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Core notion of features, usually denoted as arrays of data.
Definitions of features for all primitive types, features
with location and lists of features (both in memory and on disk).
/*
AUTOMATICALLY GENERATED BY jTemp FROM
/Users/jon/Work/openimaj/target/checkout/core/core-feature/src/main/jtemp/org/openimaj/feature/Sparse#T#FVComparison.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
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*/
package org.openimaj.feature;
import gnu.trove.set.hash.TIntHashSet;
import org.openimaj.math.util.distance.HammingUtils;
import org.openimaj.util.array.SparseIntArray;
/**
* Comparison/distance methods for DoubleFV objects.
*
* @author Jonathon Hare ([email protected])
*/
public enum SparseIntFVComparison implements FVComparator {
/**
* Euclidean distance
* d(H1,H2) = Math.sqrt( sumI( (H1(I)-H2(I))^2 ) )
*/
EUCLIDEAN(true) {
@Override
public double compare(final SparseIntArray h1, final SparseIntArray h2) {
if (h1.length != h2.length)
throw new IllegalArgumentException("Vectors have differing lengths");
double d = 0;
for (SparseIntArray.DualEntry e : h1.unionEntries(h2)) {
double diff = e.value - e.otherValue;
d += (diff * diff);
}
return Math.sqrt(d);
}
},
/**
* Correlation
*
* d(H1,H2) = sumI( H'1(I) * H'2(I) ) / sqrt( sumI[H'1(I)2]^2 * sumI[H'2(I)^2] )
* where
* H'k(I) = Hk(I) - (1/N) * sumJ( Hk(J) ); N=number of FeatureVector bins
*
*/
CORRELATION(false) {
@Override
public double compare(final SparseIntArray h1, final SparseIntArray h2) {
if (h1.length != h2.length)
throw new IllegalArgumentException("Vectors have differing lengths");
double N = h1.length;
double SH1=0, SH2=0;
for (SparseIntArray.DualEntry e : h1.unionEntries(h2)) {
SH1 += e.value;
SH2 += e.otherValue;
}
SH1 /= N;
SH2 /= N;
double d = 0;
double SH1S = 0;
double SH2S = 0;
for (SparseIntArray.DualEntry e : h1.unionEntries(h2)) {
double h1prime = e.value - SH1;
double h2prime = e.otherValue - SH2;
d += (h1prime * h2prime);
SH1S += (h1prime * h1prime);
SH2S += (h2prime * h2prime);
}
return d / Math.sqrt(SH1S * SH2S);
}
},
/**
* Chi-squared distance
* d(H1,H2) = 0.5 * sumI[(H1(I)-H2(I))^2 / (H1(I)+H2(I))]
*/
CHI_SQUARE(true) {
@Override
public double compare(final SparseIntArray h1, final SparseIntArray h2) {
if (h1.length != h2.length)
throw new IllegalArgumentException("Vectors have differing lengths");
double d = 0;
for (SparseIntArray.DualEntry e : h1.unionEntries(h2)) {
double a = e.value - e.otherValue;
double b = e.value + e.otherValue;
if (Math.abs(b) > 0) d += a*a/b;
}
return d / 2;
}
},
/**
* Histogram intersection; assumes all values > 0.
* d(H1,H2) = sumI( min(H1(I), H2(I)) )
*/
INTERSECTION(false) {
@Override
public double compare(final SparseIntArray h1, final SparseIntArray h2) {
if (h1.length != h2.length)
throw new IllegalArgumentException("Vectors have differing lengths");
double d = 0;
for (SparseIntArray.DualEntry e : h1.intersectEntries(h2)) {
d += Math.min(e.value, e.otherValue);
}
return d;
}
},
/**
* Bhattacharyya distance
* d(H1,H2) = sqrt( 1 - (1 / sqrt(sumI(H1(I)) * sumI(H2(I))) ) * sumI( sqrt(H1(I) * H2(I)) ) )
*/
BHATTACHARYYA(true) {
@Override
public double compare(final SparseIntArray h1, final SparseIntArray h2) {
if (h1.length != h2.length)
throw new IllegalArgumentException("Vectors have differing lengths");
double SH1 = 0;
double SH2 = 0;
double d = 0;
for (SparseIntArray.DualEntry e : h1.unionEntries(h2)) {
SH1 += e.value;
SH2 += e.otherValue;
d += Math.sqrt(e.value * e.otherValue);
}
double den = SH1 * SH2;
if (den == 0) return 1;
d /= Math.sqrt(den);
return Math.sqrt(1.0 - d);
}
},
/**
* Hamming Distance
* d(H1,H2) = sumI(H1(I) == H2(I) ? 1 : 0)
*/
HAMMING(true) {
@Override
public double compare(final SparseIntArray h1, final SparseIntArray h2) {
if (h1.length != h2.length)
throw new IllegalArgumentException("Vectors have differing lengths");
int d = 0;
