void _optimize_likelihood() {
par::init();
typedef std::pair<Eigen::VectorXd, double> pair_t;
auto body = [ = ](int i) {
// we need a copy because each thread should touch a copy of the GP!
auto gp = *this;
Eigen::VectorXd v = rprop::optimize([&](const Eigen::VectorXd & v) {
return gp.log_likelihood(v);
},
[&](const Eigen::VectorXd & v) {
return gp.log_likelihood_grad(v, false);
},
this->kernel_function().h_params_size(), Params::gp_auto::n_rprop());
double lik = gp.log_likelihood(v);
return std::make_pair(v, lik);
};
auto comp = [](const pair_t& v1, const pair_t& v2) {
return v1.second > v2.second;
};
pair_t init(Eigen::VectorXd::Zero(1), -std::numeric_limits<float>::max());
auto m = par::max(init, Params::gp_auto::rprop_restart(), body, comp);
std::cout << "likelihood:" << m.second << std::endl;
this->_kernel_function.set_h_params(m.first);
}
inline double pmcmc_do (vsmc::Sampler<T> &sampler, std::size_t model_num)
{
const std::size_t nchain = static_cast<std::size_t>(sampler.size());
std::vector<double> log_likelihood(nchain);
sampler.particle().value().comp_num(model_num);
sampler.initialize();
sampler.iterate(BurninNum);
for (std::size_t k = 0; k != nchain; ++k)
log_likelihood[k] = 0;
for (std::size_t j = 0; j != IterNum; ++j) {
sampler.iterate();
for (std::size_t k = 0; k != nchain; ++k) {
log_likelihood[k] += sampler.particle().value().
state(k, 0).log_likelihood();
}
}
for (std::size_t k = 0; k != nchain; ++k)
log_likelihood[k] /= IterNum;
double ps = sampler.particle().value().log_likelihood_const();
for (std::size_t k = 1; k != nchain; ++k) {
ps += sampler.particle().value().state(k, 0).alpha_inc() *
0.5 * (log_likelihood[k -1] + log_likelihood[k]);
}
return ps;
}
/*
* Sample the parameter space uniformly. Very slow!
*/
void explore_prior_space(live_point* livpnt,double llstar,unsigned num_dim,
unsigned num_par,ellipsis_mt19937_rng* rng,
void (*log_likelihood)(double *cube, unsigned ndim, unsigned npar, double *lnew))
{
/*Evolve the object within the likelihood constraint*/
unsigned i;
double llik;
live_point* trial_lvpnt=(live_point*)malloc(sizeof(live_point));
init_live_point(trial_lvpnt,num_dim);
for(;;)
{
for(i=0;i<num_dim;++i)
{
trial_lvpnt->x[i]=genrand_uniform(rng);
}
log_likelihood(trial_lvpnt->x,num_dim,num_par,&llik);
trial_lvpnt->log_lik=llik;
/*accpet if and only if within l > lstar */
if(trial_lvpnt->log_lik > llstar)
{
copy_live_point(livpnt,trial_lvpnt);
break;
}
}
free(trial_lvpnt);
}
void CGppe::Approx_CGppe_Laplace(const VectorXd & theta_x, const VectorXd& theta_t, const double& sigma, const MatrixXd& t, const MatrixXd &x, const TypePair & all_pairs,
const VectorXd & idx_global, const VectorXd& idx_global_1, const VectorXd& idx_global_2,
const VectorXd& ind_t, const VectorXd& ind_x, int M, int N)
{
//Parameters function initialization
double eps = 1E-6, psi_new, psi_old;
M = all_pairs.rows();
int n = M * N;
f = VectorXd::Zero(n);
VectorXd fvis = VectorXd::Zero(idx_global.rows());
VectorXd deriv;
double loglike = 0;
covfunc_t->SetTheta(theta_t);
covfunc_x->SetTheta(theta_x);
MatrixXd Kt = covfunc_t->ComputeGrandMatrix(t);
Kx = covfunc_x->ComputeGrandMatrix(x);
MatrixXd K = GetMat(Kt, ind_t, ind_t).array() * GetMat(Kx, ind_x, ind_x).array();
loglike = log_likelihood( sigma, all_pairs, idx_global_1, idx_global_2, M, N);
Kinv = K.inverse();
psi_new = loglike - 0.5 * fvis.transpose() * Kinv * fvis;
psi_old = INT_MIN;
while ((psi_new - psi_old) > eps)
{
psi_old = psi_new;
deriv = deriv_log_likelihood_CGppe_fast( sigma, all_pairs, idx_global_1, idx_global_2, M, N);
W = -deriv2_log_likelihood_CGppe_fast(sigma, all_pairs, idx_global_1, idx_global_2, M, N);
W = GetMat(W, idx_global, idx_global);
llt.compute(W + Kinv);
L = llt.matrixL(); //no need to extract the triangular matrix here
fvis = llt.solve(GetVec(deriv, idx_global) + W * fvis);
for (int w = 0;w < idx_global.rows();w++)
{
f(idx_global(w)) = fvis(w);
}
loglike = log_likelihood( sigma, all_pairs, idx_global_1, idx_global_2, M, N);
