diff --git a/Makefile b/Makefile
index c30870e721d02aabad080811182b0f98ad4485af..715305665b3ff981ea3e351cc3ca0d30fc3973ce 100644
--- a/Makefile
+++ b/Makefile
@@ -3,6 +3,8 @@ SPLFR_PATH=/usr/local/sicstus4.7.1/bin/splfr
all:
make -C src/methods/bayesian_linear_regression splfr=$(SPLFR_PATH)
+ make -C src/methods/linear_regression splfr=$(SPLFR_PATH)
clean:
- make -C src/methods/bayesian_linear_regression clean
\ No newline at end of file
+ make -C src/methods/bayesian_linear_regression clean
+ make -C src/methods/linear_regression clean
diff --git a/src/methods/linear_regression/linear_regression.cpp b/src/methods/linear_regression/linear_regression.cpp
index 57a231b8430c0b972ac57ce65818317a9a48e0eb..7dd7bbd2b2f6d9a2e4b7787baff6af1df5ee36a4 100644
--- a/src/methods/linear_regression/linear_regression.cpp
+++ b/src/methods/linear_regression/linear_regression.cpp
@@ -17,61 +17,116 @@ using namespace mlpack::regression;
// Global Variable of the LinearRegression object so it can be accessed from all functions
LinearRegression regressor;
-// input: const bool , const bool , const size_t , const double
-// output: double
-void initModel(SP_integer centerData, SP_integer scaleData, SP_integer nIterMax, double tol)
+// 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, SP_integer lambda, SP_integer bool_intercept)
{
- regressor = new LinearRegression((centerData == 1), (scaleData == 1), nIterMax, tol);
+ if(matSize / matRowNum != vecSize)
+ {
+ 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);
+
+ regressor = LinearRegression(data, responses, lambda, 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, SP_integer lambda, SP_integer bool_intercept)
+{
+ if(matSize / matRowNum != vecSize)
+ {
+ 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);
+
+ regressor = LinearRegression(data, responses, weights, lambda, bool_intercept);
}
// input:
-// output: double
-SP_integer alpha()
+// output:
+void initModelEmpty(SP_integer)
+{
+ regressor = LinearRegression();
+}
+
+// input: const arma::mat &points, const arma::rowvec &responses
+// output: double L2SquardError
+SP_integer computeError(float *points_mat_arr, SP_integer matSize, SP_integer matRowNum, float *responses_arr, SP_integer vecSize)
{
- return regressor.Alpha();
+ if(matSize / matRowNum != vecSize)
+ {
+ 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);
+
+ // run the model function
+ return regressor.ComputeError(data, responses);
}
// input:
// output: double
-SP_integer beta()
+SP_integer intercept()
{
- return regressor.Beta();
+ if(regressor.Intercept()) {return 1;}
+ else {return 0;}
}
// input:
-// output: const arma::colvec &
-float * dataOffset(SP_integer *arraySize)
+// output: double Tikhonov regularization parameter
+SP_integer lambda()
{
- // save the DataOffset output in colvec
- colvec armaDataOffset = regressor.DataOffset();
-
- *arraySize = armaDataOffset.n_elem;
+ return regressor.Lambda();;
+}
- return convertArmaToArray(armaDataOffset);
+// input: double new Tikhonov regularization parameter
+// output:
+void modifyLambda(SP_integer new_lambda)
+{
+ double& lambda_ref = regressor.Lambda();
+ lambda_ref = new_lambda;
}
+
// input:
-// output: const arma::colvec &
-float * dataScale(SP_integer *arraySize)
+// output: const arma::vec & parameters b vector
+float * parameters(SP_integer *arraySize)
{
- // save the DataScale output in colvec
- colvec armaDataScale = regressor.DataScale();
+ // get b vector
+ colvec armaDataOffset = regressor.Parameters();
+
+ cout << regressor.Parameters() << endl;
+ cout << armaDataOffset << endl;
- *arraySize = armaDataScale.n_elem;
+ // set the b vector length
+ *arraySize = armaDataOffset.n_elem;
- return convertArmaToArray(armaDataScale);
+ // convert b vector to array and return
+ return convertArmaToArray(armaDataOffset);
}
-// input:
-// output: const arma::colvec &
-float * omega(SP_integer *arraySize)
+// input: arma::vec & new b vector
+// output:
+void modifyParameters(float *paramArr, SP_integer paramSize)
{
- // save the Omega output in colvec
- colvec armaOmega = regressor.Omega();
+ // convert array to arma:: vec
+ rowvec new_B_Vector = convertArrayToRowvec(paramArr, paramSize);
- *arraySize = armaOmega.n_elem;
+ // get parameter b vector ref
+ vec& bVector = regressor.Parameters();
- return convertArmaToArray(armaOmega);
+ // set new b vector
