void camellia128_init(const void* key, camellia128_ctx_t* s){
uint8_t i;
s->kll = 0; /* ((uint64_t*)key)[0]; */
/* load the key, endian-adjusted, to kll,klr */
for(i=0; i<8; ++i){
s->kll <<= 8;
s->kll |= *((uint8_t*)key);
key = (uint8_t*)key+1;
}
for(i=0; i<8; ++i){
s->klr <<= 8;
s->klr |= *((uint8_t*)key);
key = (uint8_t*)key+1;
}
s->kal = s->kll;
s->kar = s->klr;
s->kar ^= camellia_f(s->kal, SIGMA(0));
s->kal ^= camellia_f(s->kar, SIGMA(1));
s->kal ^= s->kll;
s->kar ^= s->klr;
s->kar ^= camellia_f(s->kal, SIGMA(2));
s->kal ^= camellia_f(s->kar, SIGMA(3));
}
void setOrdinaryMaterial(int m){
CA(m) = (1 - SIGMA(m)*deltaT/(2*EPSR(m)*EPSNOT )) / ( 1 + SIGMA(m)*deltaT /(2*EPSR(m)*EPSNOT));
CB(m) = (deltaT/(EPSR(m)*EPSNOT*DELTA)) / ( 1 + SIGMA(m) * deltaT /(2*EPSR(m)*EPSNOT));
DA(m) = (1 - SIGMA(m)*deltaT/(2*MUR(m)*MUNOT)) / ( 1 + SIGMA(m) * deltaT /(2*MUR(m)*MUNOT));
DB(m) = (deltaT/(MUR(m)*MUNOT*DELTA)) / ( 1 + SIGMA(m)*deltaT /(2*MUR(m)*MUNOT));
} // end setOrdinaryMaterial
int main()
{
// LOAD DATA
arma::mat X;
// A) Toy Data
// char inputFile[] = "../data_files/toyclusters/toyclusters.dat";
// B) X4.dat
//char inputFile[] = "./X4.dat";
// C) fisher data
//char inputFile[] = "./fisher.dat";
// D) MNIST data
//char inputFile[] = "../data_files/MNIST/MNIST.dat";
// E) Reduced MNIST (5000x400)
//char inputFile[] = "../data_files/MNIST/MNISTreduced.dat";
// F) Reduced MNIST (0 and 1) (1000x400)
//char inputFile[] = "../data_files/MNIST/MNISTlittle.dat";
// G) Girl.png (512x768, RGB, already unrolled)
//char inputFile[] = "girl.dat";
// H) Pool.png (383x512, RGB, already unrolled)
//char inputFile[] = "pool.dat";
// I) Cat.png (733x490, RGB, unrolled)
//char inputFile[] = "cat.dat";
// J) Airplane.png (512x512, RGB, unrolled)
//char inputFile[] = "airplane.dat";
// K) Monarch.png (512x768, RGB, unrolled)
//char inputFile[] = "monarch.dat";
// L) tulips.png (512x768 ,RGB, unrolled)
//char inputFile[] = "tulips.dat";
// M) demo.dat (2d data)
char inputFile[] = "demo.dat";
// INITIALIZE PARAMETERS
X.load(inputFile);
const arma::uword N = X.n_rows;
const arma::uword D = X.n_cols;
arma::umat ids(N,1); // needed to shuffle indices later
arma::umat shuffled_ids(N,1);
for (arma::uword i = 0; i < N; ++i) {
ids(i,0) = i;
}
arma::arma_rng::set_seed_random(); // set arma rng
// int seed = time(NULL); // set RNG seed to current time
// srand(seed);
arma::uword initial_K = 32; // initial number of clusters
arma::uword K = initial_K;
arma::umat clusters(N,1); // contains cluster assignments for each data point
for (arma::uword i = 0; i < N; ++i) {
clusters(i, 0) = i%K; // initialize as [0,1,...,K-1,0,1,...,K-1,0,...]
