diff --git a/Makefile b/Makefile
index a92e302f8010123db790131224c6119d9bf6f532..895e07e1e20d19ca280d10029f7472c13004cbdd 100644
--- a/Makefile
+++ b/Makefile
@@ -5,8 +5,10 @@ all:
make -C src/methods/bayesian_linear_regression splfr=$(SPLFR_PATH)
make -C src/methods/linear_regression splfr=$(SPLFR_PATH)
make -C src/methods/lars splfr=$(SPLFR_PATH)
+ make -C src/methods/ada_boost splfr=$(SPLFR_PATH)
clean:
make -C src/methods/bayesian_linear_regression clean
make -C src/methods/linear_regression clean
make -C src/methods/lars clean
+ make -C src/methods/ada_boost clean
diff --git a/src/methods/ada_boost/Makefile b/src/methods/ada_boost/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..537b73222daebeb655d27f6e8c604395e44558f2
--- /dev/null
+++ b/src/methods/ada_boost/Makefile
@@ -0,0 +1,8 @@
+splfr=/usr/local/sicstus4.7.1/bin/splfr
+
+METHOD_NAME=ada_boost
+
+$(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/ada_boost/ada_boost.cpp b/src/methods/ada_boost/ada_boost.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..2e6b31e56a40be2f7bb340c8f6f63b511e3be224
--- /dev/null
+++ b/src/methods/ada_boost/ada_boost.cpp
@@ -0,0 +1,235 @@
+#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 "ada_boost_glue.h"
+#include <mlpack/methods/adaboost/adaboost.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::adaboost;
+
+using namespace mlpack::tree;
+using namespace mlpack::perceptron;
+using namespace mlpack::util;
+
+// Global Variable of the GlobalMethodObject object so it can be accessed from all functions
+AdaBoost<> adaBoostPerceptron;
+AdaBoost<DecisionTree<>> adaBoostDecisionStump;
+
+bool usingPerceptron = true;
+
+
+
+
+// TODO:
+// input: const MatType & data,
+// const arma::Row< size_t > & labels,
+// const size_t numClasses,
+// const WeakLearnerType & other,
+// const size_t iterations = 100,
+// const double tolerance = 1e-6
+// output:
+// description:
+void initModelWithTraining(float *dataArr, SP_integer dataSize, SP_integer dataRowNum, float *labelsArr, SP_integer labelSize, SP_integer numClasses, char const *learner,SP_integer iterations, double tolerance)
+{
+ // convert the Prolog arrays to arma::mat
+ mat data = convertArrayToMat(dataArr, dataSize, dataRowNum);
+ // convert the Prolog arrays to arma::rowvec
+ rowvec labels = convertArrayToRowvec(labelsArr, labelSize);
+ Row<size_t> convLabels = conv_to<Row<size_t>>::from(labels);
+
+ if(strcmp(learner, "perceptron") == 0)
+ {
+ cout << "perceptron" << endl;
+ usingPerceptron = true;
+ adaBoostPerceptron = AdaBoost<Perceptron<>>(data, convLabels, numClasses, iterations, tolerance);
+ }
+ else if (strcmp(learner, "decision_stump") == 0)
+ {
+ cout << "decision_stump" << endl;
+ usingPerceptron = false;
+ adaBoostDecisionStump = AdaBoost<DecisionStump<>>(data, convLabels, numClasses, iterations, tolerance);
+ }
+ else cout << "wrong input" << endl;
+}
+
+// TODO:
+// input: const double tolerance = 1e-6
+// output:
+// description:
+void initModelNoTraining(double tol = 1e-6, char const *learner = "perceptron")
+{
+ if(strcmp(learner, "perceptron") == 0)
+ {
+ cout << "perceptron" << endl;
+ usingPerceptron = true;
+ adaBoostPerceptron = AdaBoost<Perceptron<>>(tol);
+ }
+ else if (strcmp(learner, "decision_stump") == 0)
+ {
+ cout << "decision_stump" << endl;
+ usingPerceptron = false;
+ adaBoostDecisionStump = AdaBoost<DecisionStump<>>(tol);
+ }
+ else cout << "wrong input" << endl;
+}
+
+// TODO:
+// input: const MatType & test,
+// arma::Row< size_t > & predictedLabels,
+// arma::mat & probabilities
+// output:
+// description:
+void classifyWithProb(float *testMatArr, SP_integer testMatSize, SP_integer testMatRowNum, float **predLabelsArr, SP_integer *predLabelsArrSize, float **probMatArr, SP_integer *probMatColNum, SP_integer *probMatRowNum)
+{
+ // convert the Prolog arrays to arma::mat
+ mat data = convertArrayToMat(testMatArr, testMatSize, testMatRowNum);
+
+ // get the ReturnVector get the ReturnMat
