LogisticRegression Class Reference

#include <LogisticRegression.h>

Inheritance diagram for LogisticRegression:

List of all members.

Public Member Functions
	LogisticRegression ()
	~LogisticRegression ()
virtual void	modelInit ()
virtual void	modelUpdate (REAL *input, REAL *target, uint nSamples, uint crossRun)
virtual void	predictAllOutputs (REAL *rawInputs, REAL *outputs, uint nSamples, uint crossRun)
virtual void	readSpecificMaps ()
virtual void	saveWeights (int cross)
virtual void	loadWeights (int cross)
virtual void	loadMetaWeights (int cross)
Static Public Member Functions
static string	templateGenerator (int id, string preEffect, int nameID, bool blendStop)
Private Attributes
REAL **	m_x
double	m_reg
double	m_scale
double	m_offset
bool	m_tuneOffsetScale
NumericalTools	solver

---

Detailed Description

EXPERIMENTAL ALGORITHM !!

THIS CODE IS NOT fully CORRECT!! or too slow.. Please do not use this class

Logistic regression prediction model Based on a sigmoid wrapper function with a linear model Look at: http://www.cs.cmu.edu/~ggordon/IRLS-example/

Tunable parameters are the regularization constant

Definition at line 21 of file LogisticRegression.h.

---

Constructor & Destructor Documentation

LogisticRegression::LogisticRegression

(

)

Constructor

Definition at line 8 of file LogisticRegression.cpp.

{
    cout<<"LogisticRegression"<<endl;
    // init member vars
    m_x = 0;
    m_reg = 0;
    m_tuneOffsetScale = 0;
}

LogisticRegression::~LogisticRegression

(

)

Destructor

Definition at line 20 of file LogisticRegression.cpp.

{
    cout<<"descructor LogisticRegression"<<endl;
    for ( int i=0;i<m_nCross+1;i++ )
    {
        if ( m_x )
        {
            if ( m_x[i] )
                delete[] m_x[i];
            m_x[i] = 0;
        }
    }
    if ( m_x )
        delete[] m_x;
    m_x = 0;
}

---

Member Function Documentation

void LogisticRegression::loadWeights

(

int

cross

)

[virtual]

Load the weights and all other parameters and make the model ready to predict

Implements StandardAlgorithm.

Definition at line 163 of file LogisticRegression.cpp.

{
    char buf[1024];
    sprintf ( buf,"%02d",cross );
    string name = m_datasetPath + "/" + m_tempPath + "/" + m_weightFile + "." + buf;
    cout<<"Load:"<<name<<endl;
    fstream f ( name.c_str(), ios::in );
    if ( f.is_open() == false )
        assert ( false );
    f.read ( ( char* ) &m_nTrain, sizeof ( int ) );
    f.read ( ( char* ) &m_nFeatures, sizeof ( int ) );
    f.read ( ( char* ) &m_nClass, sizeof ( int ) );
    f.read ( ( char* ) &m_nDomain, sizeof ( int ) );

    m_x = new REAL*[m_nCross+1];
    for ( int i=0;i<m_nCross+1;i++ )
        m_x[i] = 0;
    m_x[cross] = new REAL[m_nFeatures * (m_nClass-1)];

    f.read ( ( char* ) m_x[cross], sizeof ( REAL ) *m_nFeatures*(m_nClass-1) );
    f.read ( ( char* ) &m_reg, sizeof ( double ) );
    f.read ( ( char* ) &m_offset, sizeof ( double ) );
    f.read ( ( char* ) &m_scale, sizeof ( double ) );
    f.read ( ( char* ) &m_maxSwing, sizeof ( double ) );
    f.close();
}

void LogisticRegression::modelInit

(

)

[virtual]

Init the Linear Model

Implements StandardAlgorithm.

Definition at line 53 of file LogisticRegression.cpp.

{
    if(m_nDomain != 1)
        assert(false); // do not allow this
    
    // add the tunable parameter
    paramDoubleValues.push_back ( &m_reg );
    paramDoubleNames.push_back ( "reg" );
    if ( m_tuneOffsetScale )
    {
        paramDoubleValues.push_back ( &m_offset );
        paramDoubleNames.push_back ( "offset" );
        paramDoubleValues.push_back ( &m_scale );
        paramDoubleNames.push_back ( "scale" );
    }
    // alloc mem for weights
    if ( m_x == 0 )
    {
        m_x = new REAL*[m_nCross+1];
        for ( int i=0;i<m_nCross+1;i++ )
            m_x[i] = new REAL[m_nFeatures * (m_nClass - 1)];
    }
}

void LogisticRegression::modelUpdate	(	REAL *	input,
		REAL *	target,
		uint	nSamples,
		uint	crossRun
	)			[virtual]

Make a model update, set the new cross validation set or set the whole training set for retraining

Parameters:

	input	Pointer to input (can be cross validation set, or whole training set) (rows x nFeatures)
	target	The targets (can be cross validation targets)
	nSamples	The sample size (rows) in input
	crossRun	The cross validation run (for training)

Implements StandardAlgorithm.

Definition at line 129 of file LogisticRegression.cpp.