}
arma::umat cluster_sizes(N,1,arma::fill::zeros); // contains num data points in cluster k
for (arma::uword i = 0; i < N; ++i) {
cluster_sizes(clusters(i,0), 0) += 1;
}
arma::mat mu(N, D, arma::fill::zeros); // contains cluster mean parameters
arma::mat filler(D,D,arma::fill::eye);
std::vector<arma::mat> sigma(N,filler); // contains cluster covariance parameters
if (K < N) { // set parameters not belonging to any cluster to -999
mu.rows(K,N-1).fill(-999);
for (arma::uword k = K; k < N; ++k) {
sigma[k].fill(-999);
}
}
arma::umat uword_dummy(1,1); // dummy 1x1 matrix;
// for (arma::uword i = 0; i <N; ++i) {
// std::cout << sigma[i] << std::endl;
// }
// std::cout << X << std::endl
// << N << std::endl
// << D << std::endl
// << K << std::endl
// << clusters << std::endl
// << cluster_sizes << std::endl
// << ids << std::endl;
// INITIALIZE HYPER PARAMETERS
// Dirichlet Process concentration parameter is alpha:
double alpha = 1;
// Dirichlet Process base distribution (i.e. prior) is
// H(mu,sigma) = NIW(mu,Sigma|m_0,k_0,S_0,nu_0) = N(mu|m_0,Sigma/k_0)IW(Sigma|S_0,nu_0)
arma::mat perturbation(D,D,arma::fill::eye);
perturbation *= 0.000001;
//const arma::mat S_0 = arma::cov(X,X,1) + perturbation; // S_xbar / N
const arma::mat S_0(D,D,arma::fill::eye);
const double nu_0 = D + 2;
const arma::mat m_0 = mean(X).t();
const double k_0 = 0.01;
// std::cout << "S_0" << S_0 << std::endl;
// std::cout << S_0 << std::endl
// << nu_0 << std::endl
// << m_0 << std::endl
// << k_0 << std::endl;
// INITIALIZE SAMPLING PARAMETERS
arma::uword NUM_SWEEPS = 250; // number of Gibbs iterations
bool SAVE_CHAIN = false; // save output of each Gibbs iteration?
// arma::uword BURN_IN = NUM_SWEEPS - 10;
// arma::uword CHAINSIZE = NUM_SWEEPS - BURN_IN;
// std::vector<arma::uword> chain_K(CHAINSIZE, K); // Initialize chain variable to initial parameters for convinience
// std::vector<arma::umat> chain_clusters(CHAINSIZE, clusters);
// std::vector<arma::umat> chain_clusterSizes(CHAINSIZE, cluster_sizes);
// std::vector<arma::mat> chain_mu(CHAINSIZE, mu);
// std::vector<std::vector<arma::mat> > chain_sigma(CHAINSIZE, sigma);
// for (arma::uword sweep = 0; sweep < CHAINSIZE; ++sweep) {
// std::cout << sweep << " K\n" << chain_K[sweep] << std::endl
// << sweep << " clusters\n" << chain_clusters[sweep] << std::endl
// << sweep << " sizes\n" << chain_clusterSizes[sweep] << std::endl
// << sweep << " mu\n" << chain_mu[sweep] << std::endl;
// for (arma::uword i = 0; i < N; ++i) {
// std::cout << sweep << " " << i << " sigma\n" << chain_sigma[sweep][i] << std::endl;
// }
// }
// START CHAIN
std::cout << "Starting Algorithm with K = " << K << std::endl;
for (arma::uword sweep = 0; sweep < NUM_SWEEPS; ++sweep) {
