1.67992282,-0.81300074,-0.79634750,0.74872553,0.64206064,1.14109719,-0.91696692,-1.11629033,-0.56549048,-1.40649951,-1.68407476,-1.25464606,1.77369952,0.75474608,-1.82498693,-0.29015595,0.38426900,-0.22272336,-0.67225426,1.27301645,0.22757292,0.44408613,1.43947291,-0.40570605,0.37818658,-0.12805593,0.92563748,1.79805350,10 -1.88161314,-9.65088081,0.31778041,0.65588820,7.88264847,1.74049687,0.02694273,-4.41281319,-3.40317869,-0.61461020,-3.61290407,-2.54189467,-2.21613383,6.47141743,0.84208632,-1.65072918,1.21307015,2.34232807,-2.76456332,1.41576242,-1.07636023,-2.95648575,-1.99708140,-1.90036511,0.27159321,1.56854248,-0.02671146,-1.98293805,-2.18598747,-0.35729605,-1.82482255,0.76499128,0.04239963,-1.30356526,0.32054460,-0.04416737,-1.62261844,1.90388548,-0.44198835,-0.32862195,-2.89607239,-1.10999227,1.02319121,-1.54512572,-3.40954304,2.71826744,0.75802284,-3.58527255,0.55090547,-0.76844049,0.40649474,-2.51256275,1.64489853,0.84329420,-0.12159044,-1.62268686,0.30996418,0.47377259,0.91668326,0.97171891,0.68947220,-0.43963659,0.28701270,-0.42079285,10 --1.53088582,-10.18377495,-1.05586362,4.95607853,11.72995377,1.48078406,-2.80654287,0.60251486,-5.41198111,-2.16554332,-1.81692410,-1.73518682,-2.67014575,2.36184597,-0.91651678,-1.04324770,-0.12699533,0.97724938,2.45189428,0.24170804,0.88612235,-0.45230061,-0.32882819,-1.29278231,3.26811552,-0.71446496,1.72240365,-0.15173882,-0.53350163,1.32157314,-0.09270537,2.81975794,0.09049688,-0.00643176,0.24807417,-1.57864439,-2.70328665,-0.69275993,0.26129305,-0.07212028,-1.11082757,0.20025694,-0.55552864,-0.37341750,0.06848729,-0.65041727,0.12637749,-1.30352473,-0.74419487,-0.56429213,-0.23151828,-1.92648816,1.25034511,0.21997237,-0.22093552,-0.46633446,0.12835836,-0.88849413,-0.01444209,-0.78089690,1.00028646,1.40521359,0.43551397,-0.22542638,10 -11.25163364,-5.95969105,5.36724520,-0.84194517,3.71164894,-0.07346249,-1.14563513,1.65825915,-5.05081606,0.69673502,3.03931570,0.09688282,8.30646133,-1.41865528,-0.15848255,-3.89211750,0.09851420,1.55835819,-2.83189559,-0.61947179,-0.41233701,-3.43100977,0.12053594,1.97405052,1.83190691,-2.46773434,0.52619374,-2.43231297,-0.86451483,-0.37365127,0.51349056,0.70331287,2.47284079,-1.26786518,-0.18268585,-1.76336586,1.24486136,1.42289162,-0.31338394,1.16100895,0.51607549,-0.24187888,0.34940016,-2.01303530,0.83631086,-0.47801930,-0.50370347,1.34969425,1.75477505,-1.63296640,-0.71762949,0.79441214,0.62531579,-0.20679140,0.20761198,-1.14204228,1.89562750,0.00647411,-1.05016267,-1.20690036,0.99743658,0.00791150,0.76397073,0.04036858,10 diff --git a/datasets/mfeat-morphological.arff b/datasets/mfeat-morphological.arff deleted file mode 100755 index ad2de8d..0000000 --- a/datasets/mfeat-morphological.arff +++ /dev/null @@ -1,2096 +0,0 @@ -% The multi-feature digit dataset -% ------------------------------- -% -% Oowned and donated by: -% ---------------------- -% -% Robert P.W. Duin -% Department of Applied Physics -% Delft University of Technology -% P.O. Box 5046, 2600 GA Delft -% The Netherlands -% -% email: duin@ph.tn.tudelft.nl -% http : //www.ph.tn.tudelft.nl/~duin -% tel +31 15 2786143 -% -% Usage -% ----- -% A slightly different version of the database is used in -% -% M. van Breukelen, R.P.W. Duin, D.M.J. Tax, and J.E. den Hartog, Handwritten -% digit recognition by combined classifiers, Kybernetika, vol. 34, no. 4, -% 1998, 381-386. -% -% M. van Breukelen and R.P.W. Duin, Neural Network Initialization by Combined -% Classifiers, in: A.K. Jain, S. Venkatesh, B.C. Lovell (eds.), ICPR'98, -% Proc. 14th Int. Conference on Pattern Recognition (Brisbane, Aug. 16-20), -% -% The database as it is is used in: -% -% A.K. Jain, R.P.W. Duin, J. Mao, Statisitcal Pattern Recognition: A Review, -% in preparation -% -% Description -% ----------- -% -% This dataset consists of features of handwritten numerals (`0'--`9') -% extracted from a collection of Dutch utility maps. 200 patterns per -% class (for a total of 2,000 patterns) have been digitized in binary -% images. These digits are represented in terms of the following six -% feature sets (files): -% -% 1. mfeat-fou: 76 Fourier coefficients of the character shapes; -% 2. mfeat-fac: 216 profile correlations; -% 3. mfeat-kar: 64 Karhunen-Love coefficients; -% 4. mfeat-pix: 240 pixel averages in 2 x 3 windows; -% 5. mfeat-zer: 47 Zernike moments; -% 6. mfeat-mor: 6 morphological features. -% -% In each file the 2000 patterns are stored in ASCI on 2000 lines. The -% first 200 patterns are of class `0', followed by sets of 200 patterns -% for each of the classes `1' - `9'. Corresponding patterns in different -% feature sets (files) correspond to the same original character. -% -% The source image dataset is lost. Using the pixel-dataset (mfeat-pix) -% sampled versions of the original images may be obtained (15 x 16 pixels). -% -% Total number of instances: -% -------------------------- -% 2000 (200 instances per class) -% -% Total number of attributes: -% --------------------------- -% 649 (distributed over 6 datasets,see above) -% -% no missing attributes -% -% Total number of classes: -% ------------------------ -% 10 -% -% Format: -% ------ -% 6 files, see above. -% Each file contains 2000 lines, one for each instance. -% Attributes are SPACE separated and can be loaded by Matlab as -% > load filename -% No missing attributes. Some are integer, others are real. -% -% -% Information about the dataset -% CLASSTYPE: nominal -% CLASSINDEX: last -% - -@relation mfeat - -@attribute att1 REAL -@attribute att2 REAL -@attribute att3 REAL -@attribute att4 REAL -@attribute att5 REAL -@attribute att6 REAL -@attribute class {1,2,3,4,5,6,7,8,9,10} - -@data -1.00000000,0.00000000,0.00000000,133.15086140,1.31169276,1620.22177892,1 -1.00000000,0.00000000,0.00000000,126.72486140,1.30274497,1609.33482208,1 -1.00000000,0.00000000,0.00000000,131.17386140,1.31903101,1568.97843469,1 -1.00000000,0.00000000,0.00000000,129.47886140,1.27087816,1695.05528143,1 -1.00000000,0.00000000,0.00000000,127.26286140,1.32963668,1647.72023493,1 -1.00000000,0.00000000,0.00000000,131.05886140,1.31040314,2027.21136662,1 -1.00000000,0.00000000,0.00000000,138.44286140,1.37990560,1918.75047185,1 -1.00000000,0.00000000,0.00000000,129.34686140,1.30564601,1547.26372726,1 -1.00000000,0.00000000,0.00000000,139.54286140,1.37466149,1871.05593439,1 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-1.00000000,1.00000000,1.00000000,142.92686140,1.42638097,4118.32732012,10 -1.00000000,1.00000000,1.00000000,133.92086140,1.56462053,3808.02131663,10 diff --git a/datasets/mfeat-zernike.arff b/datasets/mfeat-zernike.arff deleted file mode 100755 index 0426d02..0000000 --- a/datasets/mfeat-zernike.arff +++ /dev/null @@ -1,2137 +0,0 @@ -% The multi-feature digit dataset -% ------------------------------- -% -% Oowned and donated by: -% ---------------------- -% -% Robert P.W. Duin -% Department of Applied Physics -% Delft University of Technology -% P.O. Box 5046, 2600 GA Delft -% The Netherlands -% -% email: duin@ph.tn.tudelft.nl -% http : //www.ph.tn.tudelft.nl/~duin -% tel +31 15 2786143 -% -% Usage -% ----- -% A slightly different version of the database is used in -% -% M. van Breukelen, R.P.W. Duin, D.M.J. Tax, and J.E. den Hartog, Handwritten -% digit recognition by combined classifiers, Kybernetika, vol. 34, no. 4, -% 1998, 381-386. -% -% M. van Breukelen and R.P.W. Duin, Neural Network Initialization by Combined -% Classifiers, in: A.K. Jain, S. Venkatesh, B.C. Lovell (eds.), ICPR'98, -% Proc. 14th Int. Conference on Pattern Recognition (Brisbane, Aug. 16-20), -% -% The database as it is is used in: -% -% A.K. Jain, R.P.W. Duin, J. Mao, Statisitcal Pattern Recognition: A Review, -% in preparation -% -% Description -% ----------- -% -% This dataset consists of features of handwritten numerals (`0'--`9') -% extracted from a collection of Dutch utility maps. 200 patterns per -% class (for a total of 2,000 patterns) have been digitized in binary -% images. These digits are represented in terms of the following six -% feature sets (files): -% -% 1. mfeat-fou: 76 Fourier coefficients of the character shapes; -% 2. mfeat-fac: 216 profile correlations; -% 3. mfeat-kar: 64 Karhunen-Love coefficients; -% 4. mfeat-pix: 240 pixel averages in 2 x 3 windows; -% 5. mfeat-zer: 47 Zernike moments; -% 6. mfeat-mor: 6 morphological features. -% -% In each file the 2000 patterns are stored in ASCI on 2000 lines. The -% first 200 patterns are of class `0', followed by sets of 200 patterns -% for each of the classes `1' - `9'. Corresponding patterns in different -% feature sets (files) correspond to the same original character. -% -% The source image dataset is lost. Using the pixel-dataset (mfeat-pix) -% sampled versions of the original images may be obtained (15 x 16 pixels). -% -% Total number of instances: -% -------------------------- -% 2000 (200 instances per class) -% -% Total number of attributes: -% --------------------------- -% 649 (distributed over 6 datasets,see above) -% -% no missing attributes -% -% Total number of classes: -% ------------------------ -% 10 -% -% Format: -% ------ -% 6 files, see above. -% Each file contains 2000 lines, one for each instance. -% Attributes are SPACE separated and can be loaded by Matlab as -% > load filename -% No missing attributes. Some are integer, others are real. -% -% -% Information about the dataset -% CLASSTYPE: nominal -% CLASSINDEX: last -% - -@relation mfeat - -@attribute att1 REAL -@attribute att2 REAL -@attribute att3 REAL -@attribute att4 REAL -@attribute att5 REAL -@attribute att6 REAL -@attribute att7 REAL -@attribute att8 REAL -@attribute att9 REAL -@attribute att10 REAL -@attribute att11 REAL -@attribute att12 REAL -@attribute att13 REAL -@attribute att14 REAL -@attribute att15 REAL -@attribute att16 REAL -@attribute att17 REAL -@attribute att18 REAL -@attribute att19 REAL -@attribute att20 REAL -@attribute att21 REAL -@attribute att22 REAL -@attribute att23 REAL -@attribute att24 REAL -@attribute att25 REAL -@attribute att26 REAL -@attribute att27 REAL -@attribute att28 REAL -@attribute att29 REAL -@attribute att30 REAL -@attribute att31 REAL -@attribute att32 REAL -@attribute att33 REAL -@attribute att34 REAL -@attribute att35 REAL -@attribute att36 REAL -@attribute att37 REAL -@attribute att38 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Title of Database: Optical Recognition of Handwritten Digits -% -% 2. Source: -% E. Alpaydin, C. Kaynak -% Department of Computer Engineering -% Bogazici University, 80815 Istanbul Turkey -% alpaydin@boun.edu.tr -% July 1998 -% -% 3. Past Usage: -% C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their -% Applications to Handwritten Digit Recognition, -% MSc Thesis, Institute of Graduate Studies in Science and -% Engineering, Bogazici University. -% -% E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika, -% to appear. ftp://ftp.icsi.berkeley.edu/pub/ai/ethem/kyb.ps.Z -% -% 4. Relevant Information: -% We used preprocessing programs made available by NIST to extract -% normalized bitmaps of handwritten digits from a preprinted form. From -% a total of 43 people, 30 contributed to the training set and different -% 13 to the test set. 32x32 bitmaps are divided into nonoverlapping -% blocks of 4x4 and the number of on pixels are counted in each block. -% This generates an input matrix of 8x8 where each element is an -% integer in the range 0..16. This reduces dimensionality and gives -% invariance to small distortions. -% -% For info on NIST preprocessing routines, see -% M. D. Garris, J. L. Blue, G. T. Candela, D. L. Dimmick, J. Geist, -% P. J. Grother, S. A. Janet, and C. L. Wilson, NIST Form-Based -% Handprint Recognition System, NISTIR 5469, 1994. -% -% 5. Number of Instances -% optdigits.tra Training 3823 -% optdigits.tes Testing 1797 -% -% The way we used the dataset was to use half of training for -% actual training, one-fourth for validation and one-fourth -% for writer-dependent testing. The test set was used for -% writer-independent testing and is the actual quality measure. -% -% 6. Number of Attributes -% 64 input+1 class attribute -% -% 7. For Each Attribute: -% All input attributes are integers in the range 0..16. -% The last attribute is the class code 0..9 -% -% 8. Missing Attribute Values -% None -% -% 9. Class Distribution -% Class: No of examples in training set -% 0: 376 -% 1: 389 -% 2: 380 -% 3: 389 -% 4: 387 -% 5: 376 -% 6: 377 -% 7: 387 -% 8: 380 -% 9: 382 -% -% Class: No of examples in testing set -% 0: 178 -% 1: 182 -% 2: 177 -% 3: 183 -% 4: 181 -% 5: 182 -% 6: 181 -% 7: 179 -% 8: 174 -% 9: 180 -% -% Accuracy on the testing set with k-nn -% using Euclidean distance as the metric -% -% k = 1 : 98.00 -% k = 2 : 97.38 -% k = 3 : 97.83 -% k = 4 : 97.61 -% k = 5 : 97.89 -% k = 6 : 97.77 -% k = 7 : 97.66 -% k = 8 : 97.66 -% k = 9 : 97.72 -% k = 10 : 97.55 -% k = 11 : 97.89 - -@relation optdigits - -@attribute input1 numeric -@attribute input2 numeric -@attribute input3 numeric -@attribute input4 numeric -@attribute input5 numeric -@attribute input6 numeric -@attribute input7 numeric -@attribute input8 numeric -@attribute input9 numeric -@attribute input10 numeric -@attribute input11 numeric -@attribute input12 numeric -@attribute input13 numeric -@attribute input14 numeric -@attribute input15 numeric -@attribute input16 numeric -@attribute input17 numeric -@attribute input18 numeric -@attribute input19 numeric -@attribute input20 numeric -@attribute input21 numeric -@attribute input22 numeric -@attribute input23 numeric -@attribute input24 numeric -@attribute input25 numeric -@attribute input26 numeric -@attribute input27 numeric -@attribute input28 numeric -@attribute input29 numeric -@attribute input30 numeric -@attribute input31 numeric -@attribute input32 numeric -@attribute input33 numeric -@attribute input34 numeric -@attribute input35 numeric -@attribute input36 numeric -@attribute input37 numeric -@attribute input38 numeric -@attribute input39 numeric -@attribute input40 numeric -@attribute input41 numeric -@attribute input42 numeric -@attribute input43 numeric -@attribute input44 numeric -@attribute input45 numeric -@attribute input46 numeric -@attribute input47 numeric -@attribute input48 numeric -@attribute input49 numeric -@attribute input50 numeric -@attribute input51 numeric -@attribute input52 numeric -@attribute input53 numeric -@attribute input54 numeric -@attribute input55 numeric -@attribute input56 numeric -@attribute input57 numeric -@attribute input58 numeric -@attribute input59 numeric -@attribute input60 numeric -@attribute input61 numeric -@attribute input62 numeric -@attribute input63 numeric -@attribute input64 numeric -@attribute class {0,1,2,3,4,5,6,7,8,9} - -@data -0,1,6,15,12,1,0,0,0,7,16,6,6,10,0,0,0,8,16,2,0,11,2,0,0,5,16,3,0,5,7,0,0,7,13,3,0,8,7,0,0,4,12,0,1,13,5,0,0,0,14,9,15,9,0,0,0,0,6,14,7,1,0,0,0 -0,0,10,16,6,0,0,0,0,7,16,8,16,5,0,0,0,11,16,0,6,14,3,0,0,12,12,0,0,11,11,0,0,12,12,0,0,8,12,0,0,7,15,1,0,13,11,0,0,0,16,8,10,15,3,0,0,0,10,16,15,3,0,0,0 -0,0,8,15,16,13,0,0,0,1,11,9,11,16,1,0,0,0,0,0,7,14,0,0,0,0,3,4,14,12,2,0,0,1,16,16,16,16,10,0,0,2,12,16,10,0,0,0,0,0,2,16,4,0,0,0,0,0,9,14,0,0,0,0,7 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-0,0,10,14,8,1,0,0,0,2,16,14,6,1,0,0,0,0,15,15,8,15,0,0,0,0,5,16,16,10,0,0,0,0,12,15,15,12,0,0,0,4,16,6,4,16,6,0,0,8,16,10,8,16,8,0,0,1,8,12,14,12,1,0,8 \ No newline at end of file diff --git a/datasets/page-blocks.arff b/datasets/page-blocks.arff deleted file mode 100755 index 510093f..0000000 --- a/datasets/page-blocks.arff +++ /dev/null @@ -1,5585 +0,0 @@ -% 1. Title of Database: Blocks Classification -% 2. Sources: -% (a) Donato Malerba -% Dipartimento di Informatica -% University of Bari -% via Orabona 4 -% 70126 Bari - Italy -% phone: +39 - 80 - 5443269 -% fax: +39 - 80 - 5443196 -% malerbad@vm.csata.it -% (b) Donor: Donato Malerba -% (c) Date: July 1995 -% 3. Past Usage: -% This data set have been used to try different simplification methods -% for decision trees. A summary of the results can be found in: -% -% Malerba, D., Esposito, F., and Semeraro, G. -% "A Further Comparison of Simplification Methods for Decision-Tree Induction." -% In D. Fisher and H. Lenz (Eds.), "Learning from Data: -% Artificial Intelligence and Statistics V", Lecture Notes in Statistics, -% Springer Verlag, Berlin, 1995. -% -% The problem consists in classifying all the blocks of the page -% layout of a document that has been detected by a segmentation -% process. This is an essential step in document analysis -% in order to separate text from graphic areas. Indeed, -% the five classes are: text (1), horizontal line (2), -% picture (3), vertical line (4) and graphic (5). -% For a detailed presentation of the problem see: -% -% Esposito F., Malerba D., & Semeraro G. -% Multistrategy Learning for Document Recognition -% Applied Artificial Intelligence, 8, pp. 33-84, 1994 -% -% All instances have been personally checked so that -% low noise is present in the data. -% -% 4. Relevant Information Paragraph: -% -% The 5473 examples comes from 54 distinct documents. -% Each observation concerns one block. -% All attributes are numeric. -% Data are in a format readable by C4.5. -% -% 5. Number of Instances: 5473. -% -% 6. Number of Attributes -% -% height: integer. | Height of the block. -% lenght: integer. | Length of the block. -% area: integer. | Area of the block (height * lenght); -% eccen: continuous. | Eccentricity of the block (lenght / height); -% p_black: continuous. | Percentage of black pixels within the block (blackpix / area); -% p_and: continuous. | Percentage of black pixels after the application of the Run Length Smoothing Algorithm (RLSA) (blackand / area); -% mean_tr: continuous. | Mean number of white-black transitions (blackpix / wb_trans); -% blackpix: integer. | Total number of black pixels in the original bitmap of the block. -% blackand: integer. | Total