1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
|
//
// metroig.cpp
// Mothur
//
// Created by Sarah Westcott on 4/8/19.
// Copyright © 2019 Schloss Lab. All rights reserved.
//
#include "metroig.hpp"
/*constants for simplex minimisation*/
/***********************************************************************/
MetroIG::MetroIG(int fi, double sigA, double sigB, double sigS, int n, string stub) : sigmaA(sigA), sigmaB(sigB), sigmaS(sigS), nIters(n), fitIters(fi), outFileStub(stub), DiversityCalculator(false) {}
/***********************************************************************/
#ifdef USE_GSL
double nLogLikelihood0(const gsl_vector * x, void * params)
{
double dAlpha = gsl_vector_get(x,0), dBeta = gsl_vector_get(x,1);
int nS = (int) floor(gsl_vector_get(x, 2));
t_Data *ptData = (t_Data *) params;
int i = 0;
double dLogL = 0.0;
double dLog0 = 0.0, dLog1 = 0.0, dLog2 = 0.0, dLog3 = 0.0;
if(dAlpha <= 0.0 || dBeta <= 0.0){
return PENALTY;
}
DiversityUtils dutils("metroig");
for(i = 0; i < ptData->nNA; i++){
if (dutils.m->getControl_pressed()) { break; }
double dLogP = 0.0;
int nA = ptData->aanAbund[i][0];
dLogP = dutils.logLikelihood(nA, dAlpha, dBeta);
dLogL += ((double) ptData->aanAbund[i][1])*dLogP;
dLogL -= gsl_sf_lnfact(ptData->aanAbund[i][1]);
}
dLog0 = dutils.logLikelihood(0, dAlpha, dBeta);
dLog1 = (nS - ptData->nL)*dLog0;
dLog2 = - gsl_sf_lnfact(nS - ptData->nL);
dLog3 = gsl_sf_lnfact(nS);
dLogL += dLog1 + dLog2 + dLog3;
/*return*/
return -dLogL;
}
/***********************************************************************/
double negLogLikelihood0(double dAlpha, double dBeta, int nS, void * params)
{
t_Data *ptData = (t_Data *) params;
int i = 0;
double dLogL = 0.0;
double dLog0 = 0.0, dLog1 = 0.0, dLog2 = 0.0, dLog3 = 0.0;
if(dAlpha <= 0.0 || dBeta <= 0.0){
return PENALTY;
}
DiversityUtils dutils("metroig");
for(i = 0; i < ptData->nNA; i++){
if (dutils.m->getControl_pressed()) { break; }
double dLogP = 0.0;
int nA = ptData->aanAbund[i][0];
dLogP = dutils.logLikelihood(nA, dAlpha, dBeta);
dLogL += ((double) ptData->aanAbund[i][1])*dLogP;
dLogL -= gsl_sf_lnfact(ptData->aanAbund[i][1]);
}
dLog0 = dutils.logLikelihood(0, dAlpha, dBeta);
dLog1 = (nS - ptData->nL)*dLog0;
dLog2 = - gsl_sf_lnfact(nS - ptData->nL);
dLog3 = gsl_sf_lnfact(nS);
dLogL += dLog1 + dLog2 + dLog3;
/*return*/
return -dLogL;
}
/***********************************************************************/
void* metropolis0 (void * pvInitMetro)
{
t_MetroInit *ptMetroInit = (t_MetroInit *) pvInitMetro;
gsl_vector *ptX = ptMetroInit->ptX;
t_Data *ptData = ptMetroInit->ptData;
t_Params *ptParams = ptMetroInit->ptParams;
gsl_vector *ptXDash = gsl_vector_alloc(3); /*proposal*/
char *szSampleFile = (char *) malloc(1024*sizeof(char));
const gsl_rng_type *T;
gsl_rng *ptGSLRNG;
//FILE *sfp = nullptr;
int nS = 0, nSDash = 0, nIter = 0;
double dRand = 0.0, dNLL = 0.0;
void *pvRet = nullptr;
/*set up random number generator*/
T = gsl_rng_default;
ptGSLRNG = gsl_rng_alloc (T);
nS = (int) floor(gsl_vector_get(ptX,2));
dNLL = negLogLikelihood0(gsl_vector_get(ptX,0), gsl_vector_get(ptX,1), nS,(void*) ptData);