psi_new = loglike - 0.5 * fvis.transpose() * Kinv * fvis;
}
}
void calc_theta_loglik()
{
loglik_theta = 0.0;
for (int i=0; i!=m->nparams; ++i)
{
loglik_theta += log_likelihood(m->param_dists[i], theta[i], m->param_hyper[i]);
loglik_theta += jacobian_adj(m->param_trans[i], theta[i], m->param_hyper[i]);
}
}
float score_gmm (int gmm_index, float *input) {
gmm *g = &gmms[gmm_index];
const float *mixtures = g->mixture_weights;
float score = 0.0f;
int k=0;
for(k = 0; k < g->count; k++) {
float likelihood = log_likelihood( &g->gaussians[k], input) + mixtures[k];
score = logsum(score, likelihood);
}
return score;
}
double LRM::Loglike(Vec &g, Mat &h, uint nd)const{
if(nd>=2) return log_likelihood(Beta(), &g, &h);
if(nd==1) return log_likelihood(Beta(), &g, 0);
return log_likelihood(Beta(), 0, 0);
}
double log_likelihood() const
{
return log_likelihood(discount, strength);
}
void run(std::string tree_filename, std::string fasta_filename, std::string model_name) {
Model Mod; // The model
Counts data; // the counts
Parameters Par; // the parameters
std::vector<double> br; // branch lengths
double eps = 1e-8; // The threshold for the EM algorithm.
Parameters Parsim; // used for simulating data.
std::vector<double> brsim; // branch lengths of simulated data.
std::vector<std::vector<double> > Cov; // Covariance matrix
std::vector<double> variances; // The variances
bool simulate;
bool nonident;
std::string parameters_filename;
std::string covariances_filename;
// initialize random number generator with time(0).
random_initialize();
parameters_filename = strip_extension(fasta_filename) + ".dat";
covariances_filename = strip_extension(fasta_filename) + ".cov";
// Creates the pointers to the model-specific functions.
Mod = create_model(model_name);
std::cout << "Model: " << Mod.name << std::endl;
// Reads the tree.
Tree T = read_tree(tree_filename);
// Prints the Tree
std::cout << "Tree:" << std::endl;
print_tree(T);
// Check for possible nonidentifiability issues.
nonident = nonident_warning(T);
// Initialize the parameters for simulation of K81 data for testing
Parsim = create_parameters(T);
if (fasta_filename == ":test") { // if fasta file is :test generate random data.
simulate = true;
// Warn
std::cout << "WARNING: Using simulated data " << std::endl << std::endl;
// Generate random parameters
random_parameters_length(T, Mod, Parsim);
// Simulate the data
data = random_fake_counts(T, 1000, Parsim);
// Prints branch-lengths for future check.
branch_lengths(Parsim, brsim);
std::cout << "Simulated branch lengths:" << std::endl;
print_vector(brsim);
} else { // otherwise read the data
simulate = false;
// Read the counts.
std::cout << "Reading fasta file:" << std::endl;
read_counts(T, data, fasta_filename);
add_pseudocounts(0.01, data);
std::cout << std::endl;
}
// Check whether the data and the tree match.
if (T.nalpha != data.nalpha || T.nleaves != data.nspecies) {
throw std::invalid_argument("The order of the sequences or their number and the phylogenetic tree do not match.");
}
//Par = create_parameters(T);
//print_parameters(Par);
//print_vector(Par.r);
//clock_t
long start_time, end_time;
// Runs the EM algorithm. Par is used as initial parameters.
// After execution, Par contains the MLE computed by the algorithm.
// for local max over multiple iterations
Parameters Parmax = Par;
Model Modmax = Mod;
float likelL = 0.0;
float likelMax = -1000000.0;
float timerec;
float timemax;
int outfiles; //whether to save output
std::cout << "Starting the EM algorithm: " << std::endl;
int s;
int S = 0; //count of cases with neg branches
int iter;
int iterMax;
for (int it_runs = 0; it_runs < 10; it_runs++) {
Par = create_parameters(T);
Mod = create_model(model_name);
std::cout << it_runs << ", " ;
start_time = clock();
std::tie(likelL, iter) = EMalgorithm(T, Mod, Par, data, eps);
end_time = clock();
//print_parameters(Par);
// Choses the best permutation.
