diff --git a/src/methods/approx_kfn/approx_kfn.pl b/src/methods/approx_kfn/approx_kfn.pl
index e426b096e133e6f5bbddcb5798e24597c97586a6..78bbf176ffc5f2d18ee4550f1c96581577d3cf89 100644
--- a/src/methods/approx_kfn/approx_kfn.pl
+++ b/src/methods/approx_kfn/approx_kfn.pl
@@ -31,7 +31,7 @@
%% --Output--
%%
%% --Description--
-%% Initiates the DrusillaSearch Model but doesn’t train it.
+%% Initiates the DrusillaSearch Model but doesnt train it.
%% trainDrusilla has to be used before searchDrusilla can be used.
%%
foreign(initDrusillaModelNoTrain, c, initDrusillaModelNoTrain(+integer, +integer)).
@@ -90,7 +90,7 @@ foreign(trainDrusilla, c, trainDrusilla(+pointer(float_array), +integer, +intege
%% --Output--
%%
%% --Description--
-%% Initiates the QDAFNSearch Model but doesn’t train it.
+%% Initiates the QDAFNSearch Model but doesnt train it.
%% trainQDAFN has to be used before searchQDAFN can be used.
%%
foreign(initQDAFNModelNoTrain, c, initQDAFNModelNoTrain(+integer, +integer)).
diff --git a/src/methods/bayesian_linear_regression/bayesian_linear_regression.cpp b/src/methods/bayesian_linear_regression/bayesian_linear_regression.cpp
index 3177d62254e0920e43332ff5bafa1591669fc0ea..67751451be589f0547459a38a561a9ea3d92b95e 100644
--- a/src/methods/bayesian_linear_regression/bayesian_linear_regression.cpp
+++ b/src/methods/bayesian_linear_regression/bayesian_linear_regression.cpp
@@ -17,8 +17,11 @@ using namespace mlpack::regression;
// Global Variable of the BayesianLinearRegression object so it can be accessed from all functions
BayesianLinearRegression regressor;
-// input: const bool , const bool , const size_t , const double
-// output: double
+// input: const bool centerData,
+// const bool scaleData,
+// const size_t nIterMax,
+// const double tol
+// output:
void initModel(SP_integer centerData, SP_integer scaleData, SP_integer nIterMax, double tol)
{
regressor = new BayesianLinearRegression((centerData == 1), (scaleData == 1), nIterMax, tol);
@@ -39,119 +42,129 @@ double beta()
}
// input:
-// output: const arma::colvec &
-float * dataOffset(SP_integer *arraySize)
+// output: const arma::colvec & dataOffset
+void dataOffset(float **dataOffsetArr, SP_integer *dataOffsetArrSize)
{
- // save the DataOffset output in colvec
- colvec armaDataOffset = regressor.DataOffset();
-
- *arraySize = armaDataOffset.n_elem;
-
- return convertToArray(armaDataOffset);
+ // create the ReturnVector
+ rowvec dataOffsetReturnVector = regressor.DataOffset();
+
+ // return the Vector
+ returnVectorInformation(dataOffsetReturnVector, dataOffsetArr, dataOffsetArrSize);
}
// input:
-// output: const arma::colvec &
-float * dataScale(SP_integer *arraySize)
+// output: const arma::colvec & dataScale
+void dataScale(float **dataScaleArr, SP_integer *dataScaleArrSize)
{
- // save the DataScale output in colvec
- colvec armaDataScale = regressor.DataScale();
-
- *arraySize = armaDataScale.n_elem;
-
- return convertToArray(armaDataScale);
+ // create the ReturnVector
+ rowvec dataScaleReturnVector = regressor.DataScale();
+
+ // return the Vector
+ returnVectorInformation(dataScaleReturnVector, dataScaleArr, dataScaleArrSize);
}
// input:
-// output: const arma::colvec &
-float * omega(SP_integer *arraySize)
+// output: const arma::colvec & omega
+void omega(float **omegaArr, SP_integer *omegaArrSize)
{
- // save the Omega output in colvec
- colvec armaOmega = regressor.Omega();
-
- *arraySize = armaOmega.n_elem;
-
- return convertToArray(armaOmega);
+ // create the ReturnVector
+ rowvec omegaReturnVector = regressor.Omega();
+
+ // return the Vector
+ returnVectorInformation(omegaReturnVector, omegaArr, omegaArrSize);
}
-// input: const arma::mat &points, arma::rowvec &predictions
+// input: const arma::mat &points,
+// arma::rowvec &predictions
// output:
-void predict(float *pointsArr, SP_integer pointsSize, SP_integer pointsRowNum, float **predicArr, SP_integer *predicSize)
+void predict(float *pointsMatArr, SP_integer pointsMatSize, SP_integer pointsMatRowNum, float **predictionsArr, SP_integer *predictionsArrSize)
{
- // convert the Prolog arrays to std::vec for easy conversion to arma::mat
- mat data = convertArrayToMat(pointsArr, pointsSize, pointsRowNum);
+ // convert the Prolog array to arma::mat
+ mat points = convertArrayToMat(pointsMatArr, pointsMatSize, pointsMatRowNum);
+
+ // create the ReturnVector
+ rowvec predictionsReturnVector;
+
// run the prediction and save the result in arma::rowvec
- rowvec predictions;
- regressor.Predict(data, predictions);
-
+ regressor.Predict(points, predictionsReturnVector);
- // give back the sizes and the converted results as arrays
- *predicSize = predictions.n_elem;
- *predicArr = convertToArray(predictions);
+ // return the Vector
+ returnVectorInformation(predictionsReturnVector, predictionsArr, predictionsArrSize);
}
-// input: const arma::mat &points, arma::rowvec &predictions, arma::rowvec &std
+// input: const arma::mat &points,
+// arma::rowvec &predictions,
+// arma::rowvec &std
// output:
-void predictWithStd(float *pointsArr, SP_integer pointsSize, SP_integer pointsRowNum, float **predicArr, SP_integer *predicSize, float **stdArr, SP_integer *stdSize)
