linear_regression.cpp

#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 "linear_regression_glue.h"
#include <mlpack/methods/linear_regression/linear_regression.hpp>
#include <mlpack/core.hpp>

// including helper functions for converting between arma structures and arrays
#include "../../helper_files/helper.hpp"

using namespace arma;
using namespace mlpack;
using namespace std;
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
// output:
void initModel(float *matrix, SP_integer matSize, SP_integer matRowNum, float *responses_arr, SP_integer vecSize, 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);

	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:
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)
{
	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 intercept()
{
	if(regressor.Intercept()) {return 1;}
	else {return 0;}
}

// input:
// output: double Tikhonov regularization parameter
SP_integer lambda()
{
	return regressor.Lambda();;
}

// 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::vec & parameters b vector
float * parameters(SP_integer *arraySize)
{
	// 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);
}

// input: arma::vec & new b vector
// output:
void modifyParameters(float *paramArr, SP_integer paramSize)
{
	// convert array to arma:: vec
	rowvec new_B_Vector = convertArrayToRowvec(paramArr, paramSize);

	// get parameter b vector ref
	vec& bVector = regressor.Parameters();

	// set new b vector
	bVector = new_B_Vector;
}

// input: const arma::mat &points, arma::rowvec &predictions
// output:
void predict(float *pointsArr, SP_integer pointsSize, SP_integer pointsRowNum, float **predicArr, SP_integer *predicSize)
{
	// 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 result in arma::rowvec
	rowvec predictions;
	regressor.Predict(data, predictions);


	// give back the sizes and the converted results as arrays
	*predicSize = predictions.n_elem;

	*predicArr = convertToArray(predictions);
}

// input: const arma::mat &data, const arma::rowvec &responses, const bool intercept
// output:
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(responses_arr, vecSize);
	mat data = convertArrayToMat(matrix, matSize, matRowNum);

	// run the model train function
	return regressor.Train(data, responses, (bool_intercept == 1));
}

// input: const arma::mat &data, const arma::rowvec &responses, const arma::rowvec &weights, const bool intercept
// output:
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 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);

	// run the model train function
	return regressor.Train(data, responses, weights, (bool_intercept == 1));
}