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Add torch library

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This commit is contained in:
Ricardo Montañana Gómez
2023-10-31 10:07:24 +01:00
parent 77c33942f6
commit cb3281ed91
16 changed files with 884 additions and 27 deletions
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9
CMakeLists.txt
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@@ -1,10 +1,13 @@
cmake_minimum_required(VERSION 3.5)
project(testcpy)
project(PyWrap)
set( CMAKE_CXX_STANDARD 20)
set( CMAKE_CXX_STANDARD_REQUIRED ON )
set(CMAKE_CXX_STANDARD 20)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
find_package(Python3 3.11...3.11.9 COMPONENTS Interpreter Development REQUIRED)
find_package(Torch REQUIRED)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}")
add_subdirectory(lib/Files)
add_subdirectory(src)

332
data/glass.arff Executable file
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@@ -0,0 +1,332 @@
% 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
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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
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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
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1.51934,13.64,3.54,0.75,72.65,0.16,8.89,0.15,0.24,'vehic wind float'
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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'
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1.51829,13.24,3.9,1.41,72.33,0.55,8.31,0,0.1,'build wind non-float'
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1.5221,13.73,3.84,0.72,71.76,0.17,9.74,0,0,'build wind float'
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1.51784,12.68,3.67,1.16,73.11,0.61,8.7,0,0,'build wind float'
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1.51977,13.81,3.58,1.32,71.72,0.12,8.67,0.69,0,'build wind float'
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1.51708,13.72,3.68,1.81,72.06,0.64,7.88,0,0,'build wind non-float'
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1.51813,13.43,3.98,1.18,72.49,0.58,8.15,0,0,'build wind non-float'
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1.51655,13.41,3.39,1.28,72.64,0.52,8.65,0,0,'vehic wind float'
1.51751,12.81,3.57,1.35,73.02,0.62,8.59,0,0,'build wind float'
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1.51844,13.25,3.76,1.32,72.4,0.58,8.42,0,0,'build wind non-float'
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1.52172,13.51,3.86,0.88,71.79,0.23,9.54,0,0.11,'build wind float'
1.51602,14.85,0,2.38,73.28,0,8.76,0.64,0.09,headlamps
1.51321,13,0,3.02,70.7,6.21,6.93,0,0,containers
1.52739,11.02,0,0.75,73.08,0,14.96,0,0,'build wind non-float'
1.52213,14.21,3.82,0.47,71.77,0.11,9.57,0,0,'build wind float'
1.51747,12.84,3.5,1.14,73.27,0.56,8.55,0,0,'build wind float'
1.51839,12.85,3.67,1.24,72.57,0.62,8.68,0,0.35,'build wind non-float'