for (SparseIntArray.DualEntry e : h1.unionEntries(h2))
if (e.value != e.otherValue) d++;
return d;
}
},
/**
* Hamming Distance for packed bit strings
* d(H1,H2) = sumI(H1(I) == H2(I) ? 1 : 0)
*/
PACKED_HAMMING(true) {
@Override
public double compare(final SparseIntArray h1, final SparseIntArray h2) {
if (h1.length != h2.length)
throw new IllegalArgumentException("Vectors have differing lengths");
int d = 0;
for (SparseIntArray.DualEntry e : h1.unionEntries(h2)) {
d += HammingUtils.packedHamming(e.value, e.otherValue);
}
return d;
}
},
/**
* City-block (L1) distance
* d(H1,H2) = sumI( abs(H1(I)-H2(I)) )
*/
CITY_BLOCK(true) {
@Override
public double compare(final SparseIntArray h1, final SparseIntArray h2) {
if (h1.length != h2.length)
throw new IllegalArgumentException("Vectors have differing lengths");
double d = 0;
for (SparseIntArray.DualEntry e : h1.intersectEntries(h2)) {
d += Math.abs(e.value - e.otherValue);
}
return d;
}
},
/**
* Cosine similarity (sim of 1 means identical)
* d(H1,H2)=sumI(H1(I) * H2(I))) / (sumI(H1(I)^2) sumI(H2(I)^2))
*/
COSINE_SIM(false) {
@Override
public double compare(final SparseIntArray h1, final SparseIntArray h2) {
if (h1.length != h2.length)
throw new IllegalArgumentException("Vectors have differing lengths");
double h12 = 0;
double h11 = 0;
double h22 = 0;
for (SparseIntArray.DualEntry e : h1.unionEntries(h2)) {
h12 += e.value * e.otherValue;
h11 += e.value * e.value;
h22 += e.otherValue * e.otherValue;
}
return h12 / (Math.sqrt(h11) * Math.sqrt(h22));
}
},
/**
* Cosine distance (-COSINE_SIM)
*/
COSINE_DIST(true) {
@Override
public double compare(final SparseIntArray h1, final SparseIntArray h2) {
return -1 * COSINE_SIM.compare(h1, h2);
}
},
/**
* The arccosine of the cosine similarity
*/
ARCCOS(true) {
@Override
public double compare(final SparseIntArray h1, final SparseIntArray h2) {
return Math.acos( COSINE_SIM.compare(h1, h2) );
}
},
/**
* The symmetric Kullback-Leibler divergence. Vectors must only contain
* positive values; internally they will be converted to double arrays
* and normalised to sum to unit length.
*/
SYMMETRIC_KL_DIVERGENCE(true) {
@Override
public double compare(final SparseIntArray h1, final SparseIntArray h2) {
if (h1.length != h2.length)
throw new IllegalArgumentException("Vectors have differing lengths");
double sum1 = 0;
double sum2 = 0;
for (SparseIntArray.DualEntry e : h1.unionEntries(h2)) {
sum1 += e.value;
sum2 += e.otherValue;
}
double d = 0;
for (SparseIntArray.DualEntry e : h1.unionEntries(h2)) {
double h1n = e.value / sum1;
double h2n = e.otherValue / sum2;
double q1 = h1n / h2n;
double q2 = h2n / h1n;
if (h1n != 0) d += (h1n * Math.log(q1) / Math.log(2));
if (h2n != 0) d += (h2n * Math.log(q2) / Math.log(2));
}
return d / 2.0;
}
},
/**
* Jaccard distance. Converts each vector to a set
* for comparison.
*/
JACCARD_DISTANCE(true) {
@Override
public double compare(final SparseIntArray h1, final SparseIntArray h2) {
int[] h1v = h1.values();
int[] h2v = h2.values();
TIntHashSet union = new TIntHashSet(h1v);
union.addAll(h2v);
if (h1v.length != h1.length || h2v.length != h2.length)
union.add((int)0);
TIntHashSet intersection = new TIntHashSet(h1v);
intersection.retainAll(h2v);
if (h1v.length != h1.length && h2v.length != h2.length)
union.add((int)0);
return 1.0 - (((double)intersection.size()) / (double)union.size());
}
}
;
private boolean isDistance;
SparseIntFVComparison(boolean isDistance) {
this.isDistance = isDistance;
}
@Override
public boolean isDistance() {
return isDistance;
}
@Override
public double compare(SparseIntFV h1, SparseIntFV h2) {
return compare(h1.values, h2.values);
}
/**
* Compare two feature vectors in the form of sparse arrays,
* returning a score or distance.
*
* @param h1 the first feature array
* @param h2 the second feature array
* @return a score or distance
*/
public abstract double compare(SparseIntArray h1, SparseIntArray h2);
}
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