package org.forester.evoinference.distance;
import org.forester.evoinference.matrix.distance.BasicSymmetricalDistanceMatrix;
package org.forester.evoinference.distance;
import org.forester.evoinference.matrix.distance.BasicSymmetricalDistanceMatrix;
for( int i = 1; i < s; i++ ) {
for( int j = 0; j < i; j++ ) {
d.setValue( i, j, calcKimuraDistance( i, j ) );
for( int i = 1; i < s; i++ ) {
for( int j = 0; j < i; j++ ) {
d.setValue( i, j, calcKimuraDistance( i, j ) );
for( int i = 1; i < s; i++ ) {
for( int j = 0; j < i; j++ ) {
d.setValue( i, j, calcPoissonDistance( i, j ) );
for( int i = 1; i < s; i++ ) {
for( int j = 0; j < i; j++ ) {
d.setValue( i, j, calcPoissonDistance( i, j ) );
for( int i = 1; i < s; i++ ) {
for( int j = 0; j < i; j++ ) {
d.setValue( i, j, calcFractionalDissimilarity( i, j ) );
for( int i = 1; i < s; i++ ) {
for( int j = 0; j < i; j++ ) {
d.setValue( i, j, calcFractionalDissimilarity( i, j ) );
for( int i = 0; i < s; i++ ) {
d.setIdentifier( i, _msa.getIdentifier( i ) );
}
}
for( int i = 0; i < s; i++ ) {
d.setIdentifier( i, _msa.getIdentifier( i ) );
}
}
return new PairwiseDistanceCalculator( msa, DEFAULT_VALUE_FOR_TOO_LARGE_DISTANCE_FOR_KIMURA_FORMULA )
.calcFractionalDissimilarities();
}
return new PairwiseDistanceCalculator( msa, DEFAULT_VALUE_FOR_TOO_LARGE_DISTANCE_FOR_KIMURA_FORMULA )
.calcFractionalDissimilarities();
}
final double value_for_too_large_distance_for_kimura_formula ) {
return new PairwiseDistanceCalculator( msa, value_for_too_large_distance_for_kimura_formula )
.calcKimuraDistances();
final double value_for_too_large_distance_for_kimura_formula ) {
return new PairwiseDistanceCalculator( msa, value_for_too_large_distance_for_kimura_formula )
.calcKimuraDistances();