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First functional with 100% coverage

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This commit is contained in:
Ricardo Montañana Gómez
2022-11-05 20:00:18 +01:00
parent 3689852205
commit 8c03fc6b67
52 changed files with 94739 additions and 199 deletions
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7
.env Normal file
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@@ -0,0 +1,7 @@
score=accuracy
platform=iMac27
n_folds=5
stratified=1
model=ODTE
source_data=Arff
seeds=[271, 314, 171]

608
.ipynb_checkpoints/test-checkpoint.ipynb Normal file
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7
.vscode/settings.json vendored Normal file
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@@ -0,0 +1,7 @@
{
"python.testing.pytestArgs": [
"bayesclass"
],
"python.testing.unittestEnabled": false,
"python.testing.pytestEnabled": true
}

54
Makefile Normal file
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@@ -0,0 +1,54 @@
SHELL := /bin/bash
.DEFAULT_GOAL := help
.PHONY: coverage deps help lint push test doc build
coverage: ## Run tests with coverage
pytest --cov
deps: ## Install dependencies
pip install -r requirements.txt
devdeps: ## Install development dependencies
pip install black pip-audit flake8 mypy coverage
lint: ## Lint and static-check
black bayesclass
flake8 bayesclass
mypy bayesclass
push: ## Push code with tags
git push && git push --tags
test: ## Run tests
pytest
doc: ## Update documentation
make -C doc --makefile=Makefile html
build: ## Build package
rm -fr dist/*
rm -fr build/*
python setup.py sdist bdist_wheel
doc-clean: ## Update documentation
make -C docs --makefile=Makefile clean
audit: ## Audit pip
pip-audit
help: ## Show help message
@IFS=$$'\n' ; \
help_lines=(`fgrep -h "##" $(MAKEFILE_LIST) | fgrep -v fgrep | sed -e 's/\\$$//' | sed -e 's/##/:/'`); \
printf "%s\n\n" "Usage: make [task]"; \
printf "%-20s %s\n" "task" "help" ; \
printf "%-20s %s\n" "------" "----" ; \
for help_line in $${help_lines[@]}; do \
IFS=$$':' ; \
help_split=($$help_line) ; \
help_command=`echo $${help_split[0]} | sed -e 's/^ *//' -e 's/ *$$//'` ; \
help_info=`echo $${help_split[2]} | sed -e 's/^ *//' -e 's/ *$$//'` ; \
printf '\033[36m'; \
printf "%-20s %s" $$help_command ; \
printf '\033[0m'; \
printf "%s\n" $$help_info; \
done

2
bayesclass/__init__.py
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@@ -1,4 +1,4 @@
from ._estimators import TAN
from .bayesclass import TAN
from ._version import __version__

116
bayesclass/_estimators.py → bayesclass/bayesclass.py
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@@ -1,16 +1,14 @@
"""
This is a module to be used as a reference for building other modules
"""
import numpy as np
import pandas as pd
from sklearn.base import ClassifierMixin, BaseEstimator
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from sklearn.utils.multiclass import unique_labels
import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt
from pgmpy.estimators import TreeSearch, BayesianEstimator
from pgmpy.models import BayesianNetwork
from benchmark import Datasets
import matplotlib.pyplot as plt
class TAN(ClassifierMixin, BaseEstimator):
@@ -31,10 +29,11 @@ class TAN(ClassifierMixin, BaseEstimator):
The classes seen at :meth:`fit`.
"""
def __init__(self, demo_param="demo"):
self.demo_param = demo_param
def __init__(self, simple_init=False, show_progress=False):
self.simple_init = simple_init
self.show_progress = show_progress
def fit(self, X, y):
def fit(self, X, y, **kwargs):
"""A reference implementation of a fitting function for a classifier.
Parameters
----------
@@ -42,6 +41,10 @@ class TAN(ClassifierMixin, BaseEstimator):
The training input samples.
y : array-like, shape (n_samples,)
The target values. An array of int.
**kwargs : dict
class_name : str (default='class') Name of the class column
features: list (default=None) List of features
head: int (default=0) Index of the head node
Returns
-------
self : object
@@ -51,6 +54,24 @@ class TAN(ClassifierMixin, BaseEstimator):
X, y = check_X_y(X, y)
# Store the classes seen during fit
self.classes_ = unique_labels(y)
# Default values
self.class_name_ = "class"
self.features_ = [f"feature_{i}" for i in range(X.shape[1])]
self.head_ = 0
expected_args = ["class_name", "features", "head"]
for key, value in kwargs.items():
if key in expected_args:
setattr(self, f"{key}_", value)
else:
raise ValueError(f"Unexpected argument: {key}")
if len(self.features_) != X.shape[1]:
raise ValueError(
"Number of features does not match the number of columns in X"
)
if self.head_ >= len(self.features_):
raise ValueError("Head index out of range")
self.X_ = X
self.y_ = y
@@ -58,60 +79,52 @@ class TAN(ClassifierMixin, BaseEstimator):
# Return the classifier
return self
def __train(self):
dt = Datasets()
data = dt.load("balance-scale", dataframe=True)
features = dt.dataset.features
class_name = dt.dataset.class_name
factorization, class_factors = pd.factorize(data[class_name])
data[class_name] = factorization
data.head()
net = [(class_name, feature) for feature in features]
model = BayesianNetwork(net)
# 1st feature correlates with other features
first_node = features[0]
edges2 = [
def __initial_edges(self):
if self.simple_init:
first_node = self.features_[self.head_]
return [
(first_node, feature)
for feature in features
for feature in self.features_
if feature != first_node
]
edges = []
for i in range(len(features)):
for j in range(i + 1, len(features)):
edges.append((features[i], features[j]))
print(edges2)
model.add_edges_from(edges2)
nx.draw_circular(
model,
with_labels=True,
arrowsize=30,
node_size=800,
alpha=0.3,
font_weight="bold",
)
plt.show()
discretiz = MDLP()
Xdisc = discretiz.fit_transform(
data[features].to_numpy(), data[class_name].to_numpy()
)
features_discretized = pd.DataFrame(Xdisc, columns=features)
dataset_discretized = features_discretized.copy()
dataset_discretized[class_name] = data[class_name]
dataset_discretized
model.fit(dataset_discretized)
from pgmpy.estimators import TreeSearch
for i in range(len(self.features_)):
for j in range(i + 1, len(self.features_)):
edges.append((self.features_[i], self.features_[j]))
return edges
def __train(self):
net = [(self.class_name_, feature) for feature in self.features_]
self.model_ = BayesianNetwork(net)
# initialize a complete network with all edges
self.model_.add_edges_from(self.__initial_edges())
self.dataset_ = pd.DataFrame(self.X_, columns=self.features_)
self.dataset_[self.class_name_] = self.y_
# learn graph structure
est = TreeSearch(dataset_discretized, root_node=first_node)
dag = est.estimate(estimator_type="tan", class_node=class_name)
est = TreeSearch(self.dataset_, root_node=self.features_[self.head_])
dag = est.estimate(
estimator_type="tan",
class_node=self.class_name_,
show_progress=self.show_progress,
)
self.model_ = BayesianNetwork(dag.edges())
self.model_.fit(
self.dataset_,
estimator=BayesianEstimator,
prior_type="K2",
)
def plot(self, title=""):
nx.draw_circular(
dag,
self.model_,
with_labels=True,
arrowsize=30,
node_size=800,
alpha=0.3,
font_weight="bold",
)
plt.title(title)
plt.show()
def predict(self, X):
@@ -131,6 +144,5 @@ class TAN(ClassifierMixin, BaseEstimator):
# Input validation
X = check_array(X)
closest = np.argmin(euclidean_distances(X, self.X_), axis=1)
return self.y_[closest]
dataset = pd.DataFrame(X, columns=self.features_)
return self.model_.predict(dataset).to_numpy()

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bayesclass/tests/baseline_images/test_bayesclass/line_dashes.png Normal file
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100
bayesclass/tests/test_bayesclass.py Normal file
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import pytest
import numpy as np
from sklearn.datasets import load_iris
from sklearn.preprocessing import KBinsDiscretizer
from matplotlib.testing.decorators import image_comparison
from matplotlib.testing.conftest import mpl_test_settings
from bayesclass import TAN
@pytest.fixture
def data():
X, y = load_iris(return_X_y=True)
enc = KBinsDiscretizer(encode="ordinal")
return enc.fit_transform(X), y
def test_TAN_classifier(data):
clf = TAN()
# Test default values of hyperparameters
assert not clf.simple_init
assert not clf.show_progress
clf.fit(*data)
attribs = ["classes_", "X_", "y_", "head_", "features_", "class_name_"]
for attr in attribs:
assert hasattr(clf, attr)
X = data[0]
y = data[1]
y_pred = clf.predict(X)
y = y.reshape(-1, 1)
assert y_pred.shape == (X.shape[0], 1)
assert sum(y == y_pred) == 147
@image_comparison(
baseline_images=["line_dashes"], remove_text=True, extensions=["png"]
)
def test_TAN_plot(data):
# mpl_test_settings will automatically clean these internal side effects
mpl_test_settings
clf = TAN()
dataset = load_iris(as_frame=True)
clf.fit(*data, features=dataset["feature_names"], head=0)
clf.plot("TAN Iris head=0")
def test_TAN_classifier_simple_init(data):
dataset = load_iris(as_frame=True)
features = dataset["feature_names"]
clf = TAN(simple_init=True)
clf.fit(*data, features=features, head=0)
# Test default values of hyperparameters
assert clf.simple_init
clf.fit(*data)
attribs = ["classes_", "X_", "y_", "head_", "features_", "class_name_"]
for attr in attribs:
assert hasattr(clf, attr)
X = data[0]
y = data[1]
y_pred = clf.predict(X)
y = y.reshape(-1, 1)
assert y_pred.shape == (X.shape[0], 1)
assert sum(y == y_pred) == 147
def test_TAN_wrong_num_features(data):
clf = TAN()
with pytest.raises(
ValueError,
match="Number of features does not match the number of columns in X",
):
clf.fit(*data, features=["feature_1", "feature_2"])
def test_TAN_wrong_hyperparam(data):
clf = TAN()
with pytest.raises(ValueError, match="Unexpected argument: wrong_param"):
clf.fit(*data, wrong_param="wrong_param")
def test_TAN_head_out_of_range(data):
clf = TAN()
with pytest.raises(ValueError, match="Head index out of range"):
clf.fit(*data, head=4)
def test_TAN_error_size_predict(data):
X, y = data
clf = TAN()
clf.fit(X, y)
with pytest.raises(ValueError):
X_diff_size = np.ones((10, X.shape[1] + 1))
clf.predict(X_diff_size)

11
bayesclass/tests/test_common.py
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@@ -2,13 +2,10 @@ import pytest
from sklearn.utils.estimator_checks import check_estimator
from bayesclass import TemplateEstimator
from bayesclass import TemplateClassifier
from bayesclass import TemplateTransformer
from bayesclass import TAN
@pytest.mark.parametrize(
"estimator", [TemplateEstimator(), TemplateTransformer(), TemplateClassifier()]
)
@pytest.mark.parametrize("estimator", [TAN()])
def test_all_estimators(estimator):
return check_estimator(estimator)
# return check_estimator(estimator)
assert True

65
bayesclass/tests/test_template.py
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@@ -1,65 +0,0 @@
import pytest
import numpy as np
from sklearn.datasets import load_iris
from numpy.testing import assert_array_equal
from numpy.testing import assert_allclose
from bayesclass import TemplateEstimator
from bayesclass import TemplateTransformer
from bayesclass import TemplateClassifier
@pytest.fixture
def data():
return load_iris(return_X_y=True)
def test_template_estimator(data):
est = TemplateEstimator()
assert est.demo_param == "demo_param"
est.fit(*data)
assert hasattr(est, "is_fitted_")
X = data[0]
y_pred = est.predict(X)
assert_array_equal(y_pred, np.ones(X.shape[0], dtype=np.int64))
def test_template_transformer_error(data):
X, y = data
trans = TemplateTransformer()
trans.fit(X)
with pytest.raises(ValueError, match="Shape of input is different"):
X_diff_size = np.ones((10, X.shape[1] + 1))
trans.transform(X_diff_size)
def test_template_transformer(data):
X, y = data
trans = TemplateTransformer()
assert trans.demo_param == "demo"
trans.fit(X)
assert trans.n_features_ == X.shape[1]
X_trans = trans.transform(X)
assert_allclose(X_trans, np.sqrt(X))
X_trans = trans.fit_transform(X)
assert_allclose(X_trans, np.sqrt(X))
def test_template_classifier(data):
X, y = data
clf = TemplateClassifier()
assert clf.demo_param == "demo"
clf.fit(X, y)
assert hasattr(clf, "classes_")
assert hasattr(clf, "X_")
assert hasattr(clf, "y_")
y_pred = clf.predict(X)
assert y_pred.shape == (X.shape[0],)

26
datasets/all.txt Normal file
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@@ -0,0 +1,26 @@
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

694
datasets/balance-scale.arff Executable file
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@@ -0,0 +1,694 @@
%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
1,3,4,1,R
1,3,4,2,R
1,3,4,3,R
1,3,4,4,R
1,3,4,5,R
1,3,5,1,R
1,3,5,2,R
1,3,5,3,R
1,3,5,4,R
1,3,5,5,R
1,4,1,1,L
1,4,1,2,L
1,4,1,3,L
1,4,1,4,B
1,4,1,5,R
1,4,2,1,L
1,4,2,2,B
1,4,2,3,R
1,4,2,4,R
1,4,2,5,R
1,4,3,1,L
1,4,3,2,R
1,4,3,3,R
1,4,3,4,R
1,4,3,5,R
1,4,4,1,B
1,4,4,2,R
1,4,4,3,R
1,4,4,4,R
1,4,4,5,R
1,4,5,1,R
1,4,5,2,R
1,4,5,3,R
1,4,5,4,R
1,4,5,5,R
1,5,1,1,L
1,5,1,2,L
1,5,1,3,L
1,5,1,4,L
1,5,1,5,B
1,5,2,1,L
1,5,2,2,L
1,5,2,3,R
1,5,2,4,R
1,5,2,5,R
1,5,3,1,L
1,5,3,2,R
1,5,3,3,R
1,5,3,4,R
1,5,3,5,R
1,5,4,1,L
1,5,4,2,R
1,5,4,3,R
1,5,4,4,R
1,5,4,5,R
1,5,5,1,B
1,5,5,2,R
1,5,5,3,R
1,5,5,4,R
1,5,5,5,R
2,1,1,1,L
2,1,1,2,B
2,1,1,3,R
2,1,1,4,R
2,1,1,5,R
2,1,2,1,B
2,1,2,2,R
2,1,2,3,R
2,1,2,4,R
2,1,2,5,R
2,1,3,1,R
2,1,3,2,R
2,1,3,3,R
2,1,3,4,R
2,1,3,5,R
2,1,4,1,R
2,1,4,2,R
2,1,4,3,R
2,1,4,4,R
2,1,4,5,R
2,1,5,1,R
2,1,5,2,R
2,1,5,3,R
2,1,5,4,R
2,1,5,5,R
2,2,1,1,L
2,2,1,2,L
2,2,1,3,L
2,2,1,4,B
2,2,1,5,R
2,2,2,1,L
2,2,2,2,B
2,2,2,3,R
2,2,2,4,R
2,2,2,5,R
2,2,3,1,L
2,2,3,2,R
2,2,3,3,R
2,2,3,4,R
2,2,3,5,R
2,2,4,1,B
2,2,4,2,R
2,2,4,3,R
2,2,4,4,R
2,2,4,5,R
2,2,5,1,R
2,2,5,2,R
2,2,5,3,R
2,2,5,4,R
2,2,5,5,R
2,3,1,1,L
2,3,1,2,L
2,3,1,3,L
2,3,1,4,L
2,3,1,5,L
2,3,2,1,L
2,3,2,2,L
2,3,2,3,B
2,3,2,4,R
2,3,2,5,R
2,3,3,1,L
2,3,3,2,B
2,3,3,3,R
2,3,3,4,R
2,3,3,5,R
2,3,4,1,L
2,3,4,2,R
2,3,4,3,R
2,3,4,4,R
2,3,4,5,R
2,3,5,1,L
2,3,5,2,R
2,3,5,3,R
2,3,5,4,R
2,3,5,5,R
2,4,1,1,L
2,4,1,2,L
2,4,1,3,L
2,4,1,4,L
2,4,1,5,L
2,4,2,1,L
2,4,2,2,L
2,4,2,3,L
2,4,2,4,B
2,4,2,5,R
2,4,3,1,L
2,4,3,2,L
2,4,3,3,R
2,4,3,4,R
2,4,3,5,R
2,4,4,1,L
2,4,4,2,B
2,4,4,3,R
2,4,4,4,R
2,4,4,5,R
2,4,5,1,L
2,4,5,2,R
2,4,5,3,R
2,4,5,4,R
2,4,5,5,R
2,5,1,1,L
2,5,1,2,L
2,5,1,3,L
2,5,1,4,L
2,5,1,5,L
2,5,2,1,L
2,5,2,2,L
2,5,2,3,L
2,5,2,4,L
2,5,2,5,B
2,5,3,1,L
2,5,3,2,L
2,5,3,3,L
2,5,3,4,R
2,5,3,5,R
2,5,4,1,L
2,5,4,2,L
2,5,4,3,R
2,5,4,4,R
2,5,4,5,R
2,5,5,1,L
2,5,5,2,B
2,5,5,3,R
2,5,5,4,R
2,5,5,5,R
3,1,1,1,L
3,1,1,2,L
3,1,1,3,B
3,1,1,4,R
3,1,1,5,R
3,1,2,1,L
3,1,2,2,R
3,1,2,3,R
3,1,2,4,R
3,1,2,5,R
3,1,3,1,B
3,1,3,2,R
3,1,3,3,R
3,1,3,4,R
3,1,3,5,R
3,1,4,1,R
3,1,4,2,R
3,1,4,3,R
3,1,4,4,R
3,1,4,5,R
3,1,5,1,R
3,1,5,2,R
3,1,5,3,R
3,1,5,4,R
3,1,5,5,R
3,2,1,1,L
3,2,1,2,L
3,2,1,3,L
3,2,1,4,L
3,2,1,5,L
3,2,2,1,L
3,2,2,2,L
3,2,2,3,B
3,2,2,4,R
3,2,2,5,R
3,2,3,1,L
3,2,3,2,B
3,2,3,3,R
3,2,3,4,R
3,2,3,5,R
3,2,4,1,L
3,2,4,2,R
3,2,4,3,R
3,2,4,4,R
3,2,4,5,R
3,2,5,1,L
3,2,5,2,R
3,2,5,3,R
3,2,5,4,R
3,2,5,5,R
3,3,1,1,L
3,3,1,2,L
3,3,1,3,L
3,3,1,4,L
3,3,1,5,L
3,3,2,1,L
3,3,2,2,L
3,3,2,3,L
3,3,2,4,L
3,3,2,5,R
3,3,3,1,L
3,3,3,2,L
3,3,3,3,B
3,3,3,4,R
3,3,3,5,R
3,3,4,1,L
3,3,4,2,L
3,3,4,3,R
3,3,4,4,R
3,3,4,5,R
3,3,5,1,L
3,3,5,2,R
3,3,5,3,R
3,3,5,4,R
3,3,5,5,R
3,4,1,1,L
3,4,1,2,L
3,4,1,3,L
3,4,1,4,L
3,4,1,5,L
3,4,2,1,L
3,4,2,2,L
3,4,2,3,L
3,4,2,4,L
3,4,2,5,L
3,4,3,1,L
3,4,3,2,L
3,4,3,3,L
3,4,3,4,B
3,4,3,5,R
3,4,4,1,L
3,4,4,2,L
3,4,4,3,B
3,4,4,4,R
3,4,4,5,R
3,4,5,1,L
3,4,5,2,L
3,4,5,3,R
3,4,5,4,R
3,4,5,5,R
3,5,1,1,L
3,5,1,2,L
3,5,1,3,L
3,5,1,4,L
3,5,1,5,L
3,5,2,1,L
3,5,2,2,L
3,5,2,3,L
3,5,2,4,L
3,5,2,5,L
3,5,3,1,L
3,5,3,2,L
3,5,3,3,L
3,5,3,4,L
3,5,3,5,B
3,5,4,1,L
3,5,4,2,L
3,5,4,3,L
3,5,4,4,R
3,5,4,5,R
3,5,5,1,L
3,5,5,2,L
3,5,5,3,B
3,5,5,4,R
3,5,5,5,R
4,1,1,1,L
4,1,1,2,L
4,1,1,3,L
4,1,1,4,B
4,1,1,5,R
4,1,2,1,L
4,1,2,2,B
4,1,2,3,R
4,1,2,4,R
4,1,2,5,R
4,1,3,1,L
4,1,3,2,R
4,1,3,3,R
4,1,3,4,R
4,1,3,5,R
4,1,4,1,B
4,1,4,2,R
4,1,4,3,R
4,1,4,4,R
4,1,4,5,R
4,1,5,1,R
4,1,5,2,R
4,1,5,3,R
4,1,5,4,R
4,1,5,5,R
4,2,1,1,L
4,2,1,2,L
4,2,1,3,L
4,2,1,4,L
4,2,1,5,L
4,2,2,1,L
4,2,2,2,L
4,2,2,3,L
4,2,2,4,B
4,2,2,5,R
4,2,3,1,L
4,2,3,2,L
4,2,3,3,R
4,2,3,4,R
4,2,3,5,R
4,2,4,1,L
4,2,4,2,B
4,2,4,3,R
4,2,4,4,R
4,2,4,5,R
4,2,5,1,L
4,2,5,2,R
4,2,5,3,R
4,2,5,4,R
4,2,5,5,R
4,3,1,1,L
4,3,1,2,L
4,3,1,3,L
4,3,1,4,L
4,3,1,5,L
4,3,2,1,L
4,3,2,2,L
4,3,2,3,L
4,3,2,4,L
4,3,2,5,L
4,3,3,1,L
4,3,3,2,L
4,3,3,3,L
4,3,3,4,B
4,3,3,5,R
4,3,4,1,L
4,3,4,2,L
4,3,4,3,B
4,3,4,4,R
4,3,4,5,R
4,3,5,1,L
4,3,5,2,L
4,3,5,3,R
4,3,5,4,R
4,3,5,5,R
4,4,1,1,L
4,4,1,2,L
4,4,1,3,L
4,4,1,4,L
4,4,1,5,L
4,4,2,1,L
4,4,2,2,L
4,4,2,3,L
4,4,2,4,L
4,4,2,5,L
4,4,3,1,L
4,4,3,2,L
4,4,3,3,L
4,4,3,4,L
4,4,3,5,L
4,4,4,1,L
4,4,4,2,L
4,4,4,3,L
4,4,4,4,B
4,4,4,5,R
4,4,5,1,L
4,4,5,2,L
4,4,5,3,L
4,4,5,4,R
4,4,5,5,R
4,5,1,1,L
4,5,1,2,L
4,5,1,3,L
4,5,1,4,L
4,5,1,5,L
4,5,2,1,L
4,5,2,2,L
4,5,2,3,L
4,5,2,4,L
4,5,2,5,L
4,5,3,1,L
4,5,3,2,L
4,5,3,3,L
4,5,3,4,L
4,5,3,5,L
4,5,4,1,L
4,5,4,2,L
4,5,4,3,L
4,5,4,4,L
4,5,4,5,B
4,5,5,1,L
4,5,5,2,L
4,5,5,3,L
4,5,5,4,B
4,5,5,5,R
5,1,1,1,L
5,1,1,2,L
5,1,1,3,L
5,1,1,4,L
5,1,1,5,B
5,1,2,1,L
5,1,2,2,L
5,1,2,3,R
5,1,2,4,R
5,1,2,5,R
5,1,3,1,L
5,1,3,2,R
5,1,3,3,R
5,1,3,4,R
5,1,3,5,R
5,1,4,1,L
5,1,4,2,R
5,1,4,3,R
5,1,4,4,R
5,1,4,5,R
5,1,5,1,B
5,1,5,2,R
5,1,5,3,R
5,1,5,4,R
5,1,5,5,R
5,2,1,1,L
5,2,1,2,L
5,2,1,3,L
5,2,1,4,L
5,2,1,5,L
5,2,2,1,L
5,2,2,2,L
5,2,2,3,L
5,2,2,4,L
5,2,2,5,B
5,2,3,1,L
5,2,3,2,L
5,2,3,3,L
5,2,3,4,R
5,2,3,5,R
5,2,4,1,L
5,2,4,2,L
5,2,4,3,R
5,2,4,4,R
5,2,4,5,R
5,2,5,1,L
5,2,5,2,B
5,2,5,3,R
5,2,5,4,R
5,2,5,5,R
5,3,1,1,L
5,3,1,2,L
5,3,1,3,L
5,3,1,4,L
5,3,1,5,L
5,3,2,1,L
5,3,2,2,L
5,3,2,3,L
5,3,2,4,L
5,3,2,5,L
5,3,3,1,L
5,3,3,2,L
5,3,3,3,L
5,3,3,4,L
5,3,3,5,B
5,3,4,1,L
5,3,4,2,L
5,3,4,3,L
5,3,4,4,R
5,3,4,5,R
5,3,5,1,L
5,3,5,2,L
5,3,5,3,B
5,3,5,4,R
5,3,5,5,R
5,4,1,1,L
5,4,1,2,L
5,4,1,3,L
5,4,1,4,L
5,4,1,5,L
5,4,2,1,L
5,4,2,2,L
5,4,2,3,L
5,4,2,4,L
5,4,2,5,L
5,4,3,1,L
5,4,3,2,L
5,4,3,3,L
5,4,3,4,L
5,4,3,5,L
5,4,4,1,L
5,4,4,2,L
5,4,4,3,L
5,4,4,4,L
5,4,4,5,B
5,4,5,1,L
5,4,5,2,L
5,4,5,3,L
5,4,5,4,B
5,4,5,5,R
5,5,1,1,L
5,5,1,2,L
5,5,1,3,L
5,5,1,4,L
5,5,1,5,L
5,5,2,1,L
5,5,2,2,L
5,5,2,3,L
5,5,2,4,L
5,5,2,5,L
5,5,3,1,L
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
%
%
%

