/**
 * Copyright 2010 Neuroph Project http://neuroph.sourceforge.net
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.neuroph.util.data.norm;

import org.neuroph.core.data.DataSet;
import org.neuroph.core.data.DataSetRow;

/**
 * Max normalization method, which normalize data in regard to max element in training set (by columns)
 * Normalization is done according to formula:
 * normalizedVector[i] = vector[i] / abs(max[i])
 * @author Zoran Sevarac 
 */
public class MaxNormalizer implements Normalizer {
    double[] maxIn, maxOut; // these contain max values for in and out columns
    
    
    @Override
   public void normalize(DataSet dataSet) {

        findMaxVectors(dataSet);

        for (DataSetRow row : dataSet.getRows()) {
            double[] normalizedInput = normalizeMax(row.getInput(), maxIn);
            row.setInput(normalizedInput);
            
            if (dataSet.isSupervised()) {
                double[] normalizedOutput = normalizeMax(row.getDesiredOutput(), maxOut);
                row.setDesiredOutput(normalizedOutput);
            }
        }

    }    
   
   
   /**
    * Finds max values for columns in input and output vector for given data set
    * @param dataSet  
    */
    private void findMaxVectors(DataSet dataSet) {
        int inputSize = dataSet.getInputSize();
        int outputSize = dataSet.getOutputSize();
        
        maxIn = new double[inputSize];    
        for(int i=0; i maxIn[i]) {
                    maxIn[i] = input[i];
                }
             }
            
            double[] output = dataSetRow.getDesiredOutput();
            for (int i = 0; i < outputSize; i++) {
                if (output[i] > maxOut[i]) {
                    maxOut[i] = output[i];
                }
            }            
                                    
        }         
    }   
    
    
    public double[] normalizeMax(double[] vector, double[] max) {
        double[] normalizedVector = new double[vector.length];
        
        for(int i = 0; i < vector.length; i++) {
                normalizedVector[i] = vector[i] / max[i];
        }
        
        return normalizedVector;
    }     
   
}