+void predictWithStd(float *pointsMatArr, SP_integer pointsMatSize, SP_integer pointsMatRowNum, float **predictionsArr, SP_integer *predictionsArrSize, float **stdArr, SP_integer *stdArrSize)
{
- // convert the Prolog arrays to std::vec for easy conversion to arma::mat
- mat data = convertArrayToMat(pointsArr, pointsSize, pointsRowNum);
+ // convert the Prolog array to arma::mat
+ mat points = convertArrayToMat(pointsMatArr, pointsMatSize, pointsMatRowNum);
+
+ // create the ReturnVectors
+ rowvec predictionsReturnVector;
+ rowvec stdReturnVector;
+
- // run the prediction and save the results in arma::rowvecs
- rowvec predictions;
- rowvec std;
- regressor.Predict(data, predictions, std);
+ // run the prediction and save the result in arma::rowvec
+ regressor.Predict(points, predictionsReturnVector, stdReturnVector);
- // give back the sizes and the converted results as arrays
- *predicSize = predictions.n_elem;
- *stdSize = std.n_elem;
- *predicArr = convertToArray(predictions);
- *stdArr = convertToArray(std);
+ // return the Vectors
+ returnVectorInformation(predictionsReturnVector, predictionsArr, predictionsArrSize);
+ returnVectorInformation(stdReturnVector, stdArr, stdArrSize);
}
// input:
// output: double
-SP_integer responsesOffset()
+double responsesOffset()
{
return regressor.ResponsesOffset();
}
-// input: const arma::mat &data, const arma::rowvec &responses
+// input: const arma::mat & data,
+// const arma::rowvec & responses
// output: double
-SP_integer rmse(float *matrix, SP_integer matSize, SP_integer matRowNum, float *arr, SP_integer vecSize)
+double rmse(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float *responsesArr, SP_integer responsesArrSize)
{
- // convert the Prolog arrays to arma::rowvec and arma::mat
- rowvec responses = convertArrayToRowvec(arr, vecSize);
- mat data = convertArrayToMat(matrix, matSize, matRowNum);
+ // convert the Prolog array to arma::mat
+ mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+
+ // convert the Prolog array to arma::rowvec
+ rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
+
// run the model function and return the error
- return regressor.RMSE(data, responses);
+ return regressor.RMSE(data, responsesVector);
}
-// input: const arma::mat &data, const arma::rowvec &responses
+// input: const arma::mat & data,
+// const arma::rowvec & responses
// output:
-void train(float *matrix, SP_integer matSize, SP_integer matRowNum, float *arr, SP_integer vecSize)
+void train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float *responsesArr, SP_integer responsesArrSize)
{
- if(matSize / matRowNum != vecSize)
+ if(dataMatSize / dataMatRowNum != responsesArrSize)
{
cout << "Target dim doesnt fit to the Data dim" << endl;
return;
}
- // convert the Prolog arrays to arma::rowvec and arma::mat
- rowvec responses = convertArrayToRowvec(arr, vecSize);
- mat data = convertArrayToMat(matrix, matSize, matRowNum);
+ // convert the Prolog array to arma::mat
+ mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+
+ // convert the Prolog array to arma::rowvec
+ rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
+
// run the model function
- regressor.Train(data, responses);
+ regressor.Train(data, responsesVector);
}
// input:
// output: double
-SP_integer variance()
+double variance()
{
return regressor.Variance();
}
diff --git a/src/methods/bayesian_linear_regression/bayesian_linear_regression.pl b/src/methods/bayesian_linear_regression/bayesian_linear_regression.pl
index 396a631403c2cd69315b1c15de06aa063c7159f9..ca2f5dbbe83842eeb1c176fe104b2944af496d2a 100644
--- a/src/methods/bayesian_linear_regression/bayesian_linear_regression.pl
+++ b/src/methods/bayesian_linear_regression/bayesian_linear_regression.pl
@@ -76,7 +76,7 @@ foreign(beta, c, beta([-float32])).
%% --Description--
%% Get the mean vector computed on the features over the training points.
%%
-foreign(dataOffset, c, dataOffset(-integer, [-pointer(float_array)])).
+foreign(dataOffset, c, dataOffset(-pointer(float_array), -integer)).
%% --Input--
@@ -87,7 +87,7 @@ foreign(dataOffset, c, dataOffset(-integer, [-pointer(float_array)])).
%% --Description--
%% Get the vector of standard deviations computed on the features over the training points.
%%
-foreign(dataScale, c, dataScale(-integer, [-pointer(float_array)])).
+foreign(dataScale, c, dataScale(-pointer(float_array), -integer)).
%% --Input--
@@ -98,7 +98,7 @@ foreign(dataScale, c, dataScale(-integer, [-pointer(float_array)])).
%% --Description--
%% Get the solution vector.
%%
-foreign(omega, c, omega(-integer, [-pointer(float_array)])).
+foreign(omega, c, omega(-pointer(float_array), -integer)).
%% --Input--
@@ -141,7 +141,7 @@ foreign(predictWithStd, c, predictWithStd( +pointer(float_array), +integer,
%%
foreign(rmse, c, rmse( +pointer(float_array), +integer, +integer,
+pointer(float_array), +integer,
- [-integer])).
+ [-float32])).
%% --Input--
@@ -152,7 +152,7 @@ foreign(rmse, c, rmse( +pointer(float_array), +integer, +integer,
%%
%% --Description--
%% Run BayesianLinearRegression.
-%% The input matrix (like all mlpack matrices) should be column-major – each column is an observation and each row is a dimension.