guess_permutation(T, Mod, Par);
branch_lengths(Par, br);
//print_vector(br);
s = find_negative(br);
S +=s;
timerec = ((float)end_time - start_time) / CLOCKS_PER_SEC;
//assign the 1st iter time value, inc ase it's the best
if (it_runs == 0){
timemax = timerec;
iterMax = iter;
}
if (likelL > likelMax){
Parmax = Par;
Modmax = Mod;
timemax = timerec;
likelMax = likelL;
iterMax = iter;
}
}
// If parameters are not identifiable, the computation of the covariance matrix will
// fail as the Fisher info matrix will not be invertible.
if (!nonident) {
// Compute the covariance matrix using observed Fisher.
full_MLE_observed_covariance_matrix(T, Modmax, Parmax, data, Cov);
variances.resize(Cov.size());
for(unsigned int i=0; i < Cov.size(); i++) {
variances[i] = Cov[i][i];
}
// OUTPUT Save the sigmas into a file
//save_sigmas_to(covariances_filename, Cov);
}
std::cout << std::endl;
std::cout << "Finished." << std::endl;
std::cout << "Likelihood: " << log_likelihood(T, Parmax, data) << std::endl ;
std::cout << "Time: " << timemax << std::endl << std::endl;
std::cout << "negative branches: " << S << std::endl;
std::cout << "Iter: " << iterMax << std::endl;
//std::cout << "Branch lengths: " << std::endl;
//print_vector(br);
outfiles = 0;
if (!nonident && outfiles) {
std::cout << "Parameter variances: " << std::endl;
print_vector(variances);
}
std::cout << "Newick Tree:" << std::endl;
print_newick_tree(T, br);
// if is a simulation, print the L2 distance !
if (simulate) {
std::cout << "L2 distance: " << parameters_distance(Par, Parsim) << std::endl;
std::cout << "KL divergence: " << KL_divergence(T, Par, Parsim) << std::endl;
std::cout << std::endl;
}
// if it is not a simulation, store the parameters in a file !
if (!simulate && outfiles) {
std::fstream st;
st.precision(15);
st.setf(std::ios::fixed,std::ios::floatfield);
st.open(parameters_filename.c_str(), std::ios::out);
print_parameters(Par, st);
}
}
/*
* Explore the parameter space with MCMC.
* See Sivia & Skilling 2006, page 193 for more details
*/
void explore_prior_space_with_mcmc(live_point* livpnt,double llstar,unsigned num_dim,
unsigned num_par,ellipsis_mt19937_rng* rng,
void (*log_likelihood)(double *cube, unsigned ndim, unsigned npar, double *lnew))
{
/*Evolve the object within the likelihood constraint*/
unsigned num_samples=20;
double step=0.1;
unsigned accept=0;
unsigned reject=0;
unsigned i;
double llik;
unsigned samps=0;
double* uvals;
double* uvals_new;
double* xvals;
double* xvals_new;
double llik_new;
uvals=(double*)malloc(num_dim*sizeof(double));
uvals_new=(double*)malloc(num_dim*sizeof(double));
xvals=(double*)malloc(num_dim*sizeof(double));
xvals_new=(double*)malloc(num_dim*sizeof(double));
for(i=0;i<num_dim;++i)
{
uvals[i]=livpnt->u[i];
xvals[i]=livpnt->x[i];
llik_new=livpnt->log_lik;
}
for(;;)
{
for(i=0;i<num_dim;++i)
{
uvals_new[i]=uvals[i]+step*(2.*genrand_uniform(rng)-1.);
uvals_new[i]-=floor(uvals_new[i]);
xvals_new[i]=uvals_new[i];
}
log_likelihood(xvals_new,num_dim,num_par,&llik);
/*accpet if and only if within l > lstar */
if(llik > llstar)
{
for(i=0;i<num_dim;++i)
{
uvals[i]=uvals_new[i];
xvals[i]=xvals_new[i];
}
llik_new=llik;
++accept;
}
else
{
++reject;
}
/*refine step size to let acceptance ratio converge around 50% */
if(accept>reject)
{
step*=exp(1./(double)accept);
}
if(accept<reject)
{
step/=exp(1./(double)reject);
}
if(accept>0 && samps>=num_samples)
{
for(i=0;i<num_dim;++i)
{
livpnt->u[i]=uvals[i];
livpnt->x[i]=xvals[i];
}
livpnt->log_lik=llik_new;
break;
}
++samps;
}
free(uvals);
free(uvals_new);
free(xvals);
free(xvals_new);
}
/*
*This function implements an approximation to Mukherjee's Ellipsoidal methods.