number of black pixels in the bitmap of the block after the RLSA. -% wb_trans: integer. | Number of white-black transitions in the original bitmap of the block. -% -% -% -% 7. Missing Attribute Values: No missing value. -% -% 8. Class Distribution: -% -% Valid Cum -% Class Frequency Percent Percent Percent -% -% text 4913 89.8 89.8 89.8 -% horiz. line 329 6.0 6.0 95.8 -% graphic 28 .5 .5 96.3 -% vert. line 88 1.6 1.6 97.9 -% picture 115 2.1 2.1 100.0 -% ------- ------- ------- -% TOTAL 5473 100.0 100.0 -% -% Summary Statistics: -% -% Variable Mean Std Dev Minimum Maximum Correlation -% -% HEIGHT 10.47 18.96 1 804 .3510 -% LENGTH 89.57 114.72 1 553 -.0045 -% AREA 1198.41 4849.38 7 143993 .2343 -% ECCEN 13.75 30.70 .007 537.00 .0992 -% P_BLACK .37 .18 .052 1.00 .2130 -% P_AND .79 .17 .062 1.00 -.1771 -% MEAN_TR 6.22 69.08 1.00 4955.00 .0723 -% BLACKPIX 365.93 1270.33 7 33017 .1656 -% BLACKAND 741.11 1881.50 7 46133 .1565 -% WB_TRANS 106.66 167.31 1 3212 .0337 -% -% -% Information about the dataset -% CLASSTYPE: nominal -% CLASSINDEX: last -% - -@relation page-blocks - -@attribute height INTEGER -@attribute lenght INTEGER -@attribute area INTEGER -@attribute eccen REAL -@attribute p_black REAL -@attribute p_and 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Title of Database: Pen-Based Recognition of Handwritten Digits -% -% 2. Source: -% E. Alpaydin, F. Alimoglu -% Department of Computer Engineering -% Bogazici University, 80815 Istanbul Turkey -% alpaydin@boun.edu.tr -% July 1998 -% -% 3. Past Usage: -% F. Alimoglu (1996) Combining Multiple Classifiers for Pen-Based -% Handwritten Digit Recognition, -% MSc Thesis, Institute of Graduate Studies in Science and -% Engineering, Bogazici University. -% http://www.cmpe.boun.edu.tr/~alimoglu/alimoglu.ps.gz -% -% F. Alimoglu, E. Alpaydin, "Methods of Combining Multiple Classifiers -% Based on Different Representations for Pen-based Handwriting -% Recognition," Proceedings of the Fifth Turkish Artificial -% Intelligence and Artificial Neural Networks Symposium (TAINN 96), -% June 1996, Istanbul, Turkey. -% http://www.cmpe.boun.edu.tr/~alimoglu/tainn96.ps.gz -% -% -% 4. Relevant Information: -% -% We create a digit database by collecting 250 samples from 44 writers. -% The samples written by 30 writers are used for training, -% cross-validation and writer dependent testing, and the digits -% written by the other 14 are used for writer independent testing. This -% database is also available in the UNIPEN format. -% -% We use a WACOM PL-100V pressure sensitive tablet with an integrated -% LCD display and a cordless stylus. The input and display areas are -% located in the same place. Attached to the serial port of an Intel -% 486 based PC, it allows us to collect handwriting samples. The tablet -% sends $x$ and $y$ tablet coordinates and pressure level values of the -% pen at fixed time intervals (sampling rate) of 100 miliseconds. -% -% These writers are asked to write 250 digits in random order inside -% boxes of 500 by 500 tablet pixel resolution. Subject are monitored -% only during the first entry screens. Each screen contains five boxes -% with the digits to be written displayed above. Subjects are told to -% write only inside these boxes. If they make a mistake or are unhappy -% with their writing, they are instructed to clear the content of a box -% by using an on-screen button. The first ten digits are ignored -% because most writers are not familiar with this type of input devices, -% but subjects are not aware of this. -% -% In our study, we use only ($x, y$) coordinate information. The stylus -% pressure level values are ignored. First we apply normalization to -% make our representation invariant to translations and scale -% distortions. The raw data that we capture from the tablet consist of -% integer values between 0 and 500 (tablet input box resolution). The -% new coordinates are such that the coordinate which has the maximum -% range varies between 0 and 100. Usually $x$ stays in this range, since -% most characters are taller than they are wide. -% -% In order to train and test our classifiers, we need to represent -% digits as constant length feature vectors. A commonly used technique -% leading to good results is resampling the ( x_t, y_t) points. -% Temporal resampling (points regularly spaced in time) or spatial -% resampling (points regularly spaced in arc length) can be used here. -% Raw point data are already regularly spaced in time but the distance -% between them is variable. Previous research showed that spatial -% resampling to obtain a constant number of regularly spaced points -% on the trajectory yields much better performance, because it provides -% a better alignment between points. Our resampling algorithm uses -% simple linear interpolation between pairs of points. The resampled -% digits are represented as a sequence of T points ( x_t, y_t )_{t=1}^T, -% regularly spaced in arc length, as opposed to the input sequence, -% which is regularly spaced in time. -% -% So, the input vector size is 2*T, two times the number of points -% resampled. We considered spatial resampling to T=8,12,16 points in our -% experiments and found that T=8 gave the best trade-off between -% accuracy and complexity. -% -% -% 5. Number of Instances -% pendigits.tra Training 7494 -% pendigits.tes Testing 3498 -% -% The way we used the dataset was to use first half of training for -% actual training, one-fourth for validation and one-fourth -% for writer-dependent testing. The test set was used for -% writer-independent testing and is the actual quality measure. -% -% 6. Number of Attributes -% 16 input+1 class attribute -% -% 7. For Each Attribute: -% All input attributes are integers in the range 0..100. -% The last attribute is the class code 0..9 -% -% 8. Missing Attribute Values -% None -% -% 9. Class Distribution -% Class: No of examples in training set -% 0: 780 -% 1: 779 -% 2: 780 -% 3: 719 -% 4: 780 -% 5: 720 -% 6: 720 -% 7: 778 -% 8: 719 -% 9: 719 -% Class: No of examples in testing set -% 0: 363 -% 1: 364 -% 2: 364 -% 3: 336 -% 4: 364 -% 5: 335 -% 6: 336 -% 7: 364 -% 8: 336 -% 9: 336 -% -% Accuracy on the testing set with k-nn -% using Euclidean distance as the metric -% -% k = 1 : 97.74 -% k = 2 : 97.37 -% k = 3 : 97.80 -% k = 4 : 97.66 -% k = 5 : 97.60 -% k = 6 : 97.57 -% k = 7 : 97.54 -% k = 8 : 97.54 -% k = 9 : 97.46 -% k = 10 : 97.48 -% k = 11 : 97.34 - -@relation pendigits - -@attribute input1 numeric -@attribute input2 numeric -@attribute input3 numeric -@attribute input4 numeric -@attribute input5 numeric -@attribute input6 numeric -@attribute input7 numeric -@attribute input8 numeric -@attribute input9 numeric -@attribute input10 numeric -@attribute input11 numeric -@attribute input12 numeric -@attribute input13 numeric -@attribute input14 numeric -@attribute 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Title: Image Segmentation data -% -% 2. Source Information -% -- Creators: Vision Group, University of Massachusetts -% -- Donor: Vision Group (Carla Brodley, brodley@cs.umass.edu) -% -- Date: November, 1990 -% -% 3. Past Usage: None yet published -% -% 4. Relevant Information: -% -% The instances were drawn randomly from a database of 7 outdoor -% images. The images were handsegmented to create a classification -% for every pixel. -% -% Each instance is a 3x3 region. -% -% 5. Number of Instances: Training data: 210 Test data: 2100 -% -% 6. Number of Attributes: 19 continuous attributes -% -% 7. Attribute Information: -% -% 1. region-centroid-col: the column of the center pixel of the region. -% 2. region-centroid-row: the row of the center pixel of the region. -% 3. region-pixel-count: the number of pixels in a region = 9. -% 4. short-line-density-5: the results of a line extractoin algorithm that -% counts how many lines of length 5 (any orientation) with -% low contrast, less than or equal to 5, go through the region. -% 5. short-line-density-2: same as short-line-density-5 but counts lines -% of high contrast, greater than 5. -% 6. vedge-mean: measure the contrast of horizontally -% adjacent pixels in the region. There are 6, the mean and -% standard deviation are given. This attribute is used as -% a vertical edge detector. -% 7. vegde-sd: (see 6) -% 8. hedge-mean: measures the contrast of vertically adjacent -% pixels. Used for horizontal line detection. -% 9. hedge-sd: (see 8). -% 10. intensity-mean: the average over the region of (R + G + B)/3 -% 11. rawred-mean: the average over the region of the R value. -% 12. rawblue-mean: the average over the region of the B value. -% 13. rawgreen-mean: the average over the region of the G value. -% 14. exred-mean: measure the excess red: (2R - (G + B)) -% 15. exblue-mean: measure the excess blue: (2B - (G + R)) -% 16. exgreen-mean: measure the excess green: (2G - (R + B)) -% 17. value-mean: 3-d nonlinear transformation -% of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals -% of Interactive Computer Graphics) -% 18. saturatoin-mean: (see 17) -% 19. hue-mean: (see 17) -% -% 8. Missing Attribute Values: None -% -% 9. Class Distribution: -% -% Classes: brickface, sky, foliage, cement, window, path, grass. -% -% 30 instances per class for training data. -% 300 instances per class for test data. -% -% -% -% -% -% Relabeled values in attribute class -% From: 1 To: brickface -% From: 2 To: sky -% From: 3 To: foliage -% From: 4 To: cement -% From: 5 To: window -% From: 6 To: path -% From: 7 To: grass -% -@relation segment -@attribute region-centroid-col real -@attribute region-centroid-row real -@attribute region-pixel-count real -@attribute short-line-density-5 real -@attribute short-line-density-2 real -@attribute vedge-mean real -@attribute vegde-sd real -@attribute hedge-mean real -@attribute hedge-sd real -@attribute intensity-mean real -@attribute rawred-mean real -@attribute rawblue-mean real -@attribute rawgreen-mean real -@attribute exred-mean real -@attribute exblue-mean real -@attribute exgreen-mean real -@attribute value-mean real -@attribute saturation-mean real -@attribute hue-mean real 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is the data set used by Gorman and Sejnowski in their study -% of the classification of sonar signals using a neural network [1]. The -% task is to train a network to discriminate between sonar signals bounced -% off a metal cylinder and those bounced off a roughly cylindrical rock. -% -% SOURCE: The data set was contributed to the benchmark collection by Terry -% Sejnowski, now at the Salk Institute and the University of California at -% San Deigo. The data set was developed in collaboration with R. Paul -% Gorman of Allied-Signal Aerospace Technology Center. -% -% MAINTAINER: Scott E. Fahlman -% -% PROBLEM DESCRIPTION: -% -% The file "sonar.mines" contains 111 patterns obtained by bouncing sonar -% signals off a metal cylinder at various angles and under various -% conditions. The file "sonar.rocks" contains 97 patterns obtained from -% rocks under similar conditions. The transmitted sonar signal is a -% frequency-modulated chirp, rising in frequency. The data set contains -% signals obtained from a variety of different aspect angles, spanning 90 -% degrees for the cylinder and 180 degrees for the rock. -% -% Each pattern is a set of 60 numbers in the range 0.0 to 1.0. Each number -% represents the energy within a particular frequency band, integrated over -% a certain period of time. The integration aperture for higher frequencies -% occur later in time, since these frequencies are transmitted later during -% the chirp. -% -% The label associated with each record contains the letter "R" if the object -% is a rock and "M" if it is a mine (metal cylinder). The numbers in the -% labels are in increasing order of aspect angle, but they do not encode the -% angle directly. -% -% METHODOLOGY: -% -% This data set can be used in a number of different ways to test learning -% speed, quality of ultimate learning, ability to generalize, or combinations -% of these factors. -% -% In [1], Gorman and Sejnowski report two series of experiments: an -% "aspect-angle independent" series, in which the whole data set is used -% without controlling for aspect angle, and an "aspect-angle dependent" -% series in which the training and testing sets were carefully controlled to -% ensure that each set contained cases from each aspect angle in -% appropriate proportions. -% -% For the aspect-angle independent experiments the combined set of 208 cases -% is divided randomly into 13 disjoint sets with 16 cases in each. For each -% experiment, 12 of these sets are used as training data, while the 13th is -% reserved for testing. The experiment is repeated 13 times so that every -% case appears once as part of a test set. The reported performance is an -% average over the entire set of 13 different test sets, each run 10 times. -% -% It was observed that this random division of the sample set led to rather -% uneven performance. A few of the splits gave poor results, presumably -% because the test set contains some samples from aspect angles that are -% under-represented in the corresponding training set. This motivated Gorman -% and Sejnowski to devise a different set of experiments in which an attempt -% was made to balance the training and test sets so that each would have a -% representative number of samples from all aspect angles. Since detailed -% aspect angle information was not present in the data base of samples, the -% 208 samples were first divided into clusters, using a 60-dimensional -% Euclidian metric; each of these clusters was then divided between the -% 104-member training set and the 104-member test set. -% -% The actual training and testing samples used for the "aspect angle -% dependent" experiments are marked in the data files. The reported -% performance is an average over 10 runs with this single division of the -% data set. -% -% A standard back-propagation network was used for all experiments. The -% network had 60 inputs and 2 output units, one indicating a cylinder and the -% other a rock. Experiments were run with no hidden units (direct -% connections from each input to each output) and with a single hidden layer -% with 2, 3, 6, 12, or 24 units. Each network was trained by 300 epochs over -% the entire training set. -% -% The weight-update formulas used in this study were slightly different from -% the standard form. A learning rate of 2.0 and momentum of 0.0 was used. -% Errors less than 0.2 were treated as zero. Initial weights were uniform -% random values in the range -0.3 to +0.3. -% -% RESULTS: -% -% For the angle independent experiments, Gorman and Sejnowski report the -% following results for networks with different numbers of hidden units: -% -% Hidden % Right on Std. % Right on Std. -% Units Training set Dev. Test Set Dev. -% ------ ------------ ---- ---------- ---- -% 0 89.4 2.1 77.1 8.3 -% 2 96.5 0.7 81.9 6.2 -% 3 98.8 0.4 82.0 7.3 -% 6 99.7 0.2 83.5 5.6 -% 12 99.8 0.1 84.7 5.7 -% 24 99.8 0.1 84.5 5.7 -% -% For the angle-dependent experiments Gorman and Sejnowski report the -% following results: -% -% Hidden % Right on Std. % Right on Std. -% Units Training set Dev. Test Set Dev. -% ------ ------------ ---- ---------- ---- -% 0 79.3 3.4 73.1 4.8 -% 2 96.2 2.2 85.7 6.3 -% 3 98.1 1.5 87.6 3.0 -% 6 99.4 0.9 89.3 2.4 -% 12 99.8 0.6 90.4 1.8 -% 24 100.0 0.0 89.2 1.4 -% -% Not surprisingly, the network's performance on the test set was somewhat -% better when the aspect angles in the training and test sets were balanced. -% -% Gorman and Sejnowski further report that a nearest neighbor classifier on -% the same data gave an 82.7% probability of correct classification. -% -% Three trained human subjects were each tested on 100 signals, chosen at -% random from the set of 208 returns used to create this data set. Their -% responses ranged between 88% and 97% correct. However, they may have been -% using information from the raw sonar signal that is not preserved in the -% processed data sets presented here. -% -% REFERENCES: -% -% 1. Gorman, R. P., and Sejnowski, T. J. (1988). "Analysis of Hidden Units -% in a Layered Network Trained to Classify Sonar Targets" in Neural Networks, -% Vol. 1, pp. 75-89. -% -% -% -% -% Relabeled values in attribute 'Class' -% From: R To: Rock -% From: M To: Mine -% -@relation sonar -@attribute 'attribute_1' real -@attribute 'attribute_2' real -@attribute 'attribute_3' real -@attribute 'attribute_4' real -@attribute 'attribute_5' real -@attribute 'attribute_6' real -@attribute 'attribute_7' real -@attribute 'attribute_8' real -@attribute 'attribute_9' real -@attribute 'attribute_10' real -@attribute 'attribute_11' real -@attribute 'attribute_12' real -@attribute 'attribute_13' real -@attribute 'attribute_14' real -@attribute 'attribute_15' real -@attribute 'attribute_16' real -@attribute 'attribute_17' real -@attribute 'attribute_18' real -@attribute 'attribute_19' real -@attribute 'attribute_20' real -@attribute 'attribute_21' real -@attribute 'attribute_22' real -@attribute 'attribute_23' real -@attribute 'attribute_24' real -@attribute 'attribute_25' real -@attribute 'attribute_26' real -@attribute 'attribute_27' real -@attribute 'attribute_28' real -@attribute 'attribute_29' real -@attribute 'attribute_30' real -@attribute 'attribute_31' real -@attribute 'attribute_32' real -@attribute 'attribute_33' real -@attribute 'attribute_34' real -@attribute 'attribute_35' real -@attribute 'attribute_36' real -@attribute 'attribute_37' real -@attribute 'attribute_38' real -@attribute 'attribute_39' real -@attribute 'attribute_40' real -@attribute 'attribute_41' real -@attribute 'attribute_42' real -@attribute 'attribute_43' real -@attribute 'attribute_44' real -@attribute 'attribute_45' real -@attribute 'attribute_46' real -@attribute 'attribute_47' real -@attribute 'attribute_48' real -@attribute 'attribute_49' real -@attribute 'attribute_50' real -@attribute 'attribute_51' real -@attribute 'attribute_52' real -@attribute 'attribute_53' real -@attribute 'attribute_54' real -@attribute 'attribute_55' real 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-0.0283,0.0599,0.0656,0.0229,0.0839,0.1673,0.1154,0.1098,0.137,0.1767,0.1995,0.2869,0.3275,0.3769,0.4169,0.5036,0.618,0.8025,0.9333,0.9399,0.9275,0.945,0.8328,0.7773,0.7007,0.6154,0.581,0.4454,0.3707,0.2891,0.2185,0.1711,0.3578,0.3947,0.2867,0.2401,0.3619,0.3314,0.3763,0.4767,0.4059,0.3661,0.232,0.145,0.1017,0.1111,0.0655,0.0271,0.0244,0.0179,0.0109,0.0147,0.017,0.0158,0.0046,0.0073,0.0054,0.0033,0.0045,0.0079,Mine -0.0114,0.0222,0.0269,0.0384,0.1217,0.2062,0.1489,0.0929,0.135,0.1799,0.2486,0.2973,0.3672,0.4394,0.5258,0.6755,0.7402,0.8284,0.9033,0.9584,1,0.9982,0.8899,0.7493,0.6367,0.6744,0.7207,0.6821,0.5512,0.4789,0.3924,0.2533,0.1089,0.139,0.2551,0.3301,0.2818,0.2142,0.2266,0.2142,0.2354,0.2871,0.2596,0.1925,0.1256,0.1003,0.0951,0.121,0.0728,0.0174,0.0213,0.0269,0.0152,0.0257,0.0097,0.0041,0.005,0.0145,0.0103,0.0025,Mine -0.0414,0.0436,0.0447,0.0844,0.0419,0.1215,0.2002,0.1516,0.0818,0.1975,0.2309,0.3025,0.3938,0.505,0.5872,0.661,0.7417,0.8006,0.8456,0.7939,0.8804,0.8384,0.7852,0.8479,0.7434,0.6433,0.5514,0.3519,0.3168,0.3346,0.2056,0.1032,0.3168,0.404,0.4282,0.4538,0.3704,0.3741,0.3839,0.3494,0.438,0.4265,0.2854,0.2808,0.2395,0.0369,0.0805,0.0541,0.0177,0.0065,0.0222,0.0045,0.0136,0.0113,0.0053,0.0165,0.0141,0.0077,0.0246,0.0198,Mine -0.0094,0.0333,0.0306,0.0376,0.1296,0.1795,0.1909,0.1692,0.187,0.1725,0.2228,0.3106,0.4144,0.5157,0.5369,0.5107,0.6441,0.7326,0.8164,0.8856,0.9891,1,0.875,0.8631,0.9074,0.8674,0.775,0.66,0.5615,0.4016,0.2331,0.1164,0.1095,0.0431,0.0619,0.1956,0.212,0.3242,0.4102,0.2939,0.1911,0.1702,0.101,0.1512,0.1427,0.1097,0.1173,0.0972,0.0703,0.0281,0.0216,0.0153,0.0112,0.0241,0.0164,0.0055,0.0078,0.0055,0.0091,0.0067,Mine -0.0228,0.0106,0.013,0.0842,0.1117,0.1506,0.1776,0.0997,0.1428,0.2227,0.2621,0.3109,0.2859,0.3316,0.3755,0.4499,0.4765,0.6254,0.7304,0.8702,0.9349,0.9614,0.9126,0.9443,1,0.9455,0.8815,0.752,0.7068,0.5986,0.3857,0.251,0.2162,0.0968,0.1323,0.1344,0.225,0.3244,0.3939,0.3806,0.3258,0.3654,0.2983,0.1779,0.1535,0.1199,0.0959,0.0765,0.0649,0.0313,0.0185,0.0098,0.0178,0.0077,0.0074,0.0095,0.0055,0.0045,0.0063,0.0039,Mine -0.0363,0.0478,0.0298,0.021,0.1409,0.1916,0.1349,0.1613,0.1703,0.1444,0.1989,0.2154,0.2863,0.357,0.398,0.4359,0.5334,0.6304,0.6995,0.7435,0.8379,0.8641,0.9014,0.9432,0.9536,1,0.9547,0.9745,0.8962,0.7196,0.5462,0.3156,0.2525,0.1969,0.2189,0.1533,0.0711,0.1498,0.1755,0.2276,0.1322,0.1056,0.1973,0.1692,0.1881,0.1177,0.0779,0.0495,0.0492,0.0194,0.025,0.0115,0.019,0.0055,0.0096,0.005,0.0066,0.0114,0.0073,0.0033,Mine -0.0261,0.0266,0.0223,0.0749,0.1364,0.1513,0.1316,0.1654,0.1864,0.2013,0.289,0.365,0.351,0.3495,0.4325,0.5398,0.6237,0.6876,0.7329,0.8107,0.8396,0.8632,0.8747,0.9607,0.9716,0.9121,0.8576,0.8798,0.772,0.5711,0.4264,0.286,0.3114,0.2066,0.1165,0.0185,0.1302,0.248,0.1637,0.1103,0.2144,0.2033,0.1887,0.137,0.1376,0.0307,0.0373,0.0606,0.0399,0.0169,0.0135,0.0222,0.0175,0.0127,0.0022,0.0124,0.0054,0.0021,0.0028,0.0023,Mine -0.0346,0.0509,0.0079,0.0243,0.0432,0.0735,0.0938,0.1134,0.1228,0.1508,0.1809,0.239,0.2947,0.2866,0.401,0.5325,0.5486,0.5823,0.6041,0.6749,0.7084,0.789,0.9284,0.9781,0.9738,1,0.9702,0.9956,0.8235,0.602,0.5342,0.4867,0.3526,0.1566,0.0946,0.1613,0.2824,0.339,0.3019,0.2945,0.2978,0.2676,0.2055,0.2069,0.1625,0.1216,0.1013,0.0744,0.0386,0.005,0.0146,0.004,0.0122,0.0107,0.0112,0.0102,0.0052,0.0024,0.0079,0.0031,Mine -0.0162,0.0041,0.0239,0.0441,0.063,0.0921,0.1368,0.1078,0.1552,0.1779,0.2164,0.2568,0.3089,0.3829,0.4393,0.5335,0.5996,0.6728,0.7309,0.8092,0.8941,0.9668,1,0.9893,0.9376,0.8991,0.9184,0.9128,0.7811,0.6018,0.3765,0.33,0.228,0.0212,0.1117,0.1788,0.2373,0.2843,0.2241,0.2715,0.3363,0.2546,0.1867,0.216,0.1278,0.0768,0.107,0.0946,0.0636,0.0227,0.0128,0.0173,0.0135,0.0114,0.0062,0.0157,0.0088,0.0036,0.0053,0.003,Mine -0.0249,0.0119,0.0277,0.076,0.1218,0.1538,0.1192,0.1229,0.2119,0.2531,0.2855,0.2961,0.3341,0.4287,0.5205,0.6087,0.7236,0.7577,0.7726,0.8098,0.8995,0.9247,0.9365,0.9853,0.9776,1,0.9896,0.9076,0.7306,0.5758,0.4469,0.3719,0.2079,0.0955,0.0488,0.1406,0.2554,0.2054,0.1614,0.2232,0.1773,0.2293,0.2521,0.1464,0.0673,0.0965,0.1492,0.1128,0.0463,0.0193,0.014,0.0027,0.0068,0.015,0.0012,0.0133,0.0048,0.0244,0.0077,0.0074,Mine -0.027,0.0163,0.0341,0.0247,0.0822,0.1256,0.1323,0.1584,0.2017,0.2122,0.221,0.2399,0.2964,0.4061,0.5095,0.5512,0.6613,0.6804,0.652,0.6788,0.7811,0.8369,0.8969,0.9856,1,0.9395,0.8917,0.8105,0.6828,0.5572,0.4301,0.3339,0.2035,0.0798,0.0809,0.1525,0.2626,0.2456,0.198,0.2412,0.2409,0.1901,0.2077,0.1767,0.1119,0.0779,0.1344,0.096,0.0598,0.033,0.0197,0.0189,0.0204,0.0085,0.0043,0.0092,0.0138,0.0094,0.0105,0.0093,Mine -0.0388,0.0324,0.0688,0.0898,0.1267,0.1515,0.2134,0.2613,0.2832,0.2718,0.3645,0.3934,0.3843,0.4677,0.5364,0.4823,0.4835,0.5862,0.7579,0.6997,0.6918,0.8633,0.9107,0.9346,0.7884,0.8585,0.9261,0.708,0.5779,0.5215,0.4505,0.3129,0.1448,0.1046,0.182,0.1519,0.1017,0.1438,0.1986,0.2039,0.2778,0.2879,0.1331,0.114,0.131,0.1433,0.0624,0.01,0.0098,0.0131,0.0152,0.0255,0.0071,0.0263,0.0079,0.0111,0.0107,0.0068,0.0097,0.0067,Mine -0.0228,0.0853,0.1,0.0428,0.1117,0.1651,0.1597,0.2116,0.3295,0.3517,0.333,0.3643,0.402,0.4731,0.5196,0.6573,0.8426,0.8476,0.8344,0.8453,0.7999,0.8537,0.9642,1,0.9357,0.9409,0.907,0.7104,0.632,0.5667,0.3501,0.2447,0.1698,0.329,0.3674,0.2331,0.2413,0.2556,0.1892,0.194,0.3074,0.2785,0.0308,0.1238,0.1854,0.1753,0.1079,0.0728,0.0242,0.0191,0.0159,0.0172,0.0191,0.026,0.014,0.0125,0.0116,0.0093,0.0012,0.0036,Mine -0.0715,0.0849,0.0587,0.0218,0.0862,0.1801,0.1916,0.1896,0.296,0.4186,0.4867,0.5249,0.5959,0.6855,0.8573,0.9718,0.8693,0.8711,0.8954,0.9922,0.898,0.8158,0.8373,0.7541,0.5893,0.5488,0.5643,0.5406,0.4783,0.4439,0.3698,0.2574,0.1478,0.1743,0.1229,0.1588,0.1803,0.1436,0.1667,0.263,0.2234,0.1239,0.0869,0.2092,0.1499,0.0676,0.0899,0.0927,0.0658,0.0086,0.0216,0.0153,0.0121,0.0096,0.0196,0.0042,0.0066,0.0099,0.0083,0.0124,Mine -0.0209,0.0261,0.012,0.0768,0.1064,0.168,0.3016,0.346,0.3314,0.4125,0.3943,0.1334,0.4622,0.997,0.9137,0.8292,0.6994,0.7825,0.8789,0.8501,0.892,0.9473,1,0.8975,0.7806,0.8321,0.6502,0.4548,0.4732,0.3391,0.2747,0.0978,0.0477,0.1403,0.1834,0.2148,0.1271,0.1912,0.3391,0.3444,0.2369,0.1195,0.2665,0.2587,0.1393,0.1083,0.1383,0.1321,0.1069,0.0325,0.0316,0.0057,0.0159,0.0085,0.0372,0.0101,0.0127,0.0288,0.0129,0.0023,Mine -0.0374,0.0586,0.0628,0.0534,0.0255,0.1422,0.2072,0.2734,0.307,0.2597,0.3483,0.3999,0.4574,0.595,0.7924,0.8272,0.8087,0.8977,0.9828,0.8982,0.889,0.9367,0.9122,0.7936,0.6718,0.6318,0.4865,0.3388,0.4832,0.3822,0.3075,0.1267,0.0743,0.151,0.1906,0.1817,0.1709,0.0946,0.2829,0.3006,0.1602,0.1483,0.2875,0.2047,0.1064,0.1395,0.1065,0.0527,0.0395,0.0183,0.0353,0.0118,0.0063,0.0237,0.0032,0.0087,0.0124,0.0113,0.0098,0.0126,Mine -0.1371,0.1226,0.1385,0.1484,0.1776,0.1428,0.1773,0.2161,0.163,0.2067,0.4257,0.5484,0.7131,0.7003,0.6777,0.7939,0.9382,0.8925,0.9146,0.7832,0.796,0.7983,0.7716,0.6615,0.486,0.5572,0.4697,0.564,0.4517,0.3369,0.2684,0.2339,0.3052,0.3016,0.2753,0.1041,0.1757,0.3156,0.3603,0.2736,0.1301,0.2458,0.3404,0.1753,0.0679,0.1062,0.0643,0.0532,0.0531,0.0272,0.0171,0.0118,0.0129,0.0344,0.0065,0.0067,0.0022,0.0079,0.0146,0.0051,Mine -0.0443,0.0446,0.0235,0.1008,0.2252,0.2611,0.2061,0.1668,0.1801,0.3083,0.3794,0.5364,0.6173,0.7842,0.8392,0.9016,1,0.8911,0.8753,0.7886,0.7156,0.7581,0.6372,0.321,0.2076,0.2279,0.3309,0.2847,0.1949,0.1671,0.1025,0.1362,0.2212,0.1124,0.1677,0.1039,0.2562,0.2624,0.2236,0.118,0.1103,0.2831,0.2385,0.0255,0.1967,0.1483,0.0434,0.0627,0.0513,0.0473,0.0248,0.0274,0.0205,0.0141,0.0185,0.0055,0.0045,0.0115,0.0152,0.01,Mine -0.115,0.1163,0.0866,0.0358,0.0232,0.1267,0.2417,0.2661,0.4346,0.5378,0.3816,0.0991,0.0616,0.1795,0.3907,0.3602,0.3041,0.2428,0.406,0.8395,0.9777,0.468,0.061,0.2143,0.1348,0.2854,0.1617,0.2649,0.4565,0.6502,0.2848,0.3296,0.537,0.6627,0.8626,0.8547,0.7848,0.9016,0.8827,0.6086,0.281,0.0906,0.1177,0.2694,0.5214,0.4232,0.234,0.1928,0.1092,0.0507,0.0228,0.0099,0.0065,0.0085,0.0166,0.011,0.019,0.0141,0.0068,0.0086,Mine -0.0968,0.0821,0.0629,0.0608,0.0617,0.1207,0.0944,0.4223,0.5744,0.5025,0.3488,0.17,0.2076,0.3087,0.4224,0.5312,0.2436,0.1884,0.1908,0.8321,1,0.4076,0.096,0.1928,0.2419,0.379,0.2893,0.3451,0.3777,0.5213,0.2316,0.3335,0.4781,0.6116,0.6705,0.7375,0.7356,0.7792,0.6788,0.5259,0.2762,0.1545,0.2019,0.2231,0.4221,0.3067,0.1329,0.1349,0.1057,0.0499,0.0206,0.0073,0.0081,0.0303,0.019,0.0212,0.0126,0.0201,0.021,0.0041,Mine -0.079,0.0707,0.0352,0.166,0.133,0.0226,0.0771,0.2678,0.5664,0.6609,0.5002,0.2583,0.165,0.4347,0.4515,0.4579,0.3366,0.4,0.5325,0.901,0.9939,0.3689,0.1012,0.0248,0.2318,0.3981,0.2259,0.5247,0.6898,0.8316,0.4326,0.3741,0.5756,0.8043,0.7963,0.7174,0.7056,0.8148,0.7601,0.6034,0.4554,0.4729,0.4478,0.3722,0.4693,0.3839,0.0768,0.1467,0.0777,0.0469,0.0193,0.0298,0.039,0.0294,0.0175,0.0249,0.0141,0.0073,0.0025,0.0101,Mine -0.1083,0.107,0.0257,0.0837,0.0748,0.1125,0.3322,0.459,0.5526,0.5966,0.5304,0.2251,0.2402,0.2689,0.6646,0.6632,0.1674,0.0837,0.4331,0.8718,0.7992,0.3712,0.1703,0.1611,0.2086,0.2847,0.2211,0.6134,0.5807,0.6925,0.3825,0.4303,0.7791,0.8703,1,0.9212,0.9386,0.9303,0.7314,0.4791,0.2087,0.2016,0.1669,0.2872,0.4374,0.3097,0.1578,0.0553,0.0334,0.0209,0.0172,0.018,0.011,0.0234,0.0276,0.0032,0.0084,0.0122,0.0082,0.0143,Mine -0.0094,0.0611,0.1136,0.1203,0.0403,0.1227,0.2495,0.4566,0.6587,0.5079,0.335,0.0834,0.3004,0.3957,0.3769,0.3828,0.1247,0.1363,0.2678,0.9188,0.9779,0.3236,0.1944,0.1874,0.0885,0.3443,0.2953,0.5908,0.4564,0.7334,0.1969,0.279,0.6212,0.8681,0.8621,0.938,0.8327,0.948,0.6721,0.4436,0.5163,0.3809,0.1557,0.1449,0.2662,0.1806,0.1699,0.2559,0.1129,0.0201,0.048,0.0234,0.0175,0.0352,0.0158,0.0326,0.0201,0.0168,0.0245,0.0154,Mine -0.1088,0.1278,0.0926,0.1234,0.1276,0.1731,0.1948,0.4262,0.6828,0.5761,0.4733,0.2362,0.1023,0.2904,0.4713,0.4659,0.1415,0.0849,0.3257,0.9007,0.9312,0.4856,0.1346,0.1604,0.2737,0.5609,0.3654,0.6139,0.547,0.8474,0.5638,0.5443,0.5086,0.6253,0.8497,0.8406,0.842,0.9136,0.7713,0.4882,0.3724,0.4469,0.4586,0.4491,0.5616,0.4305,0.0945,0.0794,0.0274,0.0154,0.014,0.0455,0.0213,0.0082,0.0124,0.0167,0.0103,0.0205,0.0178,0.0187,Mine -0.043,0.0902,0.0833,0.0813,0.0165,0.0277,0.0569,0.2057,0.3887,0.7106,0.7342,0.5033,0.3,0.1951,0.2767,0.3737,0.2507,0.2507,0.3292,0.4871,0.6527,0.8454,0.9739,1,0.6665,0.5323,0.4024,0.3444,0.4239,0.4182,0.4393,0.1162,0.4336,0.6553,0.6172,0.4373,0.4118,0.3641,0.4572,0.4367,0.2964,0.4312,0.4155,0.1824,0.1487,0.0138,0.1164,0.2052,0.1069,0.0199,0.0208,0.0176,0.0197,0.021,0.0141,0.0049,0.0027,0.0162,0.0059,0.0021,Mine -0.0731,0.1249,0.1665,0.1496,0.1443,0.277,0.2555,0.1712,0.0466,0.1114,0.1739,0.316,0.3249,0.2164,0.2031,0.258,0.1796,0.2422,0.3609,0.181,0.2604,0.6572,0.9734,0.9757,0.8079,0.6521,0.4915,0.5363,0.7649,0.525,0.5101,0.4219,0.416,0.1906,0.0223,0.4219,0.5496,0.2483,0.2034,0.2729,0.2837,0.4463,0.3178,0.0807,0.1192,0.2134,0.3241,0.2945,0.1474,0.0211,0.0361,0.0444,0.023,0.029,0.0141,0.0161,0.0177,0.0194,0.0207,0.0057,Mine -0.0164,0.0627,0.0738,0.0608,0.0233,0.1048,0.1338,0.0644,0.1522,0.078,0.1791,0.2681,0.1788,0.1039,0.198,0.3234,0.3748,0.2586,0.368,0.3508,0.5606,0.5231,0.5469,0.6954,0.6352,0.6757,0.8499,0.8025,0.6563,0.8591,0.6655,0.5369,0.3118,0.3763,0.2801,0.0875,0.3319,0.4237,0.1801,0.3743,0.4627,0.1614,0.2494,0.3202,0.2265,0.1146,0.0476,0.0943,0.0824,0.0171,0.0244,0.0258,0.0143,0.0226,0.0187,0.0185,0.011,0.0094,0.0078,0.0112,Mine -0.0412,0.1135,0.0518,0.0232,0.0646,0.1124,0.1787,0.2407,0.2682,0.2058,0.1546,0.2671,0.3141,0.2904,0.3531,0.5079,0.4639,0.1859,0.4474,0.4079,0.54,0.4786,0.4332,0.6113,0.5091,0.4606,0.7243,0.8987,0.8826,0.9201,0.8005,0.6033,0.212,0.2866,0.4033,0.2803,0.3087,0.355,0.2545,0.1432,0.5869,0.6431,0.5826,0.4286,0.4894,0.5777,0.4315,0.264,0.1794,0.0772,0.0798,0.0376,0.0143,0.0272,0.0127,0.0166,0.0095,0.0225,0.0098,0.0085,Mine -0.0707,0.1252,0.1447,0.1644,0.1693,0.0844,0.0715,0.0947,0.1583,0.1247,0.234,0.1764,0.2284,0.3115,0.4725,0.5543,0.5386,0.3746,0.4583,0.5961,0.7464,0.7644,0.5711,0.6257,0.6695,0.7131,0.7567,0.8077,0.8477,0.9289,0.9513,0.7995,0.4362,0.4048,0.4952,0.1712,0.3652,0.3763,0.2841,0.0427,0.5331,0.6952,0.4288,0.3063,0.5835,0.5692,0.263,0.1196,0.0983,0.0374,0.0291,0.0156,0.0197,0.0135,0.0127,0.0138,0.0133,0.0131,0.0154,0.0218,Mine -0.0526,0.0563,0.1219,0.1206,0.0246,0.1022,0.0539,0.0439,0.2291,0.1632,0.2544,0.2807,0.3011,0.3361,0.3024,0.2285,0.291,0.1316,0.1151,0.3404,0.5562,0.6379,0.6553,0.7384,0.6534,0.5423,0.6877,0.7325,0.7726,0.8229,0.8787,0.9108,0.6705,0.6092,0.7505,0.4775,0.1666,0.3749,0.3776,0.2106,0.5886,0.5628,0.2577,0.5245,0.6149,0.5123,0.3385,0.1499,0.0546,0.027,0.038,0.0339,0.0149,0.0335,0.0376,0.0174,0.0132,0.0103,0.0364,0.0208,Mine -0.0516,0.0944,0.0622,0.0415,0.0995,0.2431,0.1777,0.2018,0.2611,0.1294,0.2646,0.2778,0.4432,0.3672,0.2035,0.2764,0.3252,0.1536,0.2784,0.3508,0.5187,0.7052,0.7143,0.6814,0.51,0.5308,0.6131,0.8388,0.9031,0.8607,0.9656,0.9168,0.7132,0.6898,0.731,0.4134,0.158,0.1819,0.1381,0.296,0.6935,0.8246,0.5351,0.4403,0.6448,0.6214,0.3016,0.1379,0.0364,0.0355,0.0456,0.0432,0.0274,0.0152,0.012,0.0129,0.002,0.0109,0.0074,0.0078,Mine -0.0299,0.0688,0.0992,0.1021,0.08,0.0629,0.013,0.0813,0.1761,0.0998,0.0523,0.0904,0.2655,0.3099,0.352,0.3892,0.3962,0.2449,0.2355,0.3045,0.3112,0.4698,0.5534,0.4532,0.4464,0.467,0.4621,0.6988,0.7626,0.7025,0.7382,0.7446,0.7927,0.5227,0.3967,0.3042,0.1309,0.2408,0.178,0.1598,0.5657,0.6443,0.4241,0.4567,0.576,0.5293,0.3287,0.1283,0.0698,0.0334,0.0342,0.0459,0.0277,0.0172,0.0087,0.0046,0.0203,0.013,0.0115,0.0015,Mine -0.0721,0.1574,0.1112,0.1085,0.0666,0.18,0.1108,0.2794,0.1408,0.0795,0.2534,0.392,0.3375,0.161,0.1889,0.3308,0.2282,0.2177,0.1853,0.5167,0.5342,0.6298,0.8437,0.6756,0.5825,0.6141,0.8809,0.8375,0.3869,0.5051,0.5455,0.4241,0.1534,0.495,0.6983,0.7109,0.5647,0.487,0.5515,0.4433,0.525,0.6075,0.5251,0.1359,0.4268,0.4442,0.2193,0.09,0.12,0.0628,0.0234,0.0309,0.0127,0.0082,0.0281,0.0117,0.0092,0.0147,0.0157,0.0129,Mine -0.1021,0.083,0.0577,0.0627,0.0635,0.1328,0.0988,0.1787,0.1199,0.1369,0.2509,0.2631,0.2796,0.2977,0.3823,0.3129,0.3956,0.2093,0.3218,0.3345,0.3184,0.2887,0.361,0.2566,0.4106,0.4591,0.4722,0.7278,0.7591,0.6579,0.7514,0.6666,0.4903,0.5962,0.6552,0.4014,0.1188,0.3245,0.3107,0.1354,0.5109,0.7988,0.7517,0.5508,0.5858,0.7292,0.5522,0.3339,0.1608,0.0475,0.1004,0.0709,0.0317,0.0309,0.0252,0.0087,0.0177,0.0214,0.0227,0.0106,Mine -0.0654,0.0649,0.0737,0.1132,0.2482,0.1257,0.1797,0.0989,0.246,0.3422,0.2128,0.1377,0.4032,0.5684,0.2398,0.4331,0.5954,0.5772,0.8176,0.8835,0.5248,0.6373,0.8375,0.6699,0.7756,0.875,0.83,0.6896,0.3372,0.6405,0.7138,0.8202,0.6657,0.5254,0.296,0.0704,0.097,0.3941,0.6028,0.3521,0.3924,0.4808,0.4602,0.4164,0.5438,0.5649,0.3195,0.2484,0.1299,0.0825,0.0243,0.021,0.0361,0.0239,0.0447,0.0394,0.0355,0.044,0.0243,0.0098,Mine -0.0712,0.0901,0.1276,0.1497,0.1284,0.1165,0.1285,0.1684,0.183,0.2127,0.2891,0.3985,0.4576,0.5821,0.5027,0.193,0.2579,0.3177,0.2745,0.6186,0.8958,0.7442,0.5188,0.2811,0.1773,0.6607,0.7576,0.5122,0.4701,0.5479,0.4347,0.1276,0.0846,0.0927,0.0313,0.0998,0.1781,0.1586,0.3001,0.2208,0.1455,0.2895,0.3203,0.1414,0.0629,0.0734,0.0805,0.0608,0.0565,0.0286,0.0154,0.0154,0.0156,0.0054,0.003,0.0048,0.0087,0.0101,0.0095,0.0068,Mine -0.0207,0.0535,0.0334,0.0818,0.074,0.0324,0.0918,0.107,0.1553,0.1234,0.1796,0.1787,0.1247,0.2577,0.337,0.399,0.1647,0.2266,0.3219,0.5356,0.8159,1,0.8701,0.6889,0.6299,0.5738,0.5707,0.5976,0.4301,0.2058,0.1,0.2247,0.2308,0.3977,0.3317,0.1726,0.1429,0.2168,0.1967,0.214,0.3674,0.2023,0.0778,0.0925,0.2388,0.34,0.2594,0.1102,0.0911,0.0462,0.0171,0.0033,0.005,0.019,0.0103,0.0121,0.0042,0.009,0.007,0.0099,Mine -0.0209,0.0278,0.0115,0.0445,0.0427,0.0766,0.1458,0.143,0.1894,0.1853,0.1748,0.1556,0.1476,0.1378,0.2584,0.3827,0.4784,0.536,0.6192,0.7912,0.9264,1,0.908,0.7435,0.5557,0.3172,0.1295,0.0598,0.2722,0.3616,0.3293,0.4855,0.3936,0.1845,0.0342,0.2489,0.3837,0.3514,0.2654,0.176,0.1599,0.0866,0.059,0.0813,0.0492,0.0417,0.0495,0.0367,0.0115,0.0118,0.0133,0.0096,0.0014,0.0049,0.0039,0.0029,0.0078,0.0047,0.0021,0.0011,Mine -0.0231,0.0315,0.017,0.0226,0.041,0.0116,0.0223,0.0805,0.2365,0.2461,0.2245,0.152,0.1732,0.3099,0.438,0.5595,0.682,0.6164,0.6803,0.8435,0.9921,1,0.7983,0.5426,0.3952,0.5179,0.565,0.3042,0.1881,0.396,0.2286,0.3544,0.4187,0.2398,0.1847,0.376,0.4331,0.3626,0.2519,0.187,0.1046,0.2339,0.1991,0.11,0.0684,0.0303,0.0674,0.0785,0.0455,0.0246,0.0151,0.0125,0.0036,0.0123,0.0043,0.0114,0.0052,0.0091,0.0008,0.0092,Mine -0.0131,0.0201,0.0045,0.0217,0.023,0.0481,0.0742,0.0333,0.1369,0.2079,0.2295,0.199,0.1184,0.1891,0.2949,0.5343,0.685,0.7923,0.822,0.729,0.7352,0.7918,0.8057,0.4898,0.1934,0.2924,0.6255,0.8546,0.8966,0.7821,0.5168,0.484,0.4038,0.3411,0.2849,0.2353,0.2699,0.4442,0.4323,0.3314,0.1195,0.1669,0.3702,0.3072,0.0945,0.1545,0.1394,0.0772,0.0615,0.023,0.0111,0.0168,0.0086,0.0045,0.0062,0.0065,0.003,0.0066,0.0029,0.0053,Mine -0.0233,0.0394,0.0416,0.0547,0.0993,0.1515,0.1674,0.1513,0.1723,0.2078,0.1239,0.0236,0.1771,0.3115,0.499,0.6707,0.7655,0.8485,0.9805,1,1,0.9992,0.9067,0.6803,0.5103,0.4716,0.498,0.6196,0.7171,0.6316,0.3554,0.2897,0.4316,0.3791,0.2421,0.0944,0.0351,0.0844,0.0436,0.113,0.2045,0.1937,0.0834,0.1502,0.1675,0.1058,0.1111,0.0849,0.0596,0.0201,0.0071,0.0104,0.0062,0.0026,0.0025,0.0061,0.0038,0.0101,0.0078,0.0006,Mine -0.0117,0.0069,0.0279,0.0583,0.0915,0.1267,0.1577,0.1927,0.2361,0.2169,0.118,0.0754,0.2782,0.3758,0.5093,0.6592,0.7071,0.7532,0.8357,0.8593,0.9615,0.9838,0.8705,0.6403,0.5067,0.5395,0.6934,0.8487,0.8213,0.5962,0.295,0.2758,0.2885,0.1893,0.1446,0.0955,0.0888,0.0836,0.0894,0.1547,0.2318,0.2225,0.1035,0.1721,0.2017,0.1787,0.1112,0.0398,0.0305,0.0084,0.0039,0.0053,0.0029,0.002,0.0013,0.0029,0.002,0.0062,0.0026,0.0052,Mine -0.0211,0.0128,0.0015,0.045,0.0711,0.1563,0.1518,0.1206,0.1666,0.1345,0.0785,0.0367,0.1227,0.2614,0.428,0.6122,0.7435,0.813,0.9006,0.9603,0.9162,0.914,0.7851,0.5134,0.3439,0.329,0.2571,0.3685,0.5765,0.619,0.4613,0.3615,0.4434,0.3864,0.3093,0.2138,0.1112,0.1386,0.1523,0.0996,0.1644,0.1902,0.1313,0.1776,0.2,0.0765,0.0727,0.0749,0.0449,0.0134,0.0174,0.0117,0.0023,0.0047,0.0049,0.0031,0.0024,0.0039,0.0051,0.0015,Mine -0.0047,0.0059,0.008,0.0554,0.0883,0.1278,0.1674,0.1373,0.2922,0.3469,0.3265,0.3263,0.2301,0.1253,0.2102,0.2401,0.1928,0.1673,0.1228,0.0902,0.1557,0.3291,0.5268,0.674,0.7906,0.8938,0.9395,0.9493,0.904,0.9151,0.8828,0.8086,0.718,0.672,0.6447,0.6879,0.6241,0.4936,0.4144,0.424,0.4546,0.4392,0.4323,0.4921,0.471,0.3196,0.2241,0.1806,0.099,0.0251,0.0129,0.0095,0.0126,0.0069,0.0039,0.0068,0.006,0.0045,0.0002,0.0029,Mine 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-0.0072,0.0027,0.0089,0.0061,0.042,0.0865,0.1182,0.0999,0.1976,0.2318,0.2472,0.288,0.2126,0.0708,0.1194,0.2808,0.4221,0.5279,0.5857,0.6153,0.6753,0.7873,0.8974,0.9828,1,0.846,0.6055,0.3036,0.0144,0.2526,0.4335,0.4918,0.5409,0.5961,0.5248,0.3777,0.2369,0.172,0.1878,0.325,0.2575,0.2423,0.2706,0.2323,0.1724,0.1457,0.1175,0.0868,0.0392,0.0131,0.0092,0.0078,0.0071,0.0081,0.0034,0.0064,0.0037,0.0036,0.0012,0.0037,Mine -0.0163,0.0198,0.0202,0.0386,0.0752,0.1444,0.1487,0.1484,0.2442,0.2822,0.3691,0.375,0.3927,0.3308,0.1085,0.1139,0.3446,0.5441,0.647,0.7276,0.7894,0.8264,0.8697,0.7836,0.714,0.5698,0.2908,0.4636,0.6409,0.7405,0.8069,0.842,1,0.9536,0.6755,0.3905,0.1249,0.3629,0.6356,0.8116,0.7664,0.5417,0.2614,0.1723,0.2814,0.2764,0.1985,0.1502,0.1219,0.0493,0.0027,0.0077,0.0026,0.0031,0.0083,0.002,0.0084,0.0108,0.0083,0.0033,Mine -0.0221,0.0065,0.0164,0.0487,0.0519,0.0849,0.0812,0.1833,0.2228,0.181,0.2549,0.2984,0.2624,0.1893,0.0668,0.2666,0.4274,0.6291,0.7782,0.7686,0.8099,0.8493,0.944,0.945,0.9655,0.8045,0.4969,0.396,0.3856,0.5574,0.7309,0.8549,0.9425,0.8726,0.6673,0.4694,0.1546,0.1748,0.3607,0.5208,0.5177,0.3702,0.224,0.0816,0.0395,0.0785,0.1052,0.1034,0.0764,0.0216,0.0167,0.0089,0.0051,0.0015,0.0075,0.0058,0.0016,0.007,0.0074,0.0038,Mine -0.0411,0.0277,0.0604,0.0525,0.0489,0.0385,0.0611,0.1117,0.1237,0.23,0.137,0.1335,0.2137,0.1526,0.0775,0.1196,0.0903,0.0689,0.2071,0.2975,0.2836,0.3353,0.3622,0.3202,0.3452,0.3562,0.3892,0.6622,0.9254,1,0.8528,0.6297,0.525,0.4012,0.2901,0.2007,0.3356,0.4799,0.6147,0.6246,0.4973,0.3492,0.2662,0.3137,0.4282,0.4262,0.3511,0.2458,0.1259,0.0327,0.0181,0.0217,0.0038,0.0019,0.0065,0.0132,0.0108,0.005,0.0085,0.0044,Mine -0.0137,0.0297,0.0116,0.0082,0.0241,0.0253,0.0279,0.013,0.0489,0.0874,0.11,0.1084,0.1094,0.1023,0.0601,0.0906,0.1313,0.2758,0.366,0.5269,0.581,0.6181,0.5875,0.4639,0.5424,0.7367,0.9089,1,0.8247,0.5441,0.3349,0.0877,0.16,0.4169,0.6576,0.739,0.7963,0.7493,0.6795,0.4713,0.2355,0.1704,0.2728,0.4016,0.4125,0.347,0.2739,0.179,0.0922,0.0276,0.0169,0.0081,0.004,0.0025,0.0036,0.0058,0.0067,0.0035,0.0043,0.0033,Mine -0.0015,0.0186,0.0289,0.0195,0.0515,0.0817,0.1005,0.0124,0.1168,0.1476,0.2118,0.2575,0.2354,0.1334,0.0092,0.1951,0.3685,0.4646,0.5418,0.626,0.742,0.8257,0.8609,0.84,0.8949,0.9945,1,0.9649,0.8747,0.6257,0.2184,0.2945,0.3645,0.5012,0.7843,0.9361,0.8195,0.6207,0.4513,0.3004,0.2674,0.2241,0.3141,0.3693,0.2986,0.2226,0.0849,0.0359,0.0289,0.0122,0.0045,0.0108,0.0075,0.0089,0.0036,0.0029,0.0013,0.001,0.0032,0.0047,Mine -0.013,0.012,0.0436,0.0624,0.0428,0.0349,0.0384,0.0446,0.1318,0.1375,0.2026,0.2389,0.2112,0.1444,0.0742,0.1533,0.3052,0.4116,0.5466,0.5933,0.6663,0.7333,0.7136,0.7014,0.7758,0.9137,0.9964,1,0.8881,0.6585,0.2707,0.1746,0.2709,0.4853,0.7184,0.8209,0.7536,0.6496,0.4708,0.3482,0.3508,0.3181,0.3524,0.3659,0.2846,0.1714,0.0694,0.0303,0.0292,0.0116,0.0024,0.0084,0.01,0.0018,0.0035,0.0058,0.0011,0.0009,0.0033,0.0026,Mine -0.0134,0.0172,0.0178,0.0363,0.0444,0.0744,0.08,0.0456,0.0368,0.125,0.2405,0.2325,0.2523,0.1472,0.0669,0.11,0.2353,0.3282,0.4416,0.5167,0.6508,0.7793,0.7978,0.7786,0.8587,0.9321,0.9454,0.8645,0.722,0.485,0.1357,0.2951,0.4715,0.6036,0.8083,0.987,0.88,0.6411,0.4276,0.2702,0.2642,0.3342,0.4335,0.4542,0.396,0.2525,0.1084,0.0372,0.0286,0.0099,0.0046,0.0094,0.0048,0.0047,0.0016,0.0008,0.0042,0.0024,0.0027,0.0041,Mine -0.0179,0.0136,0.0408,0.0633,0.0596,0.0808,0.209,0.3465,0.5276,0.5965,0.6254,0.4507,0.3693,0.2864,0.1635,0.0422,0.1785,0.4394,0.695,0.8097,0.855,0.8717,0.8601,0.9201,0.8729,0.8084,0.8694,0.8411,0.5793,0.3754,0.3485,0.4639,0.6495,0.6901,0.5666,0.5188,0.506,0.3885,0.3762,0.3738,0.2605,0.1591,0.1875,0.2267,0.1577,0.1211,0.0883,0.085,0.0355,0.0219,0.0086,0.0123,0.006,0.0187,0.0111,0.0126,0.0081,0.0155,0.016,0.0085,Mine -0.018,0.0444,0.0476,0.0698,0.1615,0.0887,0.0596,0.1071,0.3175,0.2918,0.3273,0.3035,0.3033,0.2587,0.1682,0.1308,0.2803,0.4519,0.6641,0.7683,0.696,0.4393,0.2432,0.2886,0.4974,0.8172,1,0.9238,0.8519,0.7722,0.5772,0.519,0.6824,0.622,0.5054,0.3578,0.3809,0.3813,0.3359,0.2771,0.3648,0.3834,0.3453,0.2096,0.1031,0.0798,0.0701,0.0526,0.0241,0.0117,0.0122,0.0122,0.0114,0.0098,0.0027,0.0025,0.0026,0.005,0.0073,0.0022,Mine -0.0329,0.0216,0.0386,0.0627,0.1158,0.1482,0.2054,0.1605,0.2532,0.2672,0.3056,0.3161,0.2314,0.2067,0.1804,0.2808,0.4423,0.5947,0.6601,0.5844,0.4539,0.4789,0.5646,0.5281,0.7115,1,0.9564,0.609,0.5112,0.4,0.0482,0.1852,0.2186,0.1436,0.1757,0.1428,0.1644,0.3089,0.3648,0.4441,0.3859,0.2813,0.1238,0.0953,0.1201,0.0825,0.0618,0.0141,0.0108,0.0124,0.0104,0.0095,0.0151,0.0059,0.0015,0.0053,0.0016,0.0042,0.0053,0.0074,Mine -0.0191,0.0173,0.0291,0.0301,0.0463,0.069,0.0576,0.1103,0.2423,0.3134,0.4786,0.5239,0.4393,0.344,0.2869,0.3889,0.442,0.3892,0.4088,0.5006,0.7271,0.9385,1,0.9831,0.9932,0.9161,0.8237,0.6957,0.4536,0.3281,0.2522,0.3964,0.4154,0.3308,0.1445,0.1923,0.3208,0.3367,0.5683,0.5505,0.3231,0.0448,0.3131,0.3387,0.413,0.3639,0.2069,0.0859,0.06,0.0267,0.0125,0.004,0.0136,0.0137,0.0172,0.0132,0.011,0.0122,0.0114,0.0068,Mine -0.0294,0.0123,0.0117,0.0113,0.0497,0.0998,0.1326,0.1117,0.2984,0.3473,0.4231,0.5044,0.5237,0.4398,0.3236,0.2956,0.3286,0.3231,0.4528,0.6339,0.7044,0.8314,0.8449,0.8512,0.9138,0.9985,1,0.7544,0.4661,0.3924,0.3849,0.4674,0.4245,0.3095,0.0752,0.2885,0.4072,0.317,0.2863,0.2634,0.0541,0.1874,0.3459,0.4646,0.4366,0.2581,0.1319,0.0505,0.0112,0.0059,0.0041,0.0056,0.0104,0.0079,0.0014,0.0054,0.0015,0.0006,0.0081,0.0043,Mine -0.0635,0.0709,0.0453,0.0333,0.0185,0.126,0.1015,0.1918,0.3362,0.39,0.4674,0.5632,0.5506,0.4343,0.3052,0.3492,0.3975,0.3875,0.528,0.7198,0.7702,0.8562,0.8688,0.9236,1,0.9662,0.9822,0.736,0.4158,0.2918,0.328,0.369,0.345,0.2863,0.0864,0.3724,0.4649,0.3488,0.1817,0.1142,0.122,0.2621,0.4461,0.4726,0.3263,0.1423,0.039,0.0406,0.0311,0.0086,0.0154,0.0048,0.0025,0.0087,0.0072,0.0095,0.0086,0.0085,0.004,0.0051,Mine -0.0201,0.0165,0.0344,0.033,0.0397,0.0443,0.0684,0.0903,0.1739,0.2571,0.2931,0.3108,0.3603,0.3002,0.2718,0.2007,0.1801,0.2234,0.3568,0.5492,0.7209,0.8318,0.8864,0.952,0.9637,1,0.9673,0.8664,0.7896,0.6345,0.5351,0.4056,0.2563,0.2894,0.3588,0.4296,0.4773,0.4516,0.3765,0.3051,0.1921,0.1184,0.1984,0.157,0.066,0.1294,0.0797,0.0052,0.0233,0.0152,0.0125,0.0054,0.0057,0.0137,0.0109,0.0035,0.0056,0.0105,0.0082,0.0036,Mine -0.0197,0.0394,0.0384,0.0076,0.0251,0.0629,0.0747,0.0578,0.1357,0.1695,0.1734,0.247,0.3141,0.3297,0.2759,0.2056,0.1162,0.1884,0.339,0.3926,0.4282,0.5418,0.6448,0.7223,0.7853,0.7984,0.8847,0.9582,0.899,0.6831,0.6108,0.548,0.5058,0.4476,0.2401,0.1405,0.1772,0.1742,0.3326,0.4021,0.3009,0.2075,0.1206,0.0255,0.0298,0.0691,0.0781,0.0777,0.0369,0.0057,0.0091,0.0134,0.0097,0.0042,0.0058,0.0072,0.0041,0.0045,0.0047,0.0054,Mine 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-0.0423,0.0321,0.0709,0.0108,0.107,0.0973,0.0961,0.1323,0.2462,0.2696,0.3412,0.4292,0.3682,0.394,0.2965,0.3172,0.2825,0.305,0.2408,0.542,0.6802,0.632,0.5824,0.6805,0.5984,0.8412,0.9911,0.9187,0.8005,0.6713,0.5632,0.7332,0.6038,0.2575,0.0349,0.1799,0.3039,0.476,0.5756,0.4254,0.5046,0.7179,0.6163,0.5663,0.5749,0.3593,0.2526,0.2299,0.1271,0.0356,0.0367,0.0176,0.0035,0.0093,0.0121,0.0075,0.0056,0.0021,0.0043,0.0017,Mine -0.0095,0.0308,0.0539,0.0411,0.0613,0.1039,0.1016,0.1394,0.2592,0.3745,0.4229,0.4499,0.5404,0.4303,0.3333,0.3496,0.3426,0.2851,0.4062,0.6833,0.765,0.667,0.5703,0.5995,0.6484,0.8614,0.9819,0.938,0.8435,0.6074,0.5403,0.689,0.5977,0.3244,0.0516,0.3157,0.359,0.3881,0.5716,0.4314,0.3051,0.4393,0.4302,0.4831,0.5084,0.1952,0.1539,0.2037,0.1054,0.0251,0.0357,0.0181,0.0019,0.0102,0.0133,0.004,0.0042,0.003,0.0031,0.0033,Mine -0.0096,0.0404,0.0682,0.0688,0.0887,0.0932,0.0955,0.214,0.2546,0.2952,0.4025,0.5148,0.4901,0.4127,0.3575,0.3447,0.3068,0.2945,0.4351,0.7264,0.8147,0.8103,0.6665,0.6958,0.7748,0.8688,1,0.9941,0.8793,0.6482,0.5876,0.6408,0.4972,0.2755,0.03,0.3356,0.3167,0.4133,0.6281,0.4977,0.2613,0.4697,0.4806,0.4921,0.5294,0.2216,0.1401,0.1888,0.0947,0.0134,0.031,0.0237,0.0078,0.0144,0.017,0.0012,0.0109,0.0036,0.0043,0.0018,Mine -0.0269,0.0383,0.0505,0.0707,0.1313,0.2103,0.2263,0.2524,0.3595,0.5915,0.6675,0.5679,0.5175,0.3334,0.2002,0.2856,0.2937,0.3424,0.5949,0.7526,0.8959,0.8147,0.7109,0.7378,0.7201,0.8254,0.8917,0.982,0.8179,0.4848,0.3203,0.2775,0.2382,0.2911,0.1675,0.3156,0.1869,0.3391,0.5993,0.4124,0.1181,0.3651,0.4655,0.4777,0.3517,0.092,0.1227,0.1785,0.1085,0.03,0.0346,0.0167,0.0199,0.0145,0.0081,0.0045,0.0043,0.0027,0.0055,0.0057,Mine -0.034,0.0625,0.0381,0.0257,0.0441,0.1027,0.1287,0.185,0.2647,0.4117,0.5245,0.5341,0.5554,0.3915,0.295,0.3075,0.3021,0.2719,0.5443,0.7932,0.8751,0.8667,0.7107,0.6911,0.7287,0.8792,1,0.9816,0.8984,0.6048,0.4934,0.5371,0.4586,0.2908,0.0774,0.2249,0.1602,0.3958,0.6117,0.5196,0.2321,0.437,0.3797,0.4322,0.4892,0.1901,0.094,0.1364,0.0906,0.0144,0.0329,0.0141,0.0019,0.0067,0.0099,0.0042,0.0057,0.0051,0.0033,0.0058,Mine -0.0209,0.0191,0.0411,0.0321,0.0698,0.1579,0.1438,0.1402,0.3048,0.3914,0.3504,0.3669,0.3943,0.3311,0.3331,0.3002,0.2324,0.1381,0.345,0.4428,0.489,0.3677,0.4379,0.4864,0.6207,0.7256,0.6624,0.7689,0.7981,0.8577,0.9273,0.7009,0.4851,0.3409,0.1406,0.1147,0.1433,0.182,0.3605,0.5529,0.5988,0.5077,0.5512,0.5027,0.7034,0.5904,0.4069,0.2761,0.1584,0.051,0.0054,0.0078,0.0201,0.0104,0.0039,0.0031,0.0062,0.0087,0.007,0.0042,Mine -0.0368,0.0279,0.0103,0.0566,0.0759,0.0679,0.097,0.1473,0.2164,0.2544,0.2936,0.2935,0.2657,0.3187,0.2794,0.2534,0.198,0.1929,0.2826,0.3245,0.3504,0.3324,0.4217,0.4774,0.4808,0.6325,0.8334,0.9458,1,0.8425,0.5524,0.4795,0.52,0.3968,0.194,0.1519,0.201,0.1736,0.1029,0.2244,0.3717,0.4449,0.3939,0.203,0.201,0.2187,0.184,0.1477,0.0971,0.0224,0.0151,0.0105,0.0024,0.0018,0.0057,0.0092,0.0009,0.0086,0.011,0.0052,Mine -0.0089,0.0274,0.0248,0.0237,0.0224,0.0845,0.1488,0.1224,0.1569,0.2119,0.3003,0.3094,0.2743,0.2547,0.187,0.1452,0.1457,0.2429,0.3259,0.3679,0.3355,0.31,0.3914,0.528,0.6409,0.7707,0.8754,1,0.9806,0.6969,0.4973,0.502,0.5359,0.3842,0.1848,0.1149,0.157,0.1311,0.1583,0.2631,0.3103,0.4512,0.3785,0.1269,0.1459,0.1092,0.1485,0.1385,0.0716,0.0176,0.0199,0.0096,0.0103,0.0093,0.0025,0.0044,0.0021,0.0069,0.006,0.0018,Mine -0.0158,0.0239,0.015,0.0494,0.0988,0.1425,0.1463,0.1219,0.1697,0.1923,0.2361,0.2719,0.3049,0.2986,0.2226,0.1745,0.2459,0.31,0.3572,0.4283,0.4268,0.3735,0.4585,0.6094,0.7221,0.7595,0.8706,1,0.9815,0.7187,0.5848,0.4192,0.3756,0.3263,0.1944,0.1394,0.167,0.1275,0.1666,0.2574,0.2258,0.2777,0.1613,0.1335,0.1976,0.1234,0.1554,0.1057,0.049,0.0097,0.0223,0.0121,0.0108,0.0057,0.0028,0.0079,0.0034,0.0046,0.0022,0.0021,Mine -0.0156,0.021,0.0282,0.0596,0.0462,0.0779,0.1365,0.078,0.1038,0.1567,0.2476,0.2783,0.2896,0.2956,0.3189,0.1892,0.173,0.2226,0.2427,0.3149,0.4102,0.3808,0.4896,0.6292,0.7519,0.7985,0.883,0.9915,0.9223,0.6981,0.6167,0.5069,0.3921,0.3524,0.2183,0.1245,0.1592,0.1626,0.2356,0.2483,0.2437,0.2715,0.1184,0.1157,0.1449,0.1883,0.1954,0.1492,0.0511,0.0155,0.0189,0.015,0.006,0.0082,0.0091,0.0038,0.0056,0.0056,0.0048,0.0024,Mine -0.0315,0.0252,0.0167,0.0479,0.0902,0.1057,0.1024,0.1209,0.1241,0.1533,0.2128,0.2536,0.2686,0.2803,0.1886,0.1485,0.216,0.2417,0.2989,0.3341,0.3786,0.3956,0.5232,0.6913,0.7868,0.8337,0.9199,1,0.899,0.6456,0.5967,0.4355,0.2997,0.2294,0.1866,0.0922,0.1829,0.1743,0.2452,0.2407,0.2518,0.3184,0.1685,0.0675,0.1186,0.1833,0.1878,0.1114,0.031,0.0143,0.0138,0.0108,0.0062,0.0044,0.0072,0.0007,0.0054,0.0035,0.0001,0.0055,Mine -0.0056,0.0267,0.0221,0.0561,0.0936,0.1146,0.0706,0.0996,0.1673,0.1859,0.2481,0.2712,0.2934,0.2637,0.188,0.1405,0.2028,0.2613,0.2778,0.3346,0.383,0.4003,0.5114,0.686,0.749,0.7843,0.9021,1,0.8888,0.6511,0.6083,0.4463,0.2948,0.1729,0.1488,0.0801,0.177,0.1382,0.2404,0.2046,0.197,0.2778,0.1377,0.0685,0.0664,0.1665,0.1807,0.1245,0.0516,0.0044,0.0185,0.0072,0.0055,0.0074,0.0068,0.0084,0.0037,0.0024,0.0034,0.0007,Mine -0.0203,0.0121,0.038,0.0128,0.0537,0.0874,0.1021,0.0852,0.1136,0.1747,0.2198,0.2721,0.2105,0.1727,0.204,0.1786,0.1318,0.226,0.2358,0.3107,0.3906,0.3631,0.4809,0.6531,0.7812,0.8395,0.918,0.9769,0.8937,0.7022,0.65,0.5069,0.3903,0.3009,0.1565,0.0985,0.22,0.2243,0.2736,0.2152,0.2438,0.3154,0.2112,0.0991,0.0594,0.194,0.1937,0.1082,0.0336,0.0177,0.0209,0.0134,0.0094,0.0047,0.0045,0.0042,0.0028,0.0036,0.0013,0.0016,Mine -0.0392,0.0108,0.0267,0.0257,0.041,0.0491,0.1053,0.169,0.2105,0.2471,0.268,0.3049,0.2863,0.2294,0.1165,0.2127,0.2062,0.2222,0.3241,0.433,0.5071,0.5944,0.7078,0.7641,0.8878,0.9711,0.988,0.9812,0.9464,0.8542,0.6457,0.3397,0.3828,0.3204,0.1331,0.044,0.1234,0.203,0.1652,0.1043,0.1066,0.211,0.2417,0.1631,0.0769,0.0723,0.0912,0.0812,0.0496,0.0101,0.0089,0.0083,0.008,0.0026,0.0079,0.0042,0.0071,0.0044,0.0022,0.0014,Mine -0.0129,0.0141,0.0309,0.0375,0.0767,0.0787,0.0662,0.1108,0.1777,0.2245,0.2431,0.3134,0.3206,0.2917,0.2249,0.2347,0.2143,0.2939,0.4898,0.6127,0.7531,0.7718,0.7432,0.8673,0.9308,0.9836,1,0.9595,0.8722,0.6862,0.4901,0.328,0.3115,0.1969,0.1019,0.0317,0.0756,0.0907,0.1066,0.138,0.0665,0.1475,0.247,0.2788,0.2709,0.2283,0.1818,0.1185,0.0546,0.0219,0.0204,0.0124,0.0093,0.0072,0.0019,0.0027,0.0054,0.0017,0.0024,0.0029,Mine -0.005,0.0017,0.027,0.045,0.0958,0.083,0.0879,0.122,0.1977,0.2282,0.2521,0.3484,0.3309,0.2614,0.1782,0.2055,0.2298,0.3545,0.6218,0.7265,0.8346,0.8268,0.8366,0.9408,0.951,0.9801,0.9974,1,0.9036,0.6409,0.3857,0.2908,0.204,0.1653,0.1769,0.114,0.074,0.0941,0.0621,0.0426,0.0572,0.1068,0.1909,0.2229,0.2203,0.2265,0.1766,0.1097,0.0558,0.0142,0.0281,0.0165,0.0056,0.001,0.0027,0.0062,0.0024,0.0063,0.0017,0.0028,Mine -0.0366,0.0421,0.0504,0.025,0.0596,0.0252,0.0958,0.0991,0.1419,0.1847,0.2222,0.2648,0.2508,0.2291,0.1555,0.1863,0.2387,0.3345,0.5233,0.6684,0.7766,0.7928,0.794,0.9129,0.9498,0.9835,1,0.9471,0.8237,0.6252,0.4181,0.3209,0.2658,0.2196,0.1588,0.0561,0.0948,0.17,0.1215,0.1282,0.0386,0.1329,0.2331,0.2468,0.196,0.1985,0.157,0.0921,0.0549,0.0194,0.0166,0.0132,0.0027,0.0022,0.0059,0.0016,0.0025,0.0017,0.0027,0.0027,Mine -0.0238,0.0318,0.0422,0.0399,0.0788,0.0766,0.0881,0.1143,0.1594,0.2048,0.2652,0.31,0.2381,0.1918,0.143,0.1735,0.1781,0.2852,0.5036,0.6166,0.7616,0.8125,0.7793,0.8788,0.8813,0.947,1,0.9739,0.8446,0.6151,0.4302,0.3165,0.2869,0.2017,0.1206,0.0271,0.058,0.1262,0.1072,0.1082,0.036,0.1197,0.2061,0.2054,0.1878,0.2047,0.1716,0.1069,0.0477,0.017,0.0186,0.0096,0.0071,0.0084,0.0038,0.0026,0.0028,0.0013,0.0035,0.006,Mine 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Title: SPAM E-mail Database -% -% 2. Sources: -% (a) Creators: Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt -% Hewlett-Packard Labs, 1501 Page Mill Rd., Palo Alto, CA 94304 -% (b) Donor: George Forman (gforman at nospam hpl.hp.com) 650-857-7835 -% (c) Generated: June-July 1999 -% -% 3. Past Usage: -% (a) Hewlett-Packard Internal-only Technical Report. External forthcoming. -% (b) Determine whether a given email is spam or not. -% (c) ~7% misclassification error. -% False positives (marking good mail as spam) are very undesirable. -% If we insist on zero false positives in the training/testing set, -% 20-25% of the spam passed through the filter. -% -% 4. Relevant Information: -% The "spam" concept is diverse: advertisements for products/web -% sites, make money fast schemes, chain letters, pornography... -% Our collection of spam e-mails came from our postmaster and -% individuals who had filed spam. Our collection of non-spam -% e-mails came from filed work and personal e-mails, and hence -% the word 'george' and the area code '650' are indicators of -% non-spam. These are useful when constructing a personalized -% spam filter. One would either have to blind such non-spam -% indicators or get a very wide collection of non-spam to -% generate a general purpose spam filter. -% -% For background on spam: -% Cranor, Lorrie F., LaMacchia, Brian A. Spam! -% Communications of the ACM, 41(8):74-83, 1998. -% -% 5. Number of Instances: 4601 (1813 Spam = 39.4%) -% -% 6. Number of Attributes: 58 (57 continuous, 1 nominal class label) -% -% 7. Attribute Information: -% The last column of 'spambase.data' denotes whether the e-mail was -% considered spam (1) or not (0), i.e. unsolicited commercial e-mail. -% Most of the attributes indicate whether a particular word or -% character was frequently occuring in the e-mail. The run-length -% attributes (55-57) measure the length of sequences of consecutive -% capital letters. For the statistical measures of each attribute, -% see the end of this file. Here are the definitions of the attributes: -% -% 48 continuous real [0,100] attributes of type word_freq_WORD -% = percentage of words in the e-mail that match WORD, -% i.e. 100 * (number of times the WORD appears in the e-mail) / -% total number of words in e-mail. A "word" in this case is any -% string of alphanumeric characters bounded by non-alphanumeric -% characters or end-of-string. -% -% 6 continuous real [0,100] attributes of type char_freq_CHAR -% = percentage of characters in the e-mail that match CHAR, -% i.e. 100 * (number of CHAR occurences) / total characters in e-mail -% -% 1 continuous real [1,...] attribute of type capital_run_length_average -% = average length of uninterrupted sequences of capital letters -% -% 1 continuous integer [1,...] attribute of type capital_run_length_longest -% = length of longest uninterrupted sequence of capital letters -% -% 1 continuous integer [1,...] attribute of type