+ Row< size_t > predLabelsVector;
+ mat probMat;
+
+ if(usingPerceptron)
+ {
+ adaBoostPerceptron.Classify(data, predLabelsVector, probMat);
+ }
+ else
+ {
+ adaBoostDecisionStump.Classify(data, predLabelsVector, probMat);
+ }
+
+ vec convPredLabels = conv_to<vec>::from(predLabelsVector);
+
+ // return the Vector lenght
+ *predLabelsArrSize = convPredLabels.n_elem;
+
+ // return the Vector as Array
+ *predLabelsArr = convertToArray(convPredLabels);
+
+ // return the Matrix dimensions
+ *probMatColNum = probMat.n_cols;
+ *probMatRowNum = probMat.n_rows;
+
+ // return the Matrix as one long Array
+ *probMatArr = convertToArray(probMat);
+}
+
+// TODO:
+// input: const MatType & test,
+// arma::Row< size_t > & predictedLabels
+// output:
+// description:
+void classifyNoProb(float *testMatArr, SP_integer testMatSize, SP_integer testMatRowNum, float **predLabelsArr, SP_integer *predLabelsArrSize)
+{
+ // convert the Prolog arrays to arma::mat
+ mat data = convertArrayToMat(testMatArr, testMatSize, testMatRowNum);
+
+ // get the ReturnVector
+ Row< size_t > predLabelsVector;
+
+ if(usingPerceptron)
+ {
+ adaBoostPerceptron.Classify(data, predLabelsVector);
+ }
+ else
+ {
+ adaBoostDecisionStump.Classify(data, predLabelsVector);
+ }
+
+ vec convPredLabels = conv_to<vec>::from(predLabelsVector);
+
+ // return the Vector lenght
+ *predLabelsArrSize = convPredLabels.n_elem;
+
+ // return the Vector as Array
+ *predLabelsArr = convertToArray(convPredLabels);
+}
+
+// TODO:
+// input:
+// output: size_t classesNum
+// description:
+SP_integer numClasses()
+{
+ if(usingPerceptron)
+ {
+ return adaBoostPerceptron.NumClasses();
+ }
+ else
+ {
+ return adaBoostDecisionStump.NumClasses();
+ }
+}
+
+// TODO:
+// input:
+// output: double tol
+// description:
+double getTolerance()
+{
+ if(usingPerceptron)
+ {
+ return adaBoostPerceptron.Tolerance();
+ }
+ else
+ {
+ return adaBoostDecisionStump.Tolerance();
+ }
+}
+
+// TODO:
+// input:
+// output: double& tol
+// description:
+void modifyTolerance(double newTol)
+{
+ if(usingPerceptron)
+ {
+ double& tol = adaBoostPerceptron.Tolerance();
+ tol = newTol;
+ }
+ else
+ {
+ double& tol = adaBoostDecisionStump.Tolerance();
+ tol = newTol;
+ }
+}
+
+// TODO:
+// input: const MatType & data,
+// const arma::Row< size_t > & labels,
+// const size_t numClasses,
+// const WeakLearnerType & learner,
+// const size_t iterations = 100,
+// const double tolerance = 1e-6
+//
+// output: double upper bound training error
+// description:
+double train(float *dataArr, SP_integer dataSize, SP_integer dataRowNum, float *labelsArr, SP_integer labelSize, SP_integer numClasses, char const *learner,SP_integer iterations, double tolerance)
+{
+ // convert the Prolog arrays to arma::mat
+ mat data = convertArrayToMat(dataArr, dataSize, dataRowNum);
+ // convert the Prolog arrays to arma::rowvec
+ rowvec labels = convertArrayToRowvec(labelsArr, labelSize);
+ Row<size_t> convLabels = conv_to<Row<size_t>>::from(labels);
+
+ if(strcmp(learner, "perceptron") == 0 && usingPerceptron)
+ {
+ cout << "perceptron" << endl;
+ return adaBoostPerceptron.Train(data, convLabels, numClasses, Perceptron(), iterations, tolerance);
+ }
+ else if (strcmp(learner, "decision_stump") == 0 && !usingPerceptron)
+ {
+ cout << "decision_stump" << endl;
+ return adaBoostDecisionStump.Train(data, convLabels, numClasses, DecisionStump<>(), iterations, tolerance);
+ }
+ else cout << "wrong input" << endl;
+ return 0;
+}
diff --git a/src/methods/ada_boost/ada_boost.pl b/src/methods/ada_boost/ada_boost.pl
new file mode 100644
index 0000000000000000000000000000000000000000..c51b6b780ef772b44777b895791657fb7d00b012
--- /dev/null
+++ b/src/methods/ada_boost/ada_boost.pl
@@ -0,0 +1,100 @@
+:- module(ada_boost, [
+ initModelWithTraining/9,
+ initModelNoTraining/2,
+ classifyWithProb/8,
+ classifyNoProb/5,
+ numClasses/1,
+ getTolerance/1,
+ modifyTolerance/1,
+ train/10]).