+%% The input matrix (like all mlpack matrices) should be column-major each column is an observation and each row is a dimension.
%%
foreign(train, c, train( +pointer(float_array), +integer, +integer,
+pointer(float_array), +integer)).
@@ -167,7 +167,7 @@ foreign(train, c, train( +pointer(float_array), +integer, +integer,
%% Get the estimate variance.
%% train/5 should be called before.
%%
-foreign(variance, c, variance([-integer])).
+foreign(variance, c, variance([-float32])).
%% Defines what functions should be connected from main.cpp
foreign_resource(bayesian_linear_regression, [ initModel,
diff --git a/src/methods/dbscan/dbscan.cpp b/src/methods/dbscan/dbscan.cpp
index c0a1c2ddd2f277b7dfcdb17263e8031153f424fe..54a8f4609054fbdc5c0796044b535034f24b1ba6 100644
--- a/src/methods/dbscan/dbscan.cpp
+++ b/src/methods/dbscan/dbscan.cpp
@@ -192,52 +192,4 @@ void dbscan(double epsilon, SP_integer minPoints, SP_integer batchMode, char con
cout << "wrong selectionType input" << endl;
}
-}
-
-
-void takeNumberFunction(SP_integer integerNumber, double doubleNumber, char const *string)
-{
-
-}
-
-SP_integer returnNumberFunction()
-{
- return 7;
-}
-
-void takeMatrixFunction(float *givenMatArr, SP_integer givenMatSize, SP_integer givenMatRowNum)
-{
- // convert the Prolog arrays to arma::mat
- mat data = convertArrayToMat(givenMatArr, givenMatSize, givenMatRowNum);
-}
-
-void takeArrayFunction(float *givenArr, SP_integer givenArrSize)
-{
- // convert the Prolog arrays to arma::rowvec
- rowvec givenVector = convertArrayToRowvec(givenArr, givenArrSize);
-}
-
-void returnMatrixFunction(float **returnMatArr, SP_integer *returnMatColNum, SP_integer *returnMatRowNum)
-{
- // get the ReturnMat
- mat toReturnMat = rowvec(3, 3, fill::ones);
-
- // return the Matrix dimensions
- *returnMatColNum = toReturnMat.n_cols;
- *returnMatRowNum = toReturnMat.n_rows;
-
- // return the Matrix as one long Array
- *returnMatArr = convertToArray(toReturnMat);
-}
-
-void returnArrayFunction(float **returnArr, SP_integer *returnArrSize)
-{
- // get the ReturnVector
- rowvec toReturnVector = rowvec(3, fill::ones);
-
- // return the Vector lenght
- *returnArrSize = toReturnVector.n_elem;
-
- // return the Vector as Array
- *returnArr = convertToArray(toReturnVector);
}
\ No newline at end of file
diff --git a/src/methods/kernel_pca/kernel_pca.cpp b/src/methods/kernel_pca/kernel_pca.cpp
index 9dad7df4b073b2cf85d32a56da83b7903fa4086e..579945cc4b6d895a5616b1eaebd65587cc6dc6d2 100644
--- a/src/methods/kernel_pca/kernel_pca.cpp
+++ b/src/methods/kernel_pca/kernel_pca.cpp
@@ -11,7 +11,7 @@
#include <mlpack/methods/nystroem_method/ordered_selection.hpp>
#include <mlpack/methods/nystroem_method/random_selection.hpp>
-// including helper functions for converting between arma structures and arrays
+// including helper functions for converting between arma structures and
#include "../../helper_files/helper.hpp"
// some of the most used namespaces
@@ -64,16 +64,14 @@ void kernel_pca(char const *kernel, SP_integer centerTranformedData, SP_integer
float **eigvecMatArr, SP_integer *eigvecMatColNum, SP_integer *eigvecMatRowNum,
SP_integer *newDimension)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
// create the ReturnMat
mat transformedDataReturnMat;
-
// create the ReturnVector
vec eigvalReturnVector;
-
// create the ReturnMat
mat eigvecReturnMat;
diff --git a/src/methods/kfn/kfn.cpp b/src/methods/kfn/kfn.cpp
index 53a683db9c03f0c883c6253ccda3b68ab05bad69..3fb1f7c870499e89d59e9044d1d035b5bb44c7c2 100644
--- a/src/methods/kfn/kfn.cpp
+++ b/src/methods/kfn/kfn.cpp
@@ -35,7 +35,8 @@ void initAndBuildModel(char const *treeType, char const *algorithm,
mat reference = convertArrayToMat(referenceMatArr, referenceMatSize, referenceMatRowNum);
- // select the treeType
+ // used a good part of how it was used in the .main file
+ // select the treeType
NSModel<FurthestNS>::TreeTypes tree = NSModel<FurthestNS>::KD_TREE;
if (strcmp(treeType, "kd") == 0)
@@ -110,8 +111,6 @@ void searchWithQuery( float *queryMatArr, SP_integer queryMatSize, SP_integer
// create the ReturnMat
Mat< size_t > neighborsReturnMat;
-
- // create the ReturnMat
mat distancesReturnMat;
@@ -136,19 +135,15 @@ void searchNoQuery( SP_integer k,
float **neighborsMatArr, SP_integer *neighborsMatColNum, SP_integer *neighborsMatRowNum,
float **distancesMatArr, SP_integer *distancesMatColNum, SP_integer *distancesMatRowNum)
{
- // create the ReturnMat
+ // create the ReturnMats
Mat< size_t > neighborsReturnMat;
-
- // create the ReturnMat
mat distancesReturnMat;
nsModelObj.Search((size_t)k, neighborsReturnMat, distancesReturnMat);
- // return the Matrix
+ // return the Matrices
returnMatrixInformation(neighborsReturnMat, neighborsMatArr, neighborsMatColNum, neighborsMatRowNum);
-
- // return the Matrix
returnMatrixInformation(distancesReturnMat, distancesMatArr, distancesMatColNum, distancesMatRowNum);
}
\ No newline at end of file
diff --git a/src/methods/knn/knn.cpp b/src/methods/knn/knn.cpp
index 34a4234a84e06c4d16a59fd9640bce4c62095fca..7c14966a0a8663e2ee75daced5cab95fd7edf001 100644
--- a/src/methods/knn/knn.cpp
+++ b/src/methods/knn/knn.cpp
@@ -37,7 +37,8 @@ void initAndBuildModel(char const *treeType, char const *algorithm,
mat reference = convertArrayToMat(referenceMatArr, referenceMatSize, referenceMatRowNum);
- // select the treeType
+ // used a good part of how it was used in the .main file
+ // select the treeType
NSModel<NearestNS>::TreeTypes tree = NSModel<NearestNS>::KD_TREE;
