correction calcul matrice
[valse.git] / src / sources / constructionModelesLassoMLE.c
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1d3c1faa 1#include "EMGLLF.h"
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2#include "utils.h"
3#include <stdlib.h>
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4#include <gsl/gsl_linalg.h>
5#include <omp.h>
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6
7// TODO: comment on constructionModelesLassoMLE purpose
09ab3c16 8void constructionModelesLassoMLE_core(
3ec579a0 9 // IN parameters
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10 const float* phiInit, // parametre initial de moyenne renormalisé
11 const float* rhoInit, // parametre initial de variance renormalisé
12 const float* piInit,// parametre initial des proportions
13 const float* gamInit, // paramètre initial des probabilités a posteriori de chaque échantillon
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14 int mini,// nombre minimal d'itérations dans l'algorithme EM
15 int maxi,// nombre maximal d'itérations dans l'algorithme EM
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16 float gamma,// valeur de gamma : puissance des proportions dans la pénalisation pour un Lasso adaptatif
17 const float* glambda, // valeur des paramètres de régularisation du Lasso
18 const float* X, // régresseurs
19 const float* Y, // réponse
20 float seuil,// seuil pour prendre en compte une variable
21 float tau,// seuil pour accepter la convergence
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22 const int* A1, // matrice des coefficients des parametres selectionnes
23 const int* A2, // matrice des coefficients des parametres non selectionnes
1d3c1faa 24 // OUT parameters
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25 float* phi,// estimateur ainsi calculé par le Lasso
26 float* rho,// estimateur ainsi calculé par le Lasso
27 float* pi, // estimateur ainsi calculé par le Lasso
28 float* lvraisemblance, // estimateur ainsi calculé par le Lasso
1d3c1faa 29 // additional size parameters
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30 int n, // taille de l'echantillon
31 int p, // nombre de covariables
32 int m, // taille de Y (multivarié)
33 int k, // nombre de composantes
34 int L) // taille de glambda
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35{
36 //preparation: phi = 0
3ec579a0 37 for (int u=0; u<p*m*k*L; u++)
1d3c1faa 38 phi[u] = 0.0;
3ec579a0 39
1d3c1faa 40 //initiate parallel section
3ec579a0 41 int lambdaIndex;
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42 omp_set_num_threads(OMP_NUM_THREADS);
43 #pragma omp parallel default(shared) private(lambdaIndex)
44 {
45 #pragma omp for schedule(dynamic,CHUNK_SIZE) nowait
46 for (lambdaIndex=0; lambdaIndex<L; lambdaIndex++)
47 {
48 //~ a = A1(:,1,lambdaIndex);
49 //~ a(a==0) = [];
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50 int* a = (int*)malloc(p*sizeof(int));
51 int lengthA = 0;
52 for (int j=0; j<p; j++)
1d3c1faa 53 {
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54 if (A1[ai(j,0,lambdaIndex,p,m+1,L)] != 0)
55 a[lengthA++] = A1[ai(j,0,lambdaIndex,p,m+1,L)] - 1;
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56 }
57 if (lengthA == 0)
58 continue;
3ec579a0 59
1d3c1faa 60 //Xa = X(:,a)
afa07d41 61 float* Xa = (float*)malloc(n*lengthA*sizeof(float));
3ec579a0 62 for (int i=0; i<n; i++)
1d3c1faa 63 {
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64 for (int j=0; j<lengthA; j++)
65 Xa[mi(i,j,n,lengthA)] = X[mi(i,a[j],n,p)];
1d3c1faa 66 }
3ec579a0 67
1d3c1faa 68 //phia = phiInit(a,:,:)
afa07d41 69 float* phia = (float*)malloc(lengthA*m*k*sizeof(float));
3ec579a0 70 for (int j=0; j<lengthA; j++)
1d3c1faa 71 {
3ec579a0 72 for (int mm=0; mm<m; mm++)
1d3c1faa 73 {
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74 for (int r=0; r<k; r++)
75 phia[ai(j,mm,r,lengthA,m,k)] = phiInit[ai(a[j],mm,r,p,m,k)];
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76 }
77 }
3ec579a0 78
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79 //[phiLambda,rhoLambda,piLambda,~,~] = EMGLLF(...