+
+%% 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
+
+%% TODO:
+%% input: const MatType & data,
+%% const arma::Row< size_t > & labels,
+%% const size_t numClasses,
+%% const WeakLearnerType & other,
+%% const size_t iterations = 100,
+%% const double tolerance = 1e-6
+%% output:
+%% description:
+foreign(initModelWithTraining, c, initModelWithTraining(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, +string, +integer , +float32)).
+
+%% TODO:
+%% input: const double tolerance = 1e-6
+%% output:
+%% description:
+foreign(initModelNoTraining, c, initModelNoTraining(+float32, +string)).
+
+%% TODO:
+%% input: const MatType & test,
+%% arma::Row< size_t > & predictedLabels,
+%% arma::mat & probabilities
+%% output:
+%% description:
+foreign(classifyWithProb, c, classifyWithProb(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer, -pointer(float_array), -integer, -integer)).
+
+%% TODO:
+%% input: const MatType & test,
+%% arma::Row< size_t > & predictedLabels
+%% output:
+%% description:
+foreign(classifyNoProb, c, classifyNoProb(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer)).
+
+%% TODO:
+%% input:
+%% output: size_t classesNum
+%% description:
+foreign(numClasses, c, numClasses([-integer])).
+
+%% TODO:
+%% input:
+%% output: double tol
+%% description:
+foreign(getTolerance, c, getTolerance([-float32])).
+
+%% TODO:
+%% input:
+%% output: double& tol
+%% description:
+foreign(modifyTolerance, c, modifyTolerance(+float32)).
+
+%% TODO:
+%% input: const MatType & data,
+%% const arma::Row< size_t > & labels,
+%% const size_t numClasses,
+%% const WeakLearnerType & learner,
+%% const size_t iterations = 100,
+%% const double tolerance = 1e-6
+%%
+%% output: double upper bound training error
+%% description:
+foreign(train, c, train(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, +string, +integer , +float32, [-float32])).
+
+%% Defines the functions that get connected from main.cpp
+foreign_resource(ada_boost, [
+ initModelWithTraining,
+ initModelNoTraining,
+ classifyWithProb,
+ classifyNoProb,
+ numClasses,
+ getTolerance,
+ modifyTolerance,
+ train
+ ]).
+
+:- load_foreign_resource(ada_boost).
\ No newline at end of file
diff --git a/src/methods/ada_boost/ada_boost_test.pl b/src/methods/ada_boost/ada_boost_test.pl
new file mode 100644
index 0000000000000000000000000000000000000000..ab199e7efcb4500b5aec325383ba96bf38a9c657
--- /dev/null
+++ b/src/methods/ada_boost/ada_boost_test.pl
@@ -0,0 +1,38 @@
+:- use_module(library(plunit)).
+
+:- use_module(ada_boost).
+:- use_module('../../helper_files/helper.pl').
+
+reset_Model :-
+ initModelNoTraining(0.0001, perceptron).
+
+:- begin_tests(lists).
+
+%% alpha tests
+test(tol_after_train, [true(A =:= 0.0001), true(B =:= 1)]) :-
+ 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, 2, perceptron, 50, 0.0001, B),
+ getTolerance(A).
+
+test(tol_after_train2, [true(A =:= 0.0001), true(B =:= 1)]) :-
+ 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],6, array(Xsize, Xrownum, X)),
+ convert_list_to_float_array([1,2], array(Ysize, Y)),
+ train(X,Xsize, Xrownum,Y, Ysize, 2, perceptron, 50, 0.0001, B),
+ getTolerance(A).
+
+test(tol_after_train_fail, [true(B =:= 1)]) :-
+ 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],2, array(Xsize, Xrownum, X)),
+ convert_list_to_float_array([1,2,2,2,1,1], array(Ysize, Y)),
+ train(X,Xsize, Xrownum,Y, Ysize, 2, perceptron, 50, 0.0001, B),
+ classifyNoProb(X,Xsize, Xrownum, NewY, NewYsize),
+ print(Ysize),
+ print(NewYsize),
+ convert_float_array_to_list(NewY, NewYsize, Results),
+ print([1,2,2,2,1,1]),
+ print(Results).
+
+:- end_tests(lists).
\ No newline at end of file
diff --git a/src/methods/new_method/new_method.cpp b/src/methods/new_method/new_method.cpp
index a29d1806690c3326138e05e6e07017ea8946a5b2..7c0ee9d2c9fe45e1328db10eed6daeb9974c3592 100644
--- a/src/methods/new_method/new_method.cpp
+++ b/src/methods/new_method/new_method.cpp
@@ -27,7 +27,7 @@ void sampleFunction()
}
-void takeNumberFunction(SP_integer integerNumber, double doubleNumber)
+void takeNumberFunction(SP_integer integerNumber, double doubleNumber, char const *string)
{
}