// shuffle indices
shuffled_ids = shuffle(ids);
// std::cout << shuffled_ids << std::endl;
// SAMPLE CLUSTERS
for (arma::uword j = 0; j < N; ++j){
// std::cout << "j = " << j << std::endl;
arma::uword i = shuffled_ids(j);
arma::mat x = X.row(i).t(); // current data point
// Remove i's statistics and any empty clusters
arma::uword c = clusters(i,0); // current cluster
cluster_sizes(c,0) -= 1;
//std::cout << "old c = " << c << std::endl;
if (cluster_sizes(c,0) == 0) { // remove empty cluster
cluster_sizes(c,0) = cluster_sizes(K-1,0); // move entries for K onto position c
mu.row(c) = mu.row(K-1);
sigma[c] = sigma[K-1];
uword_dummy(0,0) = c;
arma::uvec idx = find(clusters == K - 1);
clusters.each_row(idx) = uword_dummy;
cluster_sizes(K-1,0) = 0;
mu.row(K-1).fill(-999);
sigma[K-1].fill(-999);
--K;
}
// quick test of logMvnPdf:
// arma::mat m_(2,1);
// arma::mat s_(2,2);
// arma::mat t_(2,1);
// m_ << 1 << arma::endr << 2;
// s_ << 3 << -0.2 << arma::endr << -0.2 << 1;
// t_ << -3 << arma::endr << -3;
// double lpdf = logMvnPdf(t_, m_, s_);
// std::cout << lpdf << std::endl; // śhould be -19.1034 (works)
// Find categorical distribution over clusters (tested)
arma::mat logP(K+1, 1, arma::fill::zeros);
// quick test of logInvWishPdf
// arma::mat si_(2,2), s_(2,2);
// double nu_ = 4;
// si_ << 1 << 0.5 << arma::endr << 0.5 << 4;
// s_ << 3 << -0.2 << arma::endr << -0.2 << 1;
// double lpdf = logInvWishPdf(si_, s_, nu_);
// std::cout << lpdf << std::endl; // should be -7.4399 (it is)
// quick test for logNormInvWishPdf
// arma::mat si_(2,2), s_(2,2);
// arma :: mat mu_(2,1), m_(2,1);
// double k_ = 0.5, nu_ = 4;
// si_ << 1 << 0.5 << arma::endr << 0.5 << 4;
// s_ << 3 << -0.2 << arma::endr << -0.2 << 1;
// mu_ << -3 << arma::endr << -3;
// m_ << 1 << arma::endr << 2;
// double lp = logNormInvWishPdf(mu_,si_,m_,k_,s_,nu_);
// std::cout << lp << std::endl; // should equal -15.2318 (it is)
// p(existing clusters) (tested)
for (arma::uword k = 0; k < K; ++k) {
arma::mat m_ = mu.row(k).t();
arma::mat s_ = sigma[k];
logP(k,0) = log(cluster_sizes(k,0)) - log(N-1+alpha) + logMvnPdf(x,m_,s_);
}
// p(new cluster): find partition function (tested)
// arma::mat dummy_mu(D, 1, arma::fill::zeros);
// arma::mat dummy_sigma(D, D, arma::fill::eye);
// double logPrior, logLklihd, logPstr, logPartition;
// posterior hyperparameters (tested)
arma::mat m_1(D,1), S_1(D,D);
double k_1, nu_1;
k_1 = k_0 + 1;
nu_1 = nu_0 + 1;
m_1 = (k_0*m_0 + x) / k_1;
S_1 = S_0 + x * x.t() + k_0 * (m_0 * m_0.t()) - k_1 * (m_1 * m_1.t());
// std::cout << k_1 << std::endl
// << nu_1 << std::endl
// << m_1 << std::endl
// << S_1 << std::endl;
// // partition = likelihood*prior/posterior (tested)