+ bVector = new_B_Vector;
}
// input: const arma::mat &points, arma::rowvec &predictions
@@ -92,104 +147,37 @@ void predict(float *pointsArr, SP_integer pointsSize, SP_integer pointsRowNum, f
*predicArr = convertArmaToArray(predictions);
}
-// input: const arma::mat &points, arma::rowvec &predictions, arma::rowvec &std
+// input: const arma::mat &data, const arma::rowvec &responses, const bool intercept
// output:
-void predictWithStd(float *pointsArr, SP_integer pointsSize, SP_integer pointsRowNum, float **predicArr, SP_integer *predicSize, float **stdArr, SP_integer *stdSize)
-{
- // convert the Prolog arrays to std::vec for easy conversion to arma::mat
- mat data = convertArrayToMat(pointsArr, pointsSize, pointsRowNum);
-
- // run the prediction and save the results in arma::rowvecs
- rowvec predictions;
- rowvec std;
- regressor.Predict(data, predictions, std);
-
- // give back the sizes and the converted results as arrays
- *predicSize = predictions.n_elem;
- *stdSize = std.n_elem;
-
- *predicArr = convertArmaToArray(predictions);
- *stdArr = convertArmaToArray(std);
-}
-
-
-// input:
-// output: double
-SP_integer responsesOffset()
-{
- return regressor.ResponsesOffset();
-}
-
-// 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)
+SP_integer train(float *matrix, SP_integer matSize, SP_integer matRowNum, float *responses_arr, SP_integer vecSize, SP_integer bool_intercept)
{
+ if(matSize / matRowNum != vecSize)
+ {
+ 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(arr, vecSize);
+ rowvec responses = convertArrayToRowvec(responses_arr, vecSize);
mat data = convertArrayToMat(matrix, matSize, matRowNum);
- // run the model function and return the error
- return regressor.RMSE(data, responses);
+ // run the model train function
+ return regressor.Train(data, responses, (bool_intercept == 1));
}
-// input: const arma::mat &data, const arma::rowvec &responses
+// input: const arma::mat &data, const arma::rowvec &responses, const arma::rowvec &weights, const bool intercept
// output:
-void train(float *matrix, SP_integer matSize, SP_integer matRowNum, float *arr, SP_integer vecSize)
+SP_integer 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)
{
if(matSize / matRowNum != vecSize)
{
cout << "Target dim doesnt fit to the Data dim" << endl;
- return;
+ return 0;
}
// convert the Prolog arrays to arma::rowvec and arma::mat
- rowvec responses = convertArrayToRowvec(arr, vecSize);
mat data = convertArrayToMat(matrix, matSize, matRowNum);
+ rowvec responses = convertArrayToRowvec(responses_arr, vecSize);
+ rowvec weights = convertArrayToRowvec(weights_arr, weightsSize);
- // run the model function
- regressor.Train(data, responses);
-}
-
-// input:
-// output: double
-SP_integer variance()
-{
- return regressor.Variance();
-}
-
-/*
-void testing()
-{
- // load the iris data
- mat dataset;
- bool loaded = mlpack::data::Load("iris.csv", dataset);
-
- // split the data into train and test
- mat trainData = dataset.cols(1, 4);
- trainData.shed_row(trainData.n_rows - 1);
- rowvec trainTarget = trainData.row(trainData.n_rows - 1);
- trainData.shed_row(trainData.n_rows - 1);
-
- mat testData = dataset.col(dataset.n_cols - 1);
- testData.shed_row(testData.n_rows - 1);
- rowvec testTarget = testData.row(testData.n_rows - 1);
- testData.shed_row(testData.n_rows - 1);
-
-
- // init the bayesian linear regressor model
-
-
- // train the model
- regressor.Train(trainData, trainTarget);
-
- // predict with the test data
- rowvec prediction;
- regressor.Predict(testData, prediction);
-
-
- // compare test target and prediction
- cout << "Train Data: " << trainData << endl;
- cout << "Train Target: " << trainTarget << endl;
- cout << "Alpha" << alpha() << endl;
- cout << "Beta" << beta() << endl;
+ // run the model train function
+ return regressor.Train(data, responses, weights, (bool_intercept == 1));
}
-*/
diff --git a/src/methods/linear_regression/linear_regression.pl b/src/methods/linear_regression/linear_regression.pl
index dc54a17cc826c691afe03cc232b7420c04269c5f..a62fbb480b1646ecb498fa40875477d1f3f6936a 100644
--- a/src/methods/linear_regression/linear_regression.pl
+++ b/src/methods/linear_regression/linear_regression.pl
@@ -1,4 +1,4 @@
-:- module(linear_regression, [initModel/4, alpha/1, beta/1, dataOffset/2, dataScale/2, omega/2, predict/5, predictWithStd/7, rmse/6, train/5, variance/1]).
