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Commit 097a20cd authored by Jakhes's avatar Jakhes
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Adding finished lars method

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src/methods/lars/Makefile
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splfr=/usr/local/sicstus4.7.1/bin/splfr splfr=/usr/local/sicstus4.7.1/bin/splfr
METHOD_NAME=new_method METHOD_NAME=lars
$(METHOD_NAME).so: $(METHOD_NAME).pl $(METHOD_NAME).cpp $(METHOD_NAME).so: $(METHOD_NAME).pl $(METHOD_NAME).cpp
$(splfr) -larmadillo -fopenmp -lmlpack -lstdc++ -cxx --struct $(METHOD_NAME).pl $(METHOD_NAME).cpp ../../helper_files/helper.cpp $(splfr) -larmadillo -fopenmp -lmlpack -lstdc++ -cxx --struct $(METHOD_NAME).pl $(METHOD_NAME).cpp ../../helper_files/helper.cpp
......
src/methods/lars/lars.cpp
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#include <sicstus/sicstus.h> #include <sicstus/sicstus.h>
//#include "sicstus.h"
/* ex_glue.h is generated by splfr from the foreign/[2,3] facts. /* ex_glue.h is generated by splfr from the foreign/[2,3] facts.
Always include the glue header in your foreign resource code. Always include the glue header in your foreign resource code.
*/ */
...@@ -26,7 +27,7 @@ void initModelNoDataNoGram(SP_integer useCholesky, double lambda1, double lambda ...@@ -26,7 +27,7 @@ void initModelNoDataNoGram(SP_integer useCholesky, double lambda1, double lambda
// input: const bool useCholesky, const arma::mat & gramMatrix, const double lambda1, const double lambda2, const double tolerance // input: const bool useCholesky, const arma::mat & gramMatrix, const double lambda1, const double lambda2, const double tolerance
// output: // output:
void initModelNoDataWithGram(SP_integer useCholesky, float *gramArr, SP_integer gramSize, SP_integer gramRowNum, SP_integer lambda1, SP_integer lambda2, double tol) void initModelNoDataWithGram(SP_integer useCholesky, float *gramArr, SP_integer gramSize, SP_integer gramRowNum, double lambda1, double lambda2, double tol)
{ {
mat gramMatrix = convertArrayToMat(gramArr, gramSize, gramRowNum); mat gramMatrix = convertArrayToMat(gramArr, gramSize, gramRowNum);
...@@ -36,7 +37,7 @@ void initModelNoDataWithGram(SP_integer useCholesky, float *gramArr, SP_integer ...@@ -36,7 +37,7 @@ void initModelNoDataWithGram(SP_integer useCholesky, float *gramArr, SP_integer
// input: const arma::mat & dataMatrix, const arma::rowvec & responses, const bool transposeData, const bool useCholesky, const double lambda1, const double lambda2, const double tolerance // input: const arma::mat & dataMatrix, const arma::rowvec & responses, const bool transposeData, const bool useCholesky, const double lambda1, const double lambda2, const double tolerance
// output: // output:
void initModelWithDataNoGram(float *dataArr, SP_integer dataSize, SP_integer dataRowNum, float *responsesArr, SP_integer responsesSize, SP_integer transposeData, void initModelWithDataNoGram(float *dataArr, SP_integer dataSize, SP_integer dataRowNum, float *responsesArr, SP_integer responsesSize, SP_integer transposeData,
SP_integer useCholesky, SP_integer lambda1, SP_integer lambda2, double tol) SP_integer useCholesky, double lambda1, double lambda2, double tol)
{ {
if(dataSize / dataRowNum != responsesSize) if(dataSize / dataRowNum != responsesSize)
{ {
...@@ -47,13 +48,13 @@ void initModelWithDataNoGram(float *dataArr, SP_integer dataSize, SP_integer dat ...@@ -47,13 +48,13 @@ void initModelWithDataNoGram(float *dataArr, SP_integer dataSize, SP_integer dat
mat data = convertArrayToMat(dataArr, dataSize, dataRowNum); mat data = convertArrayToMat(dataArr, dataSize, dataRowNum);
rowvec responses = convertArrayToRowvec(responsesArr, responsesSize); rowvec responses = convertArrayToRowvec(responsesArr, responsesSize);
regressor = new LARS(data, responses, (transposedata == 1), (useCholesky == 1), lambda1, lambda2, tol); regressor = new LARS(data, responses, (transposeData == 1), (useCholesky == 1), lambda1, lambda2, tol);
} }