// // (perhaps a direct computation of the partition function would be better)
// logPrior = logNormInvWishPdf(dummy_mu, dummy_sigma, m_0, k_0, S_0, nu_0);
// logLklihd = logMvnPdf(x, dummy_mu, dummy_sigma);
// logPstr = logNormInvWishPdf(dummy_mu, dummy_sigma, m_1, k_1, S_1, nu_1);
// logPartition = logPrior + logLklihd - logPstr;
// std::cout << "log Prior = " << logPrior << std::endl
// << "log Likelihood = " << logLklihd << std::endl
// << "log Posterior = " << logPstr << std::endl
// << "log Partition = " << logPartition << std::endl;
// Computing partition directly
double logS0,signS0,logS1,signS1;
arma::log_det(logS0,signS0,S_0);
arma::log_det(logS1,signS1,S_1);
/*std::cout << "log(det(S_0)) = " << logS0 << std::endl
<< "log(det(S_1)) = " << logS1 << std::endl;*/
double term1 = 0.5*D*(log(k_0)-log(k_1));
double term2 = -0.5*D*log(arma::datum::pi);
double term3 = 0.5*(nu_0*logS0 - nu_1*logS1);
double term4 = lgamma(0.5*nu_1);
double term5 = -lgamma(0.5*(nu_1-D));
double logPartition = term1+term2+term3+term4+term5;
/*double logPartition = 0.5*D*(log(k_0)-log(k_1)-log(arma::datum::pi)) \
/+0.5*(nu_0*logS0 - nu_1*logS1) + lgamma(0.5*nu_1) - lgamma(0.5*(nu_1-D));*/
/* std::cout << "term1 = " << term1 << std::endl
<< "term2 = " << term2 << std::endl
<< "term3 = " << term3 << std::endl
<< "term4 = " << term4 << std::endl
<< "term5 = " << term5 << std::endl;*/
//std::cout << "logP = " << logPartition << std::endl;
// p(new cluster): (tested)
logP(K,0) = log(alpha) - log(N - 1 + alpha) + logPartition;
// std::cout << "logP(new cluster) = " << logP(K,0) << std::endl;
//if(i == 49)
//assert(false);
// sample cluster for i
arma::uword c_ = logCatRnd(logP);
clusters(i,0) = c_;
//if (j % 10 == 0){
//std::cout << "New c = " << c_ << std::endl;
//std::cout << "logP = \n" << logP << std::endl;
//}
// quick test for mvnRnd
// arma::mat mu, si;
// mu << 1 << arma::endr << 2;
// si << 1 << 0.9 << arma::endr << 0.9 << 1;
// arma::mat m = mvnRnd(mu, si);
// std::cout << m << std::endl;
// quick test for invWishRnd
// double df = 4;
// arma::mat si(2,2);
// si << 1 << 1 << arma::endr << 1 << 1;
// arma::mat S = invWishRnd(si,df);
// std::cout << S << std::endl;
if (c_ == K) { // Sample parameters for any new-born clusters from posterior
cluster_sizes(K, 0) = 1;
arma::mat si_ = invWishRnd(S_1, nu_1);
//arma::mat si_ = S_1;
arma::mat mu_ = mvnRnd(m_1, si_/k_1);
//arma::mat mu_ = m_1;
mu.row(K) = mu_.t();
sigma[K] = si_;
K += 1;
} else {
cluster_sizes(c_,0) += 1;
}
// if (sweep == 0)
// std::cout << " K = " << K << std::endl;
// // if (j == N-1) {
// // std::cout << logP << std::endl;
// // std::cout << K << std::endl;
// // assert(false);
// // }
