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evaluateGenericSpecial.c
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evaluateGenericSpecial.c
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/* RAxML-VI-HPC (version 2.2) a program for sequential and parallel estimation of phylogenetic trees
* Copyright August 2006 by Alexandros Stamatakis
*
* Partially derived from
* fastDNAml, a program for estimation of phylogenetic trees from sequences by Gary J. Olsen
*
* and
*
* Programs of the PHYLIP package by Joe Felsenstein.
* This program is free software; you may redistribute it and/or modify its
* under the terms of the GNU General Public License as published by the Free
* Software Foundation; either version 2 of the License, or (at your option)
* any later version.
*
* This program is distributed in the hope that it will be useful, but
* WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
* or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
* for more details.
*
*
* For any other enquiries send an Email to Alexandros Stamatakis
* Alexandros.Stamatakis@epfl.ch
*
* When publishing work that is based on the results from RAxML-VI-HPC please cite:
*
* Alexandros Stamatakis:"RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models".
* Bioinformatics 2006; doi: 10.1093/bioinformatics/btl446
*/
#ifndef WIN32
#include <unistd.h>
#endif
#include <math.h>
#include <time.h>
#include <stdlib.h>
#include <stdio.h>
#include <ctype.h>
#include <string.h>
#include "axml.h"
#ifdef __SIM_SSE3
#include <xmmintrin.h>
#include <pmmintrin.h>
/*#include <tmmintrin.h>*/
#endif
#ifdef _USE_PTHREADS
extern volatile double *reductionBuffer;
extern volatile int NumberOfThreads;
#endif
extern const unsigned int mask32[32];
void ascertainmentBiasSequence(unsigned char tip[32], int numStates, int dataType, int nodeNumber)
{
assert(numStates <= 32 && numStates > 1);
assert(nodeNumber > 0);
switch(dataType)
{
case BINARY_DATA:
tip[0] = 1;
tip[1] = 2;
break;
case DNA_DATA:
tip[0] = 1;
tip[1] = 2;
tip[2] = 4;
tip[3] = 8;
break;
case AA_DATA:
{
int
i;
for(i = 0; i < numStates; i++)
tip[i] = i;
}
break;
case GENERIC_32:
{
int
i;
for(i = 0; i < numStates; i++)
tip[i] = i;
}
break;
default:
assert(0);
}
}
static void calcDiagptableFlex(double z, int numberOfCategories, double *rptr, double *EIGN, double *diagptable, const int numStates)
{
int
i,
l;
double
lz,
lza[64];
const int
rates = numStates - 1;
assert(numStates <= 64);
if (z < zmin)
lz = log(zmin);
else
lz = log(z);
for(l = 0; l < rates; l++)
lza[l] = EIGN[l] * lz;
for(i = 0; i < numberOfCategories; i++)
{
diagptable[i * numStates] = 1.0;
for(l = 1; l < numStates; l++)
diagptable[i * numStates + l] = EXP(rptr[i] * lza[l - 1]);
}
}
static void calcDiagptableFlex_LG4(double z, int numberOfCategories, double *rptr, double *EIGN[4], double *diagptable, const int numStates)
{
int
i,
l;
double
lz;
assert(numStates <= 64);
if (z < zmin)
lz = log(zmin);
else
lz = log(z);
for(i = 0; i < numberOfCategories; i++)
{
diagptable[i * numStates] = 1.0;
for(l = 1; l < numStates; l++)
diagptable[i * numStates + l] = EXP(rptr[i] * EIGN[i][l - 1] * lz);
}
}
static double evaluateCatFlex(int *ex1, int *ex2, int *cptr, int *wptr,
double *x1, double *x2, double *tipVector,