{
    // solve the linear system
    solver.LogisticRegressionMultisolutionSinglecall ( input, target, m_x[crossRun], nSamples, m_nFeatures, m_nClass-1, m_reg, m_offset, m_scale );
}

void LogisticRegression::predictAllOutputs	(	REAL *	rawInputs,
		REAL *	outputs,
		uint	nSamples,
		uint	crossRun
	)			[virtual]

Prediction for outside use, predicts outputs based on raw input values

Parameters:

	rawInputs	The input feature, without normalization (raw)
	outputs	The output value (prediction of target)
	nSamples	The input size (number of rows)
	crossRun	Number of cross validation run (in training)

Implements StandardAlgorithm.

Definition at line 85 of file LogisticRegression.cpp.

{
    for ( int i=0;i<nSamples;i++ )
    {
        /* MATLAB
        function p = predictLogistic(A,x)
        N = size(A,1);
        k = size(x,2)+1;
        p0 = [ones(size(A,1),1) A]*x;
        p1 = [exp(p0) ones(N,1)];
        p2 = p1 ./ repmat(sum(p1,2),1,k);
        p = p2;
        */
        
        for(int j=0;j<m_nClass-1;j++)
        {
            REAL sum = 0.0;
            for(int k=0;k<m_nFeatures;k++)
                sum += rawInputs[i*m_nFeatures+k] * m_x[crossRun][k*(m_nClass-1) + j];
            outputs[i*m_nClass+j] = sum;
        }
        
        for(int j=0;j<m_nClass-1;j++)
            outputs[i*m_nClass+j] = exp(outputs[i*m_nClass+j]);
        outputs[i*m_nClass+m_nClass-1] = 1.0;
        
        REAL sum = 0.0;
        for(int j=0;j<m_nClass;j++)
            sum += outputs[i*m_nClass+j];
        
        for(int j=0;j<m_nClass;j++)
            outputs[i*m_nClass+j] /= sum;
    }
}

void LogisticRegression::readSpecificMaps

(

)

[virtual]

Read the Algorithm specific values from the description file

Implements StandardAlgorithm.

Definition at line 41 of file LogisticRegression.cpp.

{
    m_reg = m_doubleMap["initReg"];
    m_offset = m_doubleMap["initOffset"];
    m_scale = m_doubleMap["initScale"];
    m_tuneOffsetScale = m_boolMap["tuneOffsetScale"];
}

void LogisticRegression::saveWeights

(

int

cross

)

[virtual]

Save the weights and all other parameters for load the complete prediction model

Implements StandardAlgorithm.

Definition at line 139 of file LogisticRegression.cpp.

{
    char buf[1024];
    sprintf ( buf,"%02d",cross );
    string name = m_datasetPath + "/" + m_tempPath + "/" + m_weightFile + "." + buf;
    if ( m_inRetraining )
        cout<<"Save:"<<name<<endl;
    fstream f ( name.c_str(), ios::out );
    f.write ( ( char* ) &m_nTrain, sizeof ( int ) );
    f.write ( ( char* ) &m_nFeatures, sizeof ( int ) );
    f.write ( ( char* ) &m_nClass, sizeof ( int ) );
    f.write ( ( char* ) &m_nDomain, sizeof ( int ) );
    f.write ( ( char* ) m_x[cross], sizeof ( REAL ) *m_nFeatures*(m_nClass-1) );
    f.write ( ( char* ) &m_reg, sizeof ( double ) );
    f.write ( ( char* ) &m_offset, sizeof ( double ) );
    f.write ( ( char* ) &m_scale, sizeof ( double ) );
    f.write ( ( char* ) &m_maxSwing, sizeof ( double ) );
    f.close();
}

string LogisticRegression::templateGenerator	(	int	id,
		string	preEffect,
		int	nameID,
		bool	blendStop
	)			[static]

Generates a template of the description file

Returns:

The template string

Definition at line 221 of file LogisticRegression.cpp.

{
    stringstream s;
    s<<"ALGORITHM=LogisticRegression"<<endl;
    s<<"ID="<<id<<endl;
    s<<"TRAIN_ON_FULLPREDICTOR="<<preEffect<<endl;
    s<<"DISABLE=0"<<endl;
    s<<endl;
    s<<"[int]"<<endl;
    s<<"maxTuninigEpochs=20"<<endl;
    s<<endl;
    s<<"[double]"<<endl;
    s<<"initMaxSwing=1.0"<<endl;
    s<<"initReg=0.01"<<endl;
    s<<"initOffset=0.5"<<endl;
    s<<"initScale=2.0"<<endl;
    s<<endl;
    s<<"[bool]"<<endl;
    s<<"tuneOffsetScale=1"<<endl;
    s<<"enableClipping=1"<<endl;
    s<<"enableTuneSwing=0"<<endl;
    s<<endl;
    s<<"minimzeProbe="<< ( !blendStop ) <<endl;
    s<<"minimzeProbeClassificationError=0"<<endl;
    s<<"minimzeBlend="<<blendStop<<endl;
    s<<"minimzeBlendClassificationError=0"<<endl;
    s<<endl;
    s<<"[string]"<<endl;
    s<<"weightFile="<<"LogisticRegression_"<<nameID<<"_weights.dat"<<endl;
    s<<"fullPrediction=LogisticRegression_"<<nameID<<".dat"<<endl;

    return s.str();
}

---

The documentation for this class was generated from the following files:

• ELF/LogisticRegression.h
• ELF/LogisticRegression.cpp