if (strcmp(treeType, "kd") == 0)
@@ -115,18 +116,14 @@ void searchWithQuery( float *queryMatArr, SP_integer queryMatSize, SP_integer
// create the ReturnMat
Mat< size_t > neighborsReturnMat;
-
- // create the ReturnMat
mat distancesReturnMat;
nsModelObj.Search(move(query), (size_t)k, neighborsReturnMat, distancesReturnMat);
- // return the Matrix
+ // return the Matrices
returnMatrixInformation(neighborsReturnMat, neighborsMatArr, neighborsMatColNum, neighborsMatRowNum);
-
- // return the Matrix
returnMatrixInformation(distancesReturnMat, distancesMatArr, distancesMatColNum, distancesMatRowNum);
}
@@ -143,8 +140,6 @@ void searchNoQuery( SP_integer k,
{
// create the ReturnMat
Mat< size_t > neighborsReturnMat;
-
- // create the ReturnMat
mat distancesReturnMat;
diff --git a/src/methods/linear_regression/linear_regression.cpp b/src/methods/linear_regression/linear_regression.cpp
index 2fa7ec55068e97a9e0e702b45bd6db5d4d42a507..c0c18529859c1d56bfa6226965b93e5bf5680956 100644
--- a/src/methods/linear_regression/linear_regression.cpp
+++ b/src/methods/linear_regression/linear_regression.cpp
@@ -17,136 +17,171 @@ using namespace mlpack::regression;
// Global Variable of the LinearRegression object so it can be accessed from all functions
LinearRegression regressor;
-// input: const arma::mat &data, const arma::rowvec &responses, const double lambda, const bool intercept
+// input: const arma::mat &data,
+// const arma::rowvec &responses,
+// const double lambda,
+// const bool intercept
// output:
-void initModel(float *matrix, SP_integer matSize, SP_integer matRowNum, float *responses_arr, SP_integer vecSize, double lambda, SP_integer bool_intercept)
+void initModel(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
+ float *responsesArr, SP_integer responsesArrSize,
+ double lambda,
+ SP_integer bool_intercept)
{
- if(matSize / matRowNum != vecSize)
+ if(dataMatSize / dataMatRowNum != responsesArrSize)
{
cout << "Target dim doesnt fit to the Data dim" << endl;
return;
}
- // convert the Prolog arrays to arma::rowvec and arma::mat
- mat data = convertArrayToMat(matrix, matSize, matRowNum);
- rowvec responses = convertArrayToRowvec(responses_arr, vecSize);
+ // convert the Prolog array to arma::mat
+ mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+
+ // convert the Prolog array to arma::rowvec
+ rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
- regressor = LinearRegression(data, responses, lambda, bool_intercept);
+
+ regressor = LinearRegression(data, responsesVector, lambda, bool_intercept);
}
-// input: const arma::mat &data, const arma::rowvec &responses, const arma::rowvec &weights, const double lambda, const bool intercept
+// input: const arma::mat &data,
+// const arma::rowvec &responses,
+// const arma::rowvec &weights,
+// const double lambda,
+// const bool intercept
// output:
-void initModelWithWeights(float *matrix, SP_integer matSize, SP_integer matRowNum, float *responses_arr, SP_integer vecSize, float *weights_arr, SP_integer weightsSize, double lambda, SP_integer bool_intercept)
+void initModelWithWeights(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
+ float *responsesArr, SP_integer responsesArrSize,
+ float *weightsArr, SP_integer weightsArrSize,
+ double lambda,
+ SP_integer bool_intercept)
{
- if(matSize / matRowNum != vecSize)
+ if(dataMatSize / dataMatRowNum != responsesArrSize)
{
cout << "Target dim doesnt fit to the Data dim" << endl;
return;
}
- // convert the Prolog arrays to arma::rowvec and arma::mat
- mat data = convertArrayToMat(matrix, matSize, matRowNum);
- rowvec responses = convertArrayToRowvec(responses_arr, vecSize);
- rowvec weights = convertArrayToRowvec(weights_arr, weightsSize);
+ // convert the Prolog array to arma::mat
+ mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+ // convert the Prolog array to arma::rowvec
+ rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
+ // convert the Prolog array to arma::rowvec
+ rowvec weightsVector = convertArrayToRowvec(weightsArr, weightsArrSize);
+
- regressor = LinearRegression(data, responses, weights, lambda, bool_intercept);
+ regressor = LinearRegression(data, responsesVector, weightsVector, lambda, bool_intercept);
}
-// input: const arma::mat &points, const arma::rowvec &responses
+// input: const arma::mat &points,
+// const arma::rowvec &responses
// output: double L2SquardError
-double computeError(float *points_mat_arr, SP_integer matSize, SP_integer matRowNum, float *responses_arr, SP_integer vecSize)
+double computeError(float *pointsMatArr, SP_integer pointsMatSize, SP_integer pointsMatRowNum,
+ float *responsesArr, SP_integer responsesArrSize)
{
- if(matSize / matRowNum != vecSize)
+ if(pointsMatSize / pointsMatRowNum != responsesArrSize)
{
cout << "Target dim doesnt fit to the Data dim" << endl;
return 0;
}
- // convert the Prolog arrays to arma::rowvec and arma::mat
- mat data = convertArrayToMat(points_mat_arr, matSize, matRowNum);
- rowvec responses = convertArrayToRowvec(responses_arr, vecSize);
+ // convert the Prolog array to arma::mat
+ mat points = convertArrayToMat(pointsMatArr, pointsMatSize, pointsMatRowNum);
+ // convert the Prolog array to arma::rowvec
+ rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
+
// run the model function
- return regressor.ComputeError(data, responses);
+ return regressor.ComputeError(points, responsesVector);
}
// input:
// output: const arma::vec & parameters b vector
-float * parameters(SP_integer *arraySize)