unsigned char *tipX1, int n, double *diagptable_start, double *vector, boolean writeVector, const boolean fastScaling, const int numStates)
{
double
sum = 0.0,
term,
*diagptable,
*left,
*right;
int
i,
l;
if(tipX1)
{
if(writeVector)
for (i = 0; i < n; i++)
{
left = &(tipVector[numStates * tipX1[i]]);
right = &(x2[numStates * i]);
diagptable = &diagptable_start[numStates * cptr[i]];
for(l = 0, term = 0.0; l < numStates; l++)
term += left[l] * right[l] * diagptable[l];
if(fastScaling)
term = LOG(FABS(term));
else
term = LOG(FABS(term)) + (ex2[i] * LOG(minlikelihood));
vector[i] = term;
sum += wptr[i] * term;
}
else
for (i = 0; i < n; i++)
{
left = &(tipVector[numStates * tipX1[i]]);
right = &(x2[numStates * i]);
diagptable = &diagptable_start[numStates * cptr[i]];
for(l = 0, term = 0.0; l < numStates; l++)
term += left[l] * right[l] * diagptable[l];
if(fastScaling)
term = LOG(FABS(term));
else
term = LOG(FABS(term)) + (ex2[i] * LOG(minlikelihood));
sum += wptr[i] * term;
}
}
else
{
if(writeVector)
for (i = 0; i < n; i++)
{
left = &x1[numStates * i];
right = &x2[numStates * i];
diagptable = &diagptable_start[numStates * cptr[i]];
for(l = 0, term = 0.0; l < numStates; l++)
term += left[l] * right[l] * diagptable[l];
if(fastScaling)
term = LOG(FABS(term));
else
term = LOG(FABS(term)) + ((ex1[i] + ex2[i]) * LOG(minlikelihood));
vector[i] = term;
sum += wptr[i] * term;
}
else
for (i = 0; i < n; i++)
{
left = &x1[numStates * i];
right = &x2[numStates * i];
diagptable = &diagptable_start[numStates * cptr[i]];
for(l = 0, term = 0.0; l < numStates; l++)
term += left[l] * right[l] * diagptable[l];
if(fastScaling)
term = LOG(FABS(term));
else
term = LOG(FABS(term)) + ((ex1[i] + ex2[i]) * LOG(minlikelihood));
sum += wptr[i] * term;
}
}
return sum;
}
//asc
static double evaluateCatAsc(int *ex1, int *ex2,
double *x1, double *x2,
double *tipVector,
unsigned char *tipX1, int n, double *diagptable, const int numStates,
double *accumulator, double *weightVector, int dataType, int nodeNumber,
double *goldmanAccumulator)
{
double
exponent,
sum = 0.0,
unobserved,
term,
*left,
*right;
int
i,
l;
if(tipX1)
{
unsigned char
tip[32];
ascertainmentBiasSequence(tip, numStates, dataType, nodeNumber);
for (i = 0; i < n; i++)
{
left = &(tipVector[numStates * tip[i]]);
right = &(x2[i * numStates]);
term = 0.0;
for(l = 0; l < numStates; l++)
term += left[l] * right[l] * diagptable[l];
/* assumes that pow behaves as expected/specified for underflows
from the man page:
If result underflows, and is not representable, a range error occurs,
and 0.0 is returned.
*/
exponent = pow(minlikelihood, (double)ex2[i]);
unobserved = FABS(term) * exponent;
#ifdef _DEBUG_ASC
if(ex2[i] > 0)
{
printf("s %d\n", ex2[i]);
assert(0);
}
#endif
if(weightVector)
*accumulator += weightVector[i] * (LOG(FABS(term)) + (ex2[i] * LOG(minlikelihood)));
*goldmanAccumulator += ((LOG(FABS(term)) + (ex2[i] * LOG(minlikelihood))) * FABS(term) * exponent);
sum += unobserved;
}
}
else
{
for (i = 0; i < n; i++)
{
term = 0.0;
left = &(x1[i * numStates]);
right = &(x2[i * numStates]);
for(l = 0; l < numStates; l++)
term += left[l] * right[l] * diagptable[l];
/* assumes that pow behaves as expected/specified for underflows
from the man page:
If result underflows, and is not representable, a range error occurs,
and 0.0 is returned.