capital_run_length_total -% = sum of length of uninterrupted sequences of capital letters -% = total number of capital letters in the e-mail -% -% 1 nominal {0,1} class attribute of type spam -% = denotes whether the e-mail was considered spam (1) or not (0), -% i.e. unsolicited commercial e-mail. -% -% -% 8. Missing Attribute Values: None -% -% 9. Class Distribution: -% Spam 1813 (39.4%) -% Non-Spam 2788 (60.6%) -% -% -% Attribute Statistics: -% Min: Max: Average: Std.Dev: Coeff.Var_%: -% 1 0 4.54 0.10455 0.30536 292 -% 2 0 14.28 0.21301 1.2906 606 -% 3 0 5.1 0.28066 0.50414 180 -% 4 0 42.81 0.065425 1.3952 2130 -% 5 0 10 0.31222 0.67251 215 -% 6 0 5.88 0.095901 0.27382 286 -% 7 0 7.27 0.11421 0.39144 343 -% 8 0 11.11 0.10529 0.40107 381 -% 9 0 5.26 0.090067 0.27862 309 -% 10 0 18.18 0.23941 0.64476 269 -% 11 0 2.61 0.059824 0.20154 337 -% 12 0 9.67 0.5417 0.8617 159 -% 13 0 5.55 0.09393 0.30104 320 -% 14 0 10 0.058626 0.33518 572 -% 15 0 4.41 0.049205 0.25884 526 -% 16 0 20 0.24885 0.82579 332 -% 17 0 7.14 0.14259 0.44406 311 -% 18 0 9.09 0.18474 0.53112 287 -% 19 0 18.75 1.6621 1.7755 107 -% 20 0 18.18 0.085577 0.50977 596 -% 21 0 11.11 0.80976 1.2008 148 -% 22 0 17.1 0.1212 1.0258 846 -% 23 0 5.45 0.10165 0.35029 345 -% 24 0 12.5 0.094269 0.44264 470 -% 25 0 20.83 0.5495 1.6713 304 -% 26 0 16.66 0.26538 0.88696 334 -% 27 0 33.33 0.7673 3.3673 439 -% 28 0 9.09 0.12484 0.53858 431 -% 29 0 14.28 0.098915 0.59333 600 -% 30 0 5.88 0.10285 0.45668 444 -% 31 0 12.5 0.064753 0.40339 623 -% 32 0 4.76 0.047048 0.32856 698 -% 33 0 18.18 0.097229 0.55591 572 -% 34 0 4.76 0.047835 0.32945 689 -% 35 0 20 0.10541 0.53226 505 -% 36 0 7.69 0.097477 0.40262 413 -% 37 0 6.89 0.13695 0.42345 309 -% 38 0 8.33 0.013201 0.22065 1670 -% 39 0 11.11 0.078629 0.43467 553 -% 40 0 4.76 0.064834 0.34992 540 -% 41 0 7.14 0.043667 0.3612 827 -% 42 0 14.28 0.13234 0.76682 579 -% 43 0 3.57 0.046099 0.22381 486 -% 44 0 20 0.079196 0.62198 785 -% 45 0 21.42 0.30122 1.0117 336 -% 46 0 22.05 0.17982 0.91112 507 -% 47 0 2.17 0.0054445 0.076274 1400 -% 48 0 10 0.031869 0.28573 897 -% 49 0 4.385 0.038575 0.24347 631 -% 50 0 9.752 0.13903 0.27036 194 -% 51 0 4.081 0.016976 0.10939 644 -% 52 0 32.478 0.26907 0.81567 303 -% 53 0 6.003 0.075811 0.24588 324 -% 54 0 19.829 0.044238 0.42934 971 -% 55 1 1102.5 5.1915 31.729 611 -% 56 1 9989 52.173 194.89 374 -% 57 1 15841 283.29 606.35 214 -% 58 0 1 0.39404 0.4887 124 -% -% -% This file: 'spambase.DOCUMENTATION' at the UCI Machine Learning Repository -% http://www.ics.uci.edu/~mlearn/MLRepository.html -% -% Information about the dataset -% CLASSTYPE: nominal -% CLASSINDEX: last -% - -@relation spambase - -@attribute word_freq_make REAL -@attribute word_freq_address REAL -@attribute word_freq_all REAL -@attribute word_freq_3d REAL -@attribute word_freq_our REAL -@attribute word_freq_over REAL -@attribute word_freq_remove REAL -@attribute word_freq_internet REAL -@attribute word_freq_order REAL -@attribute word_freq_mail REAL -@attribute word_freq_receive REAL -@attribute word_freq_will REAL -@attribute word_freq_people REAL -@attribute word_freq_report REAL -@attribute word_freq_addresses REAL -@attribute word_freq_free REAL -@attribute word_freq_business REAL -@attribute word_freq_email REAL -@attribute word_freq_you REAL -@attribute word_freq_credit REAL -@attribute word_freq_your REAL -@attribute word_freq_font REAL -@attribute word_freq_000 REAL -@attribute word_freq_money REAL -@attribute word_freq_hp REAL -@attribute word_freq_hpl REAL -@attribute word_freq_george REAL -@attribute word_freq_650 REAL -@attribute word_freq_lab REAL -@attribute word_freq_labs REAL -@attribute word_freq_telnet REAL -@attribute word_freq_857 REAL -@attribute word_freq_data REAL -@attribute word_freq_415 REAL -@attribute word_freq_85 REAL -@attribute word_freq_technology REAL -@attribute word_freq_1999 REAL -@attribute word_freq_parts REAL -@attribute word_freq_pm REAL -@attribute word_freq_direct REAL -@attribute word_freq_cs REAL -@attribute word_freq_meeting REAL -@attribute word_freq_original REAL -@attribute word_freq_project REAL -@attribute word_freq_re REAL -@attribute word_freq_edu REAL -@attribute word_freq_table REAL -@attribute word_freq_conference REAL -@attribute char_freq_; REAL -@attribute char_freq_( REAL -@attribute char_freq_[ REAL -@attribute char_freq_! 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a/datasets/vehicle.arff +++ /dev/null @@ -1,1058 +0,0 @@ -% !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!IMPORTANT!!!!!!!!!!!!!!!!!!!!!!!!!!!! -% -% This dataset comes from the Turing Institute, Glasgow, Scotland. -% If you use this dataset in any publication you must acknowledge this -% source. -% -% !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! -% -% NAME -% vehicle silhouettes -% -% PURPOSE -% to classify a given silhouette as one of four types of vehicle, -% using a set of features extracted from the silhouette. The -% vehicle may be viewed from one of many different angles. -% -% PROBLEM TYPE -% classification -% -% SOURCE -% Drs.Pete Mowforth and Barry Shepherd -% Turing Institute -% George House -% 36 North Hanover St. -% Glasgow -% G1 2AD -% -% CONTACT -% Alistair Sutherland -% Statistics Dept. -% Strathclyde University -% Livingstone Tower -% 26 Richmond St. -% GLASGOW G1 1XH -% Great Britain -% -% Tel: 041 552 4400 x3033 -% -% Fax: 041 552 4711 -% -% e-mail: alistair@uk.ac.strathclyde.stams -% -% HISTORY -% This data was originally gathered at the TI in 1986-87 by -% JP Siebert. It was partially financed by Barr and Stroud Ltd. -% The original purpose was to find a method of distinguishing -% 3D objects within a 2D image by application of an ensemble of -% shape feature extractors to the 2D silhouettes of the objects. -% Measures of shape features extracted from example silhouettes -% of objects to be discriminated were used to generate a class- -% ification rule tree by means of computer induction. -% This object recognition strategy was successfully used to -% discriminate between silhouettes of model cars, vans and buses -% viewed from constrained elevation but all angles of rotation. -% The rule tree classification performance compared favourably -% to MDC (Minimum Distance Classifier) and k-NN (k-Nearest Neigh- -% bour) statistical classifiers in terms of both error rate and -% computational efficiency. An investigation of these rule trees -% generated by example indicated that the tree structure was -% heavily influenced by the orientation of the objects, and grouped -% similar object views into single decisions. -% -% DESCRIPTION -% The features were extracted from the silhouettes by the HIPS -% (Hierarchical Image Processing System) extension BINATTS, which -% extracts a combination of scale independent features utilising -% both classical moments based measures such as scaled variance, -% skewness and kurtosis about the major/minor axes and heuristic -% measures such as hollows, circularity, rectangularity and -% compactness. -% Four "Corgie" model vehicles were used for the experiment: -% a double decker bus, Cheverolet van, Saab 9000 and an Opel Manta 400. -% This particular combination of vehicles was chosen with the -% expectation that the bus, van and either one of the cars would -% be readily distinguishable, but it would be more difficult to -% distinguish between the cars. -% The images were acquired by a camera looking downwards at the -% model vehicle from a fixed angle of elevation (34.2 degrees -% to the horizontal). The vehicles were placed on a diffuse -% backlit surface (lightbox). The vehicles were painted matte black -% to minimise highlights. The images were captured using a CRS4000 -% framestore connected to a vax 750. All images were captured with -% a spatial resolution of 128x128 pixels quantised to 64 greylevels. -% These images were thresholded to produce binary vehicle silhouettes, -% negated (to comply with the processing requirements of BINATTS) and -% thereafter subjected to shrink-expand-expand-shrink HIPS modules to -% remove "salt and pepper" image noise. -% The vehicles were rotated and their angle of orientation was measured -% using a radial graticule beneath the vehicle. 0 and 180 degrees -% corresponded to "head on" and "rear" views respectively while 90 and -% 270 corresponded to profiles in opposite directions. Two sets of -% 60 images, each set covering a full 360 degree rotation, were captured -% for each vehicle. The vehicle was rotated by a fixed angle between -% images. These datasets are known as e2 and e3 respectively. -% A further two sets of images, e4 and e5, were captured with the camera -% at elevations of 37.5 degs and 30.8 degs respectively. These sets -% also contain 60 images per vehicle apart from e4.van which contains -% only 46 owing to the difficulty of containing the van in the image -% at some orientations. -% -% ATTRIBUTES -% -% COMPACTNESS (average perim)**2/area -% -% CIRCULARITY (average radius)**2/area -% -% DISTANCE CIRCULARITY area/(av.distance from border)**2 -% -% RADIUS RATIO (max.rad-min.rad)/av.radius -% -% PR.AXIS ASPECT RATIO (minor axis)/(major axis) -% -% MAX.LENGTH ASPECT RATIO (length perp. max length)/(max length) -% -% SCATTER RATIO (inertia about minor axis)/(inertia about major axis) -% -% ELONGATEDNESS area/(shrink width)**2 -% -% PR.AXIS RECTANGULARITY area/(pr.axis length*pr.axis width) -% -% MAX.LENGTH RECTANGULARITY area/(max.length*length perp. to this) -% -% SCALED VARIANCE (2nd order moment about minor axis)/area -% ALONG MAJOR AXIS -% -% SCALED VARIANCE (2nd order moment about major axis)/area -% ALONG MINOR AXIS -% -% SCALED RADIUS OF GYRATION (mavar+mivar)/area -% -% SKEWNESS ABOUT (3rd order moment about major axis)/sigma_min**3 -% MAJOR AXIS -% -% SKEWNESS ABOUT (3rd order moment about minor axis)/sigma_maj**3 -% MINOR AXIS -% -% KURTOSIS ABOUT (4th order moment about major axis)/sigma_min**4 -% MINOR AXIS -% -% KURTOSIS ABOUT (4th order moment about minor axis)/sigma_maj**4 -% MAJOR AXIS -% -% HOLLOWS RATIO (area of hollows)/(area of bounding polygon) -% -% Where sigma_maj**2 is the variance along the major axis and -% sigma_min**2 is the variance along the minor axis, and -% -% area of hollows= area of bounding poly-area of object -% -% The area of the bounding polygon is found as a side result of -% the computation to find the maximum length. Each individual -% length computation yields a pair of calipers to the object -% orientated at every 5 degrees. The object is propagated into -% an image containing the union of these calipers to obtain an -% image of the bounding polygon. -% -% NUMBER OF CLASSES -% -% 4 OPEL, SAAB, BUS, VAN -% -% NUMBER OF EXAMPLES -% -% Total no. = 946 -% -% No. in each class -% -% opel 240 -% saab 240 -% bus 240 -% van 226 -% -% -% 100 examples are being kept by Strathclyde for validation. -% So StatLog partners will receive 846 examples. -% -% NUMBER OF ATTRIBUTES -% -% No. of atts. = 18 -% -% -% BIBLIOGRAPHY -% -% Turing Institute Research Memorandum TIRM-87-018 "Vehicle -% Recognition Using Rule Based Methods" by Siebert,JP (March 1987) -% -% -@relation vehicle - -@attribute 'COMPACTNESS' real -@attribute 'CIRCULARITY' real -@attribute 'DISTANCE CIRCULARITY' real -@attribute 'RADIUS RATIO' real -@attribute 'PR.AXIS ASPECT RATIO' real -@attribute 'MAX.LENGTH ASPECT RATIO' real -@attribute 'SCATTER RATIO' real -@attribute 'ELONGATEDNESS' real -@attribute 'PR.AXIS RECTANGULARITY' real -@attribute 'MAX.LENGTH RECTANGULARITY' real -@attribute 'SCALED VARIANCE_MAJOR' real -@attribute 'SCALED VARIANCE_MINOR' real -@attribute 'SCALED RADIUS OF GYRATION' real -@attribute 'SKEWNESS ABOUT_MAJOR' real -@attribute 'SKEWNESS ABOUT_MINOR' real -@attribute 'KURTOSIS ABOUT_MAJOR' real -@attribute 'KURTOSIS ABOUT_MINOR' real -@attribute 'HOLLOWS RATIO' real -@attribute 'Class' {opel,saab,bus,van} - -@data -95,48,83,178,72,10,162,42,20,159,176,379,184,70,6,16,187,197,van -91,41,84,141,57,9,149,45,19,143,170,330,158,72,9,14,189,199,van -104,50,106,209,66,10,207,32,23,158,223,635,220,73,14,9,188,196,saab -93,41,82,159,63,9,144,46,19,143,160,309,127,63,6,10,199,207,van -85,44,70,205,103,52,149,45,19,144,241,325,188,127,9,11,180,183,bus -107,57,106,172,50,6,255,26,28,169,280,957,264,85,5,9,181,183,bus -97,43,73,173,65,6,153,42,19,143,176,361,172,66,13,1,200,204,bus -90,43,66,157,65,9,137,48,18,146,162,281,164,67,3,3,193,202,van -86,34,62,140,61,7,122,54,17,127,141,223,112,64,2,14,200,208,van -93,44,98,197,62,11,183,36,22,146,202,505,152,64,4,14,195,204,saab -86,36,70,143,61,9,133,50,18,130,153,266,127,66,2,10,194,202,van -90,34,66,136,55,6,123,54,17,118,148,224,118,65,5,26,196,202,saab -88,46,74,171,68,6,152,43,19,148,180,349,192,71,5,11,189,195,bus -89,42,85,144,58,10,152,44,19,144,173,345,161,72,8,13,187,197,van -94,49,79,203,71,5,174,37,21,154,196,465,206,71,6,2,197,199,bus -96,55,103,201,65,9,204,32,23,166,227,624,246,74,6,2,186,194,opel -89,36,51,109,52,6,118,57,17,129,137,206,125,80,2,14,181,185,van -99,41,77,197,69,6,177,36,21,139,202,485,151,72,4,10,198,199,bus -104,54,100,186,61,10,216,31,24,173,225,686,220,74,5,11,185,195,saab -101,56,100,215,69,10,208,32,24,169,227,651,223,74,6,5,186,193,opel -84,47,75,153,64,6,154,43,19,145,175,354,184,75,0,3,185,192,bus -84,37,53,121,59,5,123,55,17,125,141,221,133,82,7,1,179,183,van -94,43,64,173,69,7,150,43,19,142,169,344,177,68,9,1,199,206,bus -87,39,70,148,61,7,143,46,18,136,164,307,141,69,1,2,192,199,bus -99,53,105,219,66,11,204,32,23,165,221,623,224,68,0,6,191,201,saab -85,45,80,154,64,9,147,45,19,148,169,324,174,71,1,4,188,199,van -83,36,54,119,57,6,128,53,18,125,143,238,139,82,6,3,179,183,saab -107,54,98,203,65,11,218,31,25,167,229,696,216,72,1,28,187,199,saab -102,45,85,193,64,6,192,33,22,146,217,570,163,76,6,7,195,193,bus -80,38,63,129,55,7,146,46,19,130,168,314,158,83,9,20,180,185,saab -89,43,85,160,64,11,155,43,19,151,173,356,174,72,5,9,185,196,van -88,42,77,151,58,8,140,47,18,142,165,293,158,64,10,11,198,205,saab -93,35,66,154,59,6,142,46,18,128,162,304,120,64,5,13,197,202,opel -101,48,107,222,68,10,208,32,24,154,232,641,204,70,5,38,190,202,opel -87,38,85,177,61,8,164,40,20,129,186,402,130,63,1,25,198,205,opel -100,46,90,172,67,9,157,43,20,150,170,363,184,67,17,7,192,200,van -82,44,72,118,52,7,152,44,19,147,174,340,177,82,2,2,180,185,bus -90,48,86,306,126,49,153,44,19,156,272,346,200,118,0,15,185,194,van -106,53,98,176,54,10,216,31,24,171,235,691,218,74,1,9,187,197,saab 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-91,37,76,138,55,8,132,51,18,135,157,256,124,69,0,12,191,192,van -102,48,105,214,64,10,201,33,23,152,214,600,178,64,0,25,192,204,saab -96,44,68,190,70,7,155,41,19,145,179,372,166,67,5,7,202,206,bus -85,36,72,127,56,7,127,54,18,125,144,233,123,70,3,30,184,194,opel -103,48,96,232,71,10,205,32,23,153,226,633,197,71,2,15,188,196,opel -101,55,107,200,61,11,225,30,25,178,228,730,204,74,8,35,187,201,opel -103,52,103,170,52,7,236,28,26,160,254,816,250,82,3,23,183,184,bus -85,45,73,167,69,8,143,46,18,148,173,307,176,71,2,0,190,199,van -114,57,102,181,52,6,257,26,28,169,287,968,261,85,2,21,182,184,bus -88,40,55,114,53,7,132,53,18,139,142,249,158,87,0,7,176,183,van -86,37,77,144,54,7,154,43,19,127,179,352,145,71,14,13,186,191,opel -102,51,104,217,67,10,204,32,23,162,220,621,195,68,3,19,188,197,saab -105,51,93,160,51,7,217,30,24,165,240,703,208,81,9,25,188,188,bus -100,50,98,204,63,6,218,30,24,156,232,719,213,77,8,7,189,189,bus -96,44,85,166,66,10,155,43,19,150,167,355,159,67,3,10,192,202,van -109,52,104,199,60,12,215,31,24,162,220,691,212,67,11,7,189,199,saab -87,39,74,165,66,6,145,45,19,134,173,318,139,70,3,21,195,200,bus -90,41,78,145,55,7,138,48,18,138,161,284,158,67,0,1,192,197,opel -98,48,101,203,65,9,197,33,23,152,216,584,174,68,2,5,189,197,saab -96,46,88,174,68,10,155,43,19,148,173,354,182,69,14,15,194,202,van -85,43,69,141,62,7,152,44,19,145,178,341,179,84,1,4,181,184,bus -91,42,66,142,58,9,134,50,18,142,163,268,164,69,6,5,191,197,van -80,43,68,123,53,7,150,46,19,147,169,327,176,81,7,14,179,184,bus -93,46,85,169,66,9,151,44,19,147,169,339,179,67,0,4,195,204,van -93,51,90,209,69,8,183,36,22,156,211,506,230,70,6,1,189,196,saab -96,40,78,170,58,7,174,38,21,139,197,455,160,68,3,29,191,200,saab -85,36,51,115,56,5,119,57,17,124,139,207,127,81,13,5,181,184,van -100,36,73,199,73,6,162,40,20,127,189,401,125,72,6,19,200,204,bus -91,36,72,162,60,8,150,44,19,133,166,334,121,63,2,22,196,205,saab -91,41,64,148,61,8,129,51,18,142,161,249,153,68,6,12,194,201,van -86,39,58,125,55,5,117,57,17,134,140,204,148,69,7,6,190,194,van -95,53,95,202,65,10,193,34,22,160,220,559,237,71,3,2,188,196,saab -91,43,72,142,56,7,149,45,19,140,168,327,165,72,13,23,186,191,saab -81,45,68,137,60,7,152,45,19,151,167,333,179,81,3,13,179,183,bus -94,50,84,138,57,10,156,44,20,170,171,351,187,77,5,6,182,191,van -107,45,92,197,62,10,188,35,22,148,202,526,159,64,12,20,195,203,opel -115,51,100,201,60,12,196,34,23,162,207,573,184,62,22,1,198,208,saab -90,44,69,152,64,7,135,49,18,145,165,272,162,75,3,2,187,191,van -102,52,98,225,71,10,214,31,24,164,228,682,199,71,0,16,187,196,opel -103,53,106,172,55,10,212,32,24,168,220,660,223,73,16,24,187,197,saab -84,43,70,123,54,8,151,45,19,146,173,332,176,81,1,12,181,184,bus -98,38,78,191,65,8,169,39,20,136,197,430,141,67,9,20,192,199,saab -88,39,70,166,66,7,148,44,19,134,167,332,143,69,5,13,193,201,bus -91,46,101,199,65,9,196,34,23,146,219,574,199,73,5,8,186,194,opel -91,36,83,162,61,8,142,47,19,128,163,298,137,63,0,31,193,200,saab -80,44,68,120,53,8,151,45,19,146,170,333,190,80,4,16,180,185,bus -97,48,105,212,64,11,201,33,23,155,212,602,162,64,2,7,193,202,opel -92,52,93,204,67,9,189,35,22,161,214,536,240,72,2,5,188,195,opel -87,42,70,139,59,7,149,45,19,142,177,327,156,78,6,9,185,188,bus -94,37,72,146,60,9,133,50,18,135,161,262,128,69,2,7,192,195,van -97,38,75,188,68,6,171,37,20,129,199,450,137,74,2,6,197,199,bus -96,45,101,201,66,9,192,34,22,143,218,552,195,73,3,19,189,197,saab -106,53,98,154,47,4,237,28,26,164,263,838,222,82,4,17,185,184,bus -111,50,103,199,60,11,211,31,24,156,223,663,188,68,9,9,190,200,saab -96,39,64,111,48,8,134,52,18,141,160,258,139,80,7,20,183,184,van -109,47,96,206,64,9,198,33,23,150,219,586,191,70,4,13,190,198,saab -88,42,64,151,62,8,130,51,18,142,150,253,161,63,3,2,203,210,van -87,44,98,211,70,10,189,35,22,141,214,535,178,71,2,21,187,194,opel -107,54,96,201,62,12,218,31,24,170,221,694,217,71,2,21,187,198,saab -87,37,52,116,54,6,115,58,17,126,135,196,144,74,11,22,186,190,van -85,43,66,123,56,6,148,46,19,145,166,319,171,85,6,11,179,182,bus -107,55,103,213,68,11,219,30,25,172,221,709,216,70,10,7,187,197,saab -93,37,70,126,52,9,127,53,18,137,156,238,119,71,2,13,191,190,van -115,52,100,203,62,10,217,31,24,165,229,697,214,72,14,4,188,197,saab -90,39,85,160,59,7,163,41,20,131,189,396,158,71,7,13,186,192,saab -85,43,66,120,54,5,148,46,19,145,168,320,174,87,8,2,179,181,bus -86,43,68,150,64,9,138,48,18,143,161,285,174,69,6,0,192,201,van -100,51,104,163,52,10,206,32,23,164,217,631,193,69,5,21,188,196,opel -98,46,88,191,63,6,192,34,22,147,215,563,174,73,2,26,194,197,bus -87,41,76,165,67,7,148,45,19,140,171,327,152,72,7,13,188,195,bus -83,40,53,114,53,6,132,53,18,140,142,247,157,86,8,7,176,183,van -85,38,63,130,55,7,122,55,17,130,137,219,144,64,20,8,195,201,opel -111,53,108,211,61,11,207,32,23,167,217,636,216,64,21,2,196,205,saab -103,56,100,185,59,11,216,31,24,173,219,684,219,75,15,11,186,194,opel -92,39,76,180,71,6,152,43,19,131,179,350,143,72,6,14,195,200,bus -88,41,80,147,62,8,146,45,19,144,169,318,161,71,4,16,188,197,van -84,38,60,128,56,5,132,50,18,130,148,261,141,75,8,4,185,188,opel -89,44,80,191,66,6,162,40,20,143,189,396,180,66,13,11,194,199,opel -93,47,84,205,71,7,176,36,21,152,190,476,201,70,7,19,198,201,bus -104,52,101,206,62,10,198,33,23,161,207,587,204,64,2,5,195,204,opel -81,43,68,148,64,7,150,45,19,144,175,330,171,80,1,2,182,185,bus -88,45,82,155,56,8,154,43,19,149,180,357,170,69,3,0,188,193,saab -103,51,105,174,56,11,210,32,24,163,222,650,222,73,8,9,187,196,saab -83,46,68,139,59,6,150,44,19,146,172,336,183,74,5,3,185,191,bus -79,38,55,120,55,5,142,48,19,128,153,295,145,81,4,2,180,183,saab -97,55,103,197,63,11,215,31,24,172,219,677,219,75,5,24,185,194,opel -83,39,69,127,54,5,135,49,18,131,155,274,162,69,16,6,187,190,opel -98,38,72,192,69,5,166,38,20,131,189,427,138,70,1,3,200,202,bus -90,48,77,132,56,10,157,44,20,164,169,354,187,78,1,3,182,191,van -85,43,66,123,55,7,150,45,19,146,172,326,173,83,4,15,180,183,bus -81,45,68,154,69,22,151,45,19,147,186,335,186,88,1,10,180,185,bus -90,48,85,157,64,11,161,43,20,167,175,375,186,74,3,16,185,195,van -104,53,108,204,64,11,220,31,25,172,226,707,203,71,14,30,189,203,saab -95,43,96,202,65,10,189,35,22,143,217,534,166,71,6,27,190,197,opel -93,42,98,192,63,9,185,36,22,138,206,508,173,70,10,21,189,197,saab -87,38,71,123,53,8,137,49,18,127,158,277,145,75,0,9,181,186,saab -104,56,96,231,74,11,220,30,25,172,223,713,218,73,6,16,186,195,opel -95,41,82,170,65,9,145,46,19,145,163,314,140,64,4,8,199,207,van -105,54,105,213,67,10,200,33,23,163,214,597,214,68,10,20,190,198,opel -106,55,96,196,60,12,221,30,25,173,225,717,214,72,9,13,186,196,opel -86,39,84,149,57,8,156,43,20,133,185,358,157,74,0,23,183,190,opel -95,49,92,193,62,10,178,37,21,154,200,478,171,64,2,0,198,206,opel -99,57,100,177,54,13,224,30,25,188,223,726,213,72,4,7,185,198,opel -89,42,66,125,53,7,131,51,18,144,162,254,162,73,10,17,188,191,van -95,49,82,139,56,11,159,43,20,162,173,365,185,75,7,10,182,191,van -97,37,70,173,66,7,151,43,19,129,167,346,119,65,0,16,201,208,bus -100,47,70,185,70,7,162,40,20,153,179,406,172,68,9,6,200,205,bus -108,49,109,204,61,11,212,31,24,159,229,665,215,71,16,11,190,199,saab -92,46,83,154,56,6,160,41,20,148,185,382,184,71,10,5,186,191,saab -82,36,51,114,53,4,135,50,18,126,150,268,144,86,15,4,181,182,saab -111,58,105,183,51,6,265,26,29,174,285,1018,255,85,4,8,181,183,bus -87,45,66,139,58,8,140,47,18,148,168,294,175,73,3,12,188,196,van -94,46,77,169,60,8,158,42,20,148,181,373,181,67,12,2,193,199,saab -95,43,76,142,57,10,151,44,19,149,173,339,159,71,2,23,187,200,van -90,44,72,157,64,8,137,48,18,144,159,283,171,65,9,4,196,203,van -93,34,66,140,56,7,130,51,18,120,151,251,114,62,5,29,201,207,opel -93,39,87,183,64,8,169,40,20,134,200,422,149,72,7,25,188,195,saab -89,46,84,163,66,11,159,43,20,159,173,368,176,72,1,20,186,197,van -106,54,101,222,67,12,222,30,25,173,228,721,200,70,3,4,187,201,saab -86,36,78,146,58,7,135,50,18,124,155,270,148,66,0,25,190,195,saab -85,36,66,123,55,5,120,56,17,128,140,212,131,73,1,18,186,190,van -% -% -% diff --git a/datasets/waveform-5000.arff b/datasets/waveform-5000.arff deleted file mode 100755 index 53f89bb..0000000 --- a/datasets/waveform-5000.arff +++ /dev/null @@ -1,5086 +0,0 @@ -% 1. Title: Waveform Database Generator (written in C) -% -% 2. Source: -% (a) Breiman,L., Friedman,J.H., Olshen,R.A., & Stone,C.J. (1984). -% Classification and Regression Trees. Wadsworth International -% Group: Belmont, California. (see pages 43-49). -% (b) Donor: David Aha -% (c) Date: 11/10/1988 -% -% 3. Past Usage: -% 1. CART book (above): -% -- Optimal Bayes classification rate: 86% accuracy -% -- CART decision tree algorithm: 72% -% -- Nearest Neighbor Algorithm: 78% -% -- 300 training and 5000 test instances -% -% 4. Relevant Information: -% -- 3 classes of waves -% -- 21 attributes, all of which include noise -% -- See the book for details (49-55, 169) -% -- waveform.data.Z contains 5000 instances -% -% 5. Number of Instances: chosen by user -% -% 6. Number of Attributes: -% -- 21 attributes with continuous values between 0 and 6 -% -% 7. Attribute Information: -% -- Each class is generated from a combination of 2 of 3 "base" waves -% -- Each instance is generated f added noise (mean 0, variance 1) in -% each attribute -% -- See the book for details (49-55, 169) -% -% 8. Missing Attribute Values: none -% -% 9. Class Distribution: 33% for each of 3 classes - -@relation waveform - -@attribute x1 real -@attribute x2 real -@attribute x3 real -@attribute x4 real -@attribute x5 real -@attribute x6 real -@attribute x7 real -@attribute x8 real -@attribute x9 real -@attribute x10 real -@attribute x11 real -@attribute x12 real -@attribute x13 real -@attribute x14 real -@attribute x15 real -@attribute x16 real -@attribute x17 real -@attribute x18 real -@attribute x19 real -@attribute x20 real -@attribute x21 real -@attribute x22 real -@attribute x23 real -@attribute x24 real -@attribute x25 real -@attribute x26 real -@attribute x27 real -@attribute x28 real -@attribute x29 real -@attribute x30 real -@attribute x31 real -@attribute x32 real -@attribute x33 real -@attribute x34 real -@attribute x35 real -@attribute x36 real -@attribute x37 real -@attribute x38 real -@attribute x39 real -@attribute x40 real -@attribute class {0,1,2} - -@data - --0.23,-1.21,1.20,1.23,-0.10,0.12,2.49,1.19,1.34,0.58,1.22,2.30,4.65,5.82,4.91,3.16,2.25,4.01,0.82,2.28,1.01,0.09,1.22,-0.23,-0.14,1.11,0.02,-1.32,-0.11,-0.47,-0.63,-0.86,-0.70,0.51,0.34,-0.13,-0.87,0.56,-0.53,0.29,2 -0.38,0.38,-0.31,-0.09,1.52,1.35,1.49,3.81,2.33,1.34,1.45,3.70,3.08,5.01,3.27,3.40,2.23,2.56,2.15,-0.90,-0.37,-1.78,-0.40,0.45,-1.32,0.40,0.13,0.11,-0.78,2.36,0.31,1.28,1.40,0.00,-0.18,0.71,0.04,0.91,-0.79,0.22,0 --0.69,1.00,1.08,1.48,2.44,3.39,3.09,4.08,5.48,3.61,0.47,1.68,2.35,-1.04,0.63,-0.87,-0.80,0.63,0.35,-0.10,-1.42,2.14,0.57,1.21,-1.64,0.20,0.07,0.64,0.41,-0.12,0.93,0.29,1.12,0.60,0.28,2.17,0.18,-0.09,-1.33,1.00,1 -0.40,0.68,0.27,1.39,1.03,-0.32,-1.23,-0.50,0.11,0.87,1.27,4.41,3.51,4.88,4.66,5.71,2.72,0.95,1.94,0.47,-0.47,1.21,1.12,0.57,1.12,0.12,0.10,0.20,3.10,-0.34,1.18,0.43,-0.30,-0.07,-0.99,-0.75,1.11,1.35,-1.63,0.10,0 --0.81,1.59,-0.69,1.16,4.22,4.98,4.52,2.54,5.60,4.66,4.25,1.58,2.51,2.40,1.82,0.34,2.09,0.29,-2.47,2.47,-0.25,-0.66,0.75,0.19,0.31,0.21,1.19,-0.55,-1.43,-0.29,-0.07,0.62,0.14,1.33,-1.87,1.48,-0.02,-0.58,0.93,0.40,1 -0.59,0.77,-0.61,1.00,1.80,2.08,2.16,3.59,4.08,3.63,4.27,4.43,3.45,2.47,3.64,0.14,-0.07,0.32,-1.09,-0.03,-0.19,-0.50,-1.22,-0.42,-1.27,-0.62,0.34,-0.58,2.23,-0.65,-0.15,1.23,-0.29,-1.62,0.19,-1.04,0.43,-1.82,-1.14,-0.23,1 --0.15,0.13,2.27,2.39,4.00,6.14,5.36,4.08,3.81,3.89,2.46,1.78,-1.43,0.70,1.08,1.90,-1.00,-0.31,0.37,1.30,-0.18,0.28,-0.61,1.23,-0.19,-2.05,0.14,1.53,-1.17,0.84,0.40,0.25,-0.78,-0.12,-0.35,0.44,0.31,-0.45,-0.23,0.27,0 --0.30,-0.42,0.25,-0.61,-1.39,-0.60,1.71,4.01,2.96,5.81,6.56,5.69,5.46,2.72,1.27,2.12,1.79,0.20,1.19,0.25,1.40,0.04,0.56,0.26,2.06,-0.63,-0.19,-0.74,0.15,0.68,-0.60,-0.00,0.28,0.61,-0.40,-1.96,1.26,-0.72,2.01,0.95,2 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/dev/null @@ -1,302 +0,0 @@ -% 1. Title of Database: Wine recognition data -% Updated Sept 21, 1998 by C.Blake : Added attribute information -% -% 2. Sources: -% (a) Forina, M. et al, PARVUS - An Extendible Package for Data -% Exploration, Classification and Correlation. Institute of Pharmaceutical -% and Food Analysis and Technologies, Via Brigata Salerno, -% 16147 Genoa, Italy. -% -% (b) Stefan Aeberhard, email: stefan@coral.cs.jcu.edu.au -% (c) July 1991 -% 3. Past Usage: -% -% (1) -% S. Aeberhard, D. Coomans and O. de Vel, -% Comparison of Classifiers in High Dimensional Settings, -% Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of -% Mathematics and Statistics, James Cook University of North Queensland. -% (Also submitted to Technometrics). -% -% The data was used with many others for comparing various -% classifiers. The classes are separable, though only RDA -% has achieved 100% correct classification. -% (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) -% (All results using the leave-one-out technique) -% -% In a classification context, this is a well posed problem -% with "well behaved" class structures. A good data set -% for first testing of a new classifier, but not very -% challenging. -% -% (2) -% S. Aeberhard, D. Coomans and O. de Vel, -% "THE CLASSIFICATION PERFORMANCE OF RDA" -% Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of -% Mathematics and Statistics, James Cook University of North Queensland. -% (Also submitted to Journal of Chemometrics). -% -% Here, the data was used to illustrate the superior performance of -% the use of a new appreciation function with RDA. -% -% 4. Relevant Information: -% -% -- These data are the results of a chemical analysis of -% wines grown in the same region in Italy but derived from three -% different cultivars. -% The analysis determined the quantities of 13 constituents -% found in each of the three types of wines. -% -% -- I think that the initial data set had around 30 variables, but -% for some reason I only have the 13 dimensional version. -% I had a list of what the 30 or so variables were, but a.) -% I lost it, and b.), I would not know which 13 variables -% are included in the set. -% -% -- The attributes are (dontated by Riccardo Leardi, -% riclea@anchem.unige.it ) -% 1) Alcohol -% 2) Malic acid -% 3) Ash -% 4) Alcalinity of ash -% 5) Magnesium -% 6) Total phenols -% 7) Flavanoids -% 8) Nonflavanoid phenols -% 9) Proanthocyanins -% 10)Color intensity -% 11)Hue -% 12)OD280/OD315 of diluted wines -% 13)Proline -% -% 5. Number of Instances -% -% class 1 59 -% class 2 71 -% class 3 48 -% -% 6. Number of Attributes -% -% 13 -% -% 7. For Each Attribute: -% -% All attributes are continuous -% -% No statistics available, but suggest to standardise -% variables for certain uses (e.g. for us with classifiers -% which are NOT scale invariant) -% -% NOTE: 1st attribute is class identifier (1-3) -% -% 8. Missing Attribute Values: -% -% None -% -% 9. Class Distribution: number of instances per class -% -% class 1 59 -% class 2 71 -% class 3 48 -% -% Information about the dataset -% CLASSTYPE: nominal -% CLASSINDEX: first -% - -@relation wine - -@attribute class {1,2,3} -@attribute Alcohol REAL -@attribute Malic_acid REAL -@attribute Ash REAL -@attribute Alcalinity_of_ash REAL -@attribute Magnesium INTEGER -@attribute Total_phenols REAL -@attribute Flavanoids REAL -@attribute Nonflavanoid_phenols REAL -@attribute Proanthocyanins REAL -@attribute Color_intensity REAL -@attribute Hue REAL -@attribute OD280/OD315_of_diluted_wines REAL -@attribute Proline INTEGER - -@data -1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065 -1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050 -1,13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185 -1,14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480 -1,13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735 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b/doc/Makefile deleted file mode 100644 index 508376e..0000000 --- a/doc/Makefile +++ /dev/null @@ -1,184 +0,0 @@ -# Makefile for Sphinx documentation -# - -# You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = sphinx-build -PAPER = -BUILDDIR = _build - -# User-friendly check for sphinx-build -ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) -$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/) -endif - -# Internal variables. -PAPEROPT_a4 = -D latex_paper_size=a4 -PAPEROPT_letter = -D latex_paper_size=letter -ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . -# the i18n builder cannot share the environment and doctrees with the others -I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . - -.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext - -help: - @echo "Please use \`make ' where is one of" - @echo " html to make standalone HTML files" - @echo " dirhtml to make HTML files named index.html in directories" - @echo " singlehtml to make a single large HTML file" - @echo " pickle to make pickle files" - @echo " json to make JSON files" - @echo " htmlhelp to make HTML files and a HTML help project" - @echo " qthelp to make HTML files and a qthelp project" - @echo " devhelp to make HTML files and a Devhelp project" - @echo " epub to make an epub" - @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" - @echo " latexpdf to make LaTeX files and run them through pdflatex" - @echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx" - @echo " text to make text files" - @echo " man to make manual pages" - @echo " texinfo to make Texinfo files" - @echo " info to make Texinfo files and run them through makeinfo" - @echo " gettext to make PO message catalogs" - @echo " changes to make an overview of all changed/added/deprecated items" - @echo " xml to make Docutils-native XML files" - @echo " pseudoxml to make pseudoxml-XML files for display purposes" - @echo " linkcheck to check all external links for integrity" - @echo " doctest to run all doctests embedded in the documentation (if enabled)" - -clean: - -rm -rf $(BUILDDIR)/* - -rm -rf auto_examples/ - -rm -rf generated/* - -rm -rf modules/generated/* - -html: - # These two lines make the build a bit more lengthy, and the - # the embedding of images more robust - rm -rf $(BUILDDIR)/html/_images - #rm -rf _build/doctrees/ - $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html - @echo - @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." - -dirhtml: - $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml - @echo - @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." - -singlehtml: - $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml - @echo - @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." - -pickle: - $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle - @echo - @echo "Build finished; now you can process the pickle files." - -json: - $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json - @echo - @echo "Build finished; now you can process the JSON files." - -htmlhelp: - $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp - @echo - @echo "Build finished; now you can run HTML Help Workshop with the" \ - ".hhp project file in $(BUILDDIR)/htmlhelp." - -qthelp: - $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp - @echo - @echo "Build finished; now you can run "qcollectiongenerator" with the" \ - ".qhcp project file in $(BUILDDIR)/qthelp, like this:" - @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/project-template.qhcp" - @echo "To view the help file:" - @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/project-template.qhc" - -devhelp: - $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp - @echo - @echo "Build finished." - @echo "To view the help file:" - @echo "# mkdir -p $$HOME/.local/share/devhelp/project-template" - @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/project-template" - @echo "# devhelp" - -epub: - $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub - @echo - @echo "Build finished. The epub file is in $(BUILDDIR)/epub." - -latex: - $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex - @echo - @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." - @echo "Run \`make' in that directory to run these through (pdf)latex" \ - "(use \`make latexpdf' here to do that automatically)." - -latexpdf: - $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex - @echo "Running LaTeX files through pdflatex..." - $(MAKE) -C $(BUILDDIR)/latex all-pdf - @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." - -latexpdfja: - $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex - @echo "Running LaTeX files through platex and dvipdfmx..." - $(MAKE) -C $(BUILDDIR)/latex all-pdf-ja - @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." - -text: - $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text - @echo - @echo "Build finished. The text files are in $(BUILDDIR)/text." - -man: - $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man - @echo - @echo "Build finished. The manual pages are in $(BUILDDIR)/man." - -texinfo: - $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo - @echo - @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." - @echo "Run \`make' in that directory to run these through makeinfo" \ - "(use \`make info' here to do that automatically)." - -info: - $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo - @echo "Running