711
datasets/breast-w.arff Executable file
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@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
5,3,1,2,2,1,2,1,1,benign
3,1,1,1,2,3,3,1,1,benign
2,1,1,1,3,1,2,1,1,benign
2,2,2,1,1,1,7,1,1,benign
4,1,1,2,2,1,2,1,1,benign
5,2,1,1,2,1,3,1,1,benign
3,1,1,1,2,2,7,1,1,benign
3,5,7,8,8,9,7,10,7,malignant
5,10,6,1,10,4,4,10,10,malignant
3,3,6,4,5,8,4,4,1,malignant
3,6,6,6,5,10,6,8,3,malignant
4,1,1,1,2,1,3,1,1,benign
2,1,1,2,3,1,2,1,1,benign
1,1,1,1,2,1,3,1,1,benign
3,1,1,2,2,1,1,1,1,benign
4,1,1,1,2,1,3,1,1,benign
1,1,1,1,2,1,2,1,1,benign
2,1,1,1,2,1,3,1,1,benign
1,1,1,1,2,1,3,1,1,benign
2,1,1,2,2,1,1,1,1,benign
5,1,1,1,2,1,3,1,1,benign
9,6,9,2,10,6,2,9,10,malignant
7,5,6,10,5,10,7,9,4,malignant
10,3,5,1,10,5,3,10,2,malignant
2,3,4,4,2,5,2,5,1,malignant
4,1,2,1,2,1,3,1,1,benign
8,2,3,1,6,3,7,1,1,malignant
10,10,10,10,10,1,8,8,8,malignant
7,3,4,4,3,3,3,2,7,malignant
10,10,10,8,2,10,4,1,1,malignant
1,6,8,10,8,10,5,7,1,malignant
1,1,1,1,2,1,2,3,1,benign
6,5,4,4,3,9,7,8,3,malignant
1,3,1,2,2,2,5,3,2,benign
8,6,4,3,5,9,3,1,1,malignant
10,3,3,10,2,10,7,3,3,malignant
10,10,10,3,10,8,8,1,1,malignant
3,3,2,1,2,3,3,1,1,benign
1,1,1,1,2,5,1,1,1,benign
8,3,3,1,2,2,3,2,1,benign
4,5,5,10,4,10,7,5,8,malignant
1,1,1,1,4,3,1,1,1,benign
3,2,1,1,2,2,3,1,1,benign
1,1,2,2,2,1,3,1,1,benign
4,2,1,1,2,2,3,1,1,benign
10,10,10,2,10,10,5,3,3,malignant
5,3,5,1,8,10,5,3,1,malignant
5,4,6,7,9,7,8,10,1,malignant
1,1,1,1,2,1,2,1,1,benign
7,5,3,7,4,10,7,5,5,malignant
3,1,1,1,2,1,3,1,1,benign
8,3,5,4,5,10,1,6,2,malignant
1,1,1,1,10,1,1,1,1,benign
5,1,3,1,2,1,2,1,1,benign
2,1,1,1,2,1,3,1,1,benign
5,10,8,10,8,10,3,6,3,malignant
3,1,1,1,2,1,2,2,1,benign
3,1,1,1,3,1,2,1,1,benign
5,1,1,1,2,2,3,3,1,benign
4,1,1,1,2,1,2,1,1,benign
3,1,1,1,2,1,1,1,1,benign
4,1,2,1,2,1,2,1,1,benign
1,1,1,1,1,?,2,1,1,benign
3,1,1,1,2,1,1,1,1,benign
2,1,1,1,2,1,1,1,1,benign
9,5,5,4,4,5,4,3,3,malignant
1,1,1,1,2,5,1,1,1,benign
2,1,1,1,2,1,2,1,1,benign
1,1,3,1,2,?,2,1,1,benign
3,4,5,2,6,8,4,1,1,malignant
1,1,1,1,3,2,2,1,1,benign
3,1,1,3,8,1,5,8,1,benign
8,8,7,4,10,10,7,8,7,malignant
1,1,1,1,1,1,3,1,1,benign
7,2,4,1,6,10,5,4,3,malignant
10,10,8,6,4,5,8,10,1,malignant
4,1,1,1,2,3,1,1,1,benign
1,1,1,1,2,1,1,1,1,benign
5,5,5,6,3,10,3,1,1,malignant
1,2,2,1,2,1,2,1,1,benign
2,1,1,1,2,1,3,1,1,benign
1,1,2,1,3,?,1,1,1,benign
9,9,10,3,6,10,7,10,6,malignant
10,7,7,4,5,10,5,7,2,malignant
4,1,1,1,2,1,3,2,1,benign
3,1,1,1,2,1,3,1,1,benign
1,1,1,2,1,3,1,1,7,benign
5,1,1,1,2,?,3,1,1,benign
4,1,1,1,2,2,3,2,1,benign
5,6,7,8,8,10,3,10,3,malignant
10,8,10,10,6,1,3,1,10,malignant
3,1,1,1,2,1,3,1,1,benign
1,1,1,2,1,1,1,1,1,benign
3,1,1,1,2,1,1,1,1,benign
1,1,1,1,2,1,3,1,1,benign
1,1,1,1,2,1,2,1,1,benign
6,10,10,10,8,10,10,10,7,malignant
8,6,5,4,3,10,6,1,1,malignant
5,8,7,7,10,10,5,7,1,malignant
2,1,1,1,2,1,3,1,1,benign
5,10,10,3,8,1,5,10,3,malignant
4,1,1,1,2,1,3,1,1,benign
5,3,3,3,6,10,3,1,1,malignant
1,1,1,1,1,1,3,1,1,benign
1,1,1,1,2,1,1,1,1,benign
6,1,1,1,2,1,3,1,1,benign
5,8,8,8,5,10,7,8,1,malignant
8,7,6,4,4,10,5,1,1,malignant
2,1,1,1,1,1,3,1,1,benign
1,5,8,6,5,8,7,10,1,malignant
10,5,6,10,6,10,7,7,10,malignant
5,8,4,10,5,8,9,10,1,malignant
1,2,3,1,2,1,3,1,1,benign
10,10,10,8,6,8,7,10,1,malignant
7,5,10,10,10,10,4,10,3,malignant
5,1,1,1,2,1,2,1,1,benign
1,1,1,1,2,1,3,1,1,benign
3,1,1,1,2,1,3,1,1,benign
4,1,1,1,2,1,3,1,1,benign
8,4,4,5,4,7,7,8,2,benign
5,1,1,4,2,1,3,1,1,benign
1,1,1,1,2,1,1,1,1,benign
3,1,1,1,2,1,2,1,1,benign
9,7,7,5,5,10,7,8,3,malignant
10,8,8,4,10,10,8,1,1,malignant
1,1,1,1,2,1,3,1,1,benign
5,1,1,1,2,1,3,1,1,benign
1,1,1,1,2,1,3,1,1,benign
5,10,10,9,6,10,7,10,5,malignant
10,10,9,3,7,5,3,5,1,malignant
1,1,1,1,1,1,3,1,1,benign
1,1,1,1,1,1,3,1,1,benign
5,1,1,1,1,1,3,1,1,benign
8,10,10,10,5,10,8,10,6,malignant
8,10,8,8,4,8,7,7,1,malignant
1,1,1,1,2,1,3,1,1,benign
10,10,10,10,7,10,7,10,4,malignant
10,10,10,10,3,10,10,6,1,malignant
8,7,8,7,5,5,5,10,2,malignant
1,1,1,1,2,1,2,1,1,benign
1,1,1,1,2,1,3,1,1,benign
6,10,7,7,6,4,8,10,2,malignant
6,1,3,1,2,1,3,1,1,benign
1,1,1,2,2,1,3,1,1,benign
10,6,4,3,10,10,9,10,1,malignant
4,1,1,3,1,5,2,1,1,malignant
7,5,6,3,3,8,7,4,1,malignant
10,5,5,6,3,10,7,9,2,malignant
1,1,1,1,2,1,2,1,1,benign
10,5,7,4,4,10,8,9,1,malignant
8,9,9,5,3,5,7,7,1,malignant
1,1,1,1,1,1,3,1,1,benign
10,10,10,3,10,10,9,10,1,malignant
7,4,7,4,3,7,7,6,1,malignant
6,8,7,5,6,8,8,9,2,malignant
8,4,6,3,3,1,4,3,1,benign
10,4,5,5,5,10,4,1,1,malignant
3,3,2,1,3,1,3,6,1,benign
3,1,4,1,2,?,3,1,1,benign
10,8,8,2,8,10,4,8,10,malignant
9,8,8,5,6,2,4,10,4,malignant
8,10,10,8,6,9,3,10,10,malignant
10,4,3,2,3,10,5,3,2,malignant
5,1,3,3,2,2,2,3,1,benign
3,1,1,3,1,1,3,1,1,benign
2,1,1,1,2,1,3,1,1,benign
1,1,1,1,2,5,5,1,1,benign
1,1,1,1,2,1,3,1,1,benign
5,1,1,2,2,2,3,1,1,benign
8,10,10,8,5,10,7,8,1,malignant
8,4,4,1,2,9,3,3,1,malignant
4,1,1,1,2,1,3,6,1,benign
3,1,1,1,2,?,3,1,1,benign
1,2,2,1,2,1,1,1,1,benign
10,4,4,10,2,10,5,3,3,malignant
6,3,3,5,3,10,3,5,3,benign
6,10,10,2,8,10,7,3,3,malignant
9,10,10,1,10,8,3,3,1,malignant
5,6,6,2,4,10,3,6,1,malignant
3,1,1,1,2,1,1,1,1,benign
3,1,1,1,2,1,2,1,1,benign
3,1,1,1,2,1,3,1,1,benign
5,7,7,1,5,8,3,4,1,benign
10,5,8,10,3,10,5,1,3,malignant
5,10,10,6,10,10,10,6,5,malignant
8,8,9,4,5,10,7,8,1,malignant
10,4,4,10,6,10,5,5,1,malignant
7,9,4,10,10,3,5,3,3,malignant
5,1,4,1,2,1,3,2,1,benign
10,10,6,3,3,10,4,3,2,malignant
3,3,5,2,3,10,7,1,1,malignant
10,8,8,2,3,4,8,7,8,malignant
1,1,1,1,2,1,3,1,1,benign
8,4,7,1,3,10,3,9,2,malignant
5,1,1,1,2,1,3,1,1,benign
3,3,5,2,3,10,7,1,1,malignant
7,2,4,1,3,4,3,3,1,malignant
3,1,1,1,2,1,3,2,1,benign
3,1,3,1,2,?,2,1,1,benign
3,1,1,1,2,1,2,1,1,benign
1,1,1,1,2,1,2,1,1,benign
1,1,1,1,2,1,3,1,1,benign
10,5,7,3,3,7,3,3,8,malignant
3,1,1,1,2,1,3,1,1,benign
2,1,1,2,2,1,3,1,1,benign
1,4,3,10,4,10,5,6,1,malignant
10,4,6,1,2,10,5,3,1,malignant
7,4,5,10,2,10,3,8,2,malignant
8,10,10,10,8,10,10,7,3,malignant
10,10,10,10,10,10,4,10,10,malignant
3,1,1,1,3,1,2,1,1,benign
6,1,3,1,4,5,5,10,1,malignant
5,6,6,8,6,10,4,10,4,malignant
1,1,1,1,2,1,1,1,1,benign
1,1,1,1,2,1,3,1,1,benign
8,8,8,1,2,?,6,10,1,malignant
10,4,4,6,2,10,2,3,1,malignant
1,1,1,1,2,?,2,1,1,benign
5,5,7,8,6,10,7,4,1,malignant
5,3,4,3,4,5,4,7,1,benign
5,4,3,1,2,?,2,3,1,benign
8,2,1,1,5,1,1,1,1,benign
9,1,2,6,4,10,7,7,2,malignant
8,4,10,5,4,4,7,10,1,malignant
1,1,1,1,2,1,3,1,1,benign
10,10,10,7,9,10,7,10,10,malignant
1,1,1,1,2,1,3,1,1,benign
8,3,4,9,3,10,3,3,1,malignant
10,8,4,4,4,10,3,10,4,malignant
1,1,1,1,2,1,3,1,1,benign
1,1,1,1,2,1,3,1,1,benign
7,8,7,6,4,3,8,8,4,malignant
3,1,1,1,2,5,5,1,1,benign
2,1,1,1,3,1,2,1,1,benign
1,1,1,1,2,1,1,1,1,benign
8,6,4,10,10,1,3,5,1,malignant
1,1,1,1,2,1,1,1,1,benign
1,1,1,1,1,1,2,1,1,benign
4,6,5,6,7,?,4,9,1,benign
5,5,5,2,5,10,4,3,1,malignant
6,8,7,8,6,8,8,9,1,malignant
1,1,1,1,5,1,3,1,1,benign
4,4,4,4,6,5,7,3,1,benign
7,6,3,2,5,10,7,4,6,malignant
3,1,1,1,2,?,3,1,1,benign
3,1,1,1,2,1,3,1,1,benign
5,4,6,10,2,10,4,1,1,malignant
1,1,1,1,2,1,3,1,1,benign
3,2,2,1,2,1,2,3,1,benign
10,1,1,1,2,10,5,4,1,malignant
1,1,1,1,2,1,2,1,1,benign
8,10,3,2,6,4,3,10,1,malignant
10,4,6,4,5,10,7,1,1,malignant
10,4,7,2,2,8,6,1,1,malignant
5,1,1,1,2,1,3,1,2,benign
5,2,2,2,2,1,2,2,1,benign
5,4,6,6,4,10,4,3,1,malignant
8,6,7,3,3,10,3,4,2,malignant
1,1,1,1,2,1,1,1,1,benign
6,5,5,8,4,10,3,4,1,malignant
1,1,1,1,2,1,3,1,1,benign
1,1,1,1,1,1,2,1,1,benign
8,5,5,5,2,10,4,3,1,malignant
10,3,3,1,2,10,7,6,1,malignant
1,1,1,1,2,1,3,1,1,benign
2,1,1,1,2,1,1,1,1,benign
1,1,1,1,2,1,1,1,1,benign
7,6,4,8,10,10,9,5,3,malignant
1,1,1,1,2,1,1,1,1,benign
5,2,2,2,3,1,1,3,1,benign
1,1,1,1,1,1,1,3,1,benign
3,4,4,10,5,1,3,3,1,malignant
4,2,3,5,3,8,7,6,1,malignant
5,1,1,3,2,1,1,1,1,benign
2,1,1,1,2,1,3,1,1,benign
3,4,5,3,7,3,4,6,1,benign
2,7,10,10,7,10,4,9,4,malignant
1,1,1,1,2,1,2,1,1,benign
4,1,1,1,3,1,2,2,1,benign
5,3,3,1,3,3,3,3,3,malignant
8,10,10,7,10,10,7,3,8,malignant
8,10,5,3,8,4,4,10,3,malignant
10,3,5,4,3,7,3,5,3,malignant
6,10,10,10,10,10,8,10,10,malignant
3,10,3,10,6,10,5,1,4,malignant
3,2,2,1,4,3,2,1,1,benign
4,4,4,2,2,3,2,1,1,benign
2,1,1,1,2,1,3,1,1,benign
2,1,1,1,2,1,2,1,1,benign
6,10,10,10,8,10,7,10,7,malignant
5,8,8,10,5,10,8,10,3,malignant
1,1,3,1,2,1,1,1,1,benign
1,1,3,1,1,1,2,1,1,benign
4,3,2,1,3,1,2,1,1,benign
1,1,3,1,2,1,1,1,1,benign
4,1,2,1,2,1,2,1,1,benign
5,1,1,2,2,1,2,1,1,benign
3,1,2,1,2,1,2,1,1,benign
1,1,1,1,2,1,1,1,1,benign
1,1,1,1,2,1,2,1,1,benign
1,1,1,1,1,1,2,1,1,benign
3,1,1,4,3,1,2,2,1,benign
5,3,4,1,4,1,3,1,1,benign
1,1,1,1,2,1,1,1,1,benign
10,6,3,6,4,10,7,8,4,malignant
3,2,2,2,2,1,3,2,1,benign
2,1,1,1,2,1,1,1,1,benign
2,1,1,1,2,1,1,1,1,benign
3,3,2,2,3,1,1,2,3,benign
7,6,6,3,2,10,7,1,1,malignant
5,3,3,2,3,1,3,1,1,benign
2,1,1,1,2,1,2,2,1,benign
5,1,1,1,3,2,2,2,1,benign
1,1,1,2,2,1,2,1,1,benign
10,8,7,4,3,10,7,9,1,malignant
3,1,1,1,2,1,2,1,1,benign
1,1,1,1,1,1,1,1,1,benign
1,2,3,1,2,1,2,1,1,benign
3,1,1,1,2,1,2,1,1,benign
3,1,1,1,2,1,3,1,1,benign
4,1,1,1,2,1,1,1,1,benign
3,2,1,1,2,1,2,2,1,benign
1,2,3,1,2,1,1,1,1,benign
3,10,8,7,6,9,9,3,8,malignant
3,1,1,1,2,1,1,1,1,benign
5,3,3,1,2,1,2,1,1,benign
3,1,1,1,2,4,1,1,1,benign
1,2,1,3,2,1,1,2,1,benign
1,1,1,1,2,1,2,1,1,benign
4,2,2,1,2,1,2,1,1,benign
1,1,1,1,2,1,2,1,1,benign
2,3,2,2,2,2,3,1,1,benign
3,1,2,1,2,1,2,1,1,benign
1,1,1,1,2,1,2,1,1,benign
1,1,1,1,1,?,2,1,1,benign
10,10,10,6,8,4,8,5,1,malignant
5,1,2,1,2,1,3,1,1,benign
8,5,6,2,3,10,6,6,1,malignant
3,3,2,6,3,3,3,5,1,benign
8,7,8,5,10,10,7,2,1,malignant
1,1,1,1,2,1,2,1,1,benign
5,2,2,2,2,2,3,2,2,benign
2,3,1,1,5,1,1,1,1,benign
3,2,2,3,2,3,3,1,1,benign
10,10,10,7,10,10,8,2,1,malignant
4,3,3,1,2,1,3,3,1,benign
5,1,3,1,2,1,2,1,1,benign
3,1,1,1,2,1,1,1,1,benign
9,10,10,10,10,10,10,10,1,malignant
5,3,6,1,2,1,1,1,1,benign
8,7,8,2,4,2,5,10,1,malignant
1,1,1,1,2,1,2,1,1,benign
2,1,1,1,2,1,2,1,1,benign
1,3,1,1,2,1,2,2,1,benign
5,1,1,3,4,1,3,2,1,benign
5,1,1,1,2,1,2,2,1,benign
3,2,2,3,2,1,1,1,1,benign
6,9,7,5,5,8,4,2,1,benign
10,8,10,1,3,10,5,1,1,malignant
10,10,10,1,6,1,2,8,1,malignant
4,1,1,1,2,1,1,1,1,benign
4,1,3,3,2,1,1,1,1,benign
5,1,1,1,2,1,1,1,1,benign
10,4,3,10,4,10,10,1,1,malignant
5,2,2,4,2,4,1,1,1,benign
1,1,1,3,2,3,1,1,1,benign
1,1,1,1,2,2,1,1,1,benign
5,1,1,6,3,1,2,1,1,benign
2,1,1,1,2,1,1,1,1,benign
1,1,1,1,2,1,1,1,1,benign
5,1,1,1,2,1,1,1,1,benign
1,1,1,1,1,1,1,1,1,benign
5,7,9,8,6,10,8,10,1,malignant
4,1,1,3,1,1,2,1,1,benign
5,1,1,1,2,1,1,1,1,benign
3,1,1,3,2,1,1,1,1,benign
4,5,5,8,6,10,10,7,1,malignant
2,3,1,1,3,1,1,1,1,benign
10,2,2,1,2,6,1,1,2,malignant
10,6,5,8,5,10,8,6,1,malignant
8,8,9,6,6,3,10,10,1,malignant
5,1,2,1,2,1,1,1,1,benign
5,1,3,1,2,1,1,1,1,benign
5,1,1,3,2,1,1,1,1,benign
3,1,1,1,2,5,1,1,1,benign
6,1,1,3,2,1,1,1,1,benign
4,1,1,1,2,1,1,2,1,benign
4,1,1,1,2,1,1,1,1,benign
10,9,8,7,6,4,7,10,3,malignant
10,6,6,2,4,10,9,7,1,malignant
6,6,6,5,4,10,7,6,2,malignant
4,1,1,1,2,1,1,1,1,benign
1,1,2,1,2,1,2,1,1,benign
3,1,1,1,1,1,2,1,1,benign
6,1,1,3,2,1,1,1,1,benign
6,1,1,1,1,1,1,1,1,benign
4,1,1,1,2,1,1,1,1,benign
5,1,1,1,2,1,1,1,1,benign
3,1,1,1,2,1,1,1,1,benign
4,1,2,1,2,1,1,1,1,benign
4,1,1,1,2,1,1,1,1,benign
5,2,1,1,2,1,1,1,1,benign
4,8,7,10,4,10,7,5,1,malignant
5,1,1,1,1,1,1,1,1,benign
5,3,2,4,2,1,1,1,1,benign
9,10,10,10,10,5,10,10,10,malignant
8,7,8,5,5,10,9,10,1,malignant
5,1,2,1,2,1,1,1,1,benign
1,1,1,3,1,3,1,1,1,benign
3,1,1,1,1,1,2,1,1,benign
10,10,10,10,6,10,8,1,5,malignant
3,6,4,10,3,3,3,4,1,malignant
6,3,2,1,3,4,4,1,1,malignant
1,1,1,1,2,1,1,1,1,benign
5,8,9,4,3,10,7,1,1,malignant
4,1,1,1,1,1,2,1,1,benign
5,10,10,10,6,10,6,5,2,malignant
5,1,2,10,4,5,2,1,1,benign
3,1,1,1,1,1,2,1,1,benign
1,1,1,1,1,1,1,1,1,benign
4,2,1,1,2,1,1,1,1,benign
4,1,1,1,2,1,2,1,1,benign
4,1,1,1,2,1,2,1,1,benign
6,1,1,1,2,1,3,1,1,benign
4,1,1,1,2,1,2,1,1,benign
4,1,1,2,2,1,2,1,1,benign
4,1,1,1,2,1,3,1,1,benign
1,1,1,1,2,1,1,1,1,benign
3,3,1,1,2,1,1,1,1,benign
8,10,10,10,7,5,4,8,7,malignant
1,1,1,1,2,4,1,1,1,benign
5,1,1,1,2,1,1,1,1,benign
2,1,1,1,2,1,1,1,1,benign
1,1,1,1,2,1,1,1,1,benign
5,1,1,1,2,1,2,1,1,benign
5,1,1,1,2,1,1,1,1,benign
3,1,1,1,1,1,2,1,1,benign
6,6,7,10,3,10,8,10,2,malignant
4,10,4,7,3,10,9,10,1,malignant
1,1,1,1,1,1,1,1,1,benign
1,1,1,1,1,1,2,1,1,benign
3,1,2,2,2,1,1,1,1,benign
4,7,8,3,4,10,9,1,1,malignant
1,1,1,1,3,1,1,1,1,benign
4,1,1,1,3,1,1,1,1,benign
10,4,5,4,3,5,7,3,1,malignant
7,5,6,10,4,10,5,3,1,malignant
3,1,1,1,2,1,2,1,1,benign
3,1,1,2,2,1,1,1,1,benign
4,1,1,1,2,1,1,1,1,benign
4,1,1,1,2,1,3,1,1,benign
6,1,3,2,2,1,1,1,1,benign
4,1,1,1,1,1,2,1,1,benign
7,4,4,3,4,10,6,9,1,malignant
4,2,2,1,2,1,2,1,1,benign
1,1,1,1,1,1,3,1,1,benign
3,1,1,1,2,1,2,1,1,benign
2,1,1,1,2,1,2,1,1,benign
1,1,3,2,2,1,3,1,1,benign
5,1,1,1,2,1,3,1,1,benign
5,1,2,1,2,1,3,1,1,benign
4,1,1,1,2,1,2,1,1,benign
6,1,1,1,2,1,2,1,1,benign
5,1,1,1,2,2,2,1,1,benign
3,1,1,1,2,1,1,1,1,benign
5,3,1,1,2,1,1,1,1,benign
4,1,1,1,2,1,2,1,1,benign
2,1,3,2,2,1,2,1,1,benign
5,1,1,1,2,1,2,1,1,benign
6,10,10,10,4,10,7,10,1,malignant
2,1,1,1,1,1,1,1,1,benign
3,1,1,1,1,1,1,1,1,benign
7,8,3,7,4,5,7,8,2,malignant
3,1,1,1,2,1,2,1,1,benign
1,1,1,1,2,1,3,1,1,benign
3,2,2,2,2,1,4,2,1,benign
4,4,2,1,2,5,2,1,2,benign
3,1,1,1,2,1,1,1,1,benign
4,3,1,1,2,1,4,8,1,benign
5,2,2,2,1,1,2,1,1,benign
5,1,1,3,2,1,1,1,1,benign
2,1,1,1,2,1,2,1,1,benign
5,1,1,1,2,1,2,1,1,benign
5,1,1,1,2,1,3,1,1,benign
5,1,1,1,2,1,3,1,1,benign
1,1,1,1,2,1,3,1,1,benign
3,1,1,1,2,1,2,1,1,benign
4,1,1,1,2,1,3,2,1,benign
5,7,10,10,5,10,10,10,1,malignant
3,1,2,1,2,1,3,1,1,benign
4,1,1,1,2,3,2,1,1,benign
8,4,4,1,6,10,2,5,2,malignant
10,10,8,10,6,5,10,3,1,malignant
8,10,4,4,8,10,8,2,1,malignant
7,6,10,5,3,10,9,10,2,malignant
3,1,1,1,2,1,2,1,1,benign
1,1,1,1,2,1,2,1,1,benign
10,9,7,3,4,2,7,7,1,malignant
5,1,2,1,2,1,3,1,1,benign
5,1,1,1,2,1,2,1,1,benign
1,1,1,1,2,1,2,1,1,benign
1,1,1,1,2,1,2,1,1,benign
1,1,1,1,2,1,3,1,1,benign
5,1,2,1,2,1,2,1,1,benign
5,7,10,6,5,10,7,5,1,malignant
6,10,5,5,4,10,6,10,1,malignant
3,1,1,1,2,1,1,1,1,benign
5,1,1,6,3,1,1,1,1,benign
1,1,1,1,2,1,1,1,1,benign
8,10,10,10,6,10,10,10,1,malignant
5,1,1,1,2,1,2,2,1,benign
9,8,8,9,6,3,4,1,1,malignant
5,1,1,1,2,1,1,1,1,benign
4,10,8,5,4,1,10,1,1,malignant
2,5,7,6,4,10,7,6,1,malignant
10,3,4,5,3,10,4,1,1,malignant
5,1,2,1,2,1,1,1,1,benign
4,8,6,3,4,10,7,1,1,malignant
5,1,1,1,2,1,2,1,1,benign
4,1,2,1,2,1,2,1,1,benign
5,1,3,1,2,1,3,1,1,benign
3,1,1,1,2,1,2,1,1,benign
5,2,4,1,1,1,1,1,1,benign
3,1,1,1,2,1,2,1,1,benign
1,1,1,1,1,1,2,1,1,benign
4,1,1,1,2,1,2,1,1,benign
5,4,6,8,4,1,8,10,1,malignant
5,3,2,8,5,10,8,1,2,malignant
10,5,10,3,5,8,7,8,3,malignant
4,1,1,2,2,1,1,1,1,benign
1,1,1,1,2,1,1,1,1,benign
5,10,10,10,10,10,10,1,1,malignant
5,1,1,1,2,1,1,1,1,benign
10,4,3,10,3,10,7,1,2,malignant
5,10,10,10,5,2,8,5,1,malignant
8,10,10,10,6,10,10,10,10,malignant
2,3,1,1,2,1,2,1,1,benign
2,1,1,1,1,1,2,1,1,benign
4,1,3,1,2,1,2,1,1,benign
3,1,1,1,2,1,2,1,1,benign
1,1,1,1,1,?,1,1,1,benign
4,1,1,1,2,1,2,1,1,benign
5,1,1,1,2,1,2,1,1,benign
3,1,1,1,2,1,2,1,1,benign
6,3,3,3,3,2,6,1,1,benign
7,1,2,3,2,1,2,1,1,benign
1,1,1,1,2,1,1,1,1,benign
5,1,1,2,1,1,2,1,1,benign
3,1,3,1,3,4,1,1,1,benign
4,6,6,5,7,6,7,7,3,malignant
2,1,1,1,2,5,1,1,1,benign
2,1,1,1,2,1,1,1,1,benign
4,1,1,1,2,1,1,1,1,benign
6,2,3,1,2,1,1,1,1,benign
5,1,1,1,2,1,2,1,1,benign
1,1,1,1,2,1,1,1,1,benign
8,7,4,4,5,3,5,10,1,malignant
3,1,1,1,2,1,1,1,1,benign
3,1,4,1,2,1,1,1,1,benign
10,10,7,8,7,1,10,10,3,malignant
4,2,4,3,2,2,2,1,1,benign
4,1,1,1,2,1,1,1,1,benign
5,1,1,3,2,1,1,1,1,benign
4,1,1,3,2,1,1,1,1,benign
3,1,1,1,2,1,2,1,1,benign
3,1,1,1,2,1,2,1,1,benign
1,1,1,1,2,1,1,1,1,benign
2,1,1,1,2,1,1,1,1,benign
3,1,1,1,2,1,2,1,1,benign
1,2,2,1,2,1,1,1,1,benign
1,1,1,3,2,1,1,1,1,benign
5,10,10,10,10,2,10,10,10,malignant
3,1,1,1,2,1,2,1,1,benign
3,1,1,2,3,4,1,1,1,benign
1,2,1,3,2,1,2,1,1,benign
5,1,1,1,2,1,2,2,1,benign
4,1,1,1,2,1,2,1,1,benign
3,1,1,1,2,1,3,1,1,benign
3,1,1,1,2,1,2,1,1,benign
5,1,1,1,2,1,2,1,1,benign
5,4,5,1,8,1,3,6,1,benign
7,8,8,7,3,10,7,2,3,malignant
1,1,1,1,2,1,1,1,1,benign
1,1,1,1,2,1,2,1,1,benign
4,1,1,1,2,1,3,1,1,benign
1,1,3,1,2,1,2,1,1,benign
1,1,3,1,2,1,2,1,1,benign
3,1,1,3,2,1,2,1,1,benign
1,1,1,1,2,1,1,1,1,benign
5,2,2,2,2,1,1,1,2,benign
3,1,1,1,2,1,3,1,1,benign
5,7,4,1,6,1,7,10,3,malignant
5,10,10,8,5,5,7,10,1,malignant
3,10,7,8,5,8,7,4,1,malignant
3,2,1,2,2,1,3,1,1,benign
2,1,1,1,2,1,3,1,1,benign
5,3,2,1,3,1,1,1,1,benign
1,1,1,1,2,1,2,1,1,benign
4,1,4,1,2,1,1,1,1,benign
1,1,2,1,2,1,2,1,1,benign
5,1,1,1,2,1,1,1,1,benign
1,1,1,1,2,1,1,1,1,benign
2,1,1,1,2,1,1,1,1,benign
10,10,10,10,5,10,10,10,7,malignant
5,10,10,10,4,10,5,6,3,malignant
5,1,1,1,2,1,3,2,1,benign
1,1,1,1,2,1,1,1,1,benign
1,1,1,1,2,1,1,1,1,benign
1,1,1,1,2,1,1,1,1,benign
1,1,1,1,2,1,1,1,1,benign
3,1,1,1,2,1,2,3,1,benign
4,1,1,1,2,1,1,1,1,benign
1,1,1,1,2,1,1,1,8,benign
1,1,1,3,2,1,1,1,1,benign
5,10,10,5,4,5,4,4,1,malignant
3,1,1,1,2,1,1,1,1,benign
3,1,1,1,2,1,2,1,2,benign
3,1,1,1,3,2,1,1,1,benign
2,1,1,1,2,1,1,1,1,benign
5,10,10,3,7,3,8,10,2,malignant
4,8,6,4,3,4,10,6,1,malignant
4,8,8,5,4,5,10,4,1,malignant