*/
exponent = pow(minlikelihood, (double)(ex1[i] + ex2[i]));
unobserved = FABS(term) * exponent;
#ifdef _DEBUG_ASC
if(ex2[i] > 0 || ex1[i] > 0)
{
printf("s %d %d\n", ex1[i], ex2[i]);
assert(0);
}
#endif
if(weightVector)
*accumulator += weightVector[i] * (LOG(FABS(term)) + ((ex1[i] + ex2[i]) * LOG(minlikelihood)));
*goldmanAccumulator += ((LOG(FABS(term)) + ((ex1[i] + ex2[i]) * LOG(minlikelihood))) * FABS(term) * exponent);
sum += unobserved;
}
}
return sum;
}
static double evaluateGammaAsc(int *ex1, int *ex2,
double *x1, double *x2,
double *tipVector,
unsigned char *tipX1, int n, double *diagptable, const int numStates,
double *accumulator, double *weightVector, int dataType, int nodeNumber,
double *goldmanAccumulator)
{
double
exponent,
sum = 0.0,
unobserved,
term,
*left,
*right;
int
i,
j,
l;
const int
gammaStates = numStates * 4;
if(tipX1)
{
unsigned char
tip[32];
ascertainmentBiasSequence(tip, numStates, dataType, nodeNumber);
for (i = 0; i < n; i++)
{
left = &(tipVector[numStates * tip[i]]);
for(j = 0, term = 0.0; j < 4; j++)
{
right = &(x2[gammaStates * i + numStates * j]);
for(l = 0; l < numStates; l++)
term += left[l] * right[l] * diagptable[j * numStates + l];
}
/* assumes that pow behaves as expected/specified for underflows
from the man page:
If result underflows, and is not representable, a range error occurs,
and 0.0 is returned.
*/
exponent = pow(minlikelihood, (double)ex2[i]);
unobserved = 0.25 * FABS(term) * exponent;
#ifdef _DEBUG_ASC
if(ex2[i] > 0)
{
printf("s %d\n", ex2[i]);
assert(0);
}
#endif
if(weightVector)
*accumulator += weightVector[i] * (LOG(0.25 * FABS(term)) + (ex2[i] * LOG(minlikelihood)));
*goldmanAccumulator += ((LOG(0.25 * FABS(term)) + (ex2[i] * LOG(minlikelihood))) * 0.25 * FABS(term) * exponent);
sum += unobserved;
}
}
else
{
for (i = 0; i < n; i++)
{
for(j = 0, term = 0.0; j < 4; j++)
{
left = &(x1[gammaStates * i + numStates * j]);
right = &(x2[gammaStates * i + numStates * j]);
for(l = 0; l < numStates; l++)
term += left[l] * right[l] * diagptable[j * numStates + l];
}
/* assumes that pow behaves as expected/specified for underflows
from the man page:
If result underflows, and is not representable, a range error occurs,
and 0.0 is returned.
*/
exponent = pow(minlikelihood, (double)(ex1[i] + ex2[i]));
unobserved = 0.25 * FABS(term) * exponent;
#ifdef _DEBUG_ASC
if(ex2[i] > 0 || ex1[i] > 0)
{
printf("s %d %d\n", ex1[i], ex2[i]);
assert(0);
}
#endif
if(weightVector)
*accumulator += weightVector[i] * (LOG(0.25 * FABS(term)) + ((ex1[i] + ex2[i]) * LOG(minlikelihood)));
*goldmanAccumulator += ((LOG(0.25 * FABS(term)) + ((ex1[i] + ex2[i]) * LOG(minlikelihood))) * 0.25 * FABS(term) * exponent);
sum += unobserved;
}
}
return sum;
}
//asc
static double evaluateGammaFlex(int *ex1, int *ex2, int *wptr,
double *x1, double *x2,
double *tipVector,
unsigned char *tipX1, int n, double *diagptable, double *vector, boolean writeVector, const boolean fastScaling, const int numStates)
{