+void parameters(float **bArr, SP_integer *bArrSize)
{
- // get b vector
- colvec armaDataOffset = regressor.Parameters();
-
- cout << regressor.Parameters() << endl;
- cout << armaDataOffset << endl;
-
- // set the b vector length
- *arraySize = armaDataOffset.n_elem;
-
- // convert b vector to array and return
- return convertToArray(armaDataOffset);
+ // create the ReturnVector
+ rowvec bReturnVector = regressor.Parameters();
+
+ // return the Vector
+ returnVectorInformation(bReturnVector, bArr, bArrSize);
}
// input: arma::vec & new b vector
// output:
-void modifyParameters(float *paramArr, SP_integer paramSize)
+void modifyParameters(float *newBArr, SP_integer newBArrSize)
{
- // convert array to arma:: vec
- rowvec new_B_Vector = convertArrayToRowvec(paramArr, paramSize);
+ // convert the Prolog array to arma::rowvec
+ vec newBVector = convertArrayToRowvec(newBArr, newBArrSize);
+
// get parameter b vector ref
vec& bVector = regressor.Parameters();
// set new b vector
- bVector = new_B_Vector;
+ bVector = newBVector;
}
-// input: const arma::mat &points, arma::rowvec &predictions
+// input: const arma::mat &points,
+// arma::rowvec &predictions
// output:
-void predict(float *pointsArr, SP_integer pointsSize, SP_integer pointsRowNum, float **predicArr, SP_integer *predicSize)
+void predict(float *pointsMatArr, SP_integer pointsMatSize, SP_integer pointsMatRowNum,
+ float **predicArr, SP_integer *predicArrSize)
{
- // convert the Prolog arrays to std::vec for easy conversion to arma::mat
- mat data = convertArrayToMat(pointsArr, pointsSize, pointsRowNum);
+ // convert the Prolog array to arma::mat
+ mat points = convertArrayToMat(pointsMatArr, pointsMatSize, pointsMatRowNum);
- // run the prediction and save the result in arma::rowvec
- rowvec predictions;
- regressor.Predict(data, predictions);
+ // create the ReturnVector
+ rowvec predicReturnVector;
+
+ // run the predict function on the model
+ regressor.Predict(points, predicReturnVector);
+
- // give back the sizes and the converted results as arrays
- *predicSize = predictions.n_elem;
-
- *predicArr = convertToArray(predictions);
+ // return the Vector
+ returnVectorInformation(predicReturnVector, predicArr, predicArrSize);
}
-// input: const arma::mat &data, const arma::rowvec &responses, const bool intercept
+// input: const arma::mat &data,
+// const arma::rowvec &responses,
+// const bool intercept
// output:
-double train(float *matrix, SP_integer matSize, SP_integer matRowNum, float *responses_arr, SP_integer vecSize, SP_integer bool_intercept)
+double train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
+ float *responsesArr, SP_integer responsesArrSize,
+ SP_integer bool_intercept)
{
- if(matSize / matRowNum != vecSize)
+ if(dataMatSize / dataMatRowNum != responsesArrSize)
{
cout << "Target dim doesnt fit to the Data dim" << endl;
return 0;
}
- // convert the Prolog arrays to arma::rowvec and arma::mat
- rowvec responses = convertArrayToRowvec(responses_arr, vecSize);
- mat data = convertArrayToMat(matrix, matSize, matRowNum);
+ // convert the Prolog array to arma::mat
+ mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+ // convert the Prolog array to arma::rowvec
+ rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
+
// run the model train function
- return regressor.Train(data, responses, (bool_intercept == 1));
+ return regressor.Train(data, responsesVector, (bool_intercept == 1));
}
-// input: const arma::mat &data, const arma::rowvec &responses, const arma::rowvec &weights, const bool intercept
+// input: const arma::mat &data,
+// const arma::rowvec &responses,
+// const arma::rowvec &weights,
+// const bool intercept
// output:
-double trainWithWeights(float *matrix, SP_integer matSize, SP_integer matRowNum, float *responses_arr, SP_integer vecSize, float *weights_arr, SP_integer weightsSize, SP_integer bool_intercept)
+double trainWithWeights(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
+ float *responsesArr, SP_integer responsesArrSize,
+ float *weightsArr, SP_integer weightsArrSize,
+ SP_integer bool_intercept)
{
- if(matSize / matRowNum != vecSize)
+ if(dataMatSize / dataMatRowNum != responsesArrSize)
{
cout << "Target dim doesnt fit to the Data dim" << endl;
return 0;
}
- // convert the Prolog arrays to arma::rowvec and arma::mat
- mat data = convertArrayToMat(matrix, matSize, matRowNum);
- rowvec responses = convertArrayToRowvec(responses_arr, vecSize);
- rowvec weights = convertArrayToRowvec(weights_arr, weightsSize);
+ // convert the Prolog array to arma::mat
+ mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+ // convert the Prolog array to arma::rowvec
+ rowvec responsesVector = convertArrayToRowvec(responsesArr, responsesArrSize);
+ rowvec weightsVector = convertArrayToRowvec(weightsArr, weightsArrSize);
// run the model train function
- return regressor.Train(data, responses, weights, (bool_intercept == 1));
+ return regressor.Train(data, responsesVector, weightsVector, (bool_intercept == 1));
}
diff --git a/src/methods/linear_regression/linear_regression.pl b/src/methods/linear_regression/linear_regression.pl
index 7bc477e97a3f51382b063d63a4292edc8e1bebcb..7dbd8143622af46393042c3a56f757f99bacfc1e 100644
--- a/src/methods/linear_regression/linear_regression.pl
+++ b/src/methods/linear_regression/linear_regression.pl
@@ -82,7 +82,7 @@ foreign(computeError, c, computeError( +pointer(float_array), +integer, +intege
%% --Description--
%% Get the parameters (the b vector).