+:- module(linear_regression, [initModel/7, initModelWithWeights/9, initModelEmpty/1, computeError/6, intercept/1, lambda/1, modifyLambda/1, parameters/2, modifyParameters/2, predict/5, train/7, trainWithWeights/9]).
:- load_files(library(str_decl),
[when(compile_time), if(changed)]).
@@ -13,37 +13,21 @@
float_array = array(float32).
%% definitions for the connected function and what there inputs and output arguments are
-foreign(initModel, c, initModel(+integer, +integer, +integer, +float32)).
-foreign(alpha, c, alpha([-integer])).
-foreign(beta, c, beta([-integer])).
-foreign(dataOffset, c, dataOffset(-integer, [-pointer(float_array)])).
-foreign(dataScale, c, dataScale(-integer, [-pointer(float_array)])).
-foreign(omega, c, omega(-integer, [-pointer(float_array)])).
+foreign(initModel, c, initModel(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, +integer)).
+foreign(initModelWithWeights, c, initModelWithWeights(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +pointer(float_array), +integer, +integer, +integer)).
+foreign(initModelEmpty, c, initModelEmpty(+integer)).
+foreign(computeError, c, computeError(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, [-integer])).
+foreign(intercept, c, intercept([-integer])).
+foreign(lambda, c, lambda([-integer])).
+foreign(modifyLambda, c, modifyLambda(+integer)).
+foreign(parameters, c, parameters([-pointer(float_array)], -integer)).
+foreign(modifyParameters, c, modifyParameters(+pointer(float_array), +integer)).
foreign(predict, c, predict(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer)).
-foreign(predictWithStd, c, predictWithStd(+pointer(float_array), +integer, +integer, -pointer(float_array), -integer, -pointer(float_array), -integer)).
-foreign(rmse, c, rmse(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, [-integer])).
-foreign(train, c, train(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer)).
-foreign(variance, c, variance([-integer])).
-
-%% Defines what functions should be connected from main.cpp
-foreign_resource(linear_regression, [initModel, alpha, beta, dataOffset, dataScale, omega, predict, predictWithStd, rmse, train, variance]).
-
-:- load_foreign_resource(linear_regression).
+foreign(train, c, train(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +integer, [-integer])).
+foreign(trainWithWeights, c, trainWithWeights(+pointer(float_array), +integer, +integer, +pointer(float_array), +integer, +pointer(float_array), +integer, +integer, [-integer])).
+%% Defines what functions should be connected from main.cpp
+foreign_resource(linear_regression, [initModel, initModelWithWeights, initModelEmpty, computeError, intercept, lambda, modifyLambda, parameters, modifyParameters, predict, train, trainWithWeights]).
-%% Some funktions that use the new Method
-
-%% First trains the model and then tries to predict the targets and the std for the given data
-predict(PredictList, StdList) :-
- train,
- 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)),
- predictWithStd(X, Xsize,Xrownum, Predic, PredicSize, Std, StdSize),
- convert_float_array_to_list(Predic, PredicSize, PredictList),
- convert_float_array_to_list(Std, StdSize, StdList).
-
-%% Trains the model with the given data and targets
-train :-
- 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).
+:- load_foreign_resource(linear_regression).
\ No newline at end of file
diff --git a/src/methods/linear_regression/test.pl b/src/methods/linear_regression/test.pl
index be28fdc62705f334fc01360bf380f50207333277..8223f1c9929e95e63f5f1d8c4660d102b64e3be6 100644
--- a/src/methods/linear_regression/test.pl
+++ b/src/methods/linear_regression/test.pl
@@ -4,53 +4,49 @@
:- use_module('../../helper_files/helper.pl').
reset_Model :-
- initModel(1,0,50,0.0001).
+ initModelEmpty(1).
:- 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).
+ train(X,Xsize, Xrownum,Y, Ysize,1 ,0).
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).
+ train(X,Xsize, Xrownum,Y, Ysize, 1, 0).
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).
+ train(X,Xsize, Xrownum,Y, Ysize, 1, 0).
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).
+ train(X,Xsize, Xrownum,Y, Ysize, 1, 0).
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).
+ train(X,Xsize, Xrownum,Y, Ysize, 1, 0).
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).
+ train(X,Xsize, Xrownum,Y, Ysize, 1, 0).
+
+test(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, 1, 0),
+ predict(X, Xsize,Xrownum, Predic, PredicSize),
+ convert_float_array_to_list(Predic, PredicSize, [0.20000000298023224,0.20000000298023224,0.20000000298023224,0.20000000298023224]).
+
:- end_tests(lists).
\ No newline at end of file