* EXPERIMENTAL -> DO NOT USE
*/
void explore_prior_space_with_mcmc_var_tuned(live_point* livpnt,double llstar,
live_point* phys_live_points,unsigned num_phys_live,unsigned num_dim,unsigned num_par, ellipsis_mt19937_rng* rng,
void (*log_likelihood)(double *cube, unsigned ndim, unsigned npar, double *lnew))
{
/*Evolve the object within the likelihood constraint*/
unsigned num_samples=20;
unsigned i,j;
double llik;
unsigned samps=0;
unsigned accept=0;
unsigned reject=0;
double step=2.;
double* sigma;
double* mean;
double sum;
double sum2;
double enlarge_fact=2.;
double* uvals;
double* uvals_new;
double* xvals;
double* xvals_new;
double llik_new;
unsigned best=0;
/*From the available live points, estimate the variance in each dimension*/
sigma=(double*)malloc(num_dim*sizeof(double));
mean=(double*)malloc(num_dim*sizeof(double));
for(i=0;i<num_phys_live;++i)
{
if(phys_live_points[i].log_lik>phys_live_points[best].log_lik)
{
best=i;
}
}
/*printf("\nbest= %d\tval=%f\n",best,phys_live_points[best].log_lik);*/
for(i=0;i<num_dim;++i)
{
sum=0;
sum2=0;
for(j=0;j<num_phys_live;++j)
{
sum+=phys_live_points[j].u[i];
sum2+=phys_live_points[j].u[i]*phys_live_points[j].u[i];
}
sigma[i]=sqrt( (sum2-sum*sum/(double)num_phys_live)/(double)num_phys_live );
/*mean[i]=sum/(double)num_phys_live;*/
mean[i]=(phys_live_points[best].u[i]+sum/(double)num_phys_live)*0.5;
/*printf("%f\t",mean[i]);*/
}
/*printf("\n");*/
uvals=(double*)malloc(num_dim*sizeof(double));
uvals_new=(double*)malloc(num_dim*sizeof(double));
xvals=(double*)malloc(num_dim*sizeof(double));
xvals_new=(double*)malloc(num_dim*sizeof(double));
/*printf("\nllstar= %f\n",llstar);*/
for(i=0;i<num_dim;++i)
{
uvals[i]=livpnt->u[i];
xvals[i]=livpnt->x[i];
llik_new=livpnt->log_lik;
}
for(;;)
{
for(i=0;i<num_dim;++i)
{
uvals_new[i]=(uvals[i]+mean[i])*0.5+sigma[i]*genrand_uniform(rng);
xvals_new[i]=uvals_new[i];
}
log_likelihood(xvals_new,num_dim,num_par,&llik);
/*trial_lvpnt->log_lik=llik*/
/*accpet if and only if within l > lstar */
if(llik > llstar)
{
for(i=0;i<num_dim;++i)
{
uvals[i]=uvals_new[i];
xvals[i]=xvals_new[i];
}
llik_new=llik;
++accept;
}
else
{
++reject;
}
/*refine step size to let acceptance ratio converge around 50% */
if(accept>reject)
{
step*=exp(1./(double)accept);
}
if(accept<reject)
{
step/=exp(1./(double)reject);
}
if(accept>0 && samps>=num_samples)
{
/*copy_live_point(livpnt,trial_lvpnt);*/
for(i=0;i<num_dim;++i)
{
livpnt->u[i]=uvals[i];
livpnt->x[i]=xvals[i];
}
livpnt->log_lik=llik_new;
break;
}
++samps;
/*printf("accept=%d\tsamps= %d\n",accept,samps);*/
}
free(uvals);
free(uvals_new);
free(xvals);
free(xvals_new);
free(sigma);
free(mean);
}
/*
* Compute the following entities:
* - responsibilities r(k|ij)
* - mu_ij_k
* - Lambda_ij_k
* - log determinante Lamda_ij_k
*
* And then use these to compute likelihood and gradients
*/
void Likelihood_Protein::compute_f_df(int hessian_pseudocount)
{
//initialize parameters for second gaussian
mu_ij_k.zeros(400, this->nr_components);
lambda_ij_k.zeros(400, 400, this->nr_components);
lambda_ij_k_inv.zeros(400, 400, this->nr_components);
log_det_lambda_ij_k.zeros(this->nr_components);
responsibilities.zeros(this->nr_components);
//initialize likelihood vector
log_likelihood.zeros(this->number_of_pairs);
//intialize gradients to zero
grad_weight.zeros(this->nr_components, 2);
grad_mu.zeros(400,this-> nr_components);
grad_precMat.zeros(400, this->nr_components);
for(int pair = 0; pair < this->number_of_pairs; pair++){
int i = this->i_indices(pair);
int j = this->j_indices(pair);
int lin_index = i*(L - (i+1)/2.0 - 1) + j - 1; //i*L - i*(i+1)/2 + j-(i+1);
int contact = this->protein_contacts(pair);
//for computation of likelihood
arma::vec log_density(this->nr_components, arma::fill::zeros);
arma::vec vqij = mq_ij.col(lin_index);
double N_ij = mN_ij(i,j);
arma::vec w_ij = w_ij3d.tube(i,j);
//diagonal matrix Qij = diag(q'ji)
//q'ijab = q(x_i=a, x_j=b) - (lambda_w * wijab / N_ij) --> has been precomputed
arma::mat Qij = arma::diagmat(vqij);