%%
-foreign(parameters, c, parameters([-pointer(float_array)], -integer)).
+foreign(parameters, c, parameters(-pointer(float_array), -integer)).
%% --Input--
diff --git a/src/methods/lsh/lsh.cpp b/src/methods/lsh/lsh.cpp
index 0780526289440cd33f3597b3bfc0c7bca5b929d8..f3d2f342795508671e64e64c482677331c17ecc5 100644
--- a/src/methods/lsh/lsh.cpp
+++ b/src/methods/lsh/lsh.cpp
@@ -29,7 +29,8 @@ LSHSearch lshSearch;
// const size_t bucketSize = 500
// output:
// description:
-void initModel(float *referenceMatArr, SP_integer referenceMatSize, SP_integer referenceMatRowNum, SP_integer numProj, SP_integer numTables, double hashWidth, SP_integer secondHashSize, SP_integer bucketSize)
+void initModel(float *referenceMatArr, SP_integer referenceMatSize, SP_integer referenceMatRowNum,
+ SP_integer numProj, SP_integer numTables, double hashWidth, SP_integer secondHashSize, SP_integer bucketSize)
{
// convert the Prolog arrays to arma::mat
mat reference = convertArrayToMat(referenceMatArr, referenceMatSize, referenceMatRowNum);
@@ -42,7 +43,8 @@ void initModel(float *referenceMatArr, SP_integer referenceMatSize, SP_integer r
// const arma::Mat< size_t > & realNeighbors
// output: double recall percentage [0, 1]
// description:
-double computeRecall(float *foundNeighborsMatArr, SP_integer foundNeighborsMatSize, SP_integer foundNeighborsMatRowNum, float *realNeighborsMatArr, SP_integer realNeighborsMatSize, SP_integer realNeighborsMatRowNum)
+double computeRecall(float *foundNeighborsMatArr, SP_integer foundNeighborsMatSize, SP_integer foundNeighborsMatRowNum,
+ float *realNeighborsMatArr, SP_integer realNeighborsMatSize, SP_integer realNeighborsMatRowNum)
{
// convert the Prolog arrays to arma::mat
Mat< size_t > foundNeighbors = conv_to<Mat< size_t >>::from(convertArrayToMat(foundNeighborsMatArr, foundNeighborsMatSize, foundNeighborsMatRowNum));
diff --git a/src/methods/mean_shift/mean_shift.cpp b/src/methods/mean_shift/mean_shift.cpp
index 2280445ad4f0dd56cb539d863e8dd0cacc65cac7..7db14fbb0fff1698f6f72e92a8a6658bf12be387 100644
--- a/src/methods/mean_shift/mean_shift.cpp
+++ b/src/methods/mean_shift/mean_shift.cpp
@@ -30,30 +30,26 @@ using namespace mlpack::meanshift;
// bool useSeeds = true
// output:
// description:
-void meanShift(double radius, SP_integer maxIterations, float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float **assignmentsArr, SP_integer *assignmentsArrSize,
- float **centroidsMatArr, SP_integer *centroidsMatColNum, SP_integer *centroidsMatRowNum, SP_integer forceConvergence, SP_integer useSeeds)
+void meanShift(double radius, SP_integer maxIterations,
+ float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
+ float **assignmentsArr, SP_integer *assignmentsArrSize,
+ float **centroidsMatArr, SP_integer *centroidsMatColNum, SP_integer *centroidsMatRowNum,
+ SP_integer forceConvergence,
+ SP_integer useSeeds)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+ // create the ReturnVector
+ arma::Row<size_t> assignmentsReturnVector;
+ // create the ReturnMat
+ mat centroidsReturnMat;
- // get the ReturnVector
- arma::Row<size_t> toReturnVector;
- // get the ReturnMat
- mat toReturnMat;
+ MeanShift<>(radius, maxIterations).Cluster(data, assignmentsReturnVector, centroidsReturnMat, (forceConvergence == 1), (useSeeds == 1));
- MeanShift<>(radius, maxIterations).Cluster(data, toReturnVector, toReturnMat, (forceConvergence == 1), (useSeeds == 1));
- // return the Vector lenght
- *assignmentsArrSize = toReturnVector.n_elem;
-
- // return the Vector as Array
- *assignmentsArr = convertToArray(toReturnVector);
-
- // return the Matrix dimensions
- *centroidsMatColNum = toReturnMat.n_cols;
- *centroidsMatRowNum = toReturnMat.n_rows;
-
- // return the Matrix as one long Array
- *centroidsMatArr = convertToArray(toReturnMat);
+ // return the Matrix
+ returnMatrixInformation(centroidsReturnMat, centroidsMatArr, centroidsMatColNum, centroidsMatRowNum);
+ // return the Vector
+ returnVectorInformation(assignmentsReturnVector, assignmentsArr, assignmentsArrSize);
}
diff --git a/src/methods/nmf/nmf.cpp b/src/methods/nmf/nmf.cpp