// std::cout << "K = " << K << "\n" << std::endl;
}
// sample CLUSTER PARAMETERS FROM POSTERIOR
for (arma::uword k = 0; k < K; ++k) {
// std::cout << "k = " << k << std::endl;
// cluster data
arma::mat Xk = X.rows(find(clusters == k));
arma::uword Nk = cluster_sizes(k,0);
// posterior hyperparameters
arma::mat m_Nk(D,1), S_Nk(D,D);
double k_Nk, nu_Nk;
arma::mat sum_k = sum(Xk,0).t();
arma::mat cov_k(D, D, arma::fill::zeros);
for (arma::uword l = 0; l < Nk; ++l) {
cov_k += Xk.row(l).t() * Xk.row(l);
}
k_Nk = k_0 + Nk;
nu_Nk = nu_0 + Nk;
m_Nk = (k_0 * m_0 + sum_k) / k_Nk;
S_Nk = S_0 + cov_k + k_0 * (m_0 * m_0.t()) - k_Nk * (m_Nk * m_Nk.t());
// sample fresh parameters
arma::mat si_ = invWishRnd(S_Nk, nu_Nk);
//arma::mat si_ = S_Nk;
arma::mat mu_ = mvnRnd(m_Nk, si_/k_Nk);
//arma::mat mu_ = m_Nk;
mu.row(k) = mu_.t();
sigma[k] = si_;
}
std::cout << "Iteration " << sweep + 1 << "/" << NUM_SWEEPS<< " done. K = " << K << std::endl;
// // STORE CHAIN
// if (SAVE_CHAIN) {
// if (sweep >= BURN_IN) {
// chain_K[sweep - BURN_IN] = K;
// chain_clusters[sweep - BURN_IN] = clusters;
// chain_clusterSizes[sweep - BURN_IN] = cluster_sizes;
// chain_mu[sweep - BURN_IN] = mu;
// chain_sigma[sweep - BURN_IN] = sigma;
// }
// }
}
std::cout << "Final cluster sizes: " << std::endl
<< cluster_sizes.rows(0, K-1) << std::endl;
// WRITE OUPUT DATA TO FILE
arma::mat MU = mu.rows(0,K-1);
arma::mat SIGMA(D*K,D);
for (arma::uword k = 0; k < K; ++k) {
SIGMA.rows(k*D,(k+1)*D-1) = sigma[k];
}
arma::umat IDX = clusters;
// A) toycluster data
// char MuFile[] = "../data_files/toyclusters/dpmMU.out";
// char SigmaFile[] = "../data_files/toyclusters/dpmSIGMA.out";
// char IdxFile[] = "../data_files/toyclusters/dpmIDX.out";
// B) X4.dat
char MuFile[] = "dpmMU.out";
char SigmaFile[] = "dpmSIGMA.out";
char IdxFile[] = "dpmIDX.out";
std::ofstream KFile("K.out");
MU.save(MuFile, arma::raw_ascii);
SIGMA.save(SigmaFile, arma::raw_ascii);
IDX.save(IdxFile, arma::raw_ascii);
KFile << "K = " << K << std::endl;
if (SAVE_CHAIN) {}
// std::ofstream chainKFile("chainK.out");
// std::ofstream chainClustersFile("chainClusters.out");
// std::ofstream chainClusterSizesFile("chainClusterSizes.out");
// std::ofstream chainMuFile("chainMu.out");
// std::ofstream chainSigmaFile("chainSigma.out");
// chainKFile << "Dirichlet Process Mixture Model.\nInput: " << inputFile << std::endl
// << "Number of iterations of Gibbs Sampler: " << NUM_SWEEPS << std::endl
// << "Burn-In: " << BURN_IN << std::endl
// << "Initial number of clusters: " << initial_K << std::endl
// << "Output: Number of cluster (K)\n" << std::endl;
// chainClustersFile << "Dirichlet Process Mixture Model.\nInput: " << inputFile << std::endl
