org.openimaj.feature.IntFV Maven / Gradle / Ivy

Go to download
/*
	AUTOMATICALLY GENERATED BY jTemp FROM
	/Users/jsh2/Work/openimaj/target/checkout/core/core-feature/src/main/jtemp/org/openimaj/feature/#T#FV.jtemp
*/
/**
 * Copyright (c) 2011, The University of Southampton and the individual contributors.
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without modification,
 * are permitted provided that the following conditions are met:
 *
 *   * 	Redistributions of source code must retain the above copyright notice,
 * 	this list of conditions and the following disclaimer.
 *
 *   *	Redistributions in binary form must reproduce the above copyright notice,
 * 	this list of conditions and the following disclaimer in the documentation
 * 	and/or other materials provided with the distribution.
 *
 *   *	Neither the name of the University of Southampton nor the names of its
 * 	contributors may be used to endorse or promote products derived from this
 * 	software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
package org.openimaj.feature;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.io.PrintWriter;
import java.util.Arrays;
import java.util.List;
import java.util.Scanner;

import org.openimaj.util.concatenate.Concatenatable;

/**
 * Basic int single-dimensional feature vector implementation
 *
 * @author Jonathon Hare
 */
public class IntFV extends ArrayFeatureVector implements Concatenatable, Cloneable {
	private static final long serialVersionUID = 1L;
	
	/**
	 * Construct an empty feature vector
	 */
	public IntFV() {}

	/**
	 * Construct empty FV with given number of bins
	 * @param nbins the number of bins in each dimension
	 */
	public IntFV(int nbins) {
		values = new int[nbins];
	}
	
	/**
	 * Construct from flattened values array and dimensions 
	 * @param values the flat array of values
	 */
	public IntFV(int [] values) {
		this.values = values;
	}
	
	/**
	 * Get the element at the given flat index
	 * @param x the flattened element index
	 * @return the value corresponding to x
	 */
	public int get(int x) {
		return values[x];
	}

    /**
     * Set the element at the given flat index
     * @param value the value to set
     * @param x the flattened element index
     */
	void set(int value, int x) {
         values[x] = value;
	}
	
	/**
	 * Element-wise normalisation to 0..1 using separated expected
	 * minimum and maximum values for each element of the underlying
	 * feature vector.
	 *
	 * @param min an array containing the minimum expected values
	 * @param max an array containing the maximum expected values
	 * @return feature vector with each value normalised to 0..1 
	 */
	@Override
	public DoubleFV normaliseFV(double [] min, double [] max) {
		double [] dvals = asDoubleVector();

		for (int i=0; i1) dvals[i] = 1;
		}
		
		return new DoubleFV(dvals);
	}
	
	/**
	 * Min-Max normalisation of the FV. Each element of the underlying
	 * feature vector is normalised to 0..1 based on the provided
	 * minimum and maximum expected values.
	 *
	 * @param min the minimum expected value
	 * @param max the maximum expected value
	 * @return feature vector with each value normalised to 0..1
	 */
	@Override
	public DoubleFV normaliseFV(double min, double max) {
		double [] dvals = asDoubleVector();

		for (int i=0; i1) dvals[i] = 1;
		}
		
		return new DoubleFV(dvals);
	}
	
	/**
	 * Normalise the FV to unit area.
	 *
	 * @return feature vector with all elements summing to 1.
	 */
	@Override
	public DoubleFV normaliseFV() {
		double [] dvals = asDoubleVector();
		double sum = 0;

		for (int i=0; i ins) {
		int l = values.length;
		
		for (int i=0; i