double
sum = 0.0,
term,
*left,
*right;
int
i,
j,
l;
const int
gammaStates = numStates * 4;
if(tipX1)
{
if(writeVector)
for (i = 0; i < n; i++)
{
left = &(tipVector[numStates * tipX1[i]]);
for(j = 0, term = 0.0; j < 4; j++)
{
right = &(x2[gammaStates * i + numStates * j]);
for(l = 0; l < numStates; l++)
term += left[l] * right[l] * diagptable[j * numStates + l];
}
if(fastScaling)
term = LOG(0.25 * FABS(term));
else
term = LOG(0.25 * FABS(term)) + (ex2[i] * LOG(minlikelihood));
vector[i] = term;
sum += wptr[i] * term;
}
else
{
for (i = 0; i < n; i++)
{
left = &(tipVector[numStates * tipX1[i]]);
for(j = 0, term = 0.0; j < 4; j++)
{
right = &(x2[gammaStates * i + numStates * j]);
for(l = 0; l < numStates; l++)
term += left[l] * right[l] * diagptable[j * numStates + l];
}
if(fastScaling)
term = LOG(0.25 * FABS(term));
else
term = LOG(0.25 * FABS(term)) + (ex2[i] * LOG(minlikelihood));
sum += wptr[i] * term;
}
}
}
else
{
if(writeVector)
for (i = 0; i < n; i++)
{
for(j = 0, term = 0.0; j < 4; j++)
{
left = &(x1[gammaStates * i + numStates * j]);
right = &(x2[gammaStates * i + numStates * j]);
for(l = 0; l < numStates; l++)
term += left[l] * right[l] * diagptable[j * numStates + l];
}
if(fastScaling)
term = LOG(0.25 * FABS(term));
else
term = LOG(0.25 * FABS(term)) + ((ex1[i] + ex2[i])*LOG(minlikelihood));
vector[i] = term;
sum += wptr[i] * term;
}
else
for (i = 0; i < n; i++)
{
for(j = 0, term = 0.0; j < 4; j++)
{
left = &(x1[gammaStates * i + numStates * j]);
right = &(x2[gammaStates * i + numStates * j]);
for(l = 0; l < numStates; l++)
term += left[l] * right[l] * diagptable[j * numStates + l];
}
if(fastScaling)
term = LOG(0.25 * FABS(term));
else
term = LOG(0.25 * FABS(term)) + ((ex1[i] + ex2[i])*LOG(minlikelihood));
sum += wptr[i] * term;
}
}
return sum;
}
static double evaluateGammaFlex_LG4(int *ex1, int *ex2, int *wptr,
double *x1, double *x2,
double *tipVector[4],
unsigned char *tipX1, int n, double *diagptable, double *vector, boolean writeVector, const boolean fastScaling, const int numStates, double *weights)
{
double
sum = 0.0,
term,
*left,
*right;
int
i,
j,
l;
const int
gammaStates = numStates * 4;
if(tipX1)
{
if(writeVector)
for (i = 0; i < n; i++)
{
for(j = 0, term = 0.0; j < 4; j++)
{
double
t = 0.0;
left = &(tipVector[j][numStates * tipX1[i]]);
right = &(x2[gammaStates * i + numStates * j]);
for(l = 0; l < numStates; l++)
t += left[l] * right[l] * diagptable[j * numStates + l];
term += weights[j] * t;
}
if(fastScaling)
term = LOG(FABS(term));
else
term = LOG(FABS(term)) + (ex2[i] * LOG(minlikelihood));
vector[i] = term;
sum += wptr[i] * term;
}
else
{
for (i = 0; i < n; i++)
{
for(j = 0, term = 0.0; j < 4; j++)
{
double
t = 0.0;
left = &(tipVector[j][numStates * tipX1[i]]);
right = &(x2[gammaStates * i + numStates * j]);
for(l = 0; l < numStates; l++)
t += left[l] * right[l] * diagptable[j * numStates + l];
term += weights[j] * t;
}
if(fastScaling)
term = LOG(FABS(term));
else