Texinfo files through makeinfo..." - make -C $(BUILDDIR)/texinfo info - @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." - -gettext: - $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale - @echo - @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." - -changes: - $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes - @echo - @echo "The overview file is in $(BUILDDIR)/changes." - -linkcheck: - $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck - @echo - @echo "Link check complete; look for any errors in the above output " \ - "or in $(BUILDDIR)/linkcheck/output.txt." - -doctest: - $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest - @echo "Testing of doctests in the sources finished, look at the " \ - "results in $(BUILDDIR)/doctest/output.txt." - -xml: - $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml - @echo - @echo "Build finished. The XML files are in $(BUILDDIR)/xml." - -pseudoxml: - $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml - @echo - @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." diff --git a/doc/_static/css/project-template.css b/doc/_static/css/project-template.css deleted file mode 100644 index f6caff2..0000000 --- a/doc/_static/css/project-template.css +++ /dev/null @@ -1,16 +0,0 @@ -@import url("theme.css"); - -.highlight a { - text-decoration: underline; -} - -.deprecated p { - padding: 10px 7px 10px 10px; - color: #b94a48; - background-color: #F3E5E5; - border: 1px solid #eed3d7; -} - -.deprecated p span.versionmodified { - font-weight: bold; -} diff --git a/doc/_static/js/copybutton.js b/doc/_static/js/copybutton.js deleted file mode 100644 index d87f569..0000000 --- a/doc/_static/js/copybutton.js +++ /dev/null @@ -1,63 +0,0 @@ -$(document).ready(function() { - /* Add a [>>>] button on the top-right corner of code samples to hide - * the >>> and ... prompts and the output and thus make the code - * copyable. */ - var div = $('.highlight-python .highlight,' + - '.highlight-python3 .highlight,' + - '.highlight-pycon .highlight,' + - '.highlight-default .highlight') - var pre = div.find('pre'); - - // get the styles from the current theme - pre.parent().parent().css('position', 'relative'); - var hide_text = 'Hide the prompts and output'; - var show_text = 'Show the prompts and output'; - var border_width = pre.css('border-top-width'); - var border_style = pre.css('border-top-style'); - var border_color = pre.css('border-top-color'); - var button_styles = { - 'cursor':'pointer', 'position': 'absolute', 'top': '0', 'right': '0', - 'border-color': border_color, 'border-style': border_style, - 'border-width': border_width, 'color': border_color, 'text-size': '75%', - 'font-family': 'monospace', 'padding-left': '0.2em', 'padding-right': '0.2em', - 'border-radius': '0 3px 0 0' - } - - // create and add the button to all the code blocks that contain >>> - div.each(function(index) { - var jthis = $(this); - if (jthis.find('.gp').length > 0) { - var button = $('>>>'); - button.css(button_styles) - button.attr('title', hide_text); - button.data('hidden', 'false'); - jthis.prepend(button); - } - // tracebacks (.gt) contain bare text elements that need to be - // wrapped in a span to work with .nextUntil() (see later) - jthis.find('pre:has(.gt)').contents().filter(function() { - return ((this.nodeType == 3) && (this.data.trim().length > 0)); - }).wrap(''); - }); - - // define the behavior of the button when it's clicked - $('.copybutton').click(function(e){ - e.preventDefault(); - var button = $(this); - if (button.data('hidden') === 'false') { - // hide the code output - button.parent().find('.go, .gp, .gt').hide(); - button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden'); - button.css('text-decoration', 'line-through'); - button.attr('title', show_text); - button.data('hidden', 'true'); - } else { - // show the code output - button.parent().find('.go, .gp, .gt').show(); - button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible'); - button.css('text-decoration', 'none'); - button.attr('title', hide_text); - button.data('hidden', 'false'); - } - }); -}); diff --git a/doc/_templates/class.rst b/doc/_templates/class.rst deleted file mode 100644 index 30c38f6..0000000 --- a/doc/_templates/class.rst +++ /dev/null @@ -1,17 +0,0 @@ -:mod:`{{module}}`.{{objname}} -{{ underline }}============== - -.. currentmodule:: {{ module }} - -.. autoclass:: {{ objname }} - :members: - - {% block methods %} - .. automethod:: __init__ - {% endblock %} - -.. include:: {{module}}.{{objname}}.examples - -.. raw:: html - -
diff --git a/doc/_templates/function.rst b/doc/_templates/function.rst deleted file mode 100644 index 4ba355d..0000000 --- a/doc/_templates/function.rst +++ /dev/null @@ -1,12 +0,0 @@ -:mod:`{{module}}`.{{objname}} -{{ underline }}==================== - -.. currentmodule:: {{ module }} - -.. autofunction:: {{ objname }} - -.. include:: {{module}}.{{objname}}.examples - -.. raw:: html - -
diff --git a/doc/_templates/numpydoc_docstring.py b/doc/_templates/numpydoc_docstring.py deleted file mode 100644 index fd6a35f..0000000 --- a/doc/_templates/numpydoc_docstring.py +++ /dev/null @@ -1,16 +0,0 @@ -{{index}} -{{summary}} -{{extended_summary}} -{{parameters}} -{{returns}} -{{yields}} -{{other_parameters}} -{{attributes}} -{{raises}} -{{warns}} -{{warnings}} -{{see_also}} -{{notes}} -{{references}} -{{examples}} -{{methods}} diff --git a/doc/api.rst b/doc/api.rst deleted file mode 100644 index 32dc23e..0000000 --- a/doc/api.rst +++ /dev/null @@ -1,18 +0,0 @@ -#################### -project-template API -#################### - -This is an example on how to document the API of your own project. - -.. currentmodule:: bayesclass - - -TAN -=== - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - TAN - diff --git a/doc/conf.py b/doc/conf.py deleted file mode 100644 index 89cf2fe..0000000 --- a/doc/conf.py +++ /dev/null @@ -1,338 +0,0 @@ -# -*- coding: utf-8 -*- -# -# project-template documentation build configuration file, created by -# sphinx-quickstart on Mon Jan 18 14:44:12 2016. -# -# This file is execfile()d with the current directory set to its -# containing dir. -# -# Note that not all possible configuration values are present in this -# autogenerated file. -# -# All configuration values have a default; values that are commented out -# serve to show the default. - -import sys -import os - -import sphinx_gallery -import sphinx_rtd_theme - -# Add to sys.path the top-level directory where the package is located. -sys.path.insert(0, os.path.abspath("..")) - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# sys.path.insert(0, os.path.abspath('.')) - -# -- General configuration ------------------------------------------------ - -# If your documentation needs a minimal Sphinx version, state it here. -# needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - "sphinx.ext.autodoc", - "sphinx.ext.autosummary", - "sphinx.ext.doctest", - "sphinx.ext.intersphinx", - "sphinx.ext.viewcode", - "numpydoc", - "sphinx_gallery.gen_gallery", -] - -# this is needed for some reason... -# see https://github.com/numpy/numpydoc/issues/69 -numpydoc_show_class_members = False - -# pngmath / imgmath compatibility layer for different sphinx versions -import sphinx -from distutils.version import LooseVersion - -if LooseVersion(sphinx.__version__) < LooseVersion("1.4"): - extensions.append("sphinx.ext.pngmath") -else: - extensions.append("sphinx.ext.imgmath") - -autodoc_default_flags = ["members", "inherited-members"] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ["_templates"] - -# generate autosummary even if no references -autosummary_generate = True - -# The suffix of source filenames. -source_suffix = ".rst" - -# The encoding of source files. -# source_encoding = 'utf-8-sig' - -# Generate the plots for the gallery -plot_gallery = True - -# The master toctree document. -master_doc = "index" - -# General information about the project. -project = "sklearn-template" -copyright = "2016, Vighnesh Birodkar" - -# The version info for the project you're documenting, acts as replacement for -# |version| and |release|, also used in various other places throughout the -# built documents. -# -# The short X.Y version. -from bayesclass import __version__ - -version = __version__ -# The full version, including alpha/beta/rc tags. -release = __version__ - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# language = None - -# There are two options for replacing |today|: either, you set today to some -# non-false value, then it is used: -# today = '' -# Else, today_fmt is used as the format for a strftime call. -# today_fmt = '%B %d, %Y' - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -exclude_patterns = ["_build", "_templates"] - -# The reST default role (used for this markup: `text`) to use for all -# documents. -# default_role = None - -# If true, '()' will be appended to :func: etc. cross-reference text. -# add_function_parentheses = True - -# If true, the current module name will be prepended to all description -# unit titles (such as .. function::). -# add_module_names = True - -# If true, sectionauthor and moduleauthor directives will be shown in the -# output. They are ignored by default. -# show_authors = False - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = "sphinx" - -# Custom style -html_style = "css/project-template.css" - -# A list of ignored prefixes for module index sorting. -# modindex_common_prefix = [] - -# If true, keep warnings as "system message" paragraphs in the built documents. -# keep_warnings = False - - -# -- Options for HTML output ---------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -html_theme = "sphinx_rtd_theme" - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -# html_theme_options = {} - -# Add any paths that contain custom themes here, relative to this directory. -html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] - -# The name for this set of Sphinx documents. If None, it defaults to -# " v documentation". -# html_title = None - -# A shorter title for the navigation bar. Default is the same as html_title. -# html_short_title = None - -# The name of an image file (relative to this directory) to place at the top -# of the sidebar. -# html_logo = None - -# The name of an image file (within the static path) to use as favicon of the -# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 -# pixels large. -# html_favicon = None - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ["_static"] - -# Add any extra paths that contain custom files (such as robots.txt or -# .htaccess) here, relative to this directory. These files are copied -# directly to the root of the documentation. -# html_extra_path = [] - -# If not '', a 'Last updated on:' timestamp is inserted at every page bottom, -# using the given strftime format. -# html_last_updated_fmt = '%b %d, %Y' - -# If true, SmartyPants will be used to convert quotes and dashes to -# typographically correct entities. -# html_use_smartypants = True - -# Custom sidebar templates, maps document names to template names. -# html_sidebars = {} - -# Additional templates that should be rendered to pages, maps page names to -# template names. -# html_additional_pages = {} - -# If false, no module index is generated. -# html_domain_indices = True - -# If false, no index is generated. -# html_use_index = True - -# If true, the index is split into individual pages for each letter. -# html_split_index = False - -# If true, links to the reST sources are added to the pages. -# html_show_sourcelink = True - -# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. -# html_show_sphinx = True - -# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. -# html_show_copyright = True - -# If true, an OpenSearch description file will be output, and all pages will -# contain a tag referring to it. The value of this option must be the -# base URL from which the finished HTML is served. -# html_use_opensearch = '' - -# This is the file name suffix for HTML files (e.g. ".xhtml"). -# html_file_suffix = None - -# Output file base name for HTML help builder. -htmlhelp_basename = "project-templatedoc" - - -# -- Options for LaTeX output --------------------------------------------- - -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - #'papersize': 'letterpaper', - # The font size ('10pt', '11pt' or '12pt'). - #'pointsize': '10pt', - # Additional stuff for the LaTeX preamble. - #'preamble': '', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, -# author, documentclass [howto, manual, or own class]). -latex_documents = [ - ( - "index", - "project-template.tex", - "project-template Documentation", - "Vighnesh Birodkar", - "manual", - ), -] - -# The name of an image file (relative to this directory) to place at the top of -# the title page. -# latex_logo = None - -# For "manual" documents, if this is true, then toplevel headings are parts, -# not chapters. -# latex_use_parts = False - -# If true, show page references after internal links. -# latex_show_pagerefs = False - -# If true, show URL addresses after external links. -# latex_show_urls = False - -# Documents to append as an appendix to all manuals. -# latex_appendices = [] - -# If false, no module index is generated. -# latex_domain_indices = True - - -# -- Options for manual page output --------------------------------------- - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - ( - "index", - "project-template", - "project-template Documentation", - ["Vighnesh Birodkar"], - 1, - ) -] - -# If true, show URL addresses after external links. -# man_show_urls = False - - -# -- Options for Texinfo output ------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - ( - "index", - "project-template", - "project-template Documentation", - "Vighnesh Birodkar", - "project-template", - "One line description of project.", - "Miscellaneous", - ), -] - -# Documents to append as an appendix to all manuals. -# texinfo_appendices = [] - -# If false, no module index is generated. -# texinfo_domain_indices = True - -# How to display URL addresses: 'footnote', 'no', or 'inline'. -# texinfo_show_urls = 'footnote' - -# If true, do not generate a @detailmenu in the "Top" node's menu. -# texinfo_no_detailmenu = False - - -# Example configuration for intersphinx: refer to the Python standard library. -# intersphinx configuration -intersphinx_mapping = { - "python": ( - "https://docs.python.org/{.major}".format(sys.version_info), - None, - ), - "numpy": ("https://docs.scipy.org/doc/numpy/", None), - "scipy": ("https://docs.scipy.org/doc/scipy/reference", None), - "matplotlib": ("https://matplotlib.org/", None), - "sklearn": ("http://scikit-learn.org/stable", None), -} - -# sphinx-gallery configuration -sphinx_gallery_conf = { - "doc_module": "bayesclass", - "backreferences_dir": os.path.join("generated"), - "reference_url": {"bayesclass": None}, -} - - -def setup(app): - # a copy button to copy snippet of code from the documentation - app.add_js_file("js/copybutton.js") diff --git a/doc/index.rst b/doc/index.rst deleted file mode 100644 index ba235c8..0000000 --- a/doc/index.rst +++ /dev/null @@ -1,52 +0,0 @@ -.. project-template documentation master file, created by - sphinx-quickstart on Mon Jan 18 14:44:12 2016. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to sklearn-template's documentation! -============================================ - -This project is a reference implementation to anyone who wishes to develop -scikit-learn compatible classes. - -.. toctree:: - :maxdepth: 2 - :hidden: - :caption: Getting Started - - quick_start - -.. toctree:: - :maxdepth: 2 - :hidden: - :caption: Documentation - - user_guide - api - -.. toctree:: - :maxdepth: 2 - :hidden: - :caption: Tutorial - Examples - - auto_examples/index - -`Getting started `_ -------------------------------------- - -Information regarding this template and how to modify it for your own project. - -`User Guide `_ -------------------------------- - -An example of narrative documentation. - -`API Documentation `_ -------------------------------- - -An example of API documentation. - -`Examples `_ --------------------------------------- - -A set of examples. It complements the `User Guide `_. \ No newline at end of file diff --git a/doc/make.bat b/doc/make.bat deleted file mode 100644 index 79c1e19..0000000 --- a/doc/make.bat +++ /dev/null @@ -1,242 +0,0 @@ -@ECHO OFF - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set BUILDDIR=_build -set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% . -set I18NSPHINXOPTS=%SPHINXOPTS% . -if NOT "%PAPER%" == "" ( - set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% - set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS% -) - -if "%1" == "" goto help - -if "%1" == "help" ( - :help - echo.Please use `make ^` where ^ is one of - echo. html to make standalone HTML files - echo. dirhtml to make HTML files named index.html in directories - echo. singlehtml to make a single large HTML file - echo. pickle to make pickle files - echo. json to make JSON files - echo. htmlhelp to make HTML files and a HTML help project - echo. qthelp to make HTML files and a qthelp project - echo. devhelp to make HTML files and a Devhelp project - echo. epub to make an epub - echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter - echo. text to make text files - echo. man to make manual pages - echo. texinfo to make Texinfo files - echo. gettext to make PO message catalogs - echo. changes to make an overview over all changed/added/deprecated items - echo. xml to make Docutils-native XML files - echo. pseudoxml to make pseudoxml-XML files for display purposes - echo. linkcheck to check all external links for integrity - echo. doctest to run all doctests embedded in the documentation if enabled - goto end -) - -if "%1" == "clean" ( - for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i - del /q /s %BUILDDIR%\* - goto end -) - - -%SPHINXBUILD% 2> nul -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.http://sphinx-doc.org/ - exit /b 1 -) - -if "%1" == "html" ( - %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The HTML pages are in %BUILDDIR%/html. - goto end -) - -if "%1" == "dirhtml" ( - %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml. - goto end -) - -if "%1" == "singlehtml" ( - %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml. - goto end -) - -if "%1" == "pickle" ( - %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle - if errorlevel 1 exit /b 1 - echo. - echo.Build finished; now you can process the pickle files. - goto end -) - -if "%1" == "json" ( - %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json - if errorlevel 1 exit /b 1 - echo. - echo.Build finished; now you can process the JSON files. - goto end -) - -if "%1" == "htmlhelp" ( - %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp - if errorlevel 1 exit /b 1 - echo. - echo.Build finished; now you can run HTML Help Workshop with the ^ -.hhp project file in %BUILDDIR%/htmlhelp. - goto end -) - -if "%1" == "qthelp" ( - %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp - if errorlevel 1 exit /b 1 - echo. - echo.Build finished; now you can run "qcollectiongenerator" with the ^ -.qhcp project file in %BUILDDIR%/qthelp, like this: - echo.