//outer product
arma::mat qij_prod = vqij * vqij.t();
//determine negative Hessian = Hij
arma::mat diff = Qij - qij_prod;
arma::mat H_ij = N_ij * diff + regularizer_w; //eq 37
// remove after debugging ---------------------------------------------------------------------------------
//debugging artefacts in learning
//add counts to Hessian to see if we have problems with non-informative data
H_ij.diag() += hessian_pseudocount;
//H_ij = arma::diagmat(H_ij);
// remove after debugging ---------------------------------------------------------------------------------
//precompute product H_ij * wij
arma::vec Hij_wij_prod = H_ij * w_ij;
for(int k = 0; k < this->nr_components; k++){
//gaussian parameters of coupling prior
double weight_k = this->parameters.get_weight(k, contact);
arma::vec mu_k = this->parameters.get_mean(k);
arma::mat lambda_k = this->parameters.get_precMat(k);
//---------------- simplify computation in case that lambda_k is diagonal matrix
// A, A_inv, lambda_k, Qij are diagonal matrices
arma::vec A = N_ij * vqij + lambda_k.diag(); //represents diagonal matrix
arma::vec A_inv = 1.0 / A; //represents diagonal matrix
double log_det_A = arma::sum(arma::log(A));
arma::vec Ainv_qij_product = A_inv % vqij; ////column vector
double triple_product = arma::sum(vqij % Ainv_qij_product);
//---------------- matrix computations in case lambda_k is NOT diagonal matrix
//A is a diagonal matrix, as Qij and lambda_k are diagonal matrices
//arma::mat A = N_ij * Qij + lambda_k; //diagonal
//arma::mat A_inv = arma::diagmat(1.0 / A.diag()); //diagonal
//double log_det_A = arma::sum(arma::log(A.diag()));
//arma::vec Ainv_qij_product = arma::vec(A_inv * vqij); //400x1 dim matrix
//double triple_product = arma::as_scalar(vqij.t() * Ainv_qij_product);
//compute lambda_ij_k_mat
arma::mat lambda_ij_k_mat = H_ij - regularizer_w + lambda_k;
lambda_ij_k.slice(k) = lambda_ij_k_mat;
//debugging: we assume diagonal Hessian ================================================================
// arma::mat lambda_ij_k_mat_inv(400,400,arma::fill::zeros);
// lambda_ij_k_mat_inv.diag() = 1.0 / lambda_ij_k_mat.diag();
//debugging=======================================================================================
//compute inverse of lambda_ij_k_mat
//---------------- simplify computation in case that lambda_k is diagonal matrix
arma::mat lambda_ij_k_mat_inv = arma::diagmat(A_inv) + (Ainv_qij_product * Ainv_qij_product.t()) / (1.0/N_ij - triple_product);
//---------------- matrix computations in case lambda_k is NOT diagonal matrix
//arma::mat lambda_ij_k_mat_inv = A_inv + (Ainv_qij_product * Ainv_qij_product.t()) / (1.0/N_ij - triple_product);
lambda_ij_k_inv.slice(k) = lambda_ij_k_mat_inv;
//compute mu_ij_k from precomputed entities
arma::vec mu_ij_k_vec = lambda_ij_k_mat_inv * ( Hij_wij_prod + lambda_k * mu_k);
mu_ij_k.col(k) = mu_ij_k_vec;
//debugging: we assume diagonal Hessian ================================================================
// log_det_lambda_ij_k(k) = arma::sum(arma::log(lambda_ij_k_mat.diag()));
//debugging=======================================================================================
//save log determinant of lambda_ij_k, see page 16 Saikats theory
log_det_lambda_ij_k(k) = log(1 - N_ij * triple_product) + log_det_A;
//ratio of two gaussians in log space
// N(0 | mu_k, lambda_k)
//------------------------------
// N(0 | mu_ij_k, lambda_ij,k)
double gaussian_ratio_logdensity = log_density_gaussian_ratio( mu_k,
mu_ij_k_vec,
lambda_k,
lambda_ij_k_mat,
this->parameters.get_log_det_inv_covMat(k),
log_det_lambda_ij_k(k) );
if(this->debug > 0 and gaussian_ratio_logdensity>1000){
std::cout << protein_id << " " << i << " " << j << " " << pair << " " << contact << " " << k << std::endl;
std::cout << "Gaussian log density > 1: " << gaussian_ratio_logdensity << std::endl;
std::cout << "A_inv.max() : " << A_inv.max() << std::endl;
arma::uword max_ind = index_max(A_inv);
std::cout << "A(max_ind) : " << A(max_ind) << std::endl;