string filename = ptParams->szOutFileStub + "_" + toString(ptMetroInit->nThread) + ".sample";
ofstream out; Utils util; util.openOutputFile(filename, out);
out.setf(ios::fixed, ios::floatfield); out.setf(ios::showpoint);
/*seed random number generator*/
gsl_rng_set(ptGSLRNG, ptMetroInit->lSeed);
DiversityUtils dutils("metroig");
/*now perform simple Metropolis algorithm*/
while(nIter < ptParams->nIter){
double dA = 0.0, dNLLDash = 0.0;
if (dutils.m->getControl_pressed()) { break; }
dutils.getProposal(ptGSLRNG, ptXDash, ptX, &nSDash, nS, ptParams);
dNLLDash = negLogLikelihood0(gsl_vector_get(ptXDash,0), gsl_vector_get(ptXDash,1), nSDash, (void*) ptData);
dA = exp(dNLL - dNLLDash);
if(dA > 1.0){
dA = 1.0;
}
dRand = gsl_rng_uniform(ptGSLRNG);
if(dRand < dA){
gsl_vector_memcpy(ptX, ptXDash);
nS = nSDash;
dNLL = dNLLDash;
ptMetroInit->nAccepted++;
}
if(nIter % SLICE == 0){
out << nIter << "," << gsl_vector_get(ptX, 0) << "," << gsl_vector_get(ptX, 1) << "," << nS << "," << dNLL << endl;
}
nIter++;
}
out.close();
/*free up allocated memory*/
gsl_vector_free(ptXDash);
free(szSampleFile);
gsl_rng_free(ptGSLRNG);
return pvRet;
}
#endif
/***********************************************************************/
vector<string> MetroIG::getValues(SAbundVector* rank){
try {
t_Params tParams; tParams.nIter = nIters; tParams.dSigmaX = sigmaA; tParams.dSigmaY = sigmaB; tParams.dSigmaS = sigmaS; tParams.szOutFileStub = outFileStub; tParams.lSeed = m->getRandomSeed();
t_Data tData;
int bestSample = 0;
#ifdef USE_GSL
DiversityUtils dutils("metroig");
dutils.loadAbundance(&tData, rank);
gsl_vector* ptX = gsl_vector_alloc(3); /*parameter estimates*/
int sampled = rank->getNumSeqs(); //nj
int numOTUs = rank->getNumBins(); //nl
gsl_rng_env_setup();
gsl_set_error_handler_off();
/*set initial estimates for parameters*/
gsl_vector_set(ptX, 0, 1.0);
gsl_vector_set(ptX, 1, 5.0);
gsl_vector_set(ptX, 2, numOTUs*2);
double chaoResult = dutils.chao(&tData);
m->mothurOut("\nMetroIG - D = " + toString(numOTUs) + " L = " + toString(sampled) + " Chao = " + toString(chaoResult) + "\n");
dutils.minimiseSimplex(ptX, 3, (void*) &tData, &nLogLikelihood0, 0.1, 1.0e-2, 100000);
vector<double> parameterResults = dutils.outputResults(ptX, &tData, &nLogLikelihood0);
if(tParams.nIter > 0){
vector<double> acceptanceRates = dutils.mcmc(&tParams, &tData, ptX, &metropolis0);
if (fitIters != 0) { bestSample = dutils.fitSigma(acceptanceRates, parameterResults, fitIters, &tParams, &tData, ptX, &metropolis0); }
}
/*free up allocated memory*/
gsl_vector_free(ptX);
dutils.freeAbundance(&tData);
#endif
outputs.push_back(outFileStub + "_" + toString(bestSample) + ".sample");
if (bestSample == 0) { outputs.push_back(outFileStub + "_1.sample"); outputs.push_back(outFileStub + "_2.sample"); }
else if (bestSample == 1) { outputs.push_back(outFileStub + "_0.sample"); outputs.push_back(outFileStub + "_2.sample"); }
else if (bestSample == 2) { outputs.push_back(outFileStub + "_0.sample"); outputs.push_back(outFileStub + "_1.sample"); }
return outputs;
}
catch(exception& e) {
m->errorOut(e, "MetroIG", "getValues");
exit(1);
}
}
/***********************************************************************/
|