// input: const arma::mat & dataMatrix, const arma::rowvec & responses, const bool transposeData, const bool useCholesky, const arma::mat & gramMatrix, const double lambda1, const double lambda2, const double tolerance // input: const arma::mat & dataMatrix, const arma::rowvec & responses, const bool transposeData, const bool useCholesky, const arma::mat & gramMatrix, const double lambda1, const double lambda2, const double tolerance
// output: // output:
void initModelWithDataWithGram(float *dataArr, SP_integer dataSize, SP_integer dataRowNum, float *responsesArr, SP_integer responsesSize, SP_integer transposeData, void initModelWithDataWithGram(float *dataArr, SP_integer dataSize, SP_integer dataRowNum, float *responsesArr, SP_integer responsesSize, SP_integer transposeData,
SP_integer useCholesky, float *gramArr, SP_integer gramSize, SP_integer gramRowNum, SP_integer lambda1, SP_integer lambda2, double tol) SP_integer useCholesky, float *gramArr, SP_integer gramSize, SP_integer gramRowNum, double lambda1, double lambda2, double tol)
{ {
if(dataSize / dataRowNum != responsesSize) if(dataSize / dataRowNum != responsesSize)
{ {
...@@ -65,19 +66,19 @@ void initModelWithDataWithGram(float *dataArr, SP_integer dataSize, SP_integer d ...@@ -65,19 +66,19 @@ void initModelWithDataWithGram(float *dataArr, SP_integer dataSize, SP_integer d
rowvec responses = convertArrayToRowvec(responsesArr, responsesSize); rowvec responses = convertArrayToRowvec(responsesArr, responsesSize);
mat gramMatrix = convertArrayToMat(gramArr, gramSize, gramRowNum); mat gramMatrix = convertArrayToMat(gramArr, gramSize, gramRowNum);
regressor = new LARS(data, responses, (transposedata == 1), (useCholesky == 1), gramMatrix, lambda1, lambda2, tol); regressor = new LARS(data, responses, (transposeData == 1), (useCholesky == 1), gramMatrix, lambda1, lambda2, tol);
} }
// TODO: conversion of std::vector to array
// input: // input:
// output: const std::vector<size_t>& // output: const std::vector<size_t>&
void activeSet(SP_integer a) void activeSet(float **activeSetArr, SP_integer *activeSetSize)
{ {
vector<size_t> active = regressor.ActiveSet(); std::vector<size_t> activeVec = regressor.ActiveSet();
for (size_t i = 0; i < active.size(); i++)
{ // give back the sizes and the converted results as arrays
cout << active[i] << endl; *activeSetSize = activeVec.size();
}
*activeSetArr = convertToArray(activeVec);
} }
// input: // input:
...@@ -85,17 +86,26 @@ void activeSet(SP_integer a) ...@@ -85,17 +86,26 @@ void activeSet(SP_integer a)
void beta( float **betaArr, SP_integer *betaSize) void beta( float **betaArr, SP_integer *betaSize)
{ {
rowvec betasolution = regressor.Beta(); rowvec betasolution = regressor.Beta();
// give back the sizes and the converted results as arrays
*betaSize = betasolution.n_elem; *betaSize = betasolution.n_elem;
*betaArr = convertToArray(betasolution); *betaArr = convertToArray(betasolution);
} }
// TODO: adding matrix as a return
// input: // input:
// output: std::vector<arma::vec>& // output: std::vector<arma::vec>&
void betaPath() void betaPath(float **betaPathArr, SP_integer *betaPathColNum, SP_integer *betaPathRowNum)
{ {
// get the betaPath matrix
vector<vec> matrix = regressor.BetaPath();
// return the matrix dimensions
*betaPathColNum = matrix[0].size();
*betaPathRowNum = matrix.size();
// return the matrix as array
*betaPathArr = convertToArray(matrix);
} }
// input: const arma::mat &data, const arma::rowvec &responses, const bool rowMajor // input: const arma::mat &data, const arma::rowvec &responses, const bool rowMajor
...@@ -115,32 +125,44 @@ SP_integer computeError(float *points_mat_arr, SP_integer dataSize, SP_integer d ...@@ -115,32 +125,44 @@ SP_integer computeError(float *points_mat_arr, SP_integer dataSize, SP_integer d
return regressor.ComputeError(data, responses); return regressor.ComputeError(data, responses);
} }
// TODO: conversion of vector to array
// input: // input:
// output: std::vector<double>& // output: std::vector<double>&
void lambdaPath() void lambdaPath(float **lambdaPathArr, SP_integer *lambdaPathSize)
{ {
std::vector<double> lambdaPathVec = regressor.LambdaPath();