^> qcollectiongenerator %BUILDDIR%\qthelp\project-template.qhcp - echo.To view the help file: - echo.^> assistant -collectionFile %BUILDDIR%\qthelp\project-template.ghc - goto end -) - -if "%1" == "devhelp" ( - %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. - goto end -) - -if "%1" == "epub" ( - %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The epub file is in %BUILDDIR%/epub. - goto end -) - -if "%1" == "latex" ( - %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex - if errorlevel 1 exit /b 1 - echo. - echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. - goto end -) - -if "%1" == "latexpdf" ( - %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex - cd %BUILDDIR%/latex - make all-pdf - cd %BUILDDIR%/.. - echo. - echo.Build finished; the PDF files are in %BUILDDIR%/latex. - goto end -) - -if "%1" == "latexpdfja" ( - %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex - cd %BUILDDIR%/latex - make all-pdf-ja - cd %BUILDDIR%/.. - echo. - echo.Build finished; the PDF files are in %BUILDDIR%/latex. - goto end -) - -if "%1" == "text" ( - %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The text files are in %BUILDDIR%/text. - goto end -) - -if "%1" == "man" ( - %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The manual pages are in %BUILDDIR%/man. - goto end -) - -if "%1" == "texinfo" ( - %SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo. - goto end -) - -if "%1" == "gettext" ( - %SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The message catalogs are in %BUILDDIR%/locale. - goto end -) - -if "%1" == "changes" ( - %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes - if errorlevel 1 exit /b 1 - echo. - echo.The overview file is in %BUILDDIR%/changes. - goto end -) - -if "%1" == "linkcheck" ( - %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck - if errorlevel 1 exit /b 1 - echo. - echo.Link check complete; look for any errors in the above output ^ -or in %BUILDDIR%/linkcheck/output.txt. - goto end -) - -if "%1" == "doctest" ( - %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest - if errorlevel 1 exit /b 1 - echo. - echo.Testing of doctests in the sources finished, look at the ^ -results in %BUILDDIR%/doctest/output.txt. - goto end -) - -if "%1" == "xml" ( - %SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The XML files are in %BUILDDIR%/xml. - goto end -) - -if "%1" == "pseudoxml" ( - %SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml. - goto end -) - -:end diff --git a/doc/quick_start.rst b/doc/quick_start.rst deleted file mode 100644 index a1afae5..0000000 --- a/doc/quick_start.rst +++ /dev/null @@ -1,131 +0,0 @@ -##################################### -Quick Start with the project-template -##################################### - -This package serves as a skeleton package aiding at developing compatible -scikit-learn contribution. - -Creating your own scikit-learn contribution package -=================================================== - -1. Download and setup your repository -------------------------------------- - -To create your package, you need to clone the ``project-template`` repository:: - - $ git clone https://github.com/scikit-learn-contrib/project-template.git - -Before to reinitialize your git repository, you need to make the following -changes. Replace all occurrences of ``bayesclass`` and ``sklearn-template`` -with the name of you own contribution. You can find all the occurrences using -the following command:: - - $ git grep bayesclass - $ git grep sklearn-template - -To remove the history of the template package, you need to remove the `.git` -directory:: - - $ cd project-template - $ rm -rf .git - -Then, you need to initialize your new git repository:: - - $ git init - $ git add . - $ git commit -m 'Initial commit' - -Finally, you create an online repository on GitHub and push your code online:: - - $ git remote add origin https://github.com/your_remote/your_contribution.git - $ git push origin master - -2. Develop your own scikit-learn estimators -------------------------------------------- - -.. _check_estimator: http://scikit-learn.org/stable/modules/generated/sklearn.utils.estimator_checks.check_estimator.html#sklearn.utils.estimator_checks.check_estimator -.. _`Contributor's Guide`: http://scikit-learn.org/stable/developers/ -.. _PEP8: https://www.python.org/dev/peps/pep-0008/ -.. _PEP257: https://www.python.org/dev/peps/pep-0257/ -.. _NumPyDoc: https://github.com/numpy/numpydoc -.. _doctests: https://docs.python.org/3/library/doctest.html - -You can modify the source files as you want. However, your custom estimators -need to pass the check_estimator_ test to be scikit-learn compatible. You can -refer to the :ref:`User Guide ` to help you create a compatible -scikit-learn estimator. - -In any case, developers should endeavor to adhere to scikit-learn's -`Contributor's Guide`_ which promotes the use of: - -* algorithm-specific unit tests, in addition to ``check_estimator``'s common - tests; -* PEP8_-compliant code; -* a clearly documented API using NumpyDoc_ and PEP257_-compliant docstrings; -* references to relevant scientific literature in standard citation formats; -* doctests_ to provide succinct usage examples; -* standalone examples to illustrate the usage, model visualisation, and - benefits/benchmarks of particular algorithms; -* efficient code when the need for optimization is supported by benchmarks. - -3. Edit the documentation -------------------------- - -.. _Sphinx: http://www.sphinx-doc.org/en/stable/ - -The documentation is created using Sphinx_. In addition, the examples are -created using ``sphinx-gallery``. Therefore, to generate locally the -documentation, you are required to install the following packages:: - - $ pip install sphinx sphinx-gallery sphinx_rtd_theme matplotlib numpydoc pillow - -The documentation is made of: - -* a home page, ``doc/index.rst``; -* an API documentation, ``doc/api.rst`` in which you should add all public - objects for which the docstring should be exposed publicly. -* a User Guide documentation, ``doc/user_guide.rst``, containing the narrative - documentation of your package, to give as much intuition as possible to your - users. -* examples which are created in the `examples/` folder. Each example - illustrates some usage of the package. the example file name should start by - `plot_*.py`. - -The documentation is built with the following commands:: - - $ cd doc - $ make html - -4. Setup the continuous integration ------------------------------------ - -The project template already contains configuration files of the continuous -integration system. Basically, the following systems are set: - -* Travis CI is used to test the package in Linux. You need to activate Travis - CI for your own repository. Refer to the Travis CI documentation. -* AppVeyor is used to test the package in Windows. You need to activate - AppVeyor for your own repository. Refer to the AppVeyor documentation. -* Circle CI is used to check if the documentation is generated properly. You - need to activate Circle CI for your own repository. Refer to the Circle CI - documentation. -* ReadTheDocs is used to build and host the documentation. You need to activate - ReadTheDocs for your own repository. Refer to the ReadTheDocs documentation. -* CodeCov for tracking the code coverage of the package. You need to activate - CodeCov for you own repository. -* PEP8Speaks for automatically checking the PEP8 compliance of your project for - each Pull Request. - -Publish your package -==================== - -.. _PyPi: https://packaging.python.org/tutorials/packaging-projects/ -.. _conda-foge: https://conda-forge.org/ - -You can make your package available through PyPi_ and conda-forge_. Refer to -the associated documentation to be able to upload your packages such that -it will be installable with ``pip`` and ``conda``. Once published, it will -be possible to install your package with the following commands:: - - $ pip install your-scikit-learn-contribution - $ conda install -c conda-forge your-scikit-learn-contribution diff --git a/doc/user_guide.rst b/doc/user_guide.rst deleted file mode 100644 index 4cceb7f..0000000 --- a/doc/user_guide.rst +++ /dev/null @@ -1,180 +0,0 @@ -.. title:: User guide : contents - -.. _user_guide: - -================================================== -User guide: create your own scikit-learn estimator -================================================== - -Estimator ---------- - -The central piece of transformer, regressor, and classifier is -:class:`sklearn.base.BaseEstimator`. All estimators in scikit-learn are derived -from this class. In more details, this base class enables to set and get -parameters of the estimator. It can be imported as:: - - >>> from sklearn.base import BaseEstimator - -Once imported, you can create a class which inherate from this base class:: - - >>> class MyOwnEstimator(BaseEstimator): - ... pass - -Transformer ------------ - -Transformers are scikit-learn estimators which implement a ``transform`` method. -The use case is the following: - -* at ``fit``, some parameters can be learned from ``X`` and ``y``; -* at ``transform``, `X` will be transformed, using the parameters learned - during ``fit``. - -.. _mixin: https://en.wikipedia.org/wiki/Mixin - -In addition, scikit-learn provides a -mixin_, i.e. :class:`sklearn.base.TransformerMixin`, which -implement the combination of ``fit`` and ``transform`` called ``fit_transform``:: - -One can import the mixin class as:: - - >>> from sklearn.base import TransformerMixin - -Therefore, when creating a transformer, you need to create a class which -inherits from both :class:`sklearn.base.BaseEstimator` and -:class:`sklearn.base.TransformerMixin`. The scikit-learn API imposed ``fit`` to -**return ``self``**. The reason is that it allows to pipeline ``fit`` and -``transform`` imposed by the :class:`sklearn.base.TransformerMixin`. The -``fit`` method is expected to have ``X`` and ``y`` as inputs. Note that -``transform`` takes only ``X`` as input and is expected to return the -transformed version of ``X``:: - - >>> class MyOwnTransformer(BaseEstimator, TransformerMixin): - ... def fit(self, X, y=None): - ... return self - ... def transform(self, X): - ... return X - -We build a basic example to show that our :class:`MyOwnTransformer` is working -within a scikit-learn ``pipeline``:: - - >>> from sklearn.datasets import load_iris - >>> from sklearn.pipeline import make_pipeline - >>> from sklearn.linear_model import LogisticRegression - >>> X, y = load_iris(return_X_y=True) - >>> pipe = make_pipeline(MyOwnTransformer(), - ... LogisticRegression(random_state=10, - ... solver='lbfgs')) - >>> pipe.fit(X, y) # doctest: +ELLIPSIS - Pipeline(...) - >>> pipe.predict(X) # doctest: +ELLIPSIS - array([...]) - -Predictor ---------- - -Regressor -~~~~~~~~~ - -Similarly, regressors are scikit-learn estimators which implement a ``predict`` -method. The use case is the following: - -* at ``fit``, some parameters can be learned from ``X`` and ``y``; -* at ``predict``, predictions will be computed using ``X`` using the parameters - learned during ``fit``. - -In addition, scikit-learn provides a mixin_, i.e. -:class:`sklearn.base.RegressorMixin`, which implements the ``score`` method -which computes the :math:`R^2` score of the predictions. - -One can import the mixin as:: - - >>> from sklearn.base import RegressorMixin - -Therefore, we create a regressor, :class:`MyOwnRegressor` which inherits from -both :class:`sklearn.base.BaseEstimator` and -:class:`sklearn.base.RegressorMixin`. The method ``fit`` gets ``X`` and ``y`` -as input and should return ``self``. It should implement the ``predict`` -function which should output the predictions of your regressor:: - - >>> import numpy as np - >>> class MyOwnRegressor(BaseEstimator, RegressorMixin): - ... def fit(self, X, y): - ... return self - ... def predict(self, X): - ... return np.mean(X, axis=1) - -We illustrate that this regressor is working within a scikit-learn pipeline:: - - >>> from sklearn.datasets import load_diabetes - >>> X, y = load_diabetes(return_X_y=True) - >>> pipe = make_pipeline(MyOwnTransformer(), MyOwnRegressor()) - >>> pipe.fit(X, y) # doctest: +ELLIPSIS - Pipeline(...) - >>> pipe.predict(X) # doctest: +ELLIPSIS - array([...]) - -Since we inherit from the :class:`sklearn.base.RegressorMixin`, we can call -the ``score`` method which will return the :math:`R^2` score:: - - >>> pipe.score(X, y) # doctest: +ELLIPSIS - -3.9... - -Classifier -~~~~~~~~~~ - -Similarly to regressors, classifiers implement ``predict``. In addition, they -output the probabilities of the prediction using the ``predict_proba`` method: - -* at ``fit``, some parameters can be learned from ``X`` and ``y``; -* at ``predict``, predictions will be computed using ``X`` using the parameters - learned during ``fit``. The output corresponds to the predicted class for each sample; -* ``predict_proba`` will give a 2D matrix where each column corresponds to the - class and each entry will be the probability of the associated class. - -In addition, scikit-learn provides a mixin, i.e. -:class:`sklearn.base.ClassifierMixin`, which implements the ``score`` method -which computes the accuracy score of the predictions. - -One can import this mixin as:: - - >>> from sklearn.base import ClassifierMixin - -Therefore, we create a classifier, :class:`MyOwnClassifier` which inherits -from both :class:`slearn.base.BaseEstimator` and -:class:`sklearn.base.ClassifierMixin`. The method ``fit`` gets ``X`` and ``y`` -as input and should return ``self``. It should implement the ``predict`` -function which should output the class inferred by the classifier. -``predict_proba`` will output some probabilities instead:: - - >>> class MyOwnClassifier(BaseEstimator, ClassifierMixin): - ... def fit(self, X, y): - ... self.classes_ = np.unique(y) - ... return self - ... def predict(self, X): - ... return np.random.randint(0, self.classes_.size, - ... size=X.shape[0]) - ... def predict_proba(self, X): - ... pred = np.random.rand(X.shape[0], self.classes_.size) - ... return pred / np.sum(pred, axis=1)[:, np.newaxis] - -We illustrate that this regressor is working within a scikit-learn pipeline:: - - >>> X, y = load_iris(return_X_y=True) - >>> pipe = make_pipeline(MyOwnTransformer(), MyOwnClassifier()) - >>> pipe.fit(X, y) # doctest: +ELLIPSIS - Pipeline(...) - -Then, you can call ``predict`` and ``predict_proba``:: - - >>> pipe.predict(X) # doctest: +ELLIPSIS - array([...]) - >>> pipe.predict_proba(X) # doctest: +ELLIPSIS - array([...]) - -Since our classifier inherits from :class:`sklearn.base.ClassifierMixin`, we -can compute the accuracy by calling the ``score`` method:: - - >>> pipe.score(X, y) # doctest: +ELLIPSIS - 0... diff --git a/environment.yml b/environment.yml deleted file mode 100644 index 50cfb2b..0000000 --- a/environment.yml +++ /dev/null @@ -1,6 +0,0 @@ -name: bayesclass -dependencies: - - numpy - - scipy - - scikit-learn - - pgmpy diff --git a/example.py b/examples/example.py similarity index 96% rename from example.py rename to examples/example.py index 5bc9715..fb77d75 100644 --- a/example.py +++ b/examples/example.py @@ -2,7 +2,7 @@ import time from sklearn.model_selection import cross_val_score, StratifiedKFold from sklearn.preprocessing import KBinsDiscretizer from sklearn.datasets import load_wine -from bayesclass.bayesclass import TAN +from bayesclass.clfs import TAN import warnings diff --git a/examples/plot_classifier.py b/examples/plot_classifier.py deleted file mode 100644 index 57076ef..0000000 --- a/examples/plot_classifier.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -============================ -Plotting Template Classifier -============================ - -An example plot of :class:`bayesclass.TAN` -""" -import numpy as np -from matplotlib import pyplot as plt -from bayesclass.bayesclass import TAN - -X = [[0, 0], [1, 1]] -y = [0, 1] -clf = TAN() -clf.fit(X, y) - -rng = np.random.RandomState(13) -X_test = rng.randint(2, size=(500, 2)) -y_pred = clf.predict(X_test) - -X_0 = X_test[y_pred == 0] -X_1 = X_test[y_pred == 1] - - -p0 = plt.scatter(0, 0, c="red", s=100) -p1 = plt.scatter(1, 1, c="blue", s=100) - -ax0 = plt.scatter(X_0[:, 0], X_0[:, 1], c="crimson", s=50) -ax1 = plt.scatter(X_1[:, 0], X_1[:, 1], c="deepskyblue", s=50) - -leg = plt.legend( - [p0, p1, ax0, ax1], - ["Point 0", "Point 1", "Class 0", "Class 1"], - loc="upper left", - fancybox=True, - scatterpoints=1, -) -leg.get_frame().set_alpha(0.5) - -plt.xlabel("Feature 1") -plt.ylabel("Feature 2") -plt.xlim([-0.5, 1.5]) - -plt.show()