term = LOG(FABS(term)) + (ex2[i] * LOG(minlikelihood));
sum += wptr[i] * term;
}
}
}
else
{
if(writeVector)
for (i = 0; i < n; i++)
{
for(j = 0, term = 0.0; j < 4; j++)
{
double
t = 0.0;
left = &(x1[gammaStates * i + numStates * j]);
right = &(x2[gammaStates * i + numStates * j]);
for(l = 0; l < numStates; l++)
t += left[l] * right[l] * diagptable[j * numStates + l];
term += weights[j] * t;
}
if(fastScaling)
term = LOG(FABS(term));
else
term = LOG(FABS(term)) + ((ex1[i] + ex2[i])*LOG(minlikelihood));
vector[i] = term;
sum += wptr[i] * term;
}
else
for (i = 0; i < n; i++)
{
for(j = 0, term = 0.0; j < 4; j++)
{
double
t = 0.0;
left = &(x1[gammaStates * i + numStates * j]);
right = &(x2[gammaStates * i + numStates * j]);
for(l = 0; l < numStates; l++)
t += left[l] * right[l] * diagptable[j * numStates + l];
term += weights[j] * t;
}
if(fastScaling)
term = LOG(FABS(term));
else
term = LOG(FABS(term)) + ((ex1[i] + ex2[i])*LOG(minlikelihood));
sum += wptr[i] * term;
}
}
return sum;
}
static double evaluateGammaInvarFlex (int *ex1, int *ex2, int *wptr, int *iptr,
double *x1, double *x2,
double *tipVector,double *tFreqs, double invariants,
unsigned char *tipX1, int n, double *diagptable, double *vector, boolean writeVector, const boolean fastScaling,
const int numStates)
{
double
sum = 0.0,
term,
freqs[64],
scaler = 0.25 * (1.0 - invariants),
*left,
*right;
int
i,
j,
l;
const int gammaStates = numStates * 4;
for(i = 0; i < numStates; i++)
freqs[i] = tFreqs[i] * invariants;
if(tipX1)
{
if(writeVector)
for (i = 0; i < n; i++)
{
left = &(tipVector[numStates * tipX1[i]]);
for(j = 0, term = 0.0; j < 4; j++)
{
right = &(x2[gammaStates * i + numStates * j]);
for(l = 0; l < numStates; l++)
term += left[l] * right[l] * diagptable[j * numStates + l];
}
if(iptr[i] < numStates)
if(fastScaling)
term = LOG(((scaler * FABS(term)) + freqs[iptr[i]]));
else
term = LOG(((scaler * FABS(term)) + freqs[iptr[i]])) + ex2[i] * LOG(minlikelihood);
else
if(fastScaling)
term = LOG(scaler * FABS(term));
else
term = LOG(scaler * FABS(term)) + (ex2[i] * LOG(minlikelihood));
vector[i] = term;
sum += wptr[i] * term;
}
else
for (i = 0; i < n; i++)
{
left = &(tipVector[numStates * tipX1[i]]);
for(j = 0, term = 0.0; j < 4; j++)
{
right = &(x2[gammaStates * i + numStates * j]);
for(l = 0; l < numStates; l++)
term += left[l] * right[l] * diagptable[j * numStates + l];
}
if(iptr[i] < numStates)
if(fastScaling)
term = LOG(((scaler * FABS(term)) + freqs[iptr[i]]));
else
term = LOG(((scaler * FABS(term)) + freqs[iptr[i]])) + ex2[i] * LOG(minlikelihood);
else
if(fastScaling)
term = LOG(scaler * FABS(term));
else
term = LOG(scaler * FABS(term)) + (ex2[i] * LOG(minlikelihood));
sum += wptr[i] * term;
}
}
else
{
if(writeVector)
for (i = 0; i < n; i++)
{
for(j = 0, term = 0.0; j < 4; j++)
{
left = &(x1[gammaStates * i + numStates * j]);
right = &(x2[gammaStates * i + numStates * j]);
for(l = 0; l < numStates; l++)
term += left[l] * right[l] * diagptable[j * numStates + l];
}