80 // phiInit(a,:,:),rhoInit,piInit,gamInit,mini,maxi,gamma,0,X(:,a),Y,tau);
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81 float* phiLambda = (float*)malloc(lengthA*m*k*sizeof(float));
82 float* rhoLambda = (float*)malloc(m*m*k*sizeof(float));
83 float* piLambda = (float*)malloc(k*sizeof(float));
84 float* LLF = (float*)malloc((maxi+1)*sizeof(float));
85 float* S = (float*)malloc(lengthA*m*k*sizeof(float));
09ab3c16 86 EMGLLF_core(phia,rhoInit,piInit,gamInit,mini,maxi,gamma,0.0,Xa,Y,tau,
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87 phiLambda,rhoLambda,piLambda,LLF,S,
88 n,lengthA,m,k);
89 free(Xa);
90 free(phia);
91 free(LLF);
92 free(S);
3ec579a0 93
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94 //~ for j=1:length(a)
95 //~ phi(a(j),:,:,lambdaIndex) = phiLambda(j,:,:);
96 //~ end
3ec579a0 97 for (int j=0; j<lengthA; j++)
1d3c1faa 98 {
3ec579a0 99 for (int mm=0; mm<m; mm++)
1d3c1faa 100 {
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101 for (int r=0; r<k; r++)
102 phi[ai4(a[j],mm,r,lambdaIndex,p,m,k,L)] = phiLambda[ai(j,mm,r,p,m,k)];
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103 }
104 }
105 free(phiLambda);
106 //~ rho(:,:,:,lambdaIndex) = rhoLambda;
3ec579a0 107 for (int u=0; u<m; u++)
1d3c1faa 108 {
3ec579a0 109 for (int v=0; v<m; v++)
1d3c1faa 110 {
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111 for (int r=0; r<k; r++)
112 rho[ai4(u,v,r,lambdaIndex,m,m,k,L)] = rhoLambda[ai(u,v,r,m,m,k)];
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113 }
114 }
115 free(rhoLambda);
116 //~ pi(:,lambdaIndex) = piLambda;
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117 for (int r=0; r<k; r++)
118 pi[mi(r,lambdaIndex,k,L)] = piLambda[r];
1d3c1faa 119 free(piLambda);
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120
121 int dimension = 0;
122 int* b = (int*)malloc(m*sizeof(int));
123 for (int j=0; j<p; j++)
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124 {
125 //~ b = A2(j,2:end,lambdaIndex);
126 //~ b(b==0) = [];
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127 int lengthB = 0;
128 for (int mm=0; mm<m; mm++)
1d3c1faa 129 {
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130 if (A2[ai(j,mm+1,lambdaIndex,p,m+1,L)] != 0)
131 b[lengthB++] = A2[ai(j,mm+1,lambdaIndex,p,m+1,L)] - 1;
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132 }
133 //~ if length(b) > 0
134 //~ phi(A2(j,1,lambdaIndex),b,:,lambdaIndex) = 0.0;
135 //~ end
136 if (lengthB > 0)
137 {
3ec579a0 138 for (int mm=0; mm<lengthB; mm++)
1d3c1faa 139 {
3ec579a0 140 for (int r=0; r<k; r++)
09ab3c16 141 phi[ai4( A2[ai(j,0,lambdaIndex,p,m+1,L)]-1, b[mm], r, lambdaIndex, p, m, k, L)] = 0.;
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142 }
143 }
3ec579a0 144
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145 //~ c = A1(j,2:end,lambdaIndex);
146 //~ c(c==0) = [];
147 //~ dimension = dimension + length(c);
3ec579a0 148 for (int mm=0; mm<m; mm++)
1d3c1faa 149 {
3ec579a0 150 if (A1[ai(j,mm+1,lambdaIndex,p,m+1,L)] != 0)
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151 dimension++;
152 }
153 }
154 free(b);
3ec579a0 155
1d3c1faa 156 int signum;
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157 float* densite = (float*)calloc(L*n,sizeof(float));
158 float sumLogDensit = 0.0;
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159 gsl_matrix* matrix = gsl_matrix_alloc(m, m);
160 gsl_permutation* permutation = gsl_permutation_alloc(m);
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161 float* YiRhoR = (float*)malloc(m*sizeof(float));
162 float* XiPhiR = (float*)malloc(m*sizeof(float));
3ec579a0 163 for (int i=0; i<n; i++)
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164 {
165 //~ for r=1:k
166 //~ delta = Y(i,:)*rho(:,:,r,lambdaIndex) - (X(i,a)*(phi(a,:,r,lambdaIndex)));
167 //~ densite(i,lambdaIndex) = densite(i,lambdaIndex) +...