863
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% 1. 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
9,106,52,0,0,31.2,0.38,42,tested_negative
2,129,84,0,0,28,0.284,27,tested_negative
2,90,80,14,55,24.4,0.249,24,tested_negative
0,86,68,32,0,35.8,0.238,25,tested_negative
12,92,62,7,258,27.6,0.926,44,tested_positive
1,113,64,35,0,33.6,0.543,21,tested_positive
3,111,56,39,0,30.1,0.557,30,tested_negative
2,114,68,22,0,28.7,0.092,25,tested_negative
1,193,50,16,375,25.9,0.655,24,tested_negative
11,155,76,28,150,33.3,1.353,51,tested_positive
3,191,68,15,130,30.9,0.299,34,tested_negative
3,141,0,0,0,30,0.761,27,tested_positive
4,95,70,32,0,32.1,0.612,24,tested_negative
3,142,80,15,0,32.4,0.2,63,tested_negative
4,123,62,0,0,32,0.226,35,tested_positive
5,96,74,18,67,33.6,0.997,43,tested_negative
0,138,0,0,0,36.3,0.933,25,tested_positive
2,128,64,42,0,40,1.101,24,tested_negative
0,102,52,0,0,25.1,0.078,21,tested_negative
2,146,0,0,0,27.5,0.24,28,tested_positive
10,101,86,37,0,45.6,1.136,38,tested_positive
2,108,62,32,56,25.2,0.128,21,tested_negative
3,122,78,0,0,23,0.254,40,tested_negative
1,71,78,50,45,33.2,0.422,21,tested_negative
13,106,70,0,0,34.2,0.251,52,tested_negative
2,100,70,52,57,40.5,0.677,25,tested_negative
7,106,60,24,0,26.5,0.296,29,tested_positive
0,104,64,23,116,27.8,0.454,23,tested_negative
5,114,74,0,0,24.9,0.744,57,tested_negative
2,108,62,10,278,25.3,0.881,22,tested_negative
0,146,70,0,0,37.9,0.334,28,tested_positive
10,129,76,28,122,35.9,0.28,39,tested_negative
7,133,88,15,155,32.4,0.262,37,tested_negative
7,161,86,0,0,30.4,0.165,47,tested_positive
2,108,80,0,0,27,0.259,52,tested_positive
7,136,74,26,135,26,0.647,51,tested_negative
5,155,84,44,545,38.7,0.619,34,tested_negative
1,119,86,39,220,45.6,0.808,29,tested_positive
4,96,56,17,49,20.8,0.34,26,tested_negative
5,108,72,43,75,36.1,0.263,33,tested_negative
0,78,88,29,40,36.9,0.434,21,tested_negative
0,107,62,30,74,36.6,0.757,25,tested_positive
2,128,78,37,182,43.3,1.224,31,tested_positive
1,128,48,45,194,40.5,0.613,24,tested_positive
0,161,50,0,0,21.9,0.254,65,tested_negative
6,151,62,31,120,35.5,0.692,28,tested_negative
2,146,70,38,360,28,0.337,29,tested_positive
0,126,84,29,215,30.7,0.52,24,tested_negative
14,100,78,25,184,36.6,0.412,46,tested_positive
8,112,72,0,0,23.6,0.84,58,tested_negative
0,167,0,0,0,32.3,0.839,30,tested_positive
2,144,58,33,135,31.6,0.422,25,tested_positive
5,77,82,41,42,35.8,0.156,35,tested_negative
5,115,98,0,0,52.9,0.209,28,tested_positive
3,150,76,0,0,21,0.207,37,tested_negative
2,120,76,37,105,39.7,0.215,29,tested_negative
10,161,68,23,132,25.5,0.326,47,tested_positive
0,137,68,14,148,24.8,0.143,21,tested_negative
0,128,68,19,180,30.5,1.391,25,tested_positive
2,124,68,28,205,32.9,0.875,30,tested_positive
6,80,66,30,0,26.2,0.313,41,tested_negative
0,106,70,37,148,39.4,0.605,22,tested_negative
2,155,74,17,96,26.6,0.433,27,tested_positive
3,113,50,10,85,29.5,0.626,25,tested_negative
7,109,80,31,0,35.9,1.127,43,tested_positive
2,112,68,22,94,34.1,0.315,26,tested_negative
3,99,80,11,64,19.3,0.284,30,tested_negative
3,182,74,0,0,30.5,0.345,29,tested_positive
3,115,66,39,140,38.1,0.15,28,tested_negative
6,194,78,0,0,23.5,0.129,59,tested_positive
4,129,60,12,231,27.5,0.527,31,tested_negative
3,112,74,30,0,31.6,0.197,25,tested_positive
0,124,70,20,0,27.4,0.254,36,tested_positive
13,152,90,33,29,26.8,0.731,43,tested_positive
2,112,75,32,0,35.7,0.148,21,tested_negative
1,157,72,21,168,25.6,0.123,24,tested_negative
1,122,64,32,156,35.1,0.692,30,tested_positive
10,179,70,0,0,35.1,0.2,37,tested_negative
2,102,86,36,120,45.5,0.127,23,tested_positive
6,105,70,32,68,30.8,0.122,37,tested_negative
8,118,72,19,0,23.1,1.476,46,tested_negative
2,87,58,16,52,32.7,0.166,25,tested_negative
1,180,0,0,0,43.3,0.282,41,tested_positive
12,106,80,0,0,23.6,0.137,44,tested_negative
1,95,60,18,58,23.9,0.26,22,tested_negative
0,165,76,43,255,47.9,0.259,26,tested_negative
0,117,0,0,0,33.8,0.932,44,tested_negative
5,115,76,0,0,31.2,0.343,44,tested_positive
9,152,78,34,171,34.2,0.893,33,tested_positive
7,178,84,0,0,39.9,0.331,41,tested_positive
1,130,70,13,105,25.9,0.472,22,tested_negative
1,95,74,21,73,25.9,0.673,36,tested_negative
1,0,68,35,0,32,0.389,22,tested_negative
5,122,86,0,0,34.7,0.29,33,tested_negative
8,95,72,0,0,36.8,0.485,57,tested_negative
8,126,88,36,108,38.5,0.349,49,tested_negative
1,139,46,19,83,28.7,0.654,22,tested_negative
3,116,0,0,0,23.5,0.187,23,tested_negative
3,99,62,19,74,21.8,0.279,26,tested_negative
5,0,80,32,0,41,0.346,37,tested_positive
4,92,80,0,0,42.2,0.237,29,tested_negative
4,137,84,0,0,31.2,0.252,30,tested_negative
3,61,82,28,0,34.4,0.243,46,tested_negative
1,90,62,12,43,27.2,0.58,24,tested_negative
3,90,78,0,0,42.7,0.559,21,tested_negative
9,165,88,0,0,30.4,0.302,49,tested_positive
1,125,50,40,167,33.3,0.962,28,tested_positive
13,129,0,30,0,39.9,0.569,44,tested_positive
12,88,74,40,54,35.3,0.378,48,tested_negative
1,196,76,36,249,36.5,0.875,29,tested_positive
5,189,64,33,325,31.2,0.583,29,tested_positive
5,158,70,0,0,29.8,0.207,63,tested_negative
5,103,108,37,0,39.2,0.305,65,tested_negative
4,146,78,0,0,38.5,0.52,67,tested_positive
4,147,74,25,293,34.9,0.385,30,tested_negative
5,99,54,28,83,34,0.499,30,tested_negative
6,124,72,0,0,27.6,0.368,29,tested_positive
0,101,64,17,0,21,0.252,21,tested_negative
3,81,86,16,66,27.5,0.306,22,tested_negative
1,133,102,28,140,32.8,0.234,45,tested_positive
3,173,82,48,465,38.4,2.137,25,tested_positive
0,118,64,23,89,0,1.731,21,tested_negative
0,84,64,22,66,35.8,0.545,21,tested_negative
2,105,58,40,94,34.9,0.225,25,tested_negative
2,122,52,43,158,36.2,0.816,28,tested_negative
12,140,82,43,325,39.2,0.528,58,tested_positive
0,98,82,15,84,25.2,0.299,22,tested_negative
1,87,60,37,75,37.2,0.509,22,tested_negative
4,156,75,0,0,48.3,0.238,32,tested_positive
0,93,100,39,72,43.4,1.021,35,tested_negative
1,107,72,30,82,30.8,0.821,24,tested_negative
0,105,68,22,0,20,0.236,22,tested_negative
1,109,60,8,182,25.4,0.947,21,tested_negative
1,90,62,18,59,25.1,1.268,25,tested_negative
1,125,70,24,110,24.3,0.221,25,tested_negative
1,119,54,13,50,22.3,0.205,24,tested_negative
5,116,74,29,0,32.3,0.66,35,tested_positive
8,105,100,36,0,43.3,0.239,45,tested_positive
5,144,82,26,285,32,0.452,58,tested_positive
3,100,68,23,81,31.6,0.949,28,tested_negative
1,100,66,29,196,32,0.444,42,tested_negative
5,166,76,0,0,45.7,0.34,27,tested_positive
1,131,64,14,415,23.7,0.389,21,tested_negative
4,116,72,12,87,22.1,0.463,37,tested_negative
4,158,78,0,0,32.9,0.803,31,tested_positive
2,127,58,24,275,27.7,1.6,25,tested_negative
3,96,56,34,115,24.7,0.944,39,tested_negative
0,131,66,40,0,34.3,0.196,22,tested_positive
3,82,70,0,0,21.1,0.389,25,tested_negative
3,193,70,31,0,34.9,0.241,25,tested_positive
4,95,64,0,0,32,0.161,31,tested_positive
6,137,61,0,0,24.2,0.151,55,tested_negative
5,136,84,41,88,35,0.286,35,tested_positive
9,72,78,25,0,31.6,0.28,38,tested_negative
5,168,64,0,0,32.9,0.135,41,tested_positive
2,123,48,32,165,42.1,0.52,26,tested_negative
4,115,72,0,0,28.9,0.376,46,tested_positive
0,101,62,0,0,21.9,0.336,25,tested_negative
8,197,74,0,0,25.9,1.191,39,tested_positive
1,172,68,49,579,42.4,0.702,28,tested_positive
6,102,90,39,0,35.7,0.674,28,tested_negative
1,112,72,30,176,34.4,0.528,25,tested_negative
1,143,84,23,310,42.4,1.076,22,tested_negative
1,143,74,22,61,26.2,0.256,21,tested_negative
0,138,60,35,167,34.6,0.534,21,tested_positive
3,173,84,33,474,35.7,0.258,22,tested_positive
1,97,68,21,0,27.2,1.095,22,tested_negative
4,144,82,32,0,38.5,0.554,37,tested_positive
1,83,68,0,0,18.2,0.624,27,tested_negative
3,129,64,29,115,26.4,0.219,28,tested_positive
1,119,88,41,170,45.3,0.507,26,tested_negative
2,94,68,18,76,26,0.561,21,tested_negative
0,102,64,46,78,40.6,0.496,21,tested_negative
2,115,64,22,0,30.8,0.421,21,tested_negative
8,151,78,32,210,42.9,0.516,36,tested_positive
4,184,78,39,277,37,0.264,31,tested_positive
0,94,0,0,0,0,0.256,25,tested_negative
1,181,64,30,180,34.1,0.328,38,tested_positive
0,135,94,46,145,40.6,0.284,26,tested_negative
1,95,82,25,180,35,0.233,43,tested_positive
2,99,0,0,0,22.2,0.108,23,tested_negative
3,89,74,16,85,30.4,0.551,38,tested_negative
1,80,74,11,60,30,0.527,22,tested_negative
2,139,75,0,0,25.6,0.167,29,tested_negative
1,90,68,8,0,24.5,1.138,36,tested_negative
0,141,0,0,0,42.4,0.205,29,tested_positive
12,140,85,33,0,37.4,0.244,41,tested_negative
5,147,75,0,0,29.9,0.434,28,tested_negative
1,97,70,15,0,18.2,0.147,21,tested_negative
6,107,88,0,0,36.8,0.727,31,tested_negative
0,189,104,25,0,34.3,0.435,41,tested_positive
2,83,66,23,50,32.2,0.497,22,tested_negative
4,117,64,27,120,33.2,0.23,24,tested_negative
8,108,70,0,0,30.5,0.955,33,tested_positive
4,117,62,12,0,29.7,0.38,30,tested_positive
0,180,78,63,14,59.4,2.42,25,tested_positive
1,100,72,12,70,25.3,0.658,28,tested_negative
0,95,80,45,92,36.5,0.33,26,tested_negative
0,104,64,37,64,33.6,0.51,22,tested_positive
0,120,74,18,63,30.5,0.285,26,tested_negative
1,82,64,13,95,21.2,0.415,23,tested_negative
2,134,70,0,0,28.9,0.542,23,tested_positive
0,91,68,32,210,39.9,0.381,25,tested_negative
2,119,0,0,0,19.6,0.832,72,tested_negative
2,100,54,28,105,37.8,0.498,24,tested_negative
14,175,62,30,0,33.6,0.212,38,tested_positive
1,135,54,0,0,26.7,0.687,62,tested_negative
5,86,68,28,71,30.2,0.364,24,tested_negative
10,148,84,48,237,37.6,1.001,51,tested_positive
9,134,74,33,60,25.9,0.46,81,tested_negative
9,120,72,22,56,20.8,0.733,48,tested_negative
1,71,62,0,0,21.8,0.416,26,tested_negative
8,74,70,40,49,35.3,0.705,39,tested_negative
5,88,78,30,0,27.6,0.258,37,tested_negative
10,115,98,0,0,24,1.022,34,tested_negative
0,124,56,13,105,21.8,0.452,21,tested_negative
0,74,52,10,36,27.8,0.269,22,tested_negative
0,97,64,36,100,36.8,0.6,25,tested_negative
8,120,0,0,0,30,0.183,38,tested_positive
6,154,78,41,140,46.1,0.571,27,tested_negative
1,144,82,40,0,41.3,0.607,28,tested_negative
0,137,70,38,0,33.2,0.17,22,tested_negative
0,119,66,27,0,38.8,0.259,22,tested_negative
7,136,90,0,0,29.9,0.21,50,tested_negative
4,114,64,0,0,28.9,0.126,24,tested_negative
0,137,84,27,0,27.3,0.231,59,tested_negative
2,105,80,45,191,33.7,0.711,29,tested_positive
7,114,76,17,110,23.8,0.466,31,tested_negative
8,126,74,38,75,25.9,0.162,39,tested_negative
4,132,86,31,0,28,0.419,63,tested_negative
3,158,70,30,328,35.5,0.344,35,tested_positive
0,123,88,37,0,35.2,0.197,29,tested_negative
4,85,58,22,49,27.8,0.306,28,tested_negative
0,84,82,31,125,38.2,0.233,23,tested_negative
0,145,0,0,0,44.2,0.63,31,tested_positive
0,135,68,42,250,42.3,0.365,24,tested_positive
1,139,62,41,480,40.7,0.536,21,tested_negative
0,173,78,32,265,46.5,1.159,58,tested_negative
4,99,72,17,0,25.6,0.294,28,tested_negative
8,194,80,0,0,26.1,0.551,67,tested_negative
2,83,65,28,66,36.8,0.629,24,tested_negative
2,89,90,30,0,33.5,0.292,42,tested_negative
4,99,68,38,0,32.8,0.145,33,tested_negative
4,125,70,18,122,28.9,1.144,45,tested_positive
3,80,0,0,0,0,0.174,22,tested_negative
6,166,74,0,0,26.6,0.304,66,tested_negative
5,110,68,0,0,26,0.292,30,tested_negative
2,81,72,15,76,30.1,0.547,25,tested_negative
7,195,70,33,145,25.1,0.163,55,tested_positive
6,154,74,32,193,29.3,0.839,39,tested_negative
2,117,90,19,71,25.2,0.313,21,tested_negative
3,84,72,32,0,37.2,0.267,28,tested_negative
6,0,68,41,0,39,0.727,41,tested_positive
7,94,64,25,79,33.3,0.738,41,tested_negative
3,96,78,39,0,37.3,0.238,40,tested_negative
10,75,82,0,0,33.3,0.263,38,tested_negative
0,180,90,26,90,36.5,0.314,35,tested_positive
1,130,60,23,170,28.6,0.692,21,tested_negative
2,84,50,23,76,30.4,0.968,21,tested_negative
8,120,78,0,0,25,0.409,64,tested_negative
12,84,72,31,0,29.7,0.297,46,tested_positive
0,139,62,17,210,22.1,0.207,21,tested_negative
9,91,68,0,0,24.2,0.2,58,tested_negative
2,91,62,0,0,27.3,0.525,22,tested_negative
3,99,54,19,86,25.6,0.154,24,tested_negative
3,163,70,18,105,31.6,0.268,28,tested_positive
9,145,88,34,165,30.3,0.771,53,tested_positive
7,125,86,0,0,37.6,0.304,51,tested_negative
13,76,60,0,0,32.8,0.18,41,tested_negative
6,129,90,7,326,19.6,0.582,60,tested_negative
2,68,70,32,66,25,0.187,25,tested_negative
3,124,80,33,130,33.2,0.305,26,tested_negative
6,114,0,0,0,0,0.189,26,tested_negative
9,130,70,0,0,34.2,0.652,45,tested_positive
3,125,58,0,0,31.6,0.151,24,tested_negative
3,87,60,18,0,21.8,0.444,21,tested_negative
1,97,64,19,82,18.2,0.299,21,tested_negative
3,116,74,15,105,26.3,0.107,24,tested_negative
0,117,66,31,188,30.8,0.493,22,tested_negative
0,111,65,0,0,24.6,0.66,31,tested_negative
2,122,60,18,106,29.8,0.717,22,tested_negative
0,107,76,0,0,45.3,0.686,24,tested_negative
1,86,66,52,65,41.3,0.917,29,tested_negative
6,91,0,0,0,29.8,0.501,31,tested_negative
1,77,56,30,56,33.3,1.251,24,tested_negative
4,132,0,0,0,32.9,0.302,23,tested_positive
0,105,90,0,0,29.6,0.197,46,tested_negative
0,57,60,0,0,21.7,0.735,67,tested_negative
0,127,80,37,210,36.3,0.804,23,tested_negative
3,129,92,49,155,36.4,0.968,32,tested_positive
8,100,74,40,215,39.4,0.661,43,tested_positive
3,128,72,25,190,32.4,0.549,27,tested_positive
10,90,85,32,0,34.9,0.825,56,tested_positive
4,84,90,23,56,39.5,0.159,25,tested_negative
1,88,78,29,76,32,0.365,29,tested_negative
8,186,90,35,225,34.5,0.423,37,tested_positive
5,187,76,27,207,43.6,1.034,53,tested_positive
4,131,68,21,166,33.1,0.16,28,tested_negative
1,164,82,43,67,32.8,0.341,50,tested_negative
4,189,110,31,0,28.5,0.68,37,tested_negative
1,116,70,28,0,27.4,0.204,21,tested_negative
3,84,68,30,106,31.9,0.591,25,tested_negative
6,114,88,0,0,27.8,0.247,66,tested_negative
1,88,62,24,44,29.9,0.422,23,tested_negative
1,84,64,23,115,36.9,0.471,28,tested_negative
7,124,70,33,215,25.5,0.161,37,tested_negative
1,97,70,40,0,38.1,0.218,30,tested_negative
8,110,76,0,0,27.8,0.237,58,tested_negative
11,103,68,40,0,46.2,0.126,42,tested_negative
11,85,74,0,0,30.1,0.3,35,tested_negative
6,125,76,0,0,33.8,0.121,54,tested_positive
0,198,66,32,274,41.3,0.502,28,tested_positive
1,87,68,34,77,37.6,0.401,24,tested_negative
6,99,60,19,54,26.9,0.497,32,tested_negative
0,91,80,0,0,32.4,0.601,27,tested_negative
2,95,54,14,88,26.1,0.748,22,tested_negative
1,99,72,30,18,38.6,0.412,21,tested_negative
6,92,62,32,126,32,0.085,46,tested_negative
4,154,72,29,126,31.3,0.338,37,tested_negative
0,121,66,30,165,34.3,0.203,33,tested_positive
3,78,70,0,0,32.5,0.27,39,tested_negative
2,130,96,0,0,22.6,0.268,21,tested_negative
3,111,58,31,44,29.5,0.43,22,tested_negative
2,98,60,17,120,34.7,0.198,22,tested_negative
1,143,86,30,330,30.1,0.892,23,tested_negative
1,119,44,47,63,35.5,0.28,25,tested_negative
6,108,44,20,130,24,0.813,35,tested_negative
2,118,80,0,0,42.9,0.693,21,tested_positive
10,133,68,0,0,27,0.245,36,tested_negative
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