1.51646,13.41,3.55,1.25,72.81,0.68,8.1,0,0,'build wind non-float'
1.51609,15.01,0,2.51,73.05,0.05,8.83,0.53,0,headlamps
1.51667,12.94,3.61,1.26,72.75,0.56,8.6,0,0,'build wind non-float'
1.51588,13.12,3.41,1.58,73.26,0.07,8.39,0,0.19,'build wind non-float'
1.52667,13.99,3.7,0.71,71.57,0.02,9.82,0,0.1,'build wind float'
1.51831,14.39,0,1.82,72.86,1.41,6.47,2.88,0,headlamps
1.51918,14.04,3.58,1.37,72.08,0.56,8.3,0,0,'build wind float'
1.51613,13.88,1.78,1.79,73.1,0,8.67,0.76,0,headlamps
1.52196,14.36,3.85,0.89,71.36,0.15,9.15,0,0,'build wind float'
1.51824,12.87,3.48,1.29,72.95,0.6,8.43,0,0,'build wind float'
1.52151,11.03,1.71,1.56,73.44,0.58,11.62,0,0,containers
1.51969,14.56,0,0.56,73.48,0,11.22,0,0,tableware
1.51618,13.01,3.5,1.48,72.89,0.6,8.12,0,0,'build wind non-float'
1.51645,13.4,3.49,1.52,72.65,0.67,8.08,0,0.1,'build wind non-float'
1.51796,13.5,3.36,1.63,71.94,0.57,8.81,0,0.09,'vehic wind float'
1.52222,14.43,0,1,72.67,0.1,11.52,0,0.08,'build wind non-float'
1.51783,12.69,3.54,1.34,72.95,0.57,8.75,0,0,'build wind float'
1.51711,14.23,0,2.08,73.36,0,8.62,1.67,0,headlamps
1.51736,12.78,3.62,1.29,72.79,0.59,8.7,0,0,'build wind float'
1.51808,13.43,2.87,1.19,72.84,0.55,9.03,0,0,'build wind float'
1.5167,13.24,3.57,1.38,72.7,0.56,8.44,0,0.1,'vehic wind float'
1.52043,13.38,0,1.4,72.25,0.33,12.5,0,0,containers
1.519,13.49,3.48,1.35,71.95,0.55,9,0,0,'build wind float'
1.51778,13.21,2.81,1.29,72.98,0.51,9.02,0,0.09,'build wind float'
1.51905,14,2.39,1.56,72.37,0,9.57,0,0,tableware
1.51531,14.38,0,2.66,73.1,0.04,9.08,0.64,0,headlamps
1.51916,14.15,0,2.09,72.74,0,10.88,0,0,tableware
1.51841,13.02,3.62,1.06,72.34,0.64,9.13,0,0.15,'build wind non-float'
1.5159,13.02,3.58,1.51,73.12,0.69,7.96,0,0,'build wind non-float'
1.51593,13.25,3.45,1.43,73.17,0.61,7.86,0,0,'build wind non-float'
1.5164,12.55,3.48,1.87,73.23,0.63,8.08,0,0.09,'build wind non-float'
1.51663,12.93,3.54,1.62,72.96,0.64,8.03,0,0.21,'build wind non-float'
1.5169,13.33,3.54,1.61,72.54,0.68,8.11,0,0,'build wind non-float'
1.51869,13.19,3.37,1.18,72.72,0.57,8.83,0,0.16,'build wind float'
1.51776,13.53,3.41,1.52,72.04,0.58,8.79,0,0,'vehic wind float'
1.51775,12.85,3.48,1.23,72.97,0.61,8.56,0.09,0.22,'build wind float'
1.5186,13.36,3.43,1.43,72.26,0.51,8.6,0,0,'build wind non-float'
1.5172,13.38,3.5,1.15,72.85,0.5,8.43,0,0,'build wind float'
1.51623,14.2,0,2.79,73.46,0.04,9.04,0.4,0.09,headlamps
1.51618,13.53,3.55,1.54,72.99,0.39,7.78,0,0,'build wind float'
1.51761,12.81,3.54,1.23,73.24,0.58,8.39,0,0,'build wind float'
1.5161,13.42,3.4,1.22,72.69,0.59,8.32,0,0,'vehic wind float'
1.51592,12.86,3.52,2.12,72.66,0.69,7.97,0,0,'build wind non-float'
1.51613,13.92,3.52,1.25,72.88,0.37,7.94,0,0.14,'build wind non-float'
1.51689,12.67,2.88,1.71,73.21,0.73,8.54,0,0,'build wind non-float'
1.51852,14.09,2.19,1.66,72.67,0,9.32,0,0,tableware

225
data/iris.arff Executable file
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@@ -0,0 +1,225 @@
% 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
%
%
%

