SGDL2LR.java

package org.shanbo.feluca.classification.lr;

import java.util.Properties;

import org.shanbo.feluca.data2.Vector;
import org.shanbo.feluca.data2.DataStatistic;
import org.shanbo.feluca.paddle.common.Utilities;


public class SGDL2LR extends AbstractSGDLogisticRegression{

	/**
	 * 1->1 ; 0-> -1; 0.5->0
	 * @param y
	 * @return
	 */
	protected double transform(double y ){
		return y * 2 - 1 ; 
	}
	
	protected double transform(int y ){
		return (y * 2) - 1 ; 
	}
	
	protected double gradientDescend(Vector sample){
		double wTx = w0;
		int innerLabel = outerLabelInfo[LABELRANGEBASE + sample.getIntHeader()][0];
		double newlabel = transform( innerLabel); //{-1, +1}
		for(int i = 0 ; i < sample.getSize(); i++){//wTx
			wTx += featureWeights[sample.getFId(i)] * sample.getWeight(i);
		}
		double gradient = - newlabel * ( 1 - 1/(1 + Math.pow(Math.E, - newlabel * wTx)));
		if (w0Type == 2)
			w0 -= alpha  * (gradient + 2 * lambda * w0);
		for(int i = 0 ; i < sample.getSize(); i++){
			// w <- w + alpha * (error * partial_derivation - lambda * w) 
			featureWeights[sample.getFId(i)] -= 
					  alpha * (gradient * sample.getWeight(i) + 2 * lambda * featureWeights[sample.getFId(i)]) ;
		}
		double innerPrediction =  1/ (1+Math.pow(Math.E,  - wTx));
		return innerPrediction;
	}


	@Override
	public int estimate(Properties dataStatus, Properties parameters) {
		// TODO Auto-generated method stub
		int maxFeatureId = Utilities.getIntFromProperties(dataStatus, DataStatistic.MAX_FEATURE_ID);
		int maxVectorSize = Utilities.getIntFromProperties(dataStatus, DataStatistic.MAX_VECTORSIZE);
		int numberLines = Utilities.getIntFromProperties(dataStatus, DataStatistic.NUM_VECTORS);
		int numberFeatures = Utilities.getIntFromProperties(dataStatus, DataStatistic.TOTAL_FEATURES);

		int modelSize = maxFeatureId * 4 / 1024 ;
		modelSize += maxFeatureId * 4 / 1024 ;
		int dataSetKb = 0;
		if (parameters.containsKey("inRam")){
			// use file data 
			dataSetKb += 30 * 1024; // VectorStorage.FileStorage approximately cost
			//			dataSetKb += VectorPool.RAMEstimate( maxVectorSize);
		}else{
			//			dataSetKb += VectorStorage.RAMCompactStorage.RAMEstimate(numberLines, numberFeatures, vectorStatusPara);
		}
		return dataSetKb + modelSize;
	}



	@Override
	protected void estimateParameter(){

		this.samples = Utilities.getIntFromProperties(dataEntry.getDataStatistic(), DataStatistic.NUM_VECTORS);
		double rate = Math.log(2 + samples /((1 + biasWeightRound)/(biasWeightRound * 2.0)) /( this.maxFeatureId + 0.0));
		if (rate < 0.5)
			rate = 0.5;

		if (alpha == null){
			alpha = 0.5 / rate;
			minAlpha = alpha  / Math.pow(1 + rate, 1.8);
			System.out.println("guessing alpha:" + alpha);
		}
		if (this.lambda == null){
			lambda = 0.02 / rate;
			//			minLambda = lambda  / Math.pow(1 + rate, 1.8);
			minLambda = 0.001;
			System.out.println("guessing lambda:" + lambda);
		}
	}

}