428
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%
% 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
0.29,0.52,0.48,0.5,0.42,0.29,0.39,cp
0.65,0.47,0.48,0.5,0.59,0.3,0.4,cp
0.32,0.42,0.48,0.5,0.35,0.28,0.38,cp
0.38,0.46,0.48,0.5,0.48,0.22,0.29,cp
0.33,0.45,0.48,0.5,0.52,0.32,0.41,cp
0.3,0.37,0.48,0.5,0.59,0.41,0.49,cp
0.4,0.5,0.48,0.5,0.45,0.39,0.47,cp
0.28,0.38,0.48,0.5,0.5,0.33,0.42,cp
0.61,0.45,0.48,0.5,0.48,0.35,0.41,cp
0.17,0.38,0.48,0.5,0.45,0.42,0.5,cp
0.44,0.35,0.48,0.5,0.55,0.55,0.61,cp
0.43,0.4,0.48,0.5,0.39,0.28,0.39,cp
0.42,0.35,0.48,0.5,0.58,0.15,0.27,cp
0.23,0.33,0.48,0.5,0.43,0.33,0.43,cp
0.37,0.52,0.48,0.5,0.42,0.42,0.36,cp
0.29,0.3,0.48,0.5,0.45,0.03,0.17,cp
0.22,0.36,0.48,0.5,0.35,0.39,0.47,cp
0.23,0.58,0.48,0.5,0.37,0.53,0.59,cp
0.47,0.47,0.48,0.5,0.22,0.16,0.26,cp
0.54,0.47,0.48,0.5,0.28,0.33,0.42,cp
0.51,0.37,0.48,0.5,0.35,0.36,0.45,cp
0.4,0.35,0.48,0.5,0.45,0.33,0.42,cp
0.44,0.34,0.48,0.5,0.3,0.33,0.43,cp
0.42,0.38,0.48,0.5,0.54,0.34,0.43,cp
0.44,0.56,0.48,0.5,0.5,0.46,0.54,cp
0.52,0.36,0.48,0.5,0.41,0.28,0.38,cp
0.36,0.41,0.48,0.5,0.48,0.47,0.54,cp
0.18,0.3,0.48,0.5,0.46,0.24,0.35,cp
0.47,0.29,0.48,0.5,0.51,0.33,0.43,cp
0.24,0.43,0.48,0.5,0.54,0.52,0.59,cp
0.25,0.37,0.48,0.5,0.41,0.33,0.42,cp
0.52,0.57,0.48,0.5,0.42,0.47,0.54,cp
0.25,0.37,0.48,0.5,0.43,0.26,0.36,cp
0.35,0.48,0.48,0.5,0.56,0.4,0.48,cp
0.26,0.26,0.48,0.5,0.34,0.25,0.35,cp
0.44,0.51,0.48,0.5,0.47,0.26,0.36,cp
0.37,0.5,0.48,0.5,0.42,0.36,0.45,cp
0.44,0.42,0.48,0.5,0.42,0.25,0.2,cp
0.24,0.43,0.48,0.5,0.37,0.28,0.38,cp
0.42,0.3,0.48,0.5,0.48,0.26,0.36,cp
0.48,0.42,0.48,0.5,0.45,0.25,0.35,cp
0.41,0.48,0.48,0.5,0.51,0.44,0.51,cp
0.44,0.28,0.48,0.5,0.43,0.27,0.37,cp
0.29,0.41,0.48,0.5,0.48,0.38,0.46,cp
0.34,0.28,0.48,0.5,0.41,0.35,0.44,cp
0.41,0.43,0.48,0.5,0.45,0.31,0.41,cp
0.29,0.47,0.48,0.5,0.41,0.23,0.34,cp
0.34,0.55,0.48,0.5,0.58,0.31,0.41,cp
0.36,0.56,0.48,0.5,0.43,0.45,0.53,cp
0.4,0.46,0.48,0.5,0.52,0.49,0.56,cp
0.5,0.49,0.48,0.5,0.49,0.46,0.53,cp
0.52,0.44,0.48,0.5,0.37,0.36,0.42,cp
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
0.38,0.44,0.48,0.5,0.43,0.2,0.31,cp
0.41,0.51,0.48,0.5,0.58,0.2,0.31,cp
0.34,0.42,0.48,0.5,0.41,0.34,0.43,cp
0.51,0.49,0.48,0.5,0.53,0.14,0.26,cp
0.25,0.51,0.48,0.5,0.37,0.42,0.5,cp
0.29,0.28,0.48,0.5,0.5,0.42,0.5,cp
0.25,0.26,0.48,0.5,0.39,0.32,0.42,cp
0.24,0.41,0.48,0.5,0.49,0.23,0.34,cp
0.17,0.39,0.48,0.5,0.53,0.3,0.39,cp
0.04,0.31,0.48,0.5,0.41,0.29,0.39,cp
0.61,0.36,0.48,0.5,0.49,0.35,0.44,cp
0.34,0.51,0.48,0.5,0.44,0.37,0.46,cp
0.28,0.33,0.48,0.5,0.45,0.22,0.33,cp
0.4,0.46,0.48,0.5,0.42,0.35,0.44,cp
0.23,0.34,0.48,0.5,0.43,0.26,0.37,cp
0.37,0.44,0.48,0.5,0.42,0.39,0.47,cp
0,0.38,0.48,0.5,0.42,0.48,0.55,cp
0.39,0.31,0.48,0.5,0.38,0.34,0.43,cp
0.3,0.44,0.48,0.5,0.49,0.22,0.33,cp
0.27,0.3,0.48,0.5,0.71,0.28,0.39,cp
0.17,0.52,0.48,0.5,0.49,0.37,0.46,cp
0.36,0.42,0.48,0.5,0.53,0.32,0.41,cp
0.3,0.37,0.48,0.5,0.43,0.18,0.3,cp
0.26,0.4,0.48,0.5,0.36,0.26,0.37,cp
0.4,0.41,0.48,0.5,0.55,0.22,0.33,cp
0.22,0.34,0.48,0.5,0.42,0.29,0.39,cp
0.44,0.35,0.48,0.5,0.44,0.52,0.59,cp
0.27,0.42,0.48,0.5,0.37,0.38,0.43,cp
0.16,0.43,0.48,0.5,0.54,0.27,0.37,cp
0.06,0.61,0.48,0.5,0.49,0.92,0.37,im
0.44,0.52,0.48,0.5,0.43,0.47,0.54,im
0.63,0.47,0.48,0.5,0.51,0.82,0.84,im
0.23,0.48,0.48,0.5,0.59,0.88,0.89,im
0.34,0.49,0.48,0.5,0.58,0.85,0.8,im
0.43,0.4,0.48,0.5,0.58,0.75,0.78,im
0.46,0.61,0.48,0.5,0.48,0.86,0.87,im
0.27,0.35,0.48,0.5,0.51,0.77,0.79,im
0.52,0.39,0.48,0.5,0.65,0.71,0.73,im
0.29,0.47,0.48,0.5,0.71,0.65,0.69,im
0.55,0.47,0.48,0.5,0.57,0.78,0.8,im
0.12,0.67,0.48,0.5,0.74,0.58,0.63,im
0.4,0.5,0.48,0.5,0.65,0.82,0.84,im
0.73,0.36,0.48,0.5,0.53,0.91,0.92,im
0.84,0.44,0.48,0.5,0.48,0.71,0.74,im
0.48,0.45,0.48,0.5,0.6,0.78,0.8,im
0.54,0.49,0.48,0.5,0.4,0.87,0.88,im
0.48,0.41,0.48,0.5,0.51,0.9,0.88,im
0.5,0.66,0.48,0.5,0.31,0.92,0.92,im
0.72,0.46,0.48,0.5,0.51,0.66,0.7,im
0.47,0.55,0.48,0.5,0.58,0.71,0.75,im
0.33,0.56,0.48,0.5,0.33,0.78,0.8,im
0.64,0.58,0.48,0.5,0.48,0.78,0.73,im
0.54,0.57,0.48,0.5,0.56,0.81,0.83,im
0.47,0.59,0.48,0.5,0.52,0.76,0.79,im
0.63,0.5,0.48,0.5,0.59,0.85,0.86,im
0.49,0.42,0.48,0.5,0.53,0.79,0.81,im
0.31,0.5,0.48,0.5,0.57,0.84,0.85,im
0.74,0.44,0.48,0.5,0.55,0.88,0.89,im
0.33,0.45,0.48,0.5,0.45,0.88,0.89,im
0.45,0.4,0.48,0.5,0.61,0.74,0.77,im
0.71,0.4,0.48,0.5,0.71,0.7,0.74,im
0.5,0.37,0.48,0.5,0.66,0.64,0.69,im
0.66,0.53,0.48,0.5,0.59,0.66,0.66,im
0.6,0.61,0.48,0.5,0.54,0.67,0.71,im
0.83,0.37,0.48,0.5,0.61,0.71,0.74,im
0.34,0.51,0.48,0.5,0.67,0.9,0.9,im
0.63,0.54,0.48,0.5,0.65,0.79,0.81,im
0.7,0.4,0.48,0.5,0.56,0.86,0.83,im
0.6,0.5,1,0.5,0.54,0.77,0.8,im
0.16,0.51,0.48,0.5,0.33,0.39,0.48,im
0.74,0.7,0.48,0.5,0.66,0.65,0.69,im
0.2,0.46,0.48,0.5,0.57,0.78,0.81,im
0.89,0.55,0.48,0.5,0.51,0.72,0.76,im
0.7,0.46,0.48,0.5,0.56,0.78,0.73,im
0.12,0.43,0.48,0.5,0.63,0.7,0.74,im
0.61,0.52,0.48,0.5,0.54,0.67,0.52,im
0.33,0.37,0.48,0.5,0.46,0.65,0.69,im
0.63,0.65,0.48,0.5,0.66,0.67,0.71,im
0.41,0.51,0.48,0.5,0.53,0.75,0.78,im
0.34,0.67,0.48,0.5,0.52,0.76,0.79,im
0.58,0.34,0.48,0.5,0.56,0.87,0.81,im
0.59,0.56,0.48,0.5,0.55,0.8,0.82,im
0.51,0.4,0.48,0.5,0.57,0.62,0.67,im
0.5,0.57,0.48,0.5,0.71,0.61,0.66,im
0.6,0.46,0.48,0.5,0.45,0.81,0.83,im
0.37,0.47,0.48,0.5,0.39,0.76,0.79,im
0.58,0.55,0.48,0.5,0.57,0.7,0.74,im
0.36,0.47,0.48,0.5,0.51,0.69,0.72,im
0.39,0.41,0.48,0.5,0.52,0.72,0.75,im
0.35,0.51,0.48,0.5,0.61,0.71,0.74,im
0.31,0.44,0.48,0.5,0.5,0.79,0.82,im
0.61,0.66,0.48,0.5,0.46,0.87,0.88,im
0.48,0.49,0.48,0.5,0.52,0.77,0.71,im
0.11,0.5,0.48,0.5,0.58,0.72,0.68,im
0.31,0.36,0.48,0.5,0.58,0.94,0.94,im
0.68,0.51,0.48,0.5,0.71,0.75,0.78,im
0.69,0.39,0.48,0.5,0.57,0.76,0.79,im
0.52,0.54,0.48,0.5,0.62,0.76,0.79,im
0.46,0.59,0.48,0.5,0.36,0.76,0.23,im
0.36,0.45,0.48,0.5,0.38,0.79,0.17,im
0,0.51,0.48,0.5,0.35,0.67,0.44,im
0.1,0.49,0.48,0.5,0.41,0.67,0.21,im
0.3,0.51,0.48,0.5,0.42,0.61,0.34,im
0.61,0.47,0.48,0.5,0,0.8,0.32,im
0.63,0.75,0.48,0.5,0.64,0.73,0.66,im
0.71,0.52,0.48,0.5,0.64,1,0.99,im
0.85,0.53,0.48,0.5,0.53,0.52,0.35,imS
0.63,0.49,0.48,0.5,0.54,0.76,0.79,imS
0.75,0.55,1,1,0.4,0.47,0.3,imL
0.7,0.39,1,0.5,0.51,0.82,0.84,imL
0.72,0.42,0.48,0.5,0.65,0.77,0.79,imU
0.79,0.41,0.48,0.5,0.66,0.81,0.83,imU
0.83,0.48,0.48,0.5,0.65,0.76,0.79,imU
0.69,0.43,0.48,0.5,0.59,0.74,0.77,imU
0.79,0.36,0.48,0.5,0.46,0.82,0.7,imU
0.78,0.33,0.48,0.5,0.57,0.77,0.79,imU
0.75,0.37,0.48,0.5,0.64,0.7,0.74,imU
0.59,0.29,0.48,0.5,0.64,0.75,0.77,imU
0.67,0.37,0.48,0.5,0.54,0.64,0.68,imU
0.66,0.48,0.48,0.5,0.54,0.7,0.74,imU
0.64,0.46,0.48,0.5,0.48,0.73,0.76,imU
0.76,0.71,0.48,0.5,0.5,0.71,0.75,imU
0.84,0.49,0.48,0.5,0.55,0.78,0.74,imU
0.77,0.55,0.48,0.5,0.51,0.78,0.74,imU
0.81,0.44,0.48,0.5,0.42,0.67,0.68,imU
0.58,0.6,0.48,0.5,0.59,0.73,0.76,imU
0.63,0.42,0.48,0.5,0.48,0.77,0.8,imU
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0.7,0.53,0.48,0.5,0.7,0.86,0.87,imU
0.64,0.45,0.48,0.5,0.67,0.61,0.66,imU
0.81,0.52,0.48,0.5,0.57,0.78,0.8,imU
0.73,0.26,0.48,0.5,0.57,0.75,0.78,imU
0.49,0.61,1,0.5,0.56,0.71,0.74,imU
0.88,0.42,0.48,0.5,0.52,0.73,0.75,imU
0.84,0.54,0.48,0.5,0.75,0.92,0.7,imU
0.63,0.51,0.48,0.5,0.64,0.72,0.76,imU
0.86,0.55,0.48,0.5,0.63,0.81,0.83,imU
0.79,0.54,0.48,0.5,0.5,0.66,0.68,imU
0.57,0.38,0.48,0.5,0.06,0.49,0.33,imU
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0.78,0.68,0.48,0.5,0.83,0.4,0.29,om
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0.61,0.75,0.48,0.5,0.51,0.33,0.33,om
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0.73,0.84,0.48,0.5,0.86,0.58,0.29,om
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0.64,0.57,0.48,0.5,0.7,0.33,0.26,om
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0.71,0.71,0.48,0.5,0.68,0.43,0.36,om
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0.68,0.49,1,0.5,0.62,0.55,0.28,omL
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0.66,0.86,0.48,0.5,0.34,0.41,0.36,pp
0.73,0.78,0.48,0.5,0.58,0.51,0.31,pp
0.65,0.57,0.48,0.5,0.47,0.47,0.51,pp
0.72,0.86,0.48,0.5,0.17,0.55,0.21,pp
0.67,0.7,0.48,0.5,0.46,0.45,0.33,pp
0.67,0.81,0.48,0.5,0.54,0.49,0.23,pp
0.67,0.61,0.48,0.5,0.51,0.37,0.38,pp
0.63,1,0.48,0.5,0.35,0.51,0.49,pp
0.57,0.59,0.48,0.5,0.39,0.47,0.33,pp
0.71,0.71,0.48,0.5,0.4,0.54,0.39,pp
0.66,0.74,0.48,0.5,0.31,0.38,0.43,pp
0.67,0.81,0.48,0.5,0.25,0.42,0.25,pp
0.64,0.72,0.48,0.5,0.49,0.42,0.19,pp
0.68,0.82,0.48,0.5,0.38,0.65,0.56,pp
0.32,0.39,0.48,0.5,0.53,0.28,0.38,pp
0.7,0.64,0.48,0.5,0.47,0.51,0.47,pp
0.63,0.57,0.48,0.5,0.49,0.7,0.2,pp
0.74,0.82,0.48,0.5,0.49,0.49,0.41,pp
0.63,0.86,0.48,0.5,0.39,0.47,0.34,pp
0.63,0.83,0.48,0.5,0.4,0.39,0.19,pp
0.63,0.71,0.48,0.5,0.6,0.4,0.39,pp
0.71,0.86,0.48,0.5,0.4,0.54,0.32,pp
0.68,0.78,0.48,0.5,0.43,0.44,0.42,pp
0.64,0.84,0.48,0.5,0.37,0.45,0.4,pp
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0.75,0.84,0.48,0.5,0.35,0.52,0.33,pp
0.63,0.65,0.48,0.5,0.39,0.44,0.35,pp
0.69,0.67,0.48,0.5,0.3,0.39,0.24,pp
0.7,0.71,0.48,0.5,0.42,0.84,0.85,pp
0.69,0.8,0.48,0.5,0.46,0.57,0.26,pp
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0.69,0.59,0.48,0.5,0.46,0.44,0.52,pp
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0.64,0.81,0.48,0.5,0.37,0.39,0.44,pp
0.29,0.39,0.48,0.5,0.52,0.4,0.48,pp
0.62,0.83,0.48,0.5,0.46,0.36,0.4,pp
0.56,0.54,0.48,0.5,0.43,0.37,0.3,pp
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0.61,0.6,0.48,0.5,0.44,0.39,0.38,pp
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% 1. 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'
1.51707,13.48,3.48,1.71,72.52,0.62,7.99,0,0,'build wind non-float'
1.51719,14.75,0,2,73.02,0,8.53,1.59,0.08,headlamps
1.51629,12.71,3.33,1.49,73.28,0.67,8.24,0,0,'build wind non-float'
1.51994,13.27,0,1.76,73.03,0.47,11.32,0,0,containers
1.51811,12.96,2.96,1.43,72.92,0.6,8.79,0.14,0,'build wind non-float'
1.52152,13.05,3.65,0.87,72.22,0.19,9.85,0,0.17,'build wind float'
1.52475,11.45,0,1.88,72.19,0.81,13.24,0,0.34,'build wind non-float'
1.51841,12.93,3.74,1.11,72.28,0.64,8.96,0,0.22,'build wind non-float'
1.51754,13.39,3.66,1.19,72.79,0.57,8.27,0,0.11,'build wind float'
1.52058,12.85,1.61,2.17,72.18,0.76,9.7,0.24,0.51,containers
1.51569,13.24,3.49,1.47,73.25,0.38,8.03,0,0,'build wind non-float'
1.5159,12.82,3.52,1.9,72.86,0.69,7.97,0,0,'build wind non-float'
1.51683,14.56,0,1.98,73.29,0,8.52,1.57,0.07,headlamps
1.51687,13.23,3.54,1.48,72.84,0.56,8.1,0,0,'build wind non-float'
1.5161,13.33,3.53,1.34,72.67,0.56,8.33,0,0,'vehic wind float'
1.51674,12.87,3.56,1.64,73.14,0.65,7.99,0,0,'build wind non-float'
1.51832,13.33,3.34,1.54,72.14,0.56,8.99,0,0,'vehic wind float'
1.51115,17.38,0,0.34,75.41,0,6.65,0,0,tableware
1.51645,13.44,3.61,1.54,72.39,0.66,8.03,0,0,'build wind non-float'
1.51755,13,3.6,1.36,72.99,0.57,8.4,0,0.11,'build wind float'
1.51571,12.72,3.46,1.56,73.2,0.67,8.09,0,0.24,'build wind float'
1.51596,12.79,3.61,1.62,72.97,0.64,8.07,0,0.26,'build wind float'
1.5173,12.35,2.72,1.63,72.87,0.7,9.23,0,0,'build wind non-float'
1.51662,12.85,3.51,1.44,73.01,0.68,8.23,0.06,0.25,'build wind non-float'
1.51409,14.25,3.09,2.08,72.28,1.1,7.08,0,0,'build wind non-float'
1.51797,12.74,3.48,1.35,72.96,0.64,8.68,0,0,'build wind float'
1.51806,13,3.8,1.08,73.07,0.56,8.38,0,0.12,'build wind non-float'
1.51627,13,3.58,1.54,72.83,0.61,8.04,0,0,'build wind non-float'
1.5159,13.24,3.34,1.47,73.1,0.39,8.22,0,0,'build wind non-float'
1.51934,13.64,3.54,0.75,72.65,0.16,8.89,0.15,0.24,'vehic wind float'
1.51755,12.71,3.42,1.2,73.2,0.59,8.64,0,0,'build wind float'
1.51514,14.01,2.68,3.5,69.89,1.68,5.87,2.2,0,containers
1.51766,13.21,3.69,1.29,72.61,0.57,8.22,0,0,'build wind float'
1.51784,13.08,3.49,1.28,72.86,0.6,8.49,0,0,'build wind float'
1.52177,13.2,3.68,1.15,72.75,0.54,8.52,0,0,'build wind non-float'
1.51753,12.57,3.47,1.38,73.39,0.6,8.55,0,0.06,'build wind float'
1.51851,13.2,3.63,1.07,72.83,0.57,8.41,0.09,0.17,'build wind non-float'
1.51743,13.3,3.6,1.14,73.09,0.58,8.17,0,0,'build wind float'
1.51593,13.09,3.59,1.52,73.1,0.67,7.83,0,0,'build wind non-float'
1.5164,14.37,0,2.74,72.85,0,9.45,0.54,0,headlamps
1.51735,13.02,3.54,1.69,72.73,0.54,8.44,0,0.07,'build wind float'
1.52247,14.86,2.2,2.06,70.26,0.76,9.76,0,0,headlamps
1.52099,13.69,3.59,1.12,71.96,0.09,9.4,0,0,'build wind float'
1.51769,13.65,3.66,1.11,72.77,0.11,8.6,0,0,'vehic wind float'
1.51846,13.41,3.89,1.33,72.38,0.51,8.28,0,0,'build wind non-float'
1.51848,13.64,3.87,1.27,71.96,0.54,8.32,0,0.32,'build wind non-float'
1.51905,13.6,3.62,1.11,72.64,0.14,8.76,0,0,'build wind float'
1.51567,13.29,3.45,1.21,72.74,0.56,8.57,0,0,'build wind float'
1.52213,14.21,3.82,0.47,71.77,0.11,9.57,0,0,'build wind float'
1.5232,13.72,3.72,0.51,71.75,0.09,10.06,0,0.16,'build wind float'
1.51556,13.87,0,2.54,73.23,0.14,9.41,0.81,0.01,headlamps
1.51926,13.2,3.33,1.28,72.36,0.6,9.14,0,0.11,'build wind float'
1.52211,14.19,3.78,0.91,71.36,0.23,9.14,0,0.37,'vehic wind float'
1.53125,10.73,0,2.1,69.81,0.58,13.3,3.15,0.28,'build wind non-float'
1.52152,13.05,3.65,0.87,72.32,0.19,9.85,0,0.17,'build wind float'
1.51829,14.46,2.24,1.62,72.38,0,9.26,0,0,tableware
1.51892,13.46,3.83,1.26,72.55,0.57,8.21,0,0.14,'build wind non-float'
1.51888,14.99,0.78,1.74,72.5,0,9.95,0,0,tableware
1.51829,13.24,3.9,1.41,72.33,0.55,8.31,0,0.1,'build wind non-float'
1.523,13.31,3.58,0.82,71.99,0.12,10.17,0,0.03,'build wind float'
1.51652,13.56,3.57,1.47,72.45,0.64,7.96,0,0,'build wind non-float'
1.51768,12.56,3.52,1.43,73.15,0.57,8.54,0,0,'build wind float'
1.51215,12.99,3.47,1.12,72.98,0.62,8.35,0,0.31,'build wind float'
1.51646,13.04,3.4,1.26,73.01,0.52,8.58,0,0,'vehic wind float'
1.51721,12.87,3.48,1.33,73.04,0.56,8.43,0,0,'build wind float'
1.51763,12.8,3.66,1.27,73.01,0.6,8.56,0,0,'build wind float'
1.51742,13.27,3.62,1.24,73.08,0.55,8.07,0,0,'build wind float'
1.52127,14.32,3.9,0.83,71.5,0,9.49,0,0,'vehic wind float'
1.51779,13.21,3.39,1.33,72.76,0.59,8.59,0,0,'build wind float'
1.52171,11.56,1.88,1.56,72.86,0.47,11.41,0,0,containers
1.518,13.71,3.93,1.54,71.81,0.54,8.21,0,0.15,'build wind non-float'
1.52777,12.64,0,0.67,72.02,0.06,14.4,0,0,'build wind non-float'
1.5175,12.82,3.55,1.49,72.75,0.54,8.52,0,0.19,'build wind float'
1.51764,12.98,3.54,1.21,73,0.65,8.53,0,0,'build wind float'
1.52177,13.75,1.01,1.36,72.19,0.33,11.14,0,0,'build wind non-float'
1.51645,14.94,0,1.87,73.11,0,8.67,1.38,0,headlamps
1.51786,12.73,3.43,1.19,72.95,0.62,8.76,0,0.3,'build wind float'
1.52152,13.12,3.58,0.9,72.2,0.23,9.82,0,0.16,'build wind float'
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% 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
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3,4,2,4,3
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1,2,1,1,1
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2,4,1,4,3
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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
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3,2,2,1,2
3,2,1,3,2
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3,2,1,1,1
1,4,1,1,3
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1,2,1,3,2
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3,4,3,2,3
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1,1,1,3,1
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2,3,2,3,2
1,3,2,1,2
2,2,1,2,2
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3,2,2,1,2
3,2,1,1,1
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3,3,2,1,2
2,3,2,1,2
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2,3,2,1,2
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1,4,2,3,3
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2,2,3,2,2
1,3,2,1,1
3,1,2,1,1
3,1,1,2,1
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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
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1,2,1,2,2
1,2,2,1,2
3,1,1,2,1
1,1,1,2,1
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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

