diff --git a/Makefile b/Makefile
index dc3d0c91dbed63835e729a44f4af7de28088aeb1..628ac7199c7333f9465be8795d851af41c7513f9 100644
--- a/Makefile
+++ b/Makefile
@@ -12,6 +12,7 @@ all:
make -C src/methods/logistic_regression splfr=$(SPLFR_PATH)
make -C src/methods/mean_shift splfr=$(SPLFR_PATH)
make -C src/methods/naive_bayes_classifier splfr=$(SPLFR_PATH)
+ make -C src/methods/perceptron splfr=$(SPLFR_PATH)
clean:
make -C src/methods/ada_boost clean
@@ -24,3 +25,4 @@ clean:
make -C src/methods/logistic_regression clean
make -C src/methods/mean_shift clean
make -C src/methods/naive_bayes_classifier clean
+ make -C src/methods/perceptron clean
diff --git a/src/methods/perceptron/Makefile b/src/methods/perceptron/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..3911c137838368723f9035ef9300a5381d3b1d70
--- /dev/null
+++ b/src/methods/perceptron/Makefile
@@ -0,0 +1,8 @@
+splfr=/usr/local/sicstus4.7.1/bin/splfr
+
+METHOD_NAME=perceptron
+
+$(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
+clean:
+ rm $(METHOD_NAME).so
diff --git a/src/methods/perceptron/perceptron.cpp b/src/methods/perceptron/perceptron.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..789cf0a07725c63164a06db460a346eb8ef55875
--- /dev/null
+++ b/src/methods/perceptron/perceptron.cpp
@@ -0,0 +1,125 @@
+#include <sicstus/sicstus.h>
+/* ex_glue.h is generated by splfr from the foreign/[2,3] facts.
+ Always include the glue header in your foreign resource code.
+*/
+#include "perceptron_glue.h"
+#include <mlpack/methods/perceptron/perceptron.hpp>
+#include <mlpack/core.hpp>
+
+// including helper functions for converting between arma structures and arrays
+#include "../../helper_files/helper.hpp"
+
+// some of the most used namespaces
+using namespace arma;
+using namespace mlpack;
+using namespace std;
+using namespace mlpack::perceptron;
+
+// Global Variable of the GlobalMethodObject object so it can be accessed from all functions
+Perceptron perceptronGlobal;
+
+// TODO:
+// input: const size_t numClasses = 0,
+// const size_t dimensionality = 0,
+// const size_t maxIterations = 1000
+// output:
+// description:
+void initModelNoTrain(SP_integer numClasses, SP_integer dimensionality, SP_integer maxIterations)
+{
+ perceptronGlobal = Perceptron(numClasses, dimensionality, maxIterations);
+}
+
+// TODO:
+// input: const MatType & data,
+// const arma::Row< size_t > & labels,
+// const size_t numClasses,
+// const size_t maxIterations = 1000
+// output:
+// description:
+void initModelWithTrain(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float *labelsArr, SP_integer labelsArrSize, SP_integer numClasses, SP_integer maxIterations)
+{
+ // convert the Prolog arrays to arma::mat
+ mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+
+ // convert the Prolog arrays to arma::rowvec
+ Row< size_t > labelsVector = convertArrayToVec(labelsArr, labelsArrSize);
+
+ perceptronGlobal = Perceptron(data, labelsVector, numClasses, maxIterations);
+}
+
+// TODO:
+// input:
+// output: arma::vec& biases
+// description:
+void biases(float **biasesArr, SP_integer *biasesArrSize)
+{
+ // create the ReturnVector
+ vec biasesReturnVector = perceptronGlobal.Biases();
+
+ // return the Vector lenght
+ *biasesArrSize = biasesReturnVector.n_elem;
+
+ // return the Vector as Array
+ *biasesArr = convertToArray(biasesReturnVector);
+}
+
+// TODO:
+// input: const MatType & test,
+// arma::Row< size_t > & predictedLabels <-
+// output:
+// description:
+void classify(float *testMatArr, SP_integer testMatSize, SP_integer testMatRowNum, float **predicLabeslsArr, SP_integer *predicLabeslsArrSize)
+{
+ // convert the Prolog arrays to arma::mat
+ mat test = convertArrayToMat(testMatArr, testMatSize, testMatRowNum);
+
+ // create the ReturnVector
+ Row< size_t > predicLabeslsReturnVector;
+
+ perceptronGlobal.Classify(test, predicLabeslsReturnVector);
+
+ // return the Vector lenght
+ *predicLabeslsArrSize = predicLabeslsReturnVector.n_elem;
+
+ // return the Vector as Array
+ *predicLabeslsArr = convertToArray(predicLabeslsReturnVector);
+}
+
+// TODO:
+// input: const MatType & data,
+// const arma::Row< size_t > & labels,
+// const size_t numClasses,
+// const arma::rowvec & instanceWeights = arma::rowvec()