// << "Number of iterations of Gibbs Sampler: " << NUM_SWEEPS << std::endl
// << "Burn-In: " << BURN_IN << std::endl
// << "Initial number of clusters: " << initial_K << std::endl
// << "Output: Cluster identities (clusters)\n" << std::endl;
// chainClusterSizesFile << "Dirichlet Process Mixture Model.\nInput: " << inputFile << std::endl
// << "Number of iterations of Gibbs Sampler: " << NUM_SWEEPS << std::endl
// << "Burn-In: " << BURN_IN << std::endl
// << "Initial number of clusters: " << initial_K << std::endl
// << "Output: Size of clusters (cluster_sizes)\n" << std::endl;
// chainMuFile << "Dirichlet Process Mixture Model.\nInput: " << inputFile << std::endl
// << "Number of iterations of Gibbs Sampler: " << NUM_SWEEPS << std::endl
// << "Burn-In: " << BURN_IN << std::endl
// << "Initial number of clusters: " << initial_K << std::endl
// << "Output: Samples for cluster mean parameters (mu. Note: means stored in rows)\n" << std::endl;
// chainSigmaFile << "Dirichlet Process Mixture Model.\nInput: " << inputFile << std::endl
// << "Number of iterations of Gibbs Sampler: " << NUM_SWEEPS << std::endl
// << "Burn-In " << BURN_IN << std::endl
// << "Initial number of clusters: " << initial_K << std::endl
// << "Output: Samples for cluster covariances (sigma)\n" << std::endl;
// for (arma::uword sweep = 0; sweep < CHAINSIZE; ++sweep) {
// arma::uword K = chain_K[sweep];
// chainKFile << "Sweep #" << BURN_IN + sweep + 1 << "\n" << chain_K[sweep] << std::endl;
// chainClustersFile << "Sweep #" << BURN_IN + sweep + 1 << "\n" << chain_clusters[sweep] << std::endl;
// chainClusterSizesFile << "Sweep #" << BURN_IN + sweep + 1 << "\n" << chain_clusterSizes[sweep].rows(0, K - 1) << std::endl;
// chainMuFile << "Sweep #" << BURN_IN + sweep + 1 << "\n" << chain_mu[sweep].rows(0, K - 1) << std::endl;
// chainSigmaFile << "Sweep #" << BURN_IN + sweep + 1<< "\n";
// for (arma::uword i = 0; i < K; ++i) {
// chainSigmaFile << chain_sigma[sweep][i] << std::endl;
// }
// chainSigmaFile << std::endl;
// }
// }
return 0;
}
void gmm_fisher_save_soft_assgn(int n, const float *v, const gmm_t * g, int flags,
float *dp_dlambda,
float *word_total_soft_assignment) {
long d=g->d, k=g->k;
float *p = fvec_new(n * k);
long i,j,l;
long ii=0;
float * vp = NULL; /* v*p */
float * sum_pj = NULL; /* sum of p's for a given j */
gmm_compute_p(n,v,g,p,flags | GMM_FLAGS_W);
#define P(j,i) p[(i)*k+(j)]
#define V(l,i) v[(i)*d+(l)]
#define MU(l,j) g->mu[(j)*d+(l)]
#define SIGMA(l,j) g->sigma[(j)*d+(l)]
#define VP(l,j) vp[(j)*d+(l)]
// Save total soft assignment per centroid
if (word_total_soft_assignment != NULL) {
for (j=0; j<k; j++) {
double sum=0;
for (i=0; i<n; i++) {
sum += P(j,i);
}
if (n != 0) {