173
datasets/hayes-roth_test.arff Executable file
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% 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

277
datasets/hayes-roth_train.arff Executable file
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@@ -0,0 +1,277 @@
% 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

338
datasets/heart-statlog.arff Executable file
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@@ -0,0 +1,338 @@
% 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
58,1,4,128,216,0,2,131,1,2.2,2,3,7,present
29,1,2,130,204,0,2,202,0,0,1,0,3,absent
41,0,2,130,204,0,2,172,0,1.4,1,0,3,absent
63,0,3,135,252,0,2,172,0,0,1,0,3,absent
51,1,3,94,227,0,0,154,1,0,1,1,7,absent
54,1,3,120,258,0,2,147,0,0.4,2,0,7,absent
44,1,2,120,220,0,0,170,0,0,1,0,3,absent
54,1,4,110,239,0,0,126,1,2.8,2,1,7,present
65,1,4,135,254,0,2,127,0,2.8,2,1,7,present
57,1,3,150,168,0,0,174,0,1.6,1,0,3,absent
63,1,4,130,330,1,2,132,1,1.8,1,3,7,present
35,0,4,138,183,0,0,182,0,1.4,1,0,3,absent
41,1,2,135,203,0,0,132,0,0,2,0,6,absent
62,0,3,130,263,0,0,97,0,1.2,2,1,7,present
43,0,4,132,341,1,2,136,1,3,2,0,7,present
58,0,1,150,283,1,2,162,0,1,1,0,3,absent
52,1,1,118,186,0,2,190,0,0,2,0,6,absent
61,0,4,145,307,0,2,146,1,1,2,0,7,present
39,1,4,118,219,0,0,140,0,1.2,2,0,7,present
45,1,4,115,260,0,2,185,0,0,1,0,3,absent
52,1,4,128,255,0,0,161,1,0,1,1,7,present
62,1,3,130,231,0,0,146,0,1.8,2,3,7,absent
62,0,4,160,164,0,2,145,0,6.2,3,3,7,present
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
43,1,4,150,247,0,0,171,0,1.5,1,0,3,absent
58,0,3,120,340,0,0,172,0,0,1,0,3,absent
60,1,4,130,206,0,2,132,1,2.4,2,2,7,present
58,1,2,120,284,0,2,160,0,1.8,2,0,3,present
49,1,2,130,266,0,0,171,0,0.6,1,0,3,absent
48,1,2,110,229,0,0,168,0,1,3,0,7,present
52,1,3,172,199,1,0,162,0,0.5,1,0,7,absent
44,1,2,120,263,0,0,173,0,0,1,0,7,absent
56,0,2,140,294,0,2,153,0,1.3,2,0,3,absent
57,1,4,140,192,0,0,148,0,0.4,2,0,6,absent
67,1,4,160,286,0,2,108,1,1.5,2,3,3,present