std::cout << "vqij(max_ind): " << vqij(max_ind) << std::endl;
std::cout << "N_ij: " << N_ij << std::endl;
std::cout << "mu_ij_k_vec(max_ind) : " << mu_ij_k_vec(max_ind) << std::endl;
std::cout << "mu_k(max_ind) : " << mu_k(max_ind) << std::endl;
std::cout << "lambda_ij_k_mat(max_ind, max_ind) : " << lambda_ij_k_mat(max_ind, max_ind) << std::endl;
std::cout << "lambda_ij_k_mat_inv(max_ind, max_ind) : " << lambda_ij_k_mat_inv(max_ind, max_ind) << std::endl;
std::cout << "lambda_k(max_ind, max_ind) : " << lambda_k(max_ind, max_ind) << std::endl;
std::cout << "parameters.get_log_det_inv_covMat(k) : " << this->parameters.get_log_det_inv_covMat(k) << std::endl;
std::cout << "log_det_lambda_ij_k(k) : " << log_det_lambda_ij_k(k) << std::endl;
}
log_density(k) = log(weight_k) + gaussian_ratio_logdensity;
}//end loop over components k
//Johannes suggestion how to precisely compute the responsibilities
double a_max = arma::max(log_density);
this->responsibilities = arma::exp(log_density - a_max);//r_nk = exp( a_nk - a_max)
double sum_resp = arma::sum(this->responsibilities); //sum += r_nk
this->responsibilities /= sum_resp; //normalization of responsibilities, NOT in log space => r_nk /= sum;
//save neg likelihood of current pair
double f = log(sum_resp) + a_max;
if(! std::isnormal(f)) {
std::cout << "ERROR: likelihood cannot be computed for protein " << protein_id << ", i " << i << ", j " << j << " ("<< contact <<"): " << f << std::endl;
std::cout << "Nij: " << N_ij << ", sum_resp: " << sum_resp << ", a_max: " << a_max << std::endl;
for(int k = 0; k < this->nr_components; k++){
std::cout << "component: " << k << ", sum_precMat(k)diag: "<< arma::sum(this->parameters.get_precMat(k).diag()) << ", responsibilty:" << this->responsibilities(k) << ", log_density: " << log_density(k) << ", log_det_lambda_ij_k: " << log_det_lambda_ij_k(k) << std::endl;
}
continue;
} else log_likelihood(pair) = f;
// Compute ALL gradients for ALL components
for(int k = 0; k < this->nr_components; k++){
//weights (either for contact or non_contact)
grad_weight(k, contact) += gradient_weight_comp(k, contact);
//mu
grad_mu.col(k) += gradient_mu_comp(k);
//precMat - diagonal
arma::mat grad_precMat_protein = gradient_precisionMatrix_comp(k);
grad_precMat.col(k) += grad_precMat_protein.diag();
}//end components
}//end loop over ij pairs
}
/*
* This function calculates the Hessian and mu_ij_k and Lambda_ij_k
* for all pairs (i,j) in the batch
* and for all components k
*
*/
void Likelihood_Protein::compute_negLL(int nr_threads_prot)
{
//initialize likelihood vector
log_likelihood.zeros(this->number_of_pairs);
#pragma omp parallel for num_threads(nr_threads_prot)
for(int pair = 0; pair < this->number_of_pairs; pair++){
int i = this->i_indices(pair);
int j = this->j_indices(pair);
int lin_index = i*(L - (i+1)/2.0 - 1) + j - 1; //i*L - i*(i+1)/2 + j-(i+1);
int contact = this->protein_contacts(pair);
//for computation of likelihood
arma::vec log_density(this->nr_components, arma::fill::zeros);
arma::vec vqij = mq_ij.col(lin_index);
double N_ij = mN_ij(i,j);
arma::vec w_ij = w_ij3d.tube(i,j);
//arma::vec vqij = q_ij3d.tube(i,j);
//diagonal matrix Qij = diag(q'ji)
//q'ijab = q(x_i=a, x_j=b) - (lambda_w * wijab / N_ij) --> has been precomputed
arma::mat Qij = arma::diagmat(vqij);
//outer product
arma::mat qij_prod = vqij * vqij.t();
//determine negative Hessian = Hij
arma::mat diff = Qij - qij_prod;
arma::mat H_ij = N_ij * diff + regularizer_w; //eq 37
//precompute product H_ij * wij
arma::vec Hij_wij_prod = H_ij * w_ij;
for(int k = 0; k < this->nr_components; k++){
//gaussian parameters of coupling prior
double weight_k = this->parameters.get_weight(k, contact);
arma::vec mu_k = this->parameters.get_mean(k);
arma::mat lambda_k = this->parameters.get_precMat(k);
//---------------- simplify computation in case that lambda_k is diagonal matrix
// A, A_inv, lambda_k, Qij are diagonal matrices
arma::vec A = N_ij * vqij + lambda_k.diag(); //represents diagonal matrix