// give back the sizes and the converted results as arrays
*lambdaPathSize = lambdaPathVec.size();
*lambdaPathArr = convertToArray(lambdaPathVec);
} }
// TODO: adding matrix as a return
// input: // input:
// output: arma::mat& upper triangular cholesky factor // output: arma::mat& upper triangular cholesky factor
void matUtriCholFactor() void matUtriCholFactor(float **factorArr, SP_integer *factorColNum, SP_integer *factorRowNum)
{ {
// get the UtriCholFactor matrix
mat UtriCholFact = regressor.MatUtriCholFactor(); mat UtriCholFact = regressor.MatUtriCholFactor();
// return the matrix dimensions
*factorColNum = UtriCholFact.n_cols;
*factorRowNum = UtriCholFact.n_rows;
// return the matrix as array
*factorArr = convertToArray(UtriCholFact);
} }
// input: const arma::mat &points, arma::rowvec &predictions, const bool rowMajor // input: const arma::mat &points, arma::rowvec &predictions, const bool rowMajor
// output: // output:
void predict(float *pointsArr, SP_integer pointsSize, SP_integer pointsRowNum, float **predicArr, SP_integer *predicSize, SP_integer rowMajor) void predict(float *pointsArr, SP_integer pointsSize, SP_integer pointsRowNum, float **predicArr, SP_integer *predicSize, SP_integer rowMajor)
{ {
// convert the Prolog arrays to std::vec for easy conversion to arma::mat // convert the array to arma::mat
mat data = convertArrayToMat(pointsArr, pointsSize, pointsRowNum); mat data = convertArrayToMat(pointsArr, pointsSize, pointsRowNum);
// run the prediction and save the result in arma::rowvec // run the prediction and save the result in arma::rowvec
rowvec predictions; rowvec predictions;
regressor.Predict(data, predictions); regressor.Predict(data, predictions, (rowMajor == 1));
// give back the sizes and the converted results as arrays // give back the sizes and the converted results as arrays
...@@ -151,34 +173,36 @@ void predict(float *pointsArr, SP_integer pointsSize, SP_integer pointsRowNum, f ...@@ -151,34 +173,36 @@ void predict(float *pointsArr, SP_integer pointsSize, SP_integer pointsRowNum, f
// input: const arma::mat &data, const arma::rowvec &responses, arma::vec& beta, const bool transposeData // input: const arma::mat &data, const arma::rowvec &responses, arma::vec& beta, const bool transposeData
// output: double minimum cost error // output: double minimum cost error
void train(float *dataArr, SP_integer dataSize, SP_integer dataRowNum, float *responsesArr, SP_integer responsesSize, float **betaArr, SP_integer *betaSize, SP_integer transposeData) SP_integer train(float *dataArr, SP_integer dataSize, SP_integer dataRowNum, float *responsesArr, SP_integer responsesSize, float **betaArr, SP_integer *betaSize, SP_integer transposeData)
{ {
if(dataSize / dataRowNum != responsesSize) if(dataSize / dataRowNum != responsesSize)
{ {
cout << "Target dim doesnt fit to the Data dim" << endl; cout << "Target dim doesnt fit to the Data dim" << endl;
return; return 0;
} }
// convert the Prolog arrays to arma::rowvec and arma::mat // convert the Prolog arrays to arma::rowvec and arma::mat
mat data = convertArrayToMat(dataArr, dataSize, dataRowNum); mat data = convertArrayToMat(dataArr, dataSize, dataRowNum);
rowvec responses = convertArrayToRowvec(responsesArr, responsesSize); rowvec responses = convertArrayToRowvec(responsesArr, responsesSize);
vec betaVec;
// run the model function // run the model function
regressor.Train(data, responses); return regressor.Train(data, responses, betaVec, (transposeData == 1));
} }
// input: const arma::mat &data, const arma::rowvec &responses, const bool transposeData // input: const arma::mat &data, const arma::rowvec &responses, const bool transposeData
// output: double minimum cost error // output: double minimum cost error
void trainNoBetaReturn(float *dataArr, SP_integer dataSize, SP_integer dataRowNum, float *responsesArr, SP_integer responsesSize, SP_integer transposeData) SP_integer trainNoBetaReturn(float *dataArr, SP_integer dataSize, SP_integer dataRowNum, float *responsesArr, SP_integer responsesSize, SP_integer transposeData)
{ {
