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DistanceMetrics.cpp
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DistanceMetrics.cpp
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/*
* File: DistanceMetrics.cpp
* Author: billwhite
*
* Created on March 29, 2011, 5:23 PM
*/
#include <cmath>
#include <iostream>
#include <map>
#include <utility>
#include <cmath>
#include "Dataset.h"
#include "DistanceMetrics.h"
#include "DatasetInstance.h"
#include "Statistics.h"
using namespace std;
pair<bool, double> CheckMissing(unsigned int attributeIndex,
DatasetInstance* dsi1,
DatasetInstance* dsi2) {
int numMissing = 0;
pair<double, double> hasMissing = make_pair(false, false);
if(dsi1->attributes[attributeIndex] == MISSING_ATTRIBUTE_VALUE) {
hasMissing.first = true;
++numMissing;
}
if(dsi2->attributes[attributeIndex] == MISSING_ATTRIBUTE_VALUE) {
hasMissing.second = true;
++numMissing;
}
// RELIEF-D
if(!numMissing) {
return make_pair(false, 0.0);
} else {
return make_pair(true, 2.0 / 3.0);
}
pair<bool, double> retValue;
double diff = 0.0;
// unsigned int numLevels = 0;
// unsigned int V = 0;
// Dataset* ds = dsi1->GetDatasetPtr();
switch(numMissing) {
case 0:
retValue = make_pair(false, 0.0);
break;
case 1:
if(hasMissing.first == true) {
diff = 1.0 / 3.0;
// diff = 1.0 / (double) ds->NumLevels(attributeIndex);
// diff = ds->GetProbabilityValueGivenClass(attributeIndex,
// dsi2->attributes[attributeIndex],
// dsi1->GetClass());
} else {
diff = 1.0 / 3.0;
// diff = 1.0 / (double) ds->NumLevels(attributeIndex);
// diff = ds->GetProbabilityValueGivenClass(attributeIndex,
// dsi1->attributes[attributeIndex],
// dsi2->GetClass());
}
// cout << "One missing value, diff = " << diff << endl;
retValue = make_pair(true, 1.0 - diff);
break;
case 2:
diff = 1.0 / 3.0;
// diff = 1.0 / (double) ds->NumLevels(attributeIndex);
// numLevels = ds->NumLevels(attributeIndex);
// diff = 0.0;
// double PVCI1 = 0.0, PVCI2 = 0.0;
// for(V = 0; V < numLevels; ++V) {
// PVCI1 = ds->GetProbabilityValueGivenClass(attributeIndex, V, dsi1->GetClass());
// PVCI2 = ds->GetProbabilityValueGivenClass(attributeIndex, V, dsi2->GetClass());
// cout << PVCI1 << " * " << PVCI2 << " = " << (PVCI1 * PVCI2) << endl;
// diff += (PVCI1 * PVCI2);
// }
// cout << "Two missing values, diff = " << diff << endl;
retValue = make_pair(true, 1.0 - diff);
break;
}
return retValue;
}
pair<bool, double> CheckMissingNumeric(unsigned int numericIndex,
DatasetInstance* dsi1,
DatasetInstance* dsi2) {
int numMissing = 0;
pair<double, double> hasMissing = make_pair(false, false);
if(dsi1->numerics[numericIndex] == MISSING_NUMERIC_VALUE) {
hasMissing.first = true;
++numMissing;
}
if(dsi2->numerics[numericIndex] == MISSING_NUMERIC_VALUE) {
hasMissing.second = true;
++numMissing;
}
if(numMissing) {
// ripped from Weka ReliefFAttributeEval.java:difference(), lines 828-836
double diff = 0.0;
if(numMissing == 2) {
return make_pair(true, 1.0);
} else {
if(hasMissing.first) {
pair<double, double> thisMinMax =
dsi2->GetDatasetPtr()->GetMinMaxForNumeric(numericIndex);
diff = norm(dsi2->numerics[numericIndex], thisMinMax.first, thisMinMax.second);
} else {
pair<double, double> thisMinMax =
dsi1->GetDatasetPtr()->GetMinMaxForNumeric(numericIndex);
diff = norm(dsi1->numerics[numericIndex], thisMinMax.first, thisMinMax.second);
}
if(diff < 0.5) {
diff = 1.0 - diff;
}
return make_pair(true, diff);
}
} else {
return make_pair(false, 0.0);
}
}
double norm(double x, double minX, double maxX) {
if(minX == maxX) {
return 0;
} else {
return(x - minX) / (maxX - minX);
}
}
double diffAMM(unsigned int attributeIndex,
DatasetInstance* dsi1,
DatasetInstance* dsi2) {
double distance = 0.0;
pair<bool, double> checkMissing = CheckMissing(attributeIndex, dsi1, dsi2);
if(checkMissing.first) {
distance = checkMissing.second;
} else {
distance = (double)
abs((int) dsi1->attributes[attributeIndex] -
(int) dsi2->attributes[attributeIndex]) * 0.5;
}
return distance;
}
double diffGMM(unsigned int attributeIndex,
DatasetInstance* dsi1,
DatasetInstance* dsi2) {
double distance = 0.0;
pair<bool, double> checkMissing = CheckMissing(attributeIndex, dsi1, dsi2);