arma::vec A_inv = 1.0 / A; //represents diagonal matrix
double log_det_A = arma::sum(arma::log(A));
arma::vec Ainv_qij_product = A_inv % vqij; ////column vector
double triple_product = arma::sum(vqij % Ainv_qij_product);
//---------------- matrix computations in case lambda_k is NOT diagonal matrix
//A is a diagonal matrix, as Qij and lambda_k are diagonal matrices
//arma::mat A = N_ij * Qij + lambda_k; //diagonal
//arma::mat A_inv = arma::diagmat(1.0 / A.diag()); //diagonal
//double log_det_A = arma::sum(arma::log(A.diag()));
//arma::vec Ainv_qij_product = arma::vec(A_inv * vqij); //400x1 dim matrix
//double triple_product = arma::as_scalar(vqij.t() * Ainv_qij_product);
//compute lambda_ij_k_mat
arma::mat lambda_ij_k_mat = H_ij - regularizer_w + lambda_k;
//debugging: we assume diagonal Hessian ================================================================
// arma::mat lambda_ij_k_mat_inv(400,400,arma::fill::zeros);
// lambda_ij_k_mat_inv.diag() = 1.0 / lambda_ij_k_mat.diag();
//debugging=======================================================================================
//compute inverse of lambda_ij_k_mat
//---------------- simplify computation in case that lambda_k is diagonal matrix
arma::mat lambda_ij_k_mat_inv = arma::diagmat(A_inv) + (Ainv_qij_product * Ainv_qij_product.t()) / (1.0/N_ij - triple_product);
//---------------- matrix computations in case lambda_k is NOT diagonal matrix
//arma::mat lambda_ij_k_mat_inv = A_inv + (Ainv_qij_product * Ainv_qij_product.t()) / (1.0/N_ij - triple_product);
//compute mu_ij_k from precomputed entities
arma::vec mu_ij_k_vec = lambda_ij_k_mat_inv * ( Hij_wij_prod + lambda_k * mu_k);
//debugging: we assume diagonal Hessian ================================================================
// log_det_lambda_ij_k(k) = arma::sum(arma::log(lambda_ij_k_mat.diag()));
//debugging=======================================================================================
//save log determinant of lambda_ij_k, see page 16 Saikats theory
double log_det_lambda_ij = log(1 - N_ij * triple_product) + log_det_A;
//ratio of two gaussians in log space
// N(0 | mu_k, lambda_k)
//------------------------------
// N(0 | mu_ij_k, lambda_ij,k)
double gaussian_ratio_logdensity = log_density_gaussian_ratio( mu_k,
mu_ij_k_vec,
lambda_k,
lambda_ij_k_mat,
this->parameters.get_log_det_inv_covMat(k),
log_det_lambda_ij);
log_density(k) = log(weight_k) + gaussian_ratio_logdensity;
// if ((i == 0) && (j == 12)){
// std::cout << " " << std::endl;
// std::cout << protein_id << " i: " << i << " j: " << j << " contact=" << contact << " component: " << k << std::endl;
// std::cout << "triple_product : " << triple_product << std::endl;
// std::cout << "log_det_A : " << log_det_A << std::endl;
// std::cout << "lambda_ij_k_mat(0,1) : " << lambda_ij_k_mat(0,1) << "lambda_ij_k_mat(0,2) : " << lambda_ij_k_mat(0,2) << "lambda_ij_k_mat(2,0) : " << lambda_ij_k_mat(2,0) << std::endl;
// std::cout << "mu_ij_k_vec(0) : " << mu_ij_k_vec(0) << "mu_ij_k_vec(1) : " << mu_ij_k_vec(1) << "mu_ij_k_vec(2) : " << mu_ij_k_vec(2) << std::endl;
// std::cout << "Gaussian log density: " << gaussian_ratio_logdensity << std::endl;
// std::cout << "log_density(k): " << log_density(k)<< std::endl;
// std::cout << "log(weight_k): " << log(weight_k) << " weight_k: " << weight_k << std::endl ;
// }
}//end loop over components k
//Johannes suggestion how to precisely compute the responsibilities
double a_max = arma::max(log_density);
arma::vec resps = arma::exp(log_density - a_max);//r_nk = exp( a_nk - a_max)
double sum_resp = arma::sum(resps); //sum += r_nk
//save neg likelihood of current pair
double f = log(sum_resp) + a_max;
// if ((i == 0) && (j == 12)){
// std::cout << "a_max : " << a_max << std::endl;
// std::cout << "sum_resp : " << sum_resp << std::endl;
// std::cout << "f : " << f << std::endl;
// }
if(! std::isnormal(f)) {