if(dataSize / dataRowNum != responsesSize) if(dataSize / dataRowNum != responsesSize)
{ {
cout << "Target dim doesnt fit to the Data dim" << endl; cout << "Target dim doesnt fit to the Data dim" << endl;
return; return 0;
} }
// convert the Prolog arrays to arma::rowvec and arma::mat // convert the Prolog arrays to arma::rowvec and arma::mat
rowvec responses = convertArrayToRowvec(responsesArr, responsesSize); rowvec responses = convertArrayToRowvec(responsesArr, responsesSize);
mat data = convertArrayToMat(dataArr, dataSize, dataRowNum); mat data = convertArrayToMat(dataArr, dataSize, dataRowNum);
// run the model function // run the model function
regressor.Train(data, responses); return regressor.Train(data, responses, (transposeData == 1));
} }
src/methods/lars/lars.pl
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:- module(lars, [function/4, function/1, function/2, function/6]). :- module(lars, [initModelNoDataNoGram/4, initModelNoDataWithGram/7, initModelWithDataNoGram/10, initModelWithDataWithGram/13, activeSet/2,beta/2, betaPath/3, computeError/7, lambdaPath/2, matUtriCholFactor/3, predict/6, train/9, trainNoBetaReturn/7]).
:- load_files(library(str_decl), :- load_files(library(str_decl),
[when(compile_time), if(changed)]). [when(compile_time), if(changed)]).
...@@ -14,92 +14,73 @@ ...@@ -14,92 +14,73 @@
%% definitions for the connected function and what there inputs and output arguments are %% definitions for the connected function and what there inputs and output arguments are
%% Funktion const bool useCholesky, const double lambda1, const double lambda2, const double tolerance %% Funktion
%% input const bool useCholesky, const double lambda1, const double lambda2, const double tolerance
%% output
foreign(initModelNoDataNoGram, c, initModelNoDataNoGram(+integer, +float32, +float32, +float32)). foreign(initModelNoDataNoGram, c, initModelNoDataNoGram(+integer, +float32, +float32, +float32)).
%% Funktion const bool useCholesky, const arma::mat & gramMatrix, const double lambda1, const double lambda2, const double tolerance %% Funktion
%% input const bool useCholesky, const arma::mat & gramMatrix, const double lambda1, const double lambda2, const double tolerance
%% output
foreign(initModelNoDataWithGram, c, initModelNoDataWithGram(+integer, +pointer(float_array), +integer, +integer, +float32, +float32, +float32)). foreign(initModelNoDataWithGram, c, initModelNoDataWithGram(+integer, +pointer(float_array), +integer, +integer, +float32, +float32, +float32)).
%% Funktion const arma::mat & dataMatrix, const arma::rowvec & responses, const bool transposeData, const bool useCholesky, const double lambda1, const double lambda2, const double tolerance %% Funktion
foreign(initModelWithDataNoGram, c, initModelWithDataNoGram(+pointer(float_array), +integer, +pointer(float_array), +integer, +integer, +integer, +float32, +float32, +float32)). %% input const arma::mat & dataMatrix, const arma::rowvec & responses, const bool transposeData, const bool useCholesky, const double lambda1, const double lambda2, const double tolerance
%% output
foreign(initModelWithDataNoGram, c, initModelWithDataNoGram(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, +integer, +float32, +float32, +float32)).
%% Funktion const arma::mat & dataMatrix, const arma::rowvec & responses, const bool transposeData, const bool useCholesky, const arma::mat & gramMatrix, const double lambda1, const double lambda2, const double tolerance %% Funktion
foreign(initModelWithDataWithGram, c, initModelWithDataWithGram(+pointer(float_array), +integer, +pointer(float_array), +integer, +integer, +integer, +pointer(float_array), +integer, +integer, +float32, +float32, +float32)). %% input const arma::mat & dataMatrix, const arma::rowvec & responses, const bool transposeData, const bool useCholesky, const arma::mat & gramMatrix, const double lambda1, const double lambda2, const double tolerance
%% output
foreign(initModelWithDataWithGram, c, initModelWithDataWithGram(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, +integer, +pointer(float_array), +integer, +integer, +float32, +float32, +float32)).