if(checkMissing.first) {
distance = checkMissing.second;
} else {
distance = (dsi1->attributes[attributeIndex] !=
dsi2->attributes[attributeIndex]) ? 1.0 : 0.0;
}
return distance;
}
double diffNCA(unsigned int attributeIndex,
DatasetInstance* dsi1,
DatasetInstance* dsi2) {
double distance = 0.0;
// TODO: need special missing value checks for NCA metrics
pair<bool, double> checkMissing = CheckMissing(attributeIndex, dsi1, dsi2);
if(checkMissing.first) {
distance = checkMissing.second;
} else {
pair<char, char> alleles =
dsi1->GetDatasetPtr()->GetAttributeAlleles(attributeIndex);
string a1 = " ";
a1[0] = alleles.first;
string a2 = " ";
a2[0] = alleles.second;
map<AttributeLevel, string> genotypeMap;
genotypeMap[0] = a1 + a1;
genotypeMap[1] = a1 + a2;
genotypeMap[2] = a2 + a2;
AttributeLevel attrLevel1 = dsi1->attributes[attributeIndex];
AttributeLevel attrLevel2 = dsi2->attributes[attributeIndex];
string genotype1 = genotypeMap[attrLevel1];
string genotype2 = genotypeMap[attrLevel2];
map<char, unsigned int> nca1;
nca1['A'] = 0; nca1['T'] = 0; nca1['C'] = 0; nca1['G'] = 0;
++nca1[genotype1[0]];
++nca1[genotype1[1]];
map<char, unsigned int> nca2;
nca2['A'] = 0; nca2['T'] = 0; nca2['C'] = 0; nca2['G'] = 0;
++nca2[genotype2[0]];
++nca2[genotype2[1]];
map<char, unsigned int>::const_iterator nca1It = nca1.begin();
map<char, unsigned int>::const_iterator nca2It = nca2.begin();
for(; nca1It != nca1.end(); ++nca1It, ++nca2It) {
double nucleotideCount1 = (double) nca1It->second;
double nucleotideCount2 = (double) nca2It->second;
distance += abs(nucleotideCount1 - nucleotideCount2);
}
}
return distance;
}
double diffNCA6(unsigned int attributeIndex,
DatasetInstance* dsi1,
DatasetInstance* dsi2) {
double distance = 0.0;
pair<bool, double> checkMissing = CheckMissing(attributeIndex, dsi1, dsi2);
if(checkMissing.first) {
distance = checkMissing.second;
} else {
distance = (double)
abs((int) dsi1->attributes[attributeIndex] -
(int) dsi2->attributes[attributeIndex]) * 0.5;
}
// transition/transversion adjustment
if(dsi1->GetDatasetPtr()->GetAttributeMutationType(attributeIndex) ==
TRANSITION_MUTATION) {
distance *= 0.5;
}
return distance;
}
double diffKM(unsigned int attributeIndex,
DatasetInstance* dsi1,
DatasetInstance* dsi2) {
double distance = 0.0;
AttributeLevel dsi1Al = dsi1->GetAttribute(attributeIndex);
AttributeLevel dsi2Al = dsi2->GetAttribute(attributeIndex);
if(dsi1Al != dsi2Al) {
if(dsi1->GetDatasetPtr()->GetAttributeMutationType(attributeIndex) ==
TRANSITION_MUTATION) {
distance = 1.0;
} else {
if(dsi1->GetDatasetPtr()->GetAttributeMutationType(attributeIndex) ==
TRANSVERSION_MUTATION) {
distance = 2.0;
}
}
}
return distance;
}
double diffManhattan(unsigned int attributeIndex,
DatasetInstance* dsi1,
DatasetInstance* dsi2) {
// double wekaNormDiff = fabs(norm(dsi1->numerics[attributeIndex],
// minMax.first, minMax.second) -
// norm(dsi2->numerics[attributeIndex],
// minMax.first, minMax.second));
// the above code is equivalent; why???
double distance = 0.0;
pair<bool, double> checkMissing =
CheckMissingNumeric(attributeIndex, dsi1, dsi2);
if(checkMissing.first) {
distance = checkMissing.second;
} else {
pair<double, double> minMax =
dsi1->GetDatasetPtr()->GetMinMaxForNumeric(attributeIndex);
distance =
fabs(dsi1->numerics[attributeIndex] -
dsi2->numerics[attributeIndex]) /
(minMax.second - minMax.first);
}
return distance;
}
double diffEuclidean(unsigned int attributeIndex,
DatasetInstance* dsi1,
DatasetInstance* dsi2) {
double distance = 0.0;
pair<bool, double> checkMissing =
CheckMissingNumeric(attributeIndex, dsi1, dsi2);
if(checkMissing.first) {
distance = checkMissing.second;
} else {
distance =
hypot(dsi1->numerics[attributeIndex], dsi2->numerics[attributeIndex]);
}
return distance;
}
double diffPredictedValueTau(DatasetInstance* dsi1, DatasetInstance* dsi2) {
pair<double, double> minMax =
dsi1->GetDatasetPtr()->GetMinMaxForContinuousPhenotype();
// double diff = fabs(dsi1->GetPredictedValueTau() - dsi2->GetPredictedValueTau());
double diff =
fabs(dsi1->GetPredictedValueTau() - dsi2->GetPredictedValueTau()) /
(minMax.second - minMax.first);
return diff;
}