word_total_soft_assignment[j] = (float)(sum/n);
} else {
word_total_soft_assignment[j] = 0.0;
}
}
}
if(flags & GMM_FLAGS_W) {
for(j=1; j<k; j++) {
double accu=0;
for(i=0; i<n; i++)
accu+= P(j,i)/g->w[j] - P(0,i)/g->w[0];
/* normalization */
double f=n*(1/g->w[j]+1/g->w[0]);
dp_dlambda[ii++]=accu/sqrt(f);
}
}
if(flags & GMM_FLAGS_MU) {
float *dp_dmu=dp_dlambda+ii;
#define DP_DMU(l,j) dp_dmu[(j)*d+(l)]
if(0) { /* simple and slow */
for(j=0; j<k; j++) {
for(l=0; l<d; l++) {
double accu=0;
for(i=0; i<n; i++)
accu += P(j,i) * (V(l,i)-MU(l,j)) / SIGMA(l,j);
DP_DMU(l,j)=accu;
}
}
} else { /* complicated and fast */
/* precompute tables that may be useful for sigma too */
vp = fvec_new(k * d);
fmat_mul_tr(v,p,d,k,n,vp);
sum_pj = fvec_new(k);
for(j=0; j<k; j++) {
double sum=0;
for(i=0; i<n; i++) sum += P(j,i);
sum_pj[j] = sum;
}
for(j=0; j<k; j++) {
for(l=0; l<d; l++)
DP_DMU(l,j) = (VP(l,j) - MU(l,j) * sum_pj[j]) / SIGMA(l,j);
}
}
/* normalization */
if(!(flags & GMM_FLAGS_NO_NORM)) {
for(j=0; j<k; j++)
for(l=0; l<d; l++) {
float nf = sqrt(n*g->w[j]/SIGMA(l,j));
if(nf > 0) DP_DMU(l,j) /= nf;
}
}
#undef DP_DMU
ii+=d*k;
}
if(flags & (GMM_FLAGS_SIGMA | GMM_FLAGS_1SIGMA)) {
if(flags & GMM_FLAGS_1SIGMA) { /* fast not implemented for 1 sigma */
for(j=0; j<k; j++) {
double accu2=0;
for(l=0; l<d; l++) {
double accu=0;
for(i=0; i<n; i++)
accu += P(j,i) * (sqr(V(l,i)-MU(l,j)) / SIGMA(l,j) - 1) / sqrt(SIGMA(l,j));
if(flags & GMM_FLAGS_SIGMA) {
double f=flags & GMM_FLAGS_NO_NORM ? 1.0 : 2*n*g->w[j]/SIGMA(l,j);
dp_dlambda[ii++]=accu/sqrt(f);
}
accu2+=accu;
}
if(flags & GMM_FLAGS_1SIGMA) {
double f=flags & GMM_FLAGS_NO_NORM ? 1.0 : 2*d*n*g->w[j]/SIGMA(0,j);
dp_dlambda[ii++]=accu2/sqrt(f);
}
}
} else { /* fast and complicated */
assert(flags & GMM_FLAGS_SIGMA);
float *dp_dsigma = dp_dlambda + ii;
if(!vp) {
vp = fvec_new(k * d);
fmat_mul_tr(v,p,d,k,n,vp);
}
if(!sum_pj) {
sum_pj = fvec_new(k);
for(j=0; j<k; j++) {
double sum=0;
for(i=0; i<n; i++) sum += P(j,i);
sum_pj[j] = sum;
}
}
float *v2 = fvec_new(n * d);
for(i = n*d-1 ; i >= 0; i--) v2[i] = v[i] * v[i];
float *v2p = fvec_new(k * d);
fmat_mul_tr(v2,p,d,k,n,v2p);
free(v2);
#define V2P(l,j) v2p[(j)*d+(l)]
#define DP_DSIGMA(i,j) dp_dsigma[(i)+(j)*d]
for(j=0; j<k; j++) {
for(l=0; l<d; l++) {
double accu;
accu = V2P(l, j);
accu += VP(l, j) * (- 2 * MU(l,j));
accu += sum_pj[j] * (sqr(MU(l,j)) - SIGMA(l,j));
/* normalization */
double f;
if(flags & GMM_FLAGS_NO_NORM) {
f = pow(SIGMA(l,j), -1.5);
} else {
f = 1 / (SIGMA(l,j) * sqrt(2*n*g->w[j]));
}
DP_DSIGMA(l,j) = accu * f;
}
}
free(v2p);
#undef DP_DSIGMA
#undef V2P
ii += d * k;
}
}
assert(ii==gmm_fisher_sizeof(g,flags));
#undef P
#undef V
#undef MU
#undef SIGMA
free(p);