%% TODO: %% Funktion
%% Funktion that takes data from Prolog
%% input %% input
%% output const std::vector<size_t>& %% output const std::vector<size_t>&
foreign(activeSet, c, activeSet(+integer, +integer, +integer, +float32)). foreign(activeSet, c, activeSet(-pointer(float_array), -integer)).
%% Funktion that takes data from Prolog %% Funktion
%% input %% input
%% output arma::vec& %% output arma::vec&
foreign(beta, c, beta(+integer, +integer, +integer, +float32)). foreign(beta, c, beta(-pointer(float_array), -integer)).
%% TODO: %% Funktion
%% Funktion that takes data from Prolog
%% input %% input
%% output std::vector<arma::vec>& %% output std::vector<arma::vec>&
foreign(betaPath, c, betaPath(+integer, +integer, +integer, +float32)). foreign(betaPath, c, betaPath(-pointer(float_array), -integer, -integer)).
%% Funktion that takes data from Prolog %% Funktion
%% input: const arma::mat &data, const arma::rowvec &responses, const bool rowMajor %% input: const arma::mat &data, const arma::rowvec &responses, const bool rowMajor
%% output: double minimum cost error %% output: double minimum cost error
foreign(computeError, c, computeError(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, [-float32])). foreign(computeError, c, computeError(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, [-integer])).
%% TODO: %% Funktion
%% Funktion that takes data from Prolog
%% input %% input
%% output std::vector<double>& %% output std::vector<double>&
foreign(lambdaPath, c, lambdaPath(+integer, +integer, +integer, +float32)). foreign(lambdaPath, c, lambdaPath(-pointer(float_array), -integer)).
%% TODO: %% Funktion
%% Funktion that takes data from Prolog
%% input %% input
%% output arma::mat& upper triangular cholesky factor %% output arma::mat& upper triangular cholesky factor
foreign(matUtriCholFactor, c, matUtriCholFactor(+integer)). foreign(matUtriCholFactor, c, matUtriCholFactor(-pointer(float_array), -integer, -integer)).
%% Funktion that takes data from Prolog %% Funktion
%% input const arma::mat &points, arma::rowvec &predictions, const bool rowMajor %% input const arma::mat &points, arma::rowvec &predictions, const bool rowMajor
%% output %% output
foreign(predict, c, predict(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer, +integer)). foreign(predict, c, predict(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer, +integer)).
%% Funktion that takes data from Prolog %% Funktion
%% input const arma::mat &data, const arma::rowvec &responses, arma::vec& beta, const bool transposeData %% input const arma::mat &data, const arma::rowvec &responses, arma::vec& beta, const bool transposeData
%% output double minimum cost error %% output double minimum cost error
foreign(train, c, train(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, -pointer(float_array), -integer, +integer, [-integer])). foreign(train, c, train(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, -pointer(float_array), -integer, +integer, [-integer])).
%% Funktion that takes data from Prolog %% Funktion
%% input const arma::mat &data, const arma::rowvec &responses, const bool transposeData %% input const arma::mat &data, const arma::rowvec &responses, const bool transposeData
%% output double minimum cost error %% output double minimum cost error
foreign(trainNoBetaReturn, c, trainNoBetaReturn(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, [-integer])). foreign(trainNoBetaReturn, c, trainNoBetaReturn(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, [-integer])).
%% Funktion that takes data from Prolog
%% input
%% output
foreign(function, c, function(+integer, +integer, +integer, +float32)).
%% Funktion that return data to Prolog
foreign(function, c, function([-integer])).
%% Funktion that returns a array
foreign(function, c, function(-integer, [-pointer(float_array)])).
%% Funktion that takes a Matrix and a Array
foreign(function, c, function(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, [-integer])).