170
lib/Files/ArffFiles.cc Normal file
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#include "ArffFiles.h"
#include <fstream>
#include <sstream>
#include <map>
#include <iostream>
using namespace std;
ArffFiles::ArffFiles() = default;
vector<string> ArffFiles::getLines() const
{
return lines;
}
unsigned long int ArffFiles::getSize() const
{
return lines.size();
}
vector<pair<string, string>> ArffFiles::getAttributes() const
{
return attributes;
}
string ArffFiles::getClassName() const
{
return className;
}
string ArffFiles::getClassType() const
{
return classType;
}
vector<vector<float>>& ArffFiles::getX()
{
return X;
}
vector<int>& ArffFiles::getY()
{
return y;
}
void ArffFiles::loadCommon(string fileName)
{
ifstream file(fileName);
if (!file.is_open()) {
throw invalid_argument("Unable to open file");
}
string line;
string keyword;
string attribute;
string type;
string type_w;
while (getline(file, line)) {
if (line.empty() || line[0] == '%' || line == "\r" || line == " ") {
continue;
}
if (line.find("@attribute") != string::npos || line.find("@ATTRIBUTE") != string::npos) {
stringstream ss(line);
ss >> keyword >> attribute;
type = "";
while (ss >> type_w)
type += type_w + " ";
attributes.emplace_back(trim(attribute), trim(type));
continue;
}
if (line[0] == '@') {
continue;
}
lines.push_back(line);
}
file.close();
if (attributes.empty())
throw invalid_argument("No attributes found");
}
void ArffFiles::load(const string& fileName, bool classLast)
{
int labelIndex;
loadCommon(fileName);
if (classLast) {
className = get<0>(attributes.back());
classType = get<1>(attributes.back());
attributes.pop_back();
labelIndex = static_cast<int>(attributes.size());
} else {
className = get<0>(attributes.front());
classType = get<1>(attributes.front());
attributes.erase(attributes.begin());
labelIndex = 0;
}
generateDataset(labelIndex);
}
void ArffFiles::load(const string& fileName, const string& name)
{
int labelIndex;
loadCommon(fileName);
bool found = false;
for (int i = 0; i < attributes.size(); ++i) {
if (attributes[i].first == name) {
className = get<0>(attributes[i]);
classType = get<1>(attributes[i]);
attributes.erase(attributes.begin() + i);
labelIndex = i;
found = true;
break;
}
}
if (!found) {
throw invalid_argument("Class name not found");
}
generateDataset(labelIndex);
}
void ArffFiles::generateDataset(int labelIndex)
{
X = vector<vector<float>>(attributes.size(), vector<float>(lines.size()));
auto yy = vector<string>(lines.size(), "");
auto removeLines = vector<int>(); // Lines with missing values
for (size_t i = 0; i < lines.size(); i++) {
stringstream ss(lines[i]);
string value;
int pos = 0;
int xIndex = 0;
while (getline(ss, value, ',')) {
if (pos++ == labelIndex) {
yy[i] = value;
} else {
if (value == "?") {
X[xIndex++][i] = -1;
removeLines.push_back(i);
} else
X[xIndex++][i] = stof(value);
}
}
}
for (auto i : removeLines) {
yy.erase(yy.begin() + i);
for (auto& x : X) {
x.erase(x.begin() + i);
}
}
y = factorize(yy);
}
string ArffFiles::trim(const string& source)
{
string s(source);
s.erase(0, s.find_first_not_of(" '\n\r\t"));
s.erase(s.find_last_not_of(" '\n\r\t") + 1);
return s;
}
vector<int> ArffFiles::factorize(const vector<string>& labels_t)
{
vector<int> yy;
yy.reserve(labels_t.size());
map<string, int> labelMap;
int i = 0;
for (const string& label : labels_t) {
if (labelMap.find(label) == labelMap.end()) {
labelMap[label] = i++;
}
yy.push_back(labelMap[label]);
}
return yy;
}

34
lib/Files/ArffFiles.h Normal file
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@@ -0,0 +1,34 @@
#ifndef ARFFFILES_H
#define ARFFFILES_H
#include <string>
#include <vector>
using namespace std;
class ArffFiles {
private:
vector<string> lines;
vector<pair<string, string>> attributes;
string className;
string classType;
vector<vector<float>> X;
vector<int> y;
void generateDataset(int);
void loadCommon(string);
public:
ArffFiles();
void load(const string&, bool = true);
void load(const string&, const string&);
vector<string> getLines() const;
unsigned long int getSize() const;
string getClassName() const;
string getClassType() const;
static string trim(const string&);
vector<vector<float>>& getX();
vector<int>& getY();
vector<pair<string, string>> getAttributes() const;
static vector<int> factorize(const vector<string>& labels_t);
};
#endif

1
lib/Files/CMakeLists.txt Normal file
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@@ -0,0 +1 @@
add_library(ArffFiles ArffFiles.cc)

1
pspp.jnl Normal file
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@@ -0,0 +1 @@
GET FILE="/home/rmontanana/Code/covbench/data/covid_v9_20220630.sav".