free(sum_pj);
free(vp);
}
// draws cells
void drawCells(float xrot, float yrot, int64_t ***lattice, cell_info_t *cells) {
int i, j, x, y, z, sigma;
for(i = 1; i <= numCells; i++) {
sigma = i;
DEBUG(printf("\t\tprinting cell sig %i with volume %i, membrane: %i, type: %i.\n", i, cells[i].volume, cells[i].membraneArea, cells[i].type);)
for(j = 0; j < cells[i].volume; j++) {
x = cells[i].subcells[j].x;
y = cells[i].subcells[j].y;
z = cells[i].subcells[j].z;
glLoadIdentity(); // Reset the model-view matrix
glTranslatef((x - (MODEL_SIZE_X/2)) * translationX, (y - (MODEL_SIZE_Y/2)) * translationY, (z - (MODEL_SIZE_Z/2)) * translationZ);
glBegin(GL_QUADS);
// set collor accoring to subcell type
switch(cells[i].type){
case MEDIUM:
return;
break;
case VASCULAR:
glColor4f(1.0f, 0.0f, 0.0f, 0.4f);
break;
case TUMOR_NORM:
glColor4f(0.0f, 1.0f, 0.0f, 0.4f);
break;
case TUMOR_NECROSIS:
glColor4f(0.0f, 0.0f, 1.0f, 0.4f);
break;
case TUMOR_STEM:
glColor4f(0.2f, 0.6f, 0.8f, 0.4f);
break;
default:
glColor4f(1.0f, 1.0f, 1.0f, 0.4f);
}
// Top face
if(y >= MODEL_SIZE_Y - 1 || SIGMA(lattice[x][y + 1][z]) != sigma) {
glVertex3f( viewScaleX, viewScaleY, -viewScaleZ);
glVertex3f(-viewScaleX, viewScaleY, -viewScaleZ);
glVertex3f(-viewScaleX, viewScaleY, viewScaleZ);
glVertex3f( viewScaleX, viewScaleY, viewScaleZ);
}
// Bottom face
if(y == 0 || SIGMA(lattice[x][y - 1][z]) != sigma) {
glVertex3f( viewScaleX, -viewScaleY, viewScaleZ);
glVertex3f(-viewScaleX, -viewScaleY, viewScaleZ);
glVertex3f(-viewScaleX, -viewScaleY, -viewScaleZ);
glVertex3f( viewScaleX, -viewScaleY, -viewScaleZ);
}
// Front face
if(z >= MODEL_SIZE_Z - 1 || SIGMA(lattice[x][y][z + 1]) != sigma) {
glVertex3f( viewScaleX, viewScaleY, viewScaleZ);
glVertex3f(-viewScaleX, viewScaleY, viewScaleZ);
glVertex3f(-viewScaleX, -viewScaleY, viewScaleZ);
glVertex3f( viewScaleX, -viewScaleY, viewScaleZ);
}
// Back face
if(z == 0 || SIGMA(lattice[x][y][z - 1]) != sigma) {
glVertex3f( viewScaleX, -viewScaleY, -viewScaleZ);
glVertex3f(-viewScaleX, -viewScaleY, -viewScaleZ);
glVertex3f(-viewScaleX, viewScaleY, -viewScaleZ);
glVertex3f( viewScaleX, viewScaleY, -viewScaleZ);
}
// Left face
if(x == 0 || SIGMA(lattice[x - 1][y][z]) != sigma) {
glVertex3f(-viewScaleX, viewScaleY, viewScaleZ);
glVertex3f(-viewScaleX, viewScaleY, -viewScaleZ);
glVertex3f(-viewScaleX, -viewScaleY, -viewScaleZ);
glVertex3f(-viewScaleX, -viewScaleY, viewScaleZ);
}
// Right face
if(x >= MODEL_SIZE_X - 1 || SIGMA(lattice[x + 1][y][z]) != sigma) {
glVertex3f(viewScaleX, viewScaleY, -viewScaleZ);
glVertex3f(viewScaleX, viewScaleY, viewScaleZ);
glVertex3f(viewScaleX, -viewScaleY, viewScaleZ);
glVertex3f(viewScaleX, -viewScaleY, -viewScaleZ);
}
glEnd();
}
}