%% Defines what functions should be connected from main.cpp %% Defines what functions should be connected from main.cpp
foreign_resource(lars, [ foreign_resource(lars, [initModelNoDataNoGram, initModelNoDataWithGram, initModelWithDataNoGram, initModelWithDataWithGram, activeSet, beta, betaPath, computeError, lambdaPath, matUtriCholFactor, predict, train, trainNoBetaReturn]).
initModelNoDataNoGram,
initModelNoDataWithGram,
initModelWithDataNoGram,
initModelWithDataWithGram,
activeSet,
beta,
betaPath,
computeError,
lambdaPath,
matUtriCholFactor,
predict,
train,
trainNoBetaReturn]).
:- load_foreign_resource(lars). :- load_foreign_resource(lars).
src/methods/lars/lars_test.pl 0 → 100644
+ 47
− 0
View file @ 097a20cd
:- use_module(library(plunit)).
:- use_module(library(structs)).
:- use_module(lars).
:- use_module('../../helper_files/helper.pl').
reset_Model :-
initModelNoDataNoGram(1, 0.1, 0.3, 0.001),
convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5],3, array(Xsize, Xrownum, X)),
convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
trainNoBetaReturn(X,Xsize, Xrownum,Y, Ysize, 1, _).
:- begin_tests(lists).
%% train tests
test(train, [true(A =:= 0)]) :-
reset_Model,
convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5],3, array(Xsize, Xrownum, X)),
convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
trainNoBetaReturn(X,Xsize, Xrownum,Y, Ysize, 1, A),
predict(X, Xsize,Xrownum, Predic, PredicSize,0),
convert_float_array_to_list(Predic, PredicSize, Result),
print(Result).
test(activeSet) :-
reset_Model,
activeSet(ActSet, ActSetSize),
convert_float_array_to_list(ActSet, ActSetSize, Result),
print(Result).
test(matUtriCholFactor) :-
reset_Model,
matUtriCholFactor(Matrix, MatrixColNum, MatrixRowNum),
print(MatrixColNum),
convert_float_array_to_2d_list(Matrix, MatrixColNum, MatrixRowNum, Results),
print(Results).
test(betaPath) :-
reset_Model,
betaPath(Matrix, MatrixColNum, MatrixRowNum),
print(MatrixColNum),
convert_float_array_to_2d_list(Matrix, MatrixColNum, MatrixRowNum, Results),
print(Results).
:- end_tests(lists).
\ No newline at end of file
src/methods/lars/test.pl deleted 100644 → 0
+ 0
− 56
View file @ e2645a56
:- use_module(library(plunit)).
:- use_module(new_method).
:- use_module('../../helper_files/helper.pl').
reset_Model :-
initModel(1,0,50,0.0001).
:- begin_tests(lists).
%% alpha tests
test(alpha_std_init) :-
reset_Model,
alpha(0).
test(alpha_wrong_input, fail) :-
reset_Model,
alpha(1).
test(alpha_after_train, A =:= 9223372036854775808) :-
reset_Model,
convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5],3, array(Xsize, Xrownum, X)),
convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
train(X,Xsize, Xrownum,Y, Ysize),
alpha(A).
%% train tests
test(correct_train) :-
reset_Model,
convert_list_to_float_array([5.1,3.5,1.4,4.9,3.0,1.4,4.7,3.2,1.3,4.6,3.1,1.5],3, array(Xsize, Xrownum, X)),
convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
train(X,Xsize, Xrownum,Y, Ysize).
test(false_train, fail) :-
reset_Model,
convert_list_to_float_array([],3, array(Xsize, Xrownum, X)),
convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
train(X,Xsize, Xrownum,Y, Ysize).
test(false_train2, fail) :-
reset_Model,
convert_list_to_float_array([],0, array(Xsize, Xrownum, X)),
convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
train(X,Xsize, Xrownum,Y, Ysize).
test(false_train3, fail) :-
reset_Model,
convert_list_to_float_array([1,2],0, array(Xsize, Xrownum, X)),
convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
train(X,Xsize, Xrownum,Y, Ysize).
test(false_train3, fail) :-
reset_Model,
convert_list_to_float_array([1,2,44,3],3, array(Xsize, Xrownum, X)),
convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
train(X,Xsize, Xrownum,Y, Ysize).
test(false_train4) :-
reset_Model,
convert_list_to_float_array([1,2,44,3],2, array(Xsize, Xrownum, X)),
convert_list_to_float_array([0.2,0.2,0.2,0.2], array(Ysize, Y)),
train(X,Xsize, Xrownum,Y, Ysize).
:- end_tests(lists).
\ No newline at end of file
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