index de77a08d98784c422d08c327951e3e6044091693..4ecb455736330589714961f4e5e9490e36560dbd 100644
--- a/src/methods/nmf/nmf.cpp
+++ b/src/methods/nmf/nmf.cpp
@@ -39,10 +39,8 @@ void nmf(char const *updateRule, SP_integer maxIterations, double minResidue,
// convert the Prolog arrays to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
- // create the ReturnMat
+ // create the ReturnMats
mat WReturnMat;
-
- // create the ReturnMat
mat HReturnMat;
// decide which update rule to use
@@ -66,9 +64,7 @@ void nmf(char const *updateRule, SP_integer maxIterations, double minResidue,
cout << "wrong updateRule input" << endl;
}
- // return the Matrix
+ // return the Matrices
returnMatrixInformation(WReturnMat, WMatArr, WMatColNum, WMatRowNum);
-
- // return the Matrix
returnMatrixInformation(HReturnMat, HMatArr, HMatColNum, HMatRowNum);
}
diff --git a/src/methods/pca/pca.cpp b/src/methods/pca/pca.cpp
index 8ff5908542fb462dac01bbe09e926be4e5ffebf8..f08c5a805526bf8ee2ffc9d6f7d845f54d06898a 100644
--- a/src/methods/pca/pca.cpp
+++ b/src/methods/pca/pca.cpp
@@ -35,18 +35,17 @@ void pca(SP_integer scaleData, char const *decompositionPolicy,
float **eigValArr, SP_integer *eigValArrSize,
float **eigVecMatArr, SP_integer *eigVecMatColNum, SP_integer *eigVecMatRowNum)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
// create the ReturnMat
mat transformedReturnMat;
-
// create the ReturnVector
vec eigValReturnVector;
-
// create the ReturnMat
mat eigVecReturnMat;
+
// decide for the decomposition Policy
if (strcmp(decompositionPolicy, "exact") == 0)
{
@@ -69,12 +68,11 @@ void pca(SP_integer scaleData, char const *decompositionPolicy,
cout << "wrong decompositionPolicy input" << endl;
}
+
// return the Matrix
returnMatrixInformation(transformedReturnMat, transformedMatArr, transformedMatColNum, transformedMatRowNum);
-
// return the Vector
returnVectorInformation(eigValReturnVector, eigValArr, eigValArrSize);
-
// return the Matrix
returnMatrixInformation(eigVecReturnMat, eigVecMatArr, eigVecMatColNum, eigVecMatRowNum);
}
@@ -91,11 +89,12 @@ double pcaDimReduction(SP_integer scaleData, char const *decompositionPolicy,
SP_integer newDimension,
float **transformedMatArr, SP_integer *transformedMatColNum, SP_integer *transformedMatRowNum)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
double returnValue;
+
// decide for the decomposition Policy
if (strcmp(decompositionPolicy, "exact") == 0)
{
@@ -119,6 +118,7 @@ double pcaDimReduction(SP_integer scaleData, char const *decompositionPolicy,
returnValue = -1.0;
}
+
// return the Matrix
returnMatrixInformation(data, transformedMatArr, transformedMatColNum, transformedMatRowNum);
@@ -137,11 +137,12 @@ double pcaVarianceDimReduction(SP_integer scaleData, char const *decompositionPo
double varRetained,
float **transformedMatArr, SP_integer *transformedMatColNum, SP_integer *transformedMatRowNum)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
double returnValue;
+
// decide for the decomposition Policy
if (strcmp(decompositionPolicy, "exact") == 0)
{
@@ -165,6 +166,7 @@ double pcaVarianceDimReduction(SP_integer scaleData, char const *decompositionPo
returnValue = -1.0;
}
+
// return the Matrix
returnMatrixInformation(data, transformedMatArr, transformedMatColNum, transformedMatRowNum);
diff --git a/src/methods/pca/pca_test.pl b/src/methods/pca/pca_test.pl
index 190603f4e28181e218e06d935c24f322abc299b5..adc117cd53f19f59047e2667f78161565f7b15cb 100644
--- a/src/methods/pca/pca_test.pl
+++ b/src/methods/pca/pca_test.pl
@@ -3,54 +3,17 @@
:- use_module(pca).
:- 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).
+test(alpha_after_train) :-
+ open('/home/afkjakhes/eclipse-workspace/prolog-mlpack-libary/src/data_csv/iris.csv', read, A),
+ take_csv_row(A, 10, B),
+ convert_list_to_float_array(B , 10, array(Xsize, Xrownum, X)),
+ pca(0, randomized, X, Xsize, Xrownum, Tansfomed, TCols, TRows, EigVal, EigValSize, EigVec, EigVecCols, EigVecRows),
+ convert_float_array_to_2d_list(Tansfomed, TCols, TRows, TOut),
+ convert_float_array_to_list(EigVal, EigValSize, EigValOut),
+ convert_float_array_to_2d_list(EigVec, EigVecCols, EigVecRows, EigVecOut),
+ print(TOut).