6
src/CMakeLists.txt
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@@ -1,6 +1,8 @@
include_directories(${PyWrap_SOURCE_DIR}/lib/Files)
include_directories(${Python3_INCLUDE_DIRS})
add_executable(main main.cc STree.cc SVC.cc PyClassifier.cc PyWrap.cc)
add_executable(example example.cpp)
target_link_libraries(main ${Python3_LIBRARIES} "${TORCH_LIBRARIES}")
target_link_libraries(main ${Python3_LIBRARIES} "${TORCH_LIBRARIES}" ArffFiles)
target_link_libraries(example "${TORCH_LIBRARIES}" ArffFiles)

9
src/PyClassifier.cc
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@@ -13,10 +13,13 @@ namespace pywrap {
pyWrap->clean(module, className);
}
template<typename T>
T PyClassifier::callMethod(const std::string& method)
std::string PyClassifier::callMethod(const std::string& method)
{
return pyWrap->callMethod<T>(module, className, method);
return pyWrap->callMethodString(module, className, method);
}
PyClassifier& PyClassifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
{
}
} /* namespace PyWrap */

3
src/PyClassifier.h
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@@ -12,8 +12,7 @@ namespace pywrap {
PyClassifier(const std::string& module, const std::string& className);
virtual ~PyClassifier();
PyClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states);
template <typename T>
T callMethod(const std::string& method);
std::string callMethod(const std::string& method);
private:
PyWrap* pyWrap;
std::string module;

18
src/PyWrap.cc
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@@ -102,6 +102,24 @@ namespace pywrap {
Py_DECREF(result);
return value;
}
std::string PyWrap::callMethodString(const std::string& moduleName, const std::string& className, const std::string& method)
{
std::cout << "Llamando método" << std::endl;
auto item = moduleClassMap.find({ moduleName, className });
if (item == moduleClassMap.end()) {
errorAbort("Module " + moduleName + " and class " + className + " not found");
}
std::cout << "Clase encontrada" << std::endl;
PyObject* instance = std::get<2>(item->second);
PyObject* result;
if (!(result = PyObject_CallMethod(instance, method.c_str(), NULL)))
errorAbort("Couldn't call method " + method);
std::string value = PyUnicode_AsUTF8(result);
std::cout << "Result: " << value << std::endl;
Py_DECREF(result);
return value;
}
// void PyWrap::doCommand2()
// {
// PyObject* list = Py_BuildValue("[s]", "Stree");

21
src/PyWrap.h
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@@ -18,16 +18,17 @@ namespace pywrap {
~PyWrap();
template<typename T>
T callMethod(const std::string& moduleName, const std::string& className, const std::string& method);
template<typename T> T returnMethod(PyObject* result);
template<std::string> std::string returnMethod(PyObject* result);
template<int> int returnMethod(PyObject* result);
template<bool> bool returnMethod(PyObject* result);
template<torch::Tensor> torch::Tensor returnMethod(PyObject* result)
{
// PyObject* THPVariable_Wrap(at::Tensor t);
// at::Tensor& THPVariable_Unpack(PyObject * obj);
return THPVariable_Unpack(result);
};
std::string callMethodString(const std::string& moduleName, const std::string& className, const std::string& method);
// template<typename T> T returnMethod(PyObject* result);
// template<std::string> std::string returnMethod(PyObject* result);
// template<int> int returnMethod(PyObject* result);
// template<bool> bool returnMethod(PyObject* result);
// template<torch::Tensor> torch::Tensor returnMethod(PyObject* result)
// {
// // PyObject* THPVariable_Wrap(at::Tensor t);
// // at::Tensor& THPVariable_Unpack(PyObject * obj);
// return THPVariable_Unpack(result);
// };
void importClass(const std::string& moduleName, const std::string& className);
void clean(const std::string& moduleName, const std::string& className);
// void doCommand2();

