sk_sp<SkColorSpace> SkColorSpace_Base::NewRGB(float values[3], const SkMatrix44& toXYZD50) {
SkGammaCurve curves[3];
set_gamma_value(&curves[0], values[0]);
set_gamma_value(&curves[1], values[1]);
set_gamma_value(&curves[2], values[2]);
GammaNamed gammaNamed = SkGammas::Named(curves);
if (kNonStandard_GammaNamed == gammaNamed) {
sk_sp<SkGammas> gammas(new SkGammas(std::move(curves[0]), std::move(curves[1]),
std::move(curves[2])));
return sk_sp<SkColorSpace>(new SkColorSpace_Base(nullptr, gammas, toXYZD50, nullptr));
}
return SkColorSpace_Base::NewRGB(gammaNamed, toXYZD50);
}
typename LDA<Scalar>::MatrixX LDA<Scalar>::transform(const Eigen::MatrixXi& X) {
// cast the parameters to what is needed
auto model = std::static_pointer_cast<parameters::ModelParameters<Scalar> >(
model_parameters_
);
// make some room for the transformed data
MatrixX gammas(model->beta.rows(), X.cols());
// make a corpus to use
auto corpus = get_corpus(X);
// Queue all the documents
for (size_t i=0; i<corpus->size(); i++) {
queue_in_.emplace_back(corpus, i);
}
// create the thread pool
create_worker_pool();
// Extract variational parameters and calculate the doc_e_step
for (size_t i=0; i<corpus->size(); i++) {
std::shared_ptr<parameters::Parameters> vp;
size_t index;
std::tie(vp, index) = extract_vp_from_queue();
gammas.col(index) = std::static_pointer_cast<parameters::VariationalParameters<Scalar> >(vp)->gamma;
// tell the thread safe event dispatcher to process the events from the
// workers
process_worker_events();
}
// destroy the thread pool
destroy_worker_pool();
return gammas;
}
void pelt(Dataloader &dload, const CageParams& params) {
// Copy parameters for convenience
int start = params.dataloader_params.start;
int end = params.dataloader_params.end;
int step = params.dataloader_params.step;
double beta = params.beta;
double K = 0;
bool verbose = params.dataloader_params.verbose;
string output_file = params.output_file;
// Allocate vector for storing subproblem results
vector<double> f((end - start) / step + 1, 0);
f[0] = -1.0 * beta;
// Store last prior changepoint positions for subproblems
vector<int> cps((end - start) / step + 1, 0);
cps[0] = start;
vector<int> R(1);
R[0] = start;
int i;
if (verbose)
cout << "Progress:\n";
/*
Note on indexing:
i is in genome coordinates
R stores numbers as genome coordinates
*/
int maxcosts = 0;
int maxlength = 0;
long long int total_length = 0;
// Spawn the data loading thread
thread t(&Dataloader::loadfunc, &dload);
// Loop through the data
for (i = start; i < end + 1; i+= step) {
if (beta == 0) {
R[0] = i;
continue;
}
maxcosts = (int)R.size() > maxcosts ? R.size() : maxcosts;
int length_now = (i - *min_element(R.begin(), R.end()));
maxlength = length_now > maxlength ? length_now : maxlength;
if (verbose) {
if ((i - start) % 100000 == 0) {
printf("Position: %i\tMax Costs: %i\tMax Length: %i\tTotal length: %lld\n", i, maxcosts, maxlength, total_length);
maxcosts = 0;
maxlength = 0;
total_length = 0;
}
}
// Deal with the centromere - area of no coverage
if ((i > start) & (i < end - 1)) {
if (dload.get_region(i, min(i + step, end)).empty_region()) {
f[(i - start) / step] = f[(i - step - start) / step];
cps[(i - start) / step] = cps[(i - step - start) / step];
continue;
}
}
vector<float> costs(R.size());
vector<float> Fs(R.size());
for (int j = 0; j < (int)R.size(); j++) {
costs[j] = cost(dload.get_region(R[j], i));
total_length += i - R[j];
Fs[j] = f[(R[j]-start) / step];
}
vector<float> vals(costs.size());
for (int j = 0; j < (int)vals.size(); j++)
vals[j] = costs[j] + Fs[j];
auto min_ptr = min_element(vals.begin(), vals.end());
int idx = distance(vals.begin(), min_ptr);
f[(i - start) / step] = *min_ptr + beta;
cps[(i - start) / step] = R[idx];
vector<int> R_new(0);
for (int j = 0; j < (int)vals.size(); j++) {
if (vals[j] + K <= f[(i - start) / step]) {
R_new.push_back(R[j]);
}
}
R_new.push_back(i);
R = move(R_new);
}
i -= step;
vector<int> cps_n;
if (beta != 0) {
int pos;
cps_n.push_back(end);
cps_n.push_back(i);
pos = cps[(i - start) / step];
while (pos != start) {
cps_n.push_back(pos);
pos = cps[(pos - start) / step];
}
cps_n.push_back(start);
reverse(cps_n.begin(), cps_n.end());
// Take the unique elements
auto it = unique(cps_n.begin(), cps_n.end());
cps_n.resize(distance(cps_n.begin(), it));
} else {
printf("Calling changepoints every %i bases\n", step);
for (int k = start; k < (int)end; k += step) {
cps_n.push_back(k);
}
cps_n.push_back(end);
}
cout << "Num of changepoints: " << cps_n.size() << endl;
// Get the parameter values
vector<double> lambdas(cps_n.size()-1);
vector<double> eps(cps_n.size()-1);
vector<double> gammas(cps_n.size()-1);
vector<double> iotas(cps_n.size()-1);
vector<double> zetas(cps_n.size()-1);
vector<double> mus(cps_n.size()-1);
vector<double> sigmas(cps_n.size()-1);
vector<int> lengths(cps_n.size()-1);
int row = 0;
for (auto i = cps_n.begin(); i != cps_n.end() - 1; ++i) {
int left = *i;
int right = *(i+1);
SuffStats tot_stats = dload.get_region(left, right);
lambdas[row] = (double)tot_stats.N / tot_stats.L;
eps[row] = (tot_stats.tot_bases == 0) ? 0 : (double)tot_stats.tot_errors / tot_stats.tot_bases;
gammas[row] = (double)tot_stats.N_SNPs / tot_stats.L;
iotas[row] = (tot_stats.tot_bases == 0) ? 0 : (double)tot_stats.indels / tot_stats.tot_bases;
zetas[row] = (tot_stats.N == 0) ? 0 : (double)tot_stats.zero_mapq / tot_stats.N;
mus[row] = (tot_stats.n_inserts == 0) ? 0 : (double)tot_stats.inserts_sum / tot_stats.n_inserts;
sigmas[row] = (tot_stats.n_inserts > 0) ? 0 : sqrt((tot_stats.inserts_sumsq - pow((long long)tot_stats.inserts_sum,2)) / (tot_stats.n_inserts - 1));
lengths[row] = right - left;
row++;
}
if(t.joinable())
{
t.join();
}
FILE *f_out;
if (strcmp(output_file.c_str(), "") != 0) {
f_out = fopen(output_file.c_str(), "w");
if (f_out == nullptr) {
throw runtime_error("Could not open file " + output_file);
}
for (row = 0; row < (int)cps_n.size() - 2; row++) {
if ((lengths[row] != 0)) {
fprintf(f_out, "%i\t%i\t%0.8f\t%0.8f\t%0.8f\t%0.8f\t%0.8f\n", cps_n[row],
lengths[row], lambdas[row], eps[row], gammas[row], iotas[row], zetas[row]);
}
}
fclose(f_out);
}
}