-%% 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
diff --git a/src/methods/radical/radical.cpp b/src/methods/radical/radical.cpp
index bc814228adb343d29622d5db5ffcaddfaa83f456..e2e9ce7912f4677915defc8ef8631ee374e59ac9 100644
--- a/src/methods/radical/radical.cpp
+++ b/src/methods/radical/radical.cpp
@@ -38,25 +38,23 @@ void initModel(double noiseStdDev, SP_integer replicates, SP_integer angles, SP_
// util::Timers & timers = IO::GetTimers()
// output:
// description:
-void doRadical(float *xMatArr, SP_integer xMatSize, SP_integer xMatRowNum, float **yMatArr, SP_integer *yMatColNum, SP_integer *yMatRowNum, float **wMatArr, SP_integer *wMatColNum, SP_integer *wMatRowNum)
+void doRadical(float *xMatArr, SP_integer xMatSize, SP_integer xMatRowNum,
+ float **yMatArr, SP_integer *yMatColNum, SP_integer *yMatRowNum,
+ float **wMatArr, SP_integer *wMatColNum, SP_integer *wMatRowNum)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat x = convertArrayToMat(xMatArr, xMatSize, xMatRowNum);
- // create the ReturnMat
+ // create the ReturnMats
mat yReturnMat;
-
- // create the ReturnMat
mat wReturnMat;
radicalObj.DoRadical(x, yReturnMat, wReturnMat);
- // return the Matrix
+ // return the Matrices
returnMatrixInformation(yReturnMat, yMatArr, yMatColNum, yMatRowNum);
-
- // return the Matrix
returnMatrixInformation(wReturnMat, wMatArr, wMatColNum, wMatRowNum);
}
@@ -68,7 +66,7 @@ void doRadical(float *xMatArr, SP_integer xMatSize, SP_integer xMatRowNum, float
// description:
double doRadical2D(float *xMatArr, SP_integer xMatSize, SP_integer xMatRowNum)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat x = convertArrayToMat(xMatArr, xMatSize, xMatRowNum);
return radicalObj.DoRadical2D(x);
diff --git a/src/methods/random_forest/random_forest.cpp b/src/methods/random_forest/random_forest.cpp
index 1ba759cbed1a23fd3d12652f1f069374dd8f6504..cd23eacee8229a82e4983dd68a3009192e66f2c0 100644
--- a/src/methods/random_forest/random_forest.cpp
+++ b/src/methods/random_forest/random_forest.cpp
@@ -65,13 +65,13 @@ void initModelWithTrainNoWeights(float *dataMatArr, SP_integer dataMatSize, SP_i
void initModelWithTrainWithWeights(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float *labelsArr, SP_integer labelsArrSize, SP_integer numClasses,
float *weightsArr, SP_integer weightsArrSize, SP_integer numTrees, SP_integer minimumLeafSize, double minimumGainSplit, SP_integer maximumDepth)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
- // convert the Prolog arrays to arma::rowvec
+ // convert the Prolog array to arma::rowvec
Row< size_t > labelsVector = convertArrayToVec(labelsArr, labelsArrSize);
- // convert the Prolog arrays to arma::rowvec
+ // convert the Prolog array to arma::rowvec
rowvec weightsVector = convertArrayToRowvec(weightsArr, weightsArrSize);
@@ -86,7 +86,7 @@ void initModelWithTrainWithWeights(float *dataMatArr, SP_integer dataMatSize, SP
// description:
void classifyPoint(float *pointArr, SP_integer pointArrSize, SP_integer *prediction, float **probsArr, SP_integer *probsArrSize)
{
- // convert the Prolog arrays to arma::rowvec
+ // convert the Prolog array to arma::rowvec
rowvec pointVector = convertArrayToRowvec(pointArr, pointArrSize);
// create the ReturnVector
diff --git a/src/methods/softmax_regression/softmax_regression.cpp b/src/methods/softmax_regression/softmax_regression.cpp
index 56049bc9897f2c6a8f1a52ea1a78a7d5ccdf9aff..f369c2c4413966226baee6ce119e03176f2c70e7 100644
--- a/src/methods/softmax_regression/softmax_regression.cpp
+++ b/src/methods/softmax_regression/softmax_regression.cpp
@@ -24,7 +24,8 @@ SoftmaxRegression softmaxRegression;
// const bool fitIntercept = false//
// output:
// description:
-void initModelNoTrain(SP_integer inputSize, SP_integer numClasses, SP_integer fitIntercept)
+void initModelNoTrain(SP_integer inputSize, SP_integer numClasses,
+ SP_integer fitIntercept)
{
softmaxRegression = SoftmaxRegression(inputSize, numClasses, (fitIntercept == 1));
}
diff --git a/src/methods/sparse_coding/sparse_coding.cpp b/src/methods/sparse_coding/sparse_coding.cpp
index fe2bbeaac3996c522a25bb1defbf3948db17b208..dda7851ed5ad938df8eb07a969afd7a4e08c7d81 100644
--- a/src/methods/sparse_coding/sparse_coding.cpp
+++ b/src/methods/sparse_coding/sparse_coding.cpp
@@ -32,9 +32,10 @@ SparseCoding sparseCoding;
void initModelWithTrain(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
SP_integer atoms, double lambda1, double lambda2, SP_integer maxIterations, double objTolerance, double newtonTolerance)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+
sparseCoding = SparseCoding(data, atoms, lambda1, lambda2, maxIterations, objTolerance, newtonTolerance, RandomInitializer());
}
@@ -60,14 +61,16 @@ void initModelNoTrain(SP_integer atoms, double lambda1, double lambda2, SP_integ
void encode(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
float **codesMatArr, SP_integer *codesMatColNum, SP_integer *codesMatRowNum)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
// create the ReturnMat
mat codesReturnMat;
+
sparseCoding.Encode(data, codesReturnMat);
+
// return the Matrix
returnMatrixInformation(codesReturnMat, codesMatArr, codesMatColNum, codesMatRowNum);
}
@@ -80,8 +83,9 @@ void encode(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
// description:
double train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum)
{
- // convert the Prolog arrays to arma::mat
+ // convert the Prolog array to arma::mat
mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+
return sparseCoding.Train(data, RandomInitializer());
}
\ No newline at end of file