168 //~ pi(r,lambdaIndex)*det(rho(:,:,r,lambdaIndex))/(sqrt(2*PI))^m*exp(-dot(delta,delta)/2.0);
169 //~ end
3ec579a0 170 for (int r=0; r<k; r++)
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171 {
172 //compute det(rho(:,:,r,lambdaIndex)) [TODO: avoid re-computations]
3ec579a0 173 for (int u=0; u<m; u++)
1d3c1faa 174 {
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175 for (int v=0; v<m; v++)
176 matrix->data[u*m+v] = rho[ai4(u,v,r,lambdaIndex,m,m,k,L)];
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177 }
178 gsl_linalg_LU_decomp(matrix, permutation, &signum);
afa07d41 179 float detRhoR = gsl_linalg_LU_det(matrix, signum);
3ec579a0 180
1d3c1faa 181 //compute Y(i,:)*rho(:,:,r,lambdaIndex)
3ec579a0 182 for (int u=0; u<m; u++)
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183 {
184 YiRhoR[u] = 0.0;
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185 for (int v=0; v<m; v++)
186 YiRhoR[u] += Y[mi(i,v,n,m)] * rho[ai4(v,u,r,lambdaIndex,m,m,k,L)];
1d3c1faa 187 }
3ec579a0 188
1d3c1faa 189 //compute X(i,a)*phi(a,:,r,lambdaIndex)
3ec579a0 190 for (int u=0; u<m; u++)
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191 {
192 XiPhiR[u] = 0.0;
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193 for (int v=0; v<lengthA; v++)
194 XiPhiR[u] += X[mi(i,a[v],n,p)] * phi[ai4(a[v],u,r,lambdaIndex,p,m,k,L)];
1d3c1faa 195 }
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196 // On peut remplacer X par Xa dans ce dernier calcul, mais je ne sais pas si c'est intéressant ...
197
1d3c1faa 198 // compute dotProduct < delta . delta >
afa07d41 199 float dotProduct = 0.0;
3ec579a0 200 for (int u=0; u<m; u++)
1d3c1faa 201 dotProduct += (YiRhoR[u]-XiPhiR[u]) * (YiRhoR[u]-XiPhiR[u]);
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202
203 densite[mi(lambdaIndex,i,L,n)] += (pi[mi(r,lambdaIndex,k,L)]*detRhoR/pow(sqrt(2.0*M_PI),m))*exp(-dotProduct/2.0);
204 }
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205 sumLogDensit += log(densite[lambdaIndex*n+i]);
206 }
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207 lvraisemblance[mi(lambdaIndex,0,L,2)] = sumLogDensit;
208 lvraisemblance[mi(lambdaIndex,1,L,2)] = (dimension+m+1)*k-1;
209
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210 free(a);
211 free(YiRhoR);
212 free(XiPhiR);
213 free(densite);
214 gsl_matrix_free(matrix);
215 gsl_permutation_free(permutation);
216 }
217 }
218}