std::cout << "ERROR: likelihood cannot be computed for protein " << protein_id << ", i " << i << ", j " << j << " ("<< contact <<"): " << f << std::endl;
std::cout << "Nij " << N_ij << ", sum_resp: " << sum_resp << ", a_max: " << a_max << std::endl;
for(int k = 0; k < this->nr_components; k++){
std::cout << "component: " << k << ", sum_precMat(k)diag: "<< arma::sum(this->parameters.get_precMat(k).diag()) << ", responsibilty:" << resps(k)/sum_resp << ", log_density: " << log_density(k) << std::endl;
}
continue;
} else log_likelihood(pair) = f;
}//end of parallelized for loop over ij pairs
}
int main(int argc, char *argv[]) {
int nobs, sizex, nsample = 0;
char *location = NULL;
int ret = 0;
//////////////////////////////////////
/////////////// PARSERS //////////////
//////////////////////////////////////
// Parse the command line
ret = parse_command_line(argc,argv,&nobs,&sizex,&nsample,&location);
if( ret != PARSER_SUCCESS ) {
printf("Parsing failed ! Exiting...\n");
return EXIT_FAILURE;
}
// Parse the data on master
double *buffer_X = (double*)malloc(nobs*sizex*sizeof(double));
double *isigma = (double*)malloc(sizex*sizex*sizeof(double));
double *mu = (double*)malloc(sizex*sizeof(double));
double det_sigma = 0.0;
ret = read_data(buffer_X, isigma, &det_sigma, mu, &nobs, &sizex, location);
if( ret != PARSER_SUCCESS ) {
printf("Parsing failed ! Exiting...\n");
return EXIT_FAILURE;
}
////////////////////////////////////////
/////////////// Variables //////////////
////////////////////////////////////////
// Thread variables
int nthreads = 1;
int th_num = 0;
int th_nobs = nobs;
nthreads = get_num_threads();
// Timing variables
double tic, toc, tot_time = 0.0;
//// Arrays for all threads
// The pool is allocated inside the shared memory
double *pool_LV = (double*)malloc(nobs*sizex*sizeof(double)); // Left hand side vector (X-mu)
double *pool_tmp = (double*)malloc(nobs*sizex*sizeof(double)); // Temporary holder for (X-mu)*SIG
double *pool_ones = (double*)malloc(nobs*sizeof(double)); // Temporary holder to create LV
double *pool_res = (double*)malloc(nthreads*sizeof(double)); // Each thread puts its result in pool_res
// Use pointers to get the correct location in the array
double *LV = NULL;
double *tmp = NULL;
double *ones = NULL;
double *X = NULL;
// Holder for final sum
double final_sum = 0.0;
////////////////////////////////////////
/////////////// Algorithm //////////////
////////////////////////////////////////
//// Start time sampling
for(int k = 0; k < nsample; k++) {
tic = omp_get_wtime();
final_sum = 0.0;
// Main driver
#pragma omp parallel private(th_num,th_nobs,LV,tmp,ones,X) default(shared)
{
// Get thread number
th_num = omp_get_thread_num();
// Total number of observations for that thread
th_nobs = nobs/nthreads;
// Use the address to point to the correct location in the vector
X = &buffer_X[th_num*nobs*sizex/nthreads];
LV = &pool_LV[th_num*th_nobs*sizex];
tmp = &pool_tmp[th_num*th_nobs*sizex];
ones = &pool_ones[th_num*th_nobs];
// Each process can now calculate the term in the
// exponent for a subset of random vectors
// Naive approach: for loop on each vector X
// pool_res[th_num] += exp_term();
// Guru approach: BLAS
log_likelihood(X,isigma,mu,det_sigma,th_nobs,sizex,&pool_res[th_num],LV,tmp,ones);
#pragma omp barrier
// Reduction: sum all the intermediary results
#pragma omp for reduction(+:final_sum)
for(int i = 0; i < nthreads; i++)
final_sum = final_sum + pool_res[i];
}
toc = omp_get_wtime();
tot_time += toc-tic;
}
printf("Result: %f\n",final_sum);
printf("Total time: %f\n",tot_time/(double)nsample);
////////////////////////////////////////
/////////////// Clean up ///////////////
////////////////////////////////////////
free(pool_res);
free(pool_ones);
free(pool_tmp);
free(pool_LV);
free(buffer_X);
free(isigma);
free(mu);
free(location);
return EXIT_SUCCESS;
}
double LRM::Loglike(const Vector &beta, Vector &g, Matrix &h, uint nd)const{
if(nd>=2) return log_likelihood(beta, &g, &h);
if(nd==1) return log_likelihood(beta, &g, 0);
return log_likelihood(beta, 0, 0);
}