2
src/STree.cc
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@@ -5,7 +5,7 @@ namespace pywrap {
void STree::version()
{
std::cout << "Version: " << callMethod<std::string>("version") << std::endl;
std::cout << "Version: " << callMethod("version") << std::endl;
}
} /* namespace pywrap */

2
src/SVC.cc
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@@ -5,7 +5,7 @@ namespace pywrap {
void SVC::version()
{
//std::cout << "repr_html: " << callMethod<std::string>("_repr_html_") << std::endl;
std::cout << "repr_html: " << callMethod("_repr_html_") << std::endl;
}
} /* namespace pywrap */

36
src/example.cpp
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@@ -1,7 +1,10 @@
#include <torch/torch.h>
#include "ArffFiles.h"
#include<string>
#include<iostream>
using namespace std;
using namespace torch;
class Test {
public:
Test(const string& c) : c(c) {};
@@ -13,18 +16,45 @@ public:
std::cout << "Llamando a metodo" << std::endl;
return parameter;
}
private:
string c;
};
tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(const string& name, bool class_last)
{
auto handler = ArffFiles();
handler.load(static_cast<string>(name) + ".arff", class_last);
// Get Dataset X, y
vector<vector<float>> X = handler.getX();
vector<int> y = handler.getY();
// // Get className & Features
auto className = handler.getClassName();
vector<string> features;
auto attributes = handler.getAttributes();
transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
torch::Tensor Xd;
auto states = map<string, vector<int>>();
auto yt = torch::tensor(y, torch::kInt32);
Xd = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kFloat32);
for (int i = 0; i < features.size(); ++i) {
Xd.index_put_({ i, "..." }, torch::tensor(X[i], torch::kFloat32));
}
return make_tuple(Xd, yt, features, className, states);
}
int main()
{
Test t("hola");
cout << t.callMethod<string>("hola") << endl;
cout << t.callMethod<int>(1) << endl;
cout << t.callMethod<double>(7.3) << endl;
vector<vector<float>> X;
vector<int> y = { 1, 2, 3 };
X.push_back({ 1.1, 2.2, 3.3 });
vector<float> v = { 1.1, 2.2, 3.3 };
torch::Tensor matrix = torch::tensor(X[0], torch::kFloat32);
cout << "X:" << matrix << endl;
cout << "y:" << torch::tensor(y, torch::kInt32) << endl;
return 0;
}

42
src/main.cc
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@@ -1,10 +1,48 @@
#include <torch/torch.h>
#include "ArffFiles.h"
#include <vector>
#include <string>
#include <iostream>
#include <map>
#include <tuple>
#include "STree.h"
#include "SVC.h"
using namespace std;
using namespace torch;
class Paths {
public:
static string datasets()
{
return "/home/rmontanana/Code/discretizbench/datasets/";
}
};
tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(const string& name, bool class_last)
{
auto handler = ArffFiles();
handler.load(Paths::datasets() + static_cast<string>(name) + ".arff", class_last);
// Get Dataset X, y
vector<vector<float>> X = handler.getX();
vector<int> y = handler.getY();
// Get className & Features
auto className = handler.getClassName();
vector<string> features;
auto attributes = handler.getAttributes();
transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
Tensor Xd;
auto states = map<string, vector<int>>();
Xd = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kFloat32);
for (int i = 0; i < features.size(); ++i) {
Xd.index_put_({ i, "..." }, torch::tensor(X[i], torch::kFloat32));
}
return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
}
int main(int argc, char* argv[])
{
// auto wrap = pywrap::PyWrap("stree", "Stree");
// wrap.callMethod("version");
auto [X, y, features, className, states] = loadDataset("iris", true);
auto stree = pywrap::STree();
stree.version();
auto svc = pywrap::SVC();