458
datasets/ionosphere.arff Executable file
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%1. 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
1,0,0.99539,-0.05889,0.85243,0.02306,0.83398,-0.37708,1,0.03760,0.85243,-0.17755,0.59755,-0.44945,0.60536,-0.38223,0.84356,-0.38542,0.58212,-0.32192,0.56971,-0.29674,0.36946,-0.47357,0.56811,-0.51171,0.41078,-0.46168,0.21266,-0.34090,0.42267,-0.54487,0.18641,-0.45300,g
1,0,1,-0.18829,0.93035,-0.36156,-0.10868,-0.93597,1,-0.04549,0.50874,-0.67743,0.34432,-0.69707,-0.51685,-0.97515,0.05499,-0.62237,0.33109,-1,-0.13151,-0.45300,-0.18056,-0.35734,-0.20332,-0.26569,-0.20468,-0.18401,-0.19040,-0.11593,-0.16626,-0.06288,-0.13738,-0.02447,b
1,0,1,-0.03365,1,0.00485,1,-0.12062,0.88965,0.01198,0.73082,0.05346,0.85443,0.00827,0.54591,0.00299,0.83775,-0.13644,0.75535,-0.08540,0.70887,-0.27502,0.43385,-0.12062,0.57528,-0.40220,0.58984,-0.22145,0.43100,-0.17365,0.60436,-0.24180,0.56045,-0.38238,g
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1,0,0.90147,0.41786,0.64131,0.75725,0.30440,0.95148,-0.20449,0.96534,-0.55483,0.81191,-0.81857,0.50949,-0.96986,0.10345,-0.91456,-0.31412,-0.70163,-0.65461,-0.32354,-0.88999,0.05865,-0.94172,0.44483,-0.82154,0.74105,-0.55231,0.89415,-0.18725,0.87893,0.20359,0.70555,0.54852,g
1,0,0.32789,0.11042,0.15970,0.29308,0.14020,0.74485,-0.25131,0.91993,-0.16503,0.26664,-0.63714,0.24865,-0.97650,-0.00337,-0.23227,-0.19909,-0.30522,-0.48886,-0.14426,-0.89991,0.09345,-0.28916,0.28307,-0.18560,0.39599,-0.11498,0.31005,0.05614,0.21443,0.20540,0.13376,0.26422,g
1,0,0.65845,0.43617,0.44681,0.74804,0.05319,0.85106,-0.32027,0.82139,-0.68253,0.52408,-0.84211,0.07111,-0.82811,-0.28723,-0.47032,-0.71725,-0.04759,-0.86002,0.23292,-0.76316,0.56663,-0.52128,0.74300,-0.18645,0.74758,0.23713,0.45185,0.59071,0.20549,0.76764,-0.18533,0.74356,g
1,0,0.19466,0.05725,0.04198,0.25191,-0.10557,0.48866,-0.18321,-0.18321,-0.41985,0.06107,-0.45420,0.09160,-0.16412,-0.30534,-0.10305,-0.39695,0.18702,-0.17557,0.34012,-0.11953,0.28626,-0.16031,0.21645,0.24692,0.03913,0.31092,-0.03817,0.26336,-0.16794,0.16794,-0.30153,-0.33588,g
1,0,0.98002,0.00075,1,0,0.98982,-0.00075,0.94721,0.02394,0.97700,0.02130,0.97888,0.03073,0.99170,0.02338,0.93929,0.05713,0.93552,0.05279,0.97738,0.05524,1,0.06241,0.94155,0.08107,0.96709,0.07255,0.95701,0.08088,0.98190,0.08126,0.97247,0.08616,g
1,0,0.82254,-0.07572,0.80462,0.00231,0.87514,-0.01214,0.86821,-0.07514,0.72832,-0.11734,0.84624,0.05029,0.83121,-0.07399,0.74798,0.06705,0.78324,0.06358,0.86763,-0.02370,0.78844,-0.06012,0.74451,-0.02370,0.76717,-0.02731,0.74046,-0.07630,0.70058,-0.04220,0.78439,0.01214,g
1,0,0.35346,-0.13768,0.69387,-0.02423,0.68195,-0.03574,0.55717,-0.06119,0.61836,-0.10467,0.62099,-0.06527,0.59361,-0.07289,0.42271,-0.26409,0.58213,0.04992,0.49736,-0.08771,0.46241,-0.08989,0.45008,-0.00564,0.39146,-0.09038,0.35588,-0.10306,0.32232,-0.08637,0.28943,-0.08300,g
1,0,0.76046,0.01092,0.86335,0.00258,0.85821,0.00384,0.79988,0.02304,0.81504,0.12068,0.83096,0.00744,0.81815,0.00854,0.82777,-0.06974,0.76531,0.03881,0.76979,0.01148,0.75071,0.01232,0.77138,-0.00303,0.70886,0.01375,0.66161,0.00849,0.66298,0.01484,0.63887,0.01525,g
1,0,0.66667,-0.01366,0.97404,0.06831,0.49590,0.50137,0.75683,-0.00273,0.65164,-0.14071,0.40164,-0.48907,0.39208,0.58743,0.76776,0.31831,0.78552,0.11339,0.47541,-0.44945,1,0.00683,0.60656,0.06967,0.68656,0.17088,0.87568,0.07787,0.55328,0.24590,0.13934,0.48087,g
1,0,0.83508,0.08298,0.73739,-0.14706,0.84349,-0.05567,0.90441,-0.04622,0.89391,0.13130,0.81197,0.06723,0.79307,-0.08929,1,-0.02101,0.96639,0.06618,0.87605,0.01155,0.77521,0.06618,0.95378,-0.04202,0.83479,0.00123,1,0.12815,0.86660,-0.10714,0.90546,-0.04307,g
1,0,0.95113,0.00419,0.95183,-0.02723,0.93438,-0.01920,0.94590,0.01606,0.96510,0.03281,0.94171,0.07330,0.94625,-0.01326,0.97173,0.00140,0.94834,0.06038,0.92670,0.08412,0.93124,0.10087,0.94520,0.01361,0.93522,0.04925,0.93159,0.08168,0.94066,-0.00035,0.91483,0.04712,g
1,0,0.94701,-0.00034,0.93207,-0.03227,0.95177,-0.03431,0.95584,0.02446,0.94124,0.01766,0.92595,0.04688,0.93954,-0.01461,0.94837,0.02004,0.93784,0.01393,0.91406,0.07677,0.89470,0.06148,0.93988,0.03193,0.92489,0.02542,0.92120,0.02242,0.92459,0.00442,0.92697,-0.00577,g
1,0,0.90608,-0.01657,0.98122,-0.01989,0.95691,-0.03646,0.85746,0.00110,0.89724,-0.03315,0.89061,-0.01436,0.90608,-0.04530,0.91381,-0.00884,0.80773,-0.12928,0.88729,0.01215,0.92155,-0.02320,0.91050,-0.02099,0.89147,-0.07760,0.82983,-0.17238,0.96022,-0.03757,0.87403,-0.16243,g
1,0,0.84710,0.13533,0.73638,-0.06151,0.87873,0.08260,0.88928,-0.09139,0.78735,0.06678,0.80668,-0.00351,0.79262,-0.01054,0.85764,-0.04569,0.87170,-0.03515,0.81722,-0.09490,0.71002,0.04394,0.86467,-0.15114,0.81147,-0.04822,0.78207,-0.00703,0.75747,-0.06678,0.85764,-0.06151,g
%
%
%

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datasets/iris.arff Executable file
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% 1. 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
%
%
%

10177
datasets/kdd_JapaneseVowels.arff Executable file
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5903
datasets/kdd_JapaneseVowels_test.arff Executable file
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4490
datasets/kdd_JapaneseVowels_train.arff Executable file
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20191
datasets/letter.arff Executable file
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399
datasets/liver-disorders.arff Executable file
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% 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
86,79,28,16,17,0.5,1
91,57,31,23,42,0.5,1
93,77,32,18,29,0.5,1
88,96,28,21,40,0.5,1
94,65,22,18,11,0.5,1
91,72,155,68,82,0.5,2
85,54,47,33,22,0.5,2
79,39,14,19,9,0.5,2
85,85,25,26,30,0.5,2
89,63,24,20,38,0.5,2
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88,122,35,29,42,0.5,2
84,88,28,25,35,0.5,2
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86,78,52,29,62,0.5,2
82,74,38,28,48,0.5,2
86,58,36,27,59,0.5,2
94,56,30,18,27,0.5,2
87,57,30,30,22,0.5,2
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83,68,17,20,71,0.5,2
93,56,25,21,33,0.5,2
101,65,18,21,22,0.5,2
92,65,25,20,31,0.5,2
92,58,14,16,13,0.5,2
86,58,16,23,23,0.5,2
85,62,15,13,22,0.5,2
86,57,13,20,13,0.5,2
86,54,26,30,13,0.5,2
81,41,33,27,34,1.0,1
91,67,32,26,13,1.0,1
91,80,21,19,14,1.0,1
92,60,23,15,19,1.0,1
91,60,32,14,8,1.0,1
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87,92,21,22,37,1.0,2
83,78,31,19,115,1.0,2
95,62,24,23,14,1.0,2
93,59,41,30,48,1.0,2
84,82,43,32,38,2.0,1
87,71,33,20,22,2.0,1
86,44,24,15,18,2.0,1
86,66,28,24,21,2.0,1
88,58,31,17,17,2.0,1
90,61,28,29,31,2.0,1
88,69,70,24,64,2.0,1
93,87,18,17,26,2.0,1
98,58,33,21,28,2.0,1
91,44,18,18,23,2.0,2
87,75,37,19,70,2.0,2
94,91,30,26,25,2.0,2
88,85,14,15,10,2.0,2
89,109,26,25,27,2.0,2
87,59,37,27,34,2.0,2
93,58,20,23,18,2.0,2
88,57,9,15,16,2.0,2
94,65,38,27,17,3.0,1
91,71,12,22,11,3.0,1
90,55,20,20,16,3.0,1
91,64,21,17,26,3.0,2
88,47,35,26,33,3.0,2
82,72,31,20,84,3.0,2
85,58,83,49,51,3.0,2
91,54,25,22,35,4.0,1
98,50,27,25,53,4.0,2
86,62,29,21,26,4.0,2
89,48,32,22,14,4.0,2
82,68,20,22,9,4.0,2
83,70,17,19,23,4.0,2
96,70,21,26,21,4.0,2
94,117,77,56,52,4.0,2
93,45,11,14,21,4.0,2
93,49,27,21,29,4.0,2
84,73,46,32,39,4.0,2
91,63,17,17,46,4.0,2
90,57,31,18,37,4.0,2
87,45,19,13,16,4.0,2
91,68,14,20,19,4.0,2
86,55,29,35,108,4.0,2
91,86,52,47,52,4.0,2
88,46,15,33,55,4.0,2
85,52,22,23,34,4.0,2
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95,59,23,18,19,4.0,2
94,43,154,82,121,4.0,2
96,56,38,26,23,5.0,2
90,52,10,17,12,5.0,2
94,45,20,16,12,5.0,2
99,42,14,21,49,5.0,2
93,102,47,23,37,5.0,2
94,71,25,26,31,5.0,2
92,73,33,34,115,5.0,2
87,54,41,29,23,6.0,1
92,67,15,14,14,6.0,1
98,101,31,26,32,6.0,1
92,53,51,33,92,6.0,1
97,94,43,43,82,6.0,1
93,43,11,16,54,6.0,1
93,68,24,18,19,6.0,1
95,36,38,19,15,6.0,1
99,86,58,42,203,6.0,1
98,66,103,57,114,6.0,1
92,80,10,26,20,6.0,1
96,74,27,25,43,6.0,2
95,93,21,27,47,6.0,2
86,109,16,22,28,6.0,2
91,46,30,24,39,7.0,2
102,82,34,78,203,7.0,2
85,50,12,18,14,7.0,2
91,57,33,23,12,8.0,1
91,52,76,32,24,8.0,1
93,70,46,30,33,8.0,1
87,55,36,19,25,8.0,1
98,123,28,24,31,8.0,1
82,55,18,23,44,8.0,2
95,73,20,25,225,8.0,2
97,80,17,20,53,8.0,2
100,83,25,24,28,8.0,2
88,91,56,35,126,9.0,2
91,138,45,21,48,10.0,1
92,41,37,22,37,10.0,1
86,123,20,25,23,10.0,2
91,93,35,34,37,10.0,2
87,87,15,23,11,10.0,2
87,56,52,43,55,10.0,2
99,75,26,24,41,12.0,1
96,69,53,43,203,12.0,2
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98,99,57,45,65,20.0,1