+// output:
+// description:
+void train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum, float *labelsArr, SP_integer labelsArrSize, SP_integer numClasses, float *instanceWeightsArr, SP_integer instanceWeightsArrSize)
+{
+ // convert the Prolog arrays to arma::mat
+ mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
+
+ // convert the Prolog arrays to arma::rowvec
+ Row< size_t > labelsVector = convertArrayToVec(labelsArr, labelsArrSize);
+
+ // convert the Prolog arrays to arma::rowvec
+ rowvec instanceWeightsVector = convertArrayToRowvec(instanceWeightsArr, instanceWeightsArrSize);
+
+ // use the model function
+ perceptronGlobal.Train(data, labelsVector, numClasses, instanceWeightsVector);
+}
+
+// TODO:
+// input:
+// output: const arma::mat& weights
+// description:
+void weights(float **weightsMatArr, SP_integer *weightsMatColNum, SP_integer *weightsMatRowNum)
+{
+ // create the ReturnMat
+ mat weightsReturnMat = perceptronGlobal.Weights();
+
+ // return the Matrix dimensions
+ *weightsMatColNum = weightsReturnMat.n_cols;
+ *weightsMatRowNum = weightsReturnMat.n_rows;
+
+ // return the Matrix as one long Array
+ *weightsMatArr = convertToArray(weightsReturnMat);
+}
diff --git a/src/methods/perceptron/perceptron.pl b/src/methods/perceptron/perceptron.pl
new file mode 100644
index 0000000000000000000000000000000000000000..5e119794091bccfda93b4c004c9a1aafad41bc5a
--- /dev/null
+++ b/src/methods/perceptron/perceptron.pl
@@ -0,0 +1,87 @@
+:- module(perceptron, [ initModelNoTrain/3,
+ initModelWithTrain/7,
+ biases/2,
+ classify/5,
+ train/8,
+ weights/3]).
+
+%% requirements of library(struct)
+:- load_files(library(str_decl),
+ [when(compile_time), if(changed)]).
+
+%% needed for using the array type
+:- use_module(library(structs)).
+:- use_module('../../helper_files/helper.pl').
+
+%% type definitions for the float array
+:- foreign_type
+ float32 = float_32,
+ float_array = array(float32).
+
+%% definitions for the connected function
+
+%% --Input--
+%% int numClasses => 0,
+%% int dimensionality => 0,
+%% int maxIterations => 1000
+%%
+%% --Output--:
+%% --Description--
+foreign(initModelNoTrain, c, initModelNoTrain(+integer, +integer, +integer)).
+
+%% --Input--
+%% mat data => data(float_array), dataSize, dataRowNum,
+%% vec labels => labels(float_array), labelsSize,
+%% int numClasses,
+%% int maxIterations => 1000
+%%
+%% --Output--
+%% --Description--
+foreign(initModelWithTrain, c, initModelWithTrain(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, +integer)).
+
+%% --Input--
+%%
+%% --Output--
+%% vec biases => biases(float_array), biasesSize
+%%
+%% --Description--
+foreign(biases, c, biases(-pointer(float_array), -integer)).
+
+%% --Input--
+%% mat test => test(float_array), testSize, testRowNum,
+%%
+%% --Output--
+%% vec predicLabels => predicLabels(float_array), predicLabelsSize
+%%
+%% --Description--
+foreign(classify, c, classify(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer)).
+
+%% TODO:
+%% --Input--
+%% mat data => data(float_array), dataSize, dataRowNum,
+%% vec labels => labels(float_array), labelsSize,
+%% int numClasses,
+%% vec instWeights => instWeights(float_array), instWeightsSize = arma::rowvec()
+%%
+%% --Output--
+%% --Description--
+foreign(train, c, train(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, +pointer(float_array), +integer)).
+
+%% --Input--
+%%
+%% --Output--
+%% mat weights => weights(float_array), weightsColNum, weightsRowNum,
+%%
+%% --Description--
+foreign(weights, c, weights(-pointer(float_array), -integer, -integer)).
+
+
+%% Defines the functions that get connected from main.cpp
+foreign_resource(perceptron, [ initModelNoTrain,
+ initModelWithTrain,
+ biases,
+ classify,
+ train,
+ weights]).
+
+:- load_foreign_resource(perceptron).
\ No newline at end of file
diff --git a/src/methods/perceptron/perceptron_test.pl b/src/methods/perceptron/perceptron_test.pl
new file mode 100644
index 0000000000000000000000000000000000000000..5562eaee7b879bb400701829fcf6a3ee9cea2339
--- /dev/null
+++ b/src/methods/perceptron/perceptron_test.pl
@@ -0,0 +1,56 @@
+:- use_module(library(plunit)).
+
+:- use_module(perceptron).
+:- 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