if(iptr[i] < numStates)
if(fastScaling)
term = LOG(((scaler * FABS(term)) + freqs[iptr[i]]));
else
term = LOG(((scaler * FABS(term)) + freqs[iptr[i]])) + (ex1[i] + ex2[i]) * LOG(minlikelihood);
else
if(fastScaling)
term = LOG(scaler * FABS(term));
else
term = LOG(scaler * FABS(term)) + (ex1[i] + ex2[i]) * LOG(minlikelihood);
vector[i] = term;
sum += wptr[i] * term;
}
else
for (i = 0; i < n; i++)
{
for(j = 0, term = 0.0; j < 4; j++)
{
left = &(x1[gammaStates * i + numStates * j]);
right = &(x2[gammaStates * i + numStates * j]);
for(l = 0; l < numStates; l++)
term += left[l] * right[l] * diagptable[j * numStates + l];
}
if(iptr[i] < numStates)
if(fastScaling)
term = LOG(((scaler * FABS(term)) + freqs[iptr[i]]));
else
term = LOG(((scaler * FABS(term)) + freqs[iptr[i]])) + (ex1[i] + ex2[i]) * LOG(minlikelihood);
else
if(fastScaling)
term = LOG(scaler * FABS(term));
else
term = LOG(scaler * FABS(term)) + (ex1[i] + ex2[i]) * LOG(minlikelihood);
sum += wptr[i] * term;
}
}
return sum;
}
void calcDiagptable(double z, int data, int numberOfCategories, double *rptr, double *EIGN, double *diagptable)
{
int i, l;
double lz;
if (z < zmin)
lz = log(zmin);
else
lz = log(z);
switch(data)
{
case BINARY_DATA:
{
double lz1;
lz1 = EIGN[0] * lz;
for(i = 0; i < numberOfCategories; i++)
{
diagptable[2 * i] = 1.0;
diagptable[2 * i + 1] = EXP(rptr[i] * lz1);
}
}
break;
case DNA_DATA:
{
double lz1, lz2, lz3;
lz1 = EIGN[0] * lz;
lz2 = EIGN[1] * lz;
lz3 = EIGN[2] * lz;
for(i = 0; i < numberOfCategories; i++)
{
diagptable[4 * i] = 1.0;
diagptable[4 * i + 1] = EXP(rptr[i] * lz1);
diagptable[4 * i + 2] = EXP(rptr[i] * lz2);
diagptable[4 * i + 3] = EXP(rptr[i] * lz3);
}
}
break;
case AA_DATA:
{
double lza[19];
for(l = 0; l < 19; l++)
lza[l] = EIGN[l] * lz;
for(i = 0; i < numberOfCategories; i++)
{
diagptable[i * 20] = 1.0;
for(l = 1; l < 20; l++)
diagptable[i * 20 + l] = EXP(rptr[i] * lza[l - 1]);
}
}
break;
case SECONDARY_DATA:
{
double lza[15];
for(l = 0; l < 15; l++)
lza[l] = EIGN[l] * lz;
for(i = 0; i < numberOfCategories; i++)
{
diagptable[i * 16] = 1.0;
for(l = 1; l < 16; l++)
diagptable[i * 16 + l] = EXP(rptr[i] * lza[l - 1]);
}
}
break;
case SECONDARY_DATA_6:
{
double lza[5];
for(l = 0; l < 5; l++)
lza[l] = EIGN[l] * lz;
for(i = 0; i < numberOfCategories; i++)
{
diagptable[i * 6] = 1.0;
for(l = 1; l < 6; l++)
diagptable[i * 6 + l] = EXP(rptr[i] * lza[l - 1]);
}
}
break;
case SECONDARY_DATA_7:
{
double lza[6];
for(l = 0; l < 6; l++)
lza[l] = EIGN[l] * lz;
for(i = 0; i < numberOfCategories; i++)
{
diagptable[i * 7] = 1.0;
for(l = 1; l < 7; l++)
diagptable[i * 7 + l] = EXP(rptr[i] * lza[l - 1]);
}
}
break;
default:
assert(0);
}
}
#ifdef __SIM_SSE3
static double evaluateGTRCATPROT_SAVE (int *ex1, int *ex2, int *cptr, int *wptr,
double *x1, double *x2, double *tipVector,
unsigned char *tipX1, int n, double *diagptable_start, const boolean fastScaling,
double *x1_gapColumn, double *x2_gapColumn, unsigned int *x1_gap, unsigned int *x2_gap)