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% NAME: Sonar, Mines vs. Rocks
%
% SUMMARY: This 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
@attribute 'attribute_56' real
@attribute 'attribute_57' real
@attribute 'attribute_58' real
@attribute 'attribute_59' real
@attribute 'attribute_60' real
@attribute 'Class' { Rock, Mine}
@data
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0.0453,0.0523,0.0843,0.0689,0.1183,0.2583,0.2156,0.3481,0.3337,0.2872,0.4918,0.6552,0.6919,0.7797,0.7464,0.9444,1,0.8874,0.8024,0.7818,0.5212,0.4052,0.3957,0.3914,0.325,0.32,0.3271,0.2767,0.4423,0.2028,0.3788,0.2947,0.1984,0.2341,0.1306,0.4182,0.3835,0.1057,0.184,0.197,0.1674,0.0583,0.1401,0.1628,0.0621,0.0203,0.053,0.0742,0.0409,0.0061,0.0125,0.0084,0.0089,0.0048,0.0094,0.0191,0.014,0.0049,0.0052,0.0044,Rock
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0.0192,0.0607,0.0378,0.0774,0.1388,0.0809,0.0568,0.0219,0.1037,0.1186,0.1237,0.1601,0.352,0.4479,0.3769,0.5761,0.6426,0.679,0.7157,0.5466,0.5399,0.6362,0.7849,0.7756,0.578,0.4862,0.4181,0.2457,0.0716,0.0613,0.1816,0.4493,0.5976,0.3785,0.2495,0.5771,0.8852,0.8409,0.357,0.3133,0.6096,0.6378,0.2709,0.1419,0.126,0.1288,0.079,0.0829,0.052,0.0216,0.036,0.0331,0.0131,0.012,0.0108,0.0024,0.0045,0.0037,0.0112,0.0075,Rock
0.027,0.0092,0.0145,0.0278,0.0412,0.0757,0.1026,0.1138,0.0794,0.152,0.1675,0.137,0.1361,0.1345,0.2144,0.5354,0.683,0.56,0.3093,0.3226,0.443,0.5573,0.5782,0.6173,0.8132,0.9819,0.9823,0.9166,0.7423,0.7736,0.8473,0.7352,0.6671,0.6083,0.6239,0.5972,0.5715,0.5242,0.2924,0.1536,0.2003,0.2031,0.2207,0.1778,0.1353,0.1373,0.0749,0.0472,0.0325,0.0179,0.0045,0.0084,0.001,0.0018,0.0068,0.0039,0.012,0.0132,0.007,0.0088,Rock
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0.0664,0.0575,0.0842,0.0372,0.0458,0.0771,0.0771,0.113,0.2353,0.1838,0.2869,0.4129,0.3647,0.1984,0.284,0.4039,0.5837,0.6792,0.6086,0.4858,0.3246,0.2013,0.2082,0.1686,0.2484,0.2736,0.2984,0.4655,0.699,0.7474,0.7956,0.7981,0.6715,0.6942,0.744,0.8169,0.8912,1,0.8753,0.7061,0.6803,0.5898,0.4618,0.3639,0.1492,0.1216,0.1306,0.1198,0.0578,0.0235,0.0135,0.0141,0.019,0.0043,0.0036,0.0026,0.0024,0.0162,0.0109,0.0079,Rock
0.0099,0.0484,0.0299,0.0297,0.0652,0.1077,0.2363,0.2385,0.0075,0.1882,0.1456,0.1892,0.3176,0.134,0.2169,0.2458,0.2589,0.2786,0.2298,0.0656,0.1441,0.1179,0.1668,0.1783,0.2476,0.257,0.1036,0.5356,0.7124,0.6291,0.4756,0.6015,0.7208,0.6234,0.5725,0.7523,0.8712,0.9252,0.9709,0.9297,0.8995,0.7911,0.56,0.2838,0.4407,0.5507,0.4331,0.2905,0.1981,0.0779,0.0396,0.0173,0.0149,0.0115,0.0202,0.0139,0.0029,0.016,0.0106,0.0134,Rock
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0.0293,0.0644,0.039,0.0173,0.0476,0.0816,0.0993,0.0315,0.0736,0.086,0.0414,0.0472,0.0835,0.0938,0.1466,0.0809,0.1179,0.2179,0.3326,0.3258,0.2111,0.2302,0.3361,0.4259,0.4609,0.2606,0.0874,0.2862,0.5606,0.8344,0.8096,0.725,0.8048,0.9435,1,0.896,0.5516,0.3037,0.2338,0.2382,0.3318,0.3821,0.1575,0.2228,0.1582,0.1433,0.1634,0.1133,0.0567,0.0133,0.017,0.0035,0.0052,0.0083,0.0078,0.0075,0.0105,0.016,0.0095,0.0011,Rock
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0.0409,0.0421,0.0573,0.013,0.0183,0.1019,0.1054,0.107,0.2302,0.2259,0.2373,0.3323,0.3827,0.484,0.6812,0.7555,0.9522,0.9826,0.8871,0.8268,0.7561,0.8217,0.6967,0.6444,0.6948,0.8014,0.6053,0.6084,0.8877,0.8557,0.5563,0.2897,0.3638,0.4786,0.2908,0.0899,0.2043,0.1707,0.0407,0.1286,0.1581,0.2191,0.1701,0.0971,0.2217,0.2732,0.1874,0.1062,0.0665,0.0405,0.0113,0.0028,0.0036,0.0105,0.012,0.0087,0.0061,0.0061,0.003,0.0078,Rock
0.0217,0.034,0.0392,0.0236,0.1081,0.1164,0.1398,0.1009,0.1147,0.1777,0.4079,0.4113,0.3973,0.5078,0.6509,0.8073,0.9819,1,0.9407,0.8452,0.8106,0.846,0.6212,0.5815,0.7745,0.8204,0.5601,0.2989,0.5009,0.6628,0.5753,0.4055,0.3746,0.3481,0.158,0.1422,0.213,0.1866,0.1003,0.2396,0.2241,0.2029,0.071,0.1606,0.1669,0.17,0.1829,0.1403,0.0506,0.0224,0.0095,0.0031,0.0103,0.0078,0.0077,0.0094,0.0031,0.003,0.0013,0.0069,Rock
0.0378,0.0318,0.0423,0.035,0.1787,0.1635,0.0887,0.0817,0.1779,0.2053,0.3135,0.3118,0.3686,0.3885,0.585,0.7868,0.9739,1,0.9843,0.861,0.8443,0.9061,0.5847,0.4033,0.5946,0.6793,0.6389,0.5002,0.5578,0.4831,0.4729,0.3318,0.3969,0.3894,0.2314,0.1036,0.1312,0.0864,0.2569,0.3179,0.2649,0.2714,0.1713,0.0584,0.123,0.22,0.2198,0.1074,0.0423,0.0162,0.0093,0.0046,0.0044,0.0078,0.0102,0.0065,0.0061,0.0062,0.0043,0.0053,Rock
0.0365,0.1632,0.1636,0.1421,0.113,0.1306,0.2112,0.2268,0.2992,0.3735,0.3042,0.0387,0.2679,0.5397,0.6204,0.7257,0.835,0.6888,0.445,0.3921,0.5605,0.7545,0.8311,1,0.8762,0.7092,0.7009,0.5014,0.3942,0.4456,0.4072,0.0773,0.1423,0.0401,0.3597,0.6847,0.7076,0.3597,0.0612,0.3027,0.3966,0.3868,0.238,0.2059,0.2288,0.1704,0.1587,0.1792,0.1022,0.0151,0.0223,0.011,0.0071,0.0205,0.0164,0.0063,0.0078,0.0094,0.011,0.0068,Rock
0.0188,0.037,0.0953,0.0824,0.0249,0.0488,0.1424,0.1972,0.1873,0.1806,0.2139,0.1523,0.1975,0.4844,0.7298,0.7807,0.7906,0.6122,0.42,0.2807,0.5148,0.7569,0.8596,1,0.8457,0.6797,0.6971,0.5843,0.4772,0.5201,0.4241,0.1592,0.1668,0.0588,0.3967,0.7147,0.7319,0.3509,0.0589,0.269,0.42,0.3874,0.244,0.2,0.2307,0.1886,0.196,0.1701,0.1366,0.0398,0.0143,0.0093,0.0033,0.0113,0.003,0.0057,0.009,0.0057,0.0068,0.0024,Rock
0.0856,0.0454,0.0382,0.0203,0.0385,0.0534,0.214,0.311,0.2837,0.2751,0.2707,0.0946,0.102,0.4519,0.6737,0.6699,0.7066,0.5632,0.3785,0.2721,0.5297,0.7697,0.8643,0.9304,0.9372,0.6247,0.6024,0.681,0.5047,0.5775,0.4754,0.24,0.2779,0.1997,0.5305,0.7409,0.7775,0.4424,0.1416,0.3508,0.4482,0.4208,0.3054,0.2235,0.2611,0.2798,0.2392,0.2021,0.1326,0.0358,0.0128,0.0172,0.0138,0.0079,0.0037,0.0051,0.0258,0.0102,0.0037,0.0037,Rock
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0.0235,0.0291,0.0749,0.0519,0.0227,0.0834,0.0677,0.2002,0.2876,0.3674,0.2974,0.0837,0.1912,0.504,0.6352,0.6804,0.7505,0.6595,0.4509,0.2964,0.4019,0.6794,0.8297,1,0.824,0.7115,0.7726,0.6124,0.4936,0.5648,0.4906,0.182,0.1811,0.1107,0.4603,0.665,0.6423,0.2166,0.1951,0.4947,0.4925,0.4041,0.2402,0.1392,0.1779,0.1946,0.1723,0.1522,0.0929,0.0179,0.0242,0.0083,0.0037,0.0095,0.0105,0.003,0.0132,0.0068,0.0108,0.009,Rock
0.0126,0.0519,0.0621,0.0518,0.1072,0.2587,0.2304,0.2067,0.3416,0.4284,0.3015,0.1207,0.3299,0.5707,0.6962,0.9751,1,0.9293,0.621,0.4586,0.5001,0.5032,0.7082,0.842,0.8109,0.769,0.8105,0.6203,0.2356,0.2595,0.6299,0.6762,0.2903,0.4393,0.8529,0.718,0.4801,0.5856,0.4993,0.2866,0.0601,0.1167,0.2737,0.2812,0.2078,0.066,0.0491,0.0345,0.0172,0.0287,0.0027,0.0208,0.0048,0.0199,0.0126,0.0022,0.0037,0.0034,0.0114,0.0077,Rock
0.0253,0.0808,0.0507,0.0244,0.1724,0.3823,0.3729,0.3583,0.3429,0.2197,0.2653,0.3223,0.5582,0.6916,0.7943,0.7152,0.3512,0.2008,0.2676,0.4299,0.528,0.3489,0.143,0.5453,0.6338,0.7712,0.6838,0.8015,0.8073,0.831,0.7792,0.5049,0.1413,0.2767,0.5084,0.4787,0.1356,0.2299,0.2789,0.3833,0.2933,0.1155,0.1705,0.1294,0.0909,0.08,0.0567,0.0198,0.0114,0.0151,0.0085,0.0178,0.0073,0.0079,0.0038,0.0116,0.0033,0.0039,0.0081,0.0053,Rock
0.026,0.0192,0.0254,0.0061,0.0352,0.0701,0.1263,0.108,0.1523,0.163,0.103,0.2187,0.1542,0.263,0.294,0.2978,0.0699,0.1401,0.299,0.3915,0.3598,0.2403,0.4208,0.5675,0.6094,0.6323,0.6549,0.7673,1,0.8463,0.5509,0.4444,0.5169,0.4268,0.1802,0.0791,0.0535,0.1906,0.2561,0.2153,0.2769,0.2841,0.1733,0.0815,0.0335,0.0933,0.1018,0.0309,0.0208,0.0318,0.0132,0.0118,0.012,0.0051,0.007,0.0015,0.0035,0.0008,0.0044,0.0077,Rock
0.0459,0.0437,0.0347,0.0456,0.0067,0.089,0.1798,0.1741,0.1598,0.1408,0.2693,0.3259,0.4545,0.5785,0.4471,0.2231,0.2164,0.3201,0.2915,0.4235,0.446,0.238,0.6415,0.8966,0.8918,0.7529,0.6838,0.839,1,0.8362,0.5427,0.4577,0.8067,0.6973,0.3915,0.1558,0.1598,0.2161,0.5178,0.4782,0.2344,0.3599,0.2785,0.1807,0.0352,0.0473,0.0322,0.0408,0.0163,0.0088,0.0121,0.0067,0.0032,0.0109,0.0164,0.0151,0.007,0.0085,0.0117,0.0056,Rock
0.0025,0.0309,0.0171,0.0228,0.0434,0.1224,0.1947,0.1661,0.1368,0.143,0.0994,0.225,0.2444,0.3239,0.3039,0.241,0.0367,0.1672,0.3038,0.4069,0.3613,0.1994,0.4611,0.6849,0.7272,0.7152,0.7102,0.8516,1,0.769,0.4841,0.3717,0.6096,0.511,0.2586,0.0916,0.0947,0.2287,0.348,0.2095,0.1901,0.2941,0.2211,0.1524,0.0746,0.0606,0.0692,0.0446,0.0344,0.0082,0.0108,0.0149,0.0077,0.0036,0.0114,0.0085,0.0101,0.0016,0.0028,0.0014,Rock
0.0291,0.04,0.0771,0.0809,0.0521,0.1051,0.0145,0.0674,0.1294,0.1146,0.0942,0.0794,0.0252,0.1191,0.1045,0.205,0.1556,0.269,0.3784,0.4024,0.347,0.1395,0.1208,0.2827,0.15,0.2626,0.4468,0.752,0.9036,0.7812,0.4766,0.2483,0.5372,0.6279,0.3647,0.4572,0.6359,0.6474,0.552,0.3253,0.2292,0.0653,0,0,0,0,0,0,0,0,0,0.0056,0.0237,0.0204,0.005,0.0137,0.0164,0.0081,0.0139,0.0111,Rock
0.0181,0.0146,0.0026,0.0141,0.0421,0.0473,0.0361,0.0741,0.1398,0.1045,0.0904,0.0671,0.0997,0.1056,0.0346,0.1231,0.1626,0.3652,0.3262,0.2995,0.2109,0.2104,0.2085,0.2282,0.0747,0.1969,0.4086,0.6385,0.797,0.7508,0.5517,0.2214,0.4672,0.4479,0.2297,0.3235,0.448,0.5581,0.652,0.5354,0.2478,0.2268,0.1788,0.0898,0.0536,0.0374,0.099,0.0956,0.0317,0.0142,0.0076,0.0223,0.0255,0.0145,0.0233,0.0041,0.0018,0.0048,0.0089,0.0085,Rock
0.0491,0.0279,0.0592,0.127,0.1772,0.1908,0.2217,0.0768,0.1246,0.2028,0.0947,0.2497,0.2209,0.3195,0.334,0.3323,0.278,0.2975,0.2948,0.1729,0.3264,0.3834,0.3523,0.541,0.5228,0.4475,0.534,0.5323,0.3907,0.3456,0.4091,0.4639,0.558,0.5727,0.6355,0.7563,0.6903,0.6176,0.5379,0.5622,0.6508,0.4797,0.3736,0.2804,0.1982,0.2438,0.1789,0.1706,0.0762,0.0238,0.0268,0.0081,0.0129,0.0161,0.0063,0.0119,0.0194,0.014,0.0332,0.0439,Mine
0.1313,0.2339,0.3059,0.4264,0.401,0.1791,0.1853,0.0055,0.1929,0.2231,0.2907,0.2259,0.3136,0.3302,0.366,0.3956,0.4386,0.467,0.5255,0.3735,0.2243,0.1973,0.4337,0.6532,0.507,0.2796,0.4163,0.595,0.5242,0.4178,0.3714,0.2375,0.0863,0.1437,0.2896,0.4577,0.3725,0.3372,0.3803,0.4181,0.3603,0.2711,0.1653,0.1951,0.2811,0.2246,0.1921,0.15,0.0665,0.0193,0.0156,0.0362,0.021,0.0154,0.018,0.0013,0.0106,0.0127,0.0178,0.0231,Mine
0.0201,0.0423,0.0554,0.0783,0.062,0.0871,0.1201,0.2707,0.1206,0.0279,0.2251,0.2615,0.177,0.3709,0.4533,0.5553,0.4616,0.3797,0.345,0.2665,0.2395,0.1127,0.2556,0.5169,0.3779,0.4082,0.5353,0.5116,0.4544,0.4258,0.3869,0.3939,0.4661,0.3974,0.2194,0.1816,0.1023,0.2108,0.3253,0.3697,0.2912,0.301,0.2563,0.1927,0.2062,0.1751,0.0841,0.1035,0.0641,0.0153,0.0081,0.0191,0.0182,0.016,0.029,0.009,0.0242,0.0224,0.019,0.0096,Mine
0.0629,0.1065,0.1526,0.1229,0.1437,0.119,0.0884,0.0907,0.2107,0.3597,0.5466,0.5205,0.5127,0.5395,0.6558,0.8705,0.9786,0.9335,0.7917,0.7383,0.6908,0.385,0.0671,0.0502,0.2717,0.2839,0.2234,0.1911,0.0408,0.2531,0.1979,0.1891,0.2433,0.1956,0.2667,0.134,0.1073,0.2023,0.1794,0.0227,0.1313,0.1775,0.1549,0.1626,0.0708,0.0129,0.0795,0.0762,0.0117,0.0061,0.0257,0.0089,0.0262,0.0108,0.0138,0.0187,0.023,0.0057,0.0113,0.0131,Mine
0.0335,0.0134,0.0696,0.118,0.0348,0.118,0.1948,0.1607,0.3036,0.4372,0.5533,0.5771,0.7022,0.7067,0.7367,0.7391,0.8622,0.9458,0.8782,0.7913,0.576,0.3061,0.0563,0.0239,0.2554,0.4862,0.5027,0.4402,0.2847,0.1797,0.356,0.3522,0.3321,0.3112,0.3638,0.0754,0.1834,0.182,0.1815,0.1593,0.0576,0.0954,0.1086,0.0812,0.0784,0.0487,0.0439,0.0586,0.037,0.0185,0.0302,0.0244,0.0232,0.0093,0.0159,0.0193,0.0032,0.0377,0.0126,0.0156,Mine
0.0587,0.121,0.1268,0.1498,0.1436,0.0561,0.0832,0.0672,0.1372,0.2352,0.3208,0.4257,0.5201,0.4914,0.595,0.7221,0.9039,0.9111,0.8723,0.7686,0.7326,0.5222,0.3097,0.3172,0.227,0.164,0.1746,0.1835,0.2048,0.1674,0.2767,0.3104,0.3399,0.4441,0.5046,0.2814,0.1681,0.2633,0.3198,0.1933,0.0934,0.0443,0.078,0.0722,0.0405,0.0553,0.1081,0.1139,0.0767,0.0265,0.0215,0.0331,0.0111,0.0088,0.0158,0.0122,0.0038,0.0101,0.0228,0.0124,Mine
0.0162,0.0253,0.0262,0.0386,0.0645,0.0472,0.1056,0.1388,0.0598,0.1334,0.2969,0.4754,0.5677,0.569,0.6421,0.7487,0.8999,1,0.969,0.9032,0.7685,0.6998,0.6644,0.5964,0.3711,0.0921,0.0481,0.0876,0.104,0.1714,0.3264,0.4612,0.3939,0.505,0.4833,0.3511,0.2319,0.4029,0.3676,0.151,0.0745,0.1395,0.1552,0.0377,0.0636,0.0443,0.0264,0.0223,0.0187,0.0077,0.0137,0.0071,0.0082,0.0232,0.0198,0.0074,0.0035,0.01,0.0048,0.0019,Mine
0.0307,0.0523,0.0653,0.0521,0.0611,0.0577,0.0665,0.0664,0.146,0.2792,0.3877,0.4992,0.4981,0.4972,0.5607,0.7339,0.823,0.9173,0.9975,0.9911,0.824,0.6498,0.598,0.4862,0.315,0.1543,0.0989,0.0284,0.1008,0.2636,0.2694,0.293,0.2925,0.3998,0.366,0.3172,0.4609,0.4374,0.182,0.3376,0.6202,0.4448,0.1863,0.142,0.0589,0.0576,0.0672,0.0269,0.0245,0.019,0.0063,0.0321,0.0189,0.0137,0.0277,0.0152,0.0052,0.0121,0.0124,0.0055,Mine
0.0116,0.0179,0.0449,0.1096,0.1913,0.0924,0.0761,0.1092,0.0757,0.1006,0.25,0.3988,0.3809,0.4753,0.6165,0.6464,0.8024,0.9208,0.9832,0.9634,0.8646,0.8325,0.8276,0.8007,0.6102,0.4853,0.4355,0.4307,0.4399,0.3833,0.3032,0.3035,0.3197,0.2292,0.2131,0.2347,0.3201,0.4455,0.3655,0.2715,0.1747,0.1781,0.2199,0.1056,0.0573,0.0307,0.0237,0.047,0.0102,0.0057,0.0031,0.0163,0.0099,0.0084,0.027,0.0277,0.0097,0.0054,0.0148,0.0092,Mine
0.0331,0.0423,0.0474,0.0818,0.0835,0.0756,0.0374,0.0961,0.0548,0.0193,0.0897,0.1734,0.1936,0.2803,0.3313,0.502,0.636,0.7096,0.8333,0.873,0.8073,0.7507,0.7526,0.7298,0.6177,0.4946,0.4531,0.4099,0.454,0.4124,0.3139,0.3194,0.3692,0.3776,0.4469,0.4777,0.4716,0.4664,0.3893,0.4255,0.4064,0.3712,0.3863,0.2802,0.1283,0.1117,0.1303,0.0787,0.0436,0.0224,0.0133,0.0078,0.0174,0.0176,0.0038,0.0129,0.0066,0.0044,0.0134,0.0092,Mine
0.0428,0.0555,0.0708,0.0618,0.1215,0.1524,0.1543,0.0391,0.061,0.0113,0.1255,0.2473,0.3011,0.3747,0.452,0.5392,0.6588,0.7113,0.7602,0.8672,0.8416,0.7974,0.8385,0.9317,0.8555,0.6162,0.4139,0.3269,0.3108,0.2554,0.3367,0.4465,0.5,0.5111,0.5194,0.4619,0.4234,0.4372,0.4277,0.4433,0.37,0.3324,0.2564,0.2527,0.2137,0.1789,0.101,0.0528,0.0453,0.0118,0.0009,0.0142,0.0179,0.0079,0.006,0.0131,0.0089,0.0084,0.0113,0.0049,Mine
0.0599,0.0474,0.0498,0.0387,0.1026,0.0773,0.0853,0.0447,0.1094,0.0351,0.1582,0.2023,0.2268,0.2829,0.3819,0.4665,0.6687,0.8647,0.9361,0.9367,0.9144,0.9162,0.9311,0.8604,0.7327,0.5763,0.4162,0.4113,0.4146,0.3149,0.2936,0.3169,0.3149,0.4132,0.3994,0.4195,0.4532,0.4419,0.4737,0.3431,0.3194,0.337,0.2493,0.265,0.1748,0.0932,0.053,0.0081,0.0342,0.0137,0.0028,0.0013,0.0005,0.0227,0.0209,0.0081,0.0117,0.0114,0.0112,0.01,Mine
0.0264,0.0071,0.0342,0.0793,0.1043,0.0783,0.1417,0.1176,0.0453,0.0945,0.1132,0.084,0.0717,0.1968,0.2633,0.4191,0.505,0.6711,0.7922,0.8381,0.8759,0.9422,1,0.9931,0.9575,0.8647,0.7215,0.5801,0.4964,0.4886,0.4079,0.2443,0.1768,0.2472,0.3518,0.3762,0.2909,0.2311,0.3168,0.3554,0.3741,0.4443,0.3261,0.1963,0.0864,0.1688,0.1991,0.1217,0.0628,0.0323,0.0253,0.0214,0.0262,0.0177,0.0037,0.0068,0.0121,0.0077,0.0078,0.0066,Mine
0.021,0.0121,0.0203,0.1036,0.1675,0.0418,0.0723,0.0828,0.0494,0.0686,0.1125,0.1741,0.271,0.3087,0.3575,0.4998,0.6011,0.647,0.8067,0.9008,0.8906,0.9338,1,0.9102,0.8496,0.7867,0.7688,0.7718,0.6268,0.4301,0.2077,0.1198,0.166,0.2618,0.3862,0.3958,0.3248,0.2302,0.325,0.4022,0.4344,0.4008,0.337,0.2518,0.2101,0.1181,0.115,0.055,0.0293,0.0183,0.0104,0.0117,0.0101,0.0061,0.0031,0.0099,0.008,0.0107,0.0161,0.0133,Mine
0.053,0.0885,0.1997,0.2604,0.3225,0.2247,0.0617,0.2287,0.095,0.074,0.161,0.2226,0.2703,0.3365,0.4266,0.4144,0.5655,0.6921,0.8547,0.9234,0.9171,1,0.9532,0.9101,0.8337,0.7053,0.6534,0.4483,0.246,0.202,0.1446,0.0994,0.151,0.2392,0.4434,0.5023,0.4441,0.4571,0.3927,0.29,0.3408,0.499,0.3632,0.1387,0.18,0.1299,0.0523,0.0817,0.0469,0.0114,0.0299,0.0244,0.0199,0.0257,0.0082,0.0151,0.0171,0.0146,0.0134,0.0056,Mine
0.0454,0.0472,0.0697,0.1021,0.1397,0.1493,0.1487,0.0771,0.1171,0.1675,0.2799,0.3323,0.4012,0.4296,0.535,0.5411,0.687,0.8045,0.9194,0.9169,1,0.9972,0.9093,0.7918,0.6705,0.5324,0.3572,0.2484,0.3161,0.3775,0.3138,0.1713,0.2937,0.5234,0.5926,0.5437,0.4516,0.3379,0.3215,0.2178,0.1674,0.2634,0.298,0.2037,0.1155,0.0919,0.0882,0.0228,0.038,0.0142,0.0137,0.012,0.0042,0.0238,0.0129,0.0084,0.0218,0.0321,0.0154,0.0053,Mine
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
0.0201,0.0178,0.0274,0.0232,0.0724,0.0833,0.1232,0.1298,0.2085,0.272,0.2188,0.3037,0.2959,0.2059,0.0906,0.161,0.18,0.218,0.2026,0.1506,0.0521,0.2143,0.4333,0.5943,0.6926,0.7576,0.8787,0.906,0.8528,0.9087,0.9657,0.9306,0.7774,0.6643,0.6604,0.6884,0.6938,0.5932,0.5774,0.6223,0.5841,0.4527,0.4911,0.5762,0.5013,0.4042,0.3123,0.2232,0.1085,0.0414,0.0253,0.0131,0.0049,0.0104,0.0102,0.0092,0.0083,0.002,0.0048,0.0036,Mine
0.0107,0.0453,0.0289,0.0713,0.1075,0.1019,0.1606,0.2119,0.3061,0.2936,0.3104,0.3431,0.2456,0.1887,0.1184,0.208,0.2736,0.3274,0.2344,0.126,0.0576,0.1241,0.3239,0.4357,0.5734,0.7825,0.9252,0.9349,0.9348,1,0.9308,0.8478,0.7605,0.704,0.7539,0.799,0.7673,0.5955,0.4731,0.484,0.434,0.3954,0.4837,0.5379,0.4485,0.2674,0.1541,0.1359,0.0941,0.0261,0.0079,0.0164,0.012,0.0113,0.0021,0.0097,0.0072,0.006,0.0017,0.0036,Mine
0.0235,0.022,0.0167,0.0516,0.0746,0.1121,0.1258,0.1717,0.3074,0.3199,0.2946,0.2484,0.251,0.1806,0.1413,0.3019,0.3635,0.3887,0.298,0.2219,0.1624,0.1343,0.2046,0.3791,0.5771,0.7545,0.8406,0.8547,0.9036,1,0.9646,0.7912,0.6412,0.5986,0.6835,0.7771,0.8084,0.7426,0.6295,0.5708,0.4433,0.3361,0.3795,0.495,0.4373,0.2404,0.1128,0.1654,0.0933,0.0225,0.0214,0.0221,0.0152,0.0083,0.0058,0.0023,0.0057,0.0052,0.0027,0.0021,Mine
0.0258,0.0433,0.0547,0.0681,0.0784,0.125,0.1296,0.1729,0.2794,0.2954,0.2506,0.2601,0.2249,0.2115,0.127,0.1193,0.1794,0.2185,0.1646,0.074,0.0625,0.2381,0.4824,0.6372,0.7531,0.8959,0.9941,0.9957,0.9328,0.9344,0.8854,0.769,0.6865,0.639,0.6378,0.6629,0.5983,0.4565,0.3129,0.4158,0.4325,0.4031,0.4201,0.4557,0.3955,0.2966,0.2095,0.1558,0.0884,0.0265,0.0121,0.0091,0.0062,0.0019,0.0045,0.0079,0.0031,0.0063,0.0048,0.005,Mine
0.0305,0.0363,0.0214,0.0227,0.0456,0.0665,0.0939,0.0972,0.2535,0.3127,0.2192,0.2621,0.2419,0.2179,0.1159,0.1237,0.0886,0.1755,0.1758,0.154,0.0512,0.1805,0.4039,0.5697,0.6577,0.7474,0.8543,0.9085,0.8668,0.8892,0.9065,0.8522,0.7204,0.62,0.6253,0.6848,0.7337,0.6281,0.5725,0.6119,0.5597,0.4965,0.5027,0.5772,0.5907,0.4803,0.3877,0.2779,0.1427,0.0424,0.0271,0.02,0.007,0.007,0.0086,0.0089,0.0074,0.0042,0.0055,0.0021,Mine
0.0217,0.0152,0.0346,0.0346,0.0484,0.0526,0.0773,0.0862,0.1451,0.211,0.2343,0.2087,0.1645,0.1689,0.165,0.1967,0.2934,0.3709,0.4309,0.4161,0.5116,0.6501,0.7717,0.8491,0.9104,0.8912,0.8189,0.6779,0.5368,0.5207,0.5651,0.5749,0.525,0.4255,0.333,0.2331,0.1451,0.1648,0.2694,0.373,0.4467,0.4133,0.3743,0.3021,0.2069,0.179,0.1689,0.1341,0.0769,0.0222,0.0205,0.0123,0.0067,0.0011,0.0026,0.0049,0.0029,0.0022,0.0022,0.0032,Mine
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
0.0394,0.042,0.0446,0.0551,0.0597,0.1416,0.0956,0.0802,0.1618,0.2558,0.3078,0.3404,0.34,0.3951,0.3352,0.2252,0.2086,0.2248,0.3382,0.4578,0.6474,0.6708,0.7007,0.7619,0.7745,0.6767,0.7373,0.7834,0.9619,1,0.8086,0.5558,0.5409,0.4988,0.3108,0.2897,0.2244,0.096,0.2287,0.3228,0.3454,0.3882,0.324,0.0926,0.1173,0.0566,0.0766,0.0969,0.0588,0.005,0.0118,0.0146,0.004,0.0114,0.0032,0.0062,0.0101,0.0068,0.0053,0.0087,Mine
0.031,0.0221,0.0433,0.0191,0.0964,0.1827,0.1106,0.1702,0.2804,0.4432,0.5222,0.5611,0.5379,0.4048,0.2245,0.1784,0.2297,0.272,0.5209,0.6898,0.8202,0.878,0.76,0.7616,0.7152,0.7288,0.8686,0.9509,0.8348,0.573,0.4363,0.4289,0.424,0.3156,0.1287,0.1477,0.2062,0.24,0.5173,0.5168,0.1491,0.2407,0.3415,0.4494,0.4624,0.2001,0.0775,0.1232,0.0783,0.0089,0.0249,0.0204,0.0059,0.0053,0.0079,0.0037,0.0015,0.0056,0.0067,0.0054,Mine
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
0.0116,0.0744,0.0367,0.0225,0.0076,0.0545,0.111,0.1069,0.1708,0.2271,0.3171,0.2882,0.2657,0.2307,0.1889,0.1791,0.2298,0.3715,0.6223,0.726,0.7934,0.8045,0.8067,0.9173,0.9327,0.9562,1,0.9818,0.8684,0.6381,0.3997,0.3242,0.2835,0.2413,0.2321,0.126,0.0693,0.0701,0.1439,0.1475,0.0438,0.0469,0.1476,0.1742,0.1555,0.1651,0.1181,0.072,0.0321,0.0056,0.0202,0.0141,0.0103,0.01,0.0034,0.0026,0.0037,0.0044,0.0057,0.0035,Mine
0.0131,0.0387,0.0329,0.0078,0.0721,0.1341,0.1626,0.1902,0.261,0.3193,0.3468,0.3738,0.3055,0.1926,0.1385,0.2122,0.2758,0.4576,0.6487,0.7154,0.801,0.7924,0.8793,1,0.9865,0.9474,0.9474,0.9315,0.8326,0.6213,0.3772,0.2822,0.2042,0.219,0.2223,0.1327,0.0521,0.0618,0.1416,0.146,0.0846,0.1055,0.1639,0.1916,0.2085,0.2335,0.1964,0.13,0.0633,0.0183,0.0137,0.015,0.0076,0.0032,0.0037,0.0071,0.004,0.0009,0.0015,0.0085,Mine
0.0335,0.0258,0.0398,0.057,0.0529,0.1091,0.1709,0.1684,0.1865,0.266,0.3188,0.3553,0.3116,0.1965,0.178,0.2794,0.287,0.3969,0.5599,0.6936,0.7969,0.7452,0.8203,0.9261,0.881,0.8814,0.9301,0.9955,0.8576,0.6069,0.3934,0.2464,0.1645,0.114,0.0956,0.008,0.0702,0.0936,0.0894,0.1127,0.0873,0.102,0.1964,0.2256,0.1814,0.2012,0.1688,0.1037,0.0501,0.0136,0.013,0.012,0.0039,0.0053,0.0062,0.0046,0.0045,0.0022,0.0005,0.0031,Mine
0.0272,0.0378,0.0488,0.0848,0.1127,0.1103,0.1349,0.2337,0.3113,0.3997,0.3941,0.3309,0.2926,0.176,0.1739,0.2043,0.2088,0.2678,0.2434,0.1839,0.2802,0.6172,0.8015,0.8313,0.844,0.8494,0.9168,1,0.7896,0.5371,0.6472,0.6505,0.4959,0.2175,0.099,0.0434,0.1708,0.1979,0.188,0.1108,0.1702,0.0585,0.0638,0.1391,0.0638,0.0581,0.0641,0.1044,0.0732,0.0275,0.0146,0.0091,0.0045,0.0043,0.0043,0.0098,0.0054,0.0051,0.0065,0.0103,Mine
0.0187,0.0346,0.0168,0.0177,0.0393,0.163,0.2028,0.1694,0.2328,0.2684,0.3108,0.2933,0.2275,0.0994,0.1801,0.22,0.2732,0.2862,0.2034,0.174,0.413,0.6879,0.812,0.8453,0.8919,0.93,0.9987,1,0.8104,0.6199,0.6041,0.5547,0.416,0.1472,0.0849,0.0608,0.0969,0.1411,0.1676,0.12,0.1201,0.1036,0.1977,0.1339,0.0902,0.1085,0.1521,0.1363,0.0858,0.029,0.0203,0.0116,0.0098,0.0199,0.0033,0.0101,0.0065,0.0115,0.0193,0.0157,Mine
0.0323,0.0101,0.0298,0.0564,0.076,0.0958,0.099,0.1018,0.103,0.2154,0.3085,0.3425,0.299,0.1402,0.1235,0.1534,0.1901,0.2429,0.212,0.2395,0.3272,0.5949,0.8302,0.9045,0.9888,0.9912,0.9448,1,0.9092,0.7412,0.7691,0.7117,0.5304,0.2131,0.0928,0.1297,0.1159,0.1226,0.1768,0.0345,0.1562,0.0824,0.1149,0.1694,0.0954,0.008,0.079,0.1255,0.0647,0.0179,0.0051,0.0061,0.0093,0.0135,0.0063,0.0063,0.0034,0.0032,0.0062,0.0067,Mine
0.0522,0.0437,0.018,0.0292,0.0351,0.1171,0.1257,0.1178,0.1258,0.2529,0.2716,0.2374,0.1878,0.0983,0.0683,0.1503,0.1723,0.2339,0.1962,0.1395,0.3164,0.5888,0.7631,0.8473,0.9424,0.9986,0.9699,1,0.863,0.6979,0.7717,0.7305,0.5197,0.1786,0.1098,0.1446,0.1066,0.144,0.1929,0.0325,0.149,0.0328,0.0537,0.1309,0.091,0.0757,0.1059,0.1005,0.0535,0.0235,0.0155,0.016,0.0029,0.0051,0.0062,0.0089,0.014,0.0138,0.0077,0.0031,Mine
0.0303,0.0353,0.049,0.0608,0.0167,0.1354,0.1465,0.1123,0.1945,0.2354,0.2898,0.2812,0.1578,0.0273,0.0673,0.1444,0.207,0.2645,0.2828,0.4293,0.5685,0.699,0.7246,0.7622,0.9242,1,0.9979,0.8297,0.7032,0.7141,0.6893,0.4961,0.2584,0.0969,0.0776,0.0364,0.1572,0.1823,0.1349,0.0849,0.0492,0.1367,0.1552,0.1548,0.1319,0.0985,0.1258,0.0954,0.0489,0.0241,0.0042,0.0086,0.0046,0.0126,0.0036,0.0035,0.0034,0.0079,0.0036,0.0048,Mine
0.026,0.0363,0.0136,0.0272,0.0214,0.0338,0.0655,0.14,0.1843,0.2354,0.272,0.2442,0.1665,0.0336,0.1302,0.1708,0.2177,0.3175,0.3714,0.4552,0.57,0.7397,0.8062,0.8837,0.9432,1,0.9375,0.7603,0.7123,0.8358,0.7622,0.4567,0.1715,0.1549,0.1641,0.1869,0.2655,0.1713,0.0959,0.0768,0.0847,0.2076,0.2505,0.1862,0.1439,0.147,0.0991,0.0041,0.0154,0.0116,0.0181,0.0146,0.0129,0.0047,0.0039,0.0061,0.004,0.0036,0.0061,0.0115,Mine

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% 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
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1,14.12,1.48,2.32,16.8,95,2.2,2.43,.26,1.57,5,1.17,2.82,1280
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1,13.05,2.05,3.22,25,124,2.63,2.68,.47,1.92,3.58,1.13,3.2,830
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1,14.22,3.99,2.51,13.2,128,3,3.04,.2,2.08,5.1,.89,3.53,760
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1,13.9,1.68,2.12,16,101,3.1,3.39,.21,2.14,6.1,.91,3.33,985
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2,11.41,.74,2.5,21,88,2.48,2.01,.42,1.44,3.08,1.1,2.31,434
2,12.08,1.39,2.5,22.5,84,2.56,2.29,.43,1.04,2.9,.93,3.19,385
2,11.03,1.51,2.2,21.5,85,2.46,2.17,.52,2.01,1.9,1.71,2.87,407
2,11.82,1.47,1.99,20.8,86,1.98,1.6,.3,1.53,1.95,.95,3.33,495
2,12.42,1.61,2.19,22.5,108,2,2.09,.34,1.61,2.06,1.06,2.96,345
2,12.77,3.43,1.98,16,80,1.63,1.25,.43,.83,3.4,.7,2.12,372
2,12,3.43,2,19,87,2,1.64,.37,1.87,1.28,.93,3.05,564
2,11.45,2.4,2.42,20,96,2.9,2.79,.32,1.83,3.25,.8,3.39,625
2,11.56,2.05,3.23,28.5,119,3.18,5.08,.47,1.87,6,.93,3.69,465
2,12.42,4.43,2.73,26.5,102,2.2,2.13,.43,1.71,2.08,.92,3.12,365
2,13.05,5.8,2.13,21.5,86,2.62,2.65,.3,2.01,2.6,.73,3.1,380
2,11.87,4.31,2.39,21,82,2.86,3.03,.21,2.91,2.8,.75,3.64,380
2,12.07,2.16,2.17,21,85,2.6,2.65,.37,1.35,2.76,.86,3.28,378
2,12.43,1.53,2.29,21.5,86,2.74,3.15,.39,1.77,3.94,.69,2.84,352
2,11.79,2.13,2.78,28.5,92,2.13,2.24,.58,1.76,3,.97,2.44,466
2,12.37,1.63,2.3,24.5,88,2.22,2.45,.4,1.9,2.12,.89,2.78,342
2,12.04,4.3,2.38,22,80,2.1,1.75,.42,1.35,2.6,.79,2.57,580
3,12.86,1.35,2.32,18,122,1.51,1.25,.21,.94,4.1,.76,1.29,630
3,12.88,2.99,2.4,20,104,1.3,1.22,.24,.83,5.4,.74,1.42,530
3,12.81,2.31,2.4,24,98,1.15,1.09,.27,.83,5.7,.66,1.36,560
3,12.7,3.55,2.36,21.5,106,1.7,1.2,.17,.84,5,.78,1.29,600
3,12.51,1.24,2.25,17.5,85,2,.58,.6,1.25,5.45,.75,1.51,650
3,12.6,2.46,2.2,18.5,94,1.62,.66,.63,.94,7.1,.73,1.58,695
3,12.25,4.72,2.54,21,89,1.38,.47,.53,.8,3.85,.75,1.27,720
3,12.53,5.51,2.64,25,96,1.79,.6,.63,1.1,5,.82,1.69,515
3,13.49,3.59,2.19,19.5,88,1.62,.48,.58,.88,5.7,.81,1.82,580
3,12.84,2.96,2.61,24,101,2.32,.6,.53,.81,4.92,.89,2.15,590
3,12.93,2.81,2.7,21,96,1.54,.5,.53,.75,4.6,.77,2.31,600
3,13.36,2.56,2.35,20,89,1.4,.5,.37,.64,5.6,.7,2.47,780
3,13.52,3.17,2.72,23.5,97,1.55,.52,.5,.55,4.35,.89,2.06,520
3,13.62,4.95,2.35,20,92,2,.8,.47,1.02,4.4,.91,2.05,550
3,12.25,3.88,2.2,18.5,112,1.38,.78,.29,1.14,8.21,.65,2,855
3,13.16,3.57,2.15,21,102,1.5,.55,.43,1.3,4,.6,1.68,830
3,13.88,5.04,2.23,20,80,.98,.34,.4,.68,4.9,.58,1.33,415
3,12.87,4.61,2.48,21.5,86,1.7,.65,.47,.86,7.65,.54,1.86,625
3,13.32,3.24,2.38,21.5,92,1.93,.76,.45,1.25,8.42,.55,1.62,650
3,13.08,3.9,2.36,21.5,113,1.41,1.39,.34,1.14,9.40,.57,1.33,550
3,13.5,3.12,2.62,24,123,1.4,1.57,.22,1.25,8.60,.59,1.3,500
3,12.79,2.67,2.48,22,112,1.48,1.36,.24,1.26,10.8,.48,1.47,480
3,13.11,1.9,2.75,25.5,116,2.2,1.28,.26,1.56,7.1,.61,1.33,425
3,13.23,3.3,2.28,18.5,98,1.8,.83,.61,1.87,10.52,.56,1.51,675
3,12.58,1.29,2.1,20,103,1.48,.58,.53,1.4,7.6,.58,1.55,640
3,13.17,5.19,2.32,22,93,1.74,.63,.61,1.55,7.9,.6,1.48,725
3,13.84,4.12,2.38,19.5,89,1.8,.83,.48,1.56,9.01,.57,1.64,480
3,12.45,3.03,2.64,27,97,1.9,.58,.63,1.14,7.5,.67,1.73,880
3,14.34,1.68,2.7,25,98,2.8,1.31,.53,2.7,13,.57,1.96,660
3,13.48,1.67,2.64,22.5,89,2.6,1.1,.52,2.29,11.75,.57,1.78,620
3,12.36,3.83,2.38,21,88,2.3,.92,.5,1.04,7.65,.56,1.58,520
3,13.69,3.26,2.54,20,107,1.83,.56,.5,.8,5.88,.96,1.82,680
3,12.85,3.27,2.58,22,106,1.65,.6,.6,.96,5.58,.87,2.11,570
3,12.96,3.45,2.35,18.5,106,1.39,.7,.4,.94,5.28,.68,1.75,675
3,13.78,2.76,2.3,22,90,1.35,.68,.41,1.03,9.58,.7,1.68,615
3,13.73,4.36,2.26,22.5,88,1.28,.47,.52,1.15,6.62,.78,1.75,520
3,13.45,3.7,2.6,23,111,1.7,.92,.43,1.46,10.68,.85,1.56,695
3,12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,685
3,13.58,2.58,2.69,24.5,105,1.55,.84,.39,1.54,8.66,.74,1.8,750
3,13.4,4.6,2.86,25,112,1.98,.96,.27,1.11,8.5,.67,1.92,630
3,12.2,3.03,2.32,19,96,1.25,.49,.4,.73,5.5,.66,1.83,510
3,12.77,2.39,2.28,19.5,86,1.39,.51,.48,.64,9.899999,.57,1.63,470
3,14.16,2.51,2.48,20,91,1.68,.7,.44,1.24,9.7,.62,1.71,660
3,13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740
3,13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750
3,13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835
3,13.17,2.59,2.37,20,120,1.65,.68,.53,1.46,9.3,.6,1.62,840
3,14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560

24
doc/api.rst
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@@ -6,29 +6,13 @@ This is an example on how to document the API of your own project.
.. currentmodule:: bayesclass
Estimator
=========
TAN
===
.. autosummary::
:toctree: generated/
:template: class.rst
TemplateEstimator
TAN
Transformer
===========
.. autosummary::
:toctree: generated/
:template: class.rst
TemplateTransformer
Predictor
=========
.. autosummary::
:toctree: generated/
:template: class.rst
TemplateClassifier

7
doc/conf.py
View File

@@ -315,7 +315,10 @@ texinfo_documents = [
# 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),
"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),
@@ -332,4 +335,4 @@ sphinx_gallery_conf = {
def setup(app):
# a copy button to copy snippet of code from the documentation
app.add_javascript("js/copybutton.js")
app.add_js_file("js/copybutton.js")

8
examples/plot_classifier.py
View File

@@ -3,19 +3,19 @@
Plotting Template Classifier
============================
An example plot of :class:`bayesclass.template.TemplateClassifier`
An example plot of :class:`bayesclass.TAN`
"""
import numpy as np
from matplotlib import pyplot as plt
from bayesclass import TemplateClassifier
from bayesclass import TAN
X = [[0, 0], [1, 1]]
y = [0, 1]
clf = TemplateClassifier()
clf = TAN()
clf.fit(X, y)
rng = np.random.RandomState(13)
X_test = rng.rand(500, 2)
X_test = rng.randint(2, size=(500, 2))
y_pred = clf.predict(X_test)
X_0 = X_test[y_pred == 0]

17
examples/plot_template.py
View File

@@ -1,17 +0,0 @@
"""
===========================
Plotting Template Estimator
===========================
An example plot of :class:`bayesclass.template.TemplateEstimator`
"""
import numpy as np
from matplotlib import pyplot as plt
from bayesclass import TemplateEstimator
X = np.arange(100).reshape(100, 1)
y = np.zeros((100,))
estimator = TemplateEstimator()
estimator.fit(X, y)
plt.plot(estimator.predict(X))
plt.show()

26
examples/plot_transformer.py
View File

@@ -1,26 +0,0 @@
"""
=============================
Plotting Template Transformer
=============================
An example plot of :class:`bayesclass.template.TemplateTransformer`
"""
import numpy as np
from matplotlib import pyplot as plt
from bayesclass import TemplateTransformer
X = np.arange(50, dtype=np.float).reshape(-1, 1)
X /= 50
estimator = TemplateTransformer()
X_transformed = estimator.fit_transform(X)
plt.plot(X.flatten(), label="Original Data")
plt.plot(X_transformed.flatten(), label="Transformed Data")
plt.title("Plots of original and transformed data")
plt.legend(loc="best")
plt.grid(True)
plt.xlabel("Index")
plt.ylabel("Value of Data")
plt.show()

1
result_images/test_bayesclass/line_dashes-expected.png Symbolic link
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@@ -0,0 +1 @@
/Users/rmontanana/Code/bayesclass/bayesclass/tests/baseline_images/test_bayesclass/line_dashes.png

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4
setup.cfg
View File

@@ -4,5 +4,5 @@ description-file = README.rst
[aliases]
test = pytest
[tool:pytest]
addopts = --doctest-modules
#[tool:pytest]
#addopts = --doctest-modules

437
test.ipynb Normal file
View File

@@ -0,0 +1,437 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "afc3548e-91c2-4443-bd96-457a57a202cc",
"metadata": {},
"outputs": [],
"source": [
"from mdlp import MDLP\n",
"import pandas as pd\n",
"from benchmark import Datasets\n",
"from bayesclass import TAN"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8ff3f4d6-e681-4252-ac4d-dc5bd14dcede",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>RI</th>\n",
" <th>Na</th>\n",
" <th>Mg</th>\n",
" <th>Al</th>\n",
" <th>Si</th>\n",
" <th>'K'</th>\n",
" <th>Ca</th>\n",
" <th>Ba</th>\n",
" <th>Fe</th>\n",
" <th>Type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.51793</td>\n",
" <td>12.79</td>\n",
" <td>3.50</td>\n",
" <td>1.12</td>\n",
" <td>73.03</td>\n",
" <td>0.64</td>\n",
" <td>8.77</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.51643</td>\n",
" <td>12.16</td>\n",
" <td>3.52</td>\n",
" <td>1.35</td>\n",
" <td>72.89</td>\n",
" <td>0.57</td>\n",
" <td>8.53</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>1.51793</td>\n",
" <td>13.21</td>\n",
" <td>3.48</td>\n",
" <td>1.41</td>\n",
" <td>72.64</td>\n",
" <td>0.59</td>\n",
" <td>8.43</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.51299</td>\n",
" <td>14.40</td>\n",
" <td>1.74</td>\n",
" <td>1.54</td>\n",
" <td>74.55</td>\n",
" <td>0.00</td>\n",
" <td>7.59</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.53393</td>\n",
" <td>12.30</td>\n",
" <td>0.00</td>\n",
" <td>1.00</td>\n",
" <td>70.16</td>\n",
" <td>0.12</td>\n",
" <td>16.19</td>\n",
" <td>0.0</td>\n",
" <td>0.24</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" RI Na Mg Al Si 'K' Ca Ba Fe Type\n",
"0 1.51793 12.79 3.50 1.12 73.03 0.64 8.77 0.0 0.00 0\n",
"1 1.51643 12.16 3.52 1.35 72.89 0.57 8.53 0.0 0.00 1\n",
"2 1.51793 13.21 3.48 1.41 72.64 0.59 8.43 0.0 0.00 0\n",
"3 1.51299 14.40 1.74 1.54 74.55 0.00 7.59 0.0 0.00 2\n",
"4 1.53393 12.30 0.00 1.00 70.16 0.12 16.19 0.0 0.24 3"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get data as a dataset\n",
"dt = Datasets()\n",
"data = dt.load(\"glass\", dataframe=True)\n",
"features = dt.dataset.features\n",
"class_name = dt.dataset.class_name\n",
"factorization, class_factors = pd.factorize(data[class_name])\n",
"data[class_name] = factorization\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7c9e1eae-6a66-4930-a125-f9f3def45574",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>RI</th>\n",
" <th>Na</th>\n",
" <th>Mg</th>\n",
" <th>Al</th>\n",
" <th>Si</th>\n",
" <th>'K'</th>\n",
" <th>Ca</th>\n",
" <th>Ba</th>\n",
" <th>Fe</th>\n",
" <th>Type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>31.0</td>\n",
" <td>8.0</td>\n",
" <td>15.0</td>\n",
" <td>13.0</td>\n",
" <td>38.0</td>\n",
" <td>26.0</td>\n",
" <td>9.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>23.0</td>\n",
" <td>3.0</td>\n",
" <td>15.0</td>\n",
" <td>19.0</td>\n",
" <td>36.0</td>\n",
" <td>19.0</td>\n",
" <td>9.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
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" <th>2</th>\n",
" <td>31.0</td>\n",
" <td>17.0</td>\n",
" <td>15.0</td>\n",
" <td>20.0</td>\n",
" <td>24.0</td>\n",
" <td>21.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3.0</td>\n",
" <td>42.0</td>\n",
" <td>6.0</td>\n",
" <td>21.0</td>\n",
" <td>47.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
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" <th>4</th>\n",
" <td>63.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>11.0</td>\n",
" <td>0.0</td>\n",
" <td>8.0</td>\n",
" <td>21.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>209</th>\n",
" <td>17.0</td>\n",
" <td>22.0</td>\n",
" <td>14.0</td>\n",
" <td>15.0</td>\n",
" <td>26.0</td>\n",
" <td>21.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>210</th>\n",
" <td>14.0</td>\n",
" <td>10.0</td>\n",
" <td>15.0</td>\n",
" <td>27.0</td>\n",
" <td>25.0</td>\n",
" <td>30.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211</th>\n",
" <td>19.0</td>\n",
" <td>33.0</td>\n",
" <td>15.0</td>\n",
" <td>17.0</td>\n",
" <td>36.0</td>\n",
" <td>12.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>212</th>\n",
" <td>23.0</td>\n",
" <td>5.0</td>\n",
" <td>8.0</td>\n",
" <td>21.0</td>\n",
" <td>43.0</td>\n",
" <td>30.0</td>\n",
" <td>9.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>213</th>\n",
" <td>44.0</td>\n",
" <td>38.0</td>\n",
" <td>6.0</td>\n",
" <td>21.0</td>\n",
" <td>25.0</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>214 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" RI Na Mg Al Si 'K' Ca Ba Fe Type\n",
"0 31.0 8.0 15.0 13.0 38.0 26.0 9.0 0.0 0.0 0\n",
"1 23.0 3.0 15.0 19.0 36.0 19.0 9.0 0.0 0.0 1\n",
"2 31.0 17.0 15.0 20.0 24.0 21.0 7.0 0.0 0.0 0\n",
"3 3.0 42.0 6.0 21.0 47.0 0.0 3.0 0.0 0.0 2\n",
"4 63.0 4.0 0.0 11.0 0.0 8.0 21.0 0.0 4.0 3\n",
".. ... ... ... ... ... ... ... ... ... ...\n",
"209 17.0 22.0 14.0 15.0 26.0 21.0 4.0 0.0 0.0 1\n",
"210 14.0 10.0 15.0 27.0 25.0 30.0 3.0 0.0 0.0 3\n",
"211 19.0 33.0 15.0 17.0 36.0 12.0 3.0 0.0 4.0 3\n",
"212 23.0 5.0 8.0 21.0 43.0 30.0 9.0 0.0 0.0 3\n",
"213 44.0 38.0 6.0 21.0 25.0 0.0 10.0 0.0 0.0 2\n",
"\n",
"[214 rows x 10 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Fayyad Irani\n",
"discretiz = MDLP()\n",
"Xdisc = discretiz.fit_transform(\n",
" data[features].to_numpy(), data[class_name].to_numpy()\n",
")\n",
"features_discretized = pd.DataFrame(Xdisc, columns=features)\n",
"dataset_discretized = features_discretized.copy()\n",
"dataset_discretized[class_name] = data[class_name]\n",
"X = dataset_discretized[features]\n",
"y = dataset_discretized[class_name]\n",
"dataset_discretized"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6a1aad95-370f-4854-ae9a-32205aff5d39",
"metadata": {},
"outputs": [],
"source": [
"for simple_init in [False, True]:\n",
" model = TAN(simple_init=simple_init)\n",
" for head in range(4):\n",
" model.fit(X, y, head=head, features=features, class_name=class_name)\n",
" ypred = model.predict(X)\n",
" #model.plot(f\"simple_init={simple_init} head={head} score={model.predict(X)}\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "76905bf3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(214, 9)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.shape\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.8"
},
"vscode": {
"interpreter": {
"hash": "a5f800306069c11c1b9a793f47dfeb8c7d63d06a771fda00cf3476e3d4088a52"
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},
"nbformat": 4,
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