void netcdf_write_blitz(NcVar *nc_var, blitz::Array<T, rank> const &val)
{
long counts[rank];
for (int i=0; i<rank; ++i) counts[i] = val.extent(i);
//printf("netcdf_write_blitz: %p %p\n", nc_var, val.data());
nc_var->put(val.data(), counts);
}
void GridDomain::global_to_local(
blitz::Array<double,1> const &global,
std::vector<blitz::Array<double,1>> &olocal)
{
if (olocal.size() != this->num_local_indices) {
fprintf(stderr, "MatrixDomainer::get_rows() had bad dimension 1 = %d (expected %ld)\n", olocal.extent(1), this->num_local_indices);
throw std::exception();
}
for (auto ii = olocal.begin(); ii != olocal.end(); ++ii) {
// Make sure it has the right dimensions
if (olocal[i].extent(0) != global.extent(0)) {
fprintf(stderr, "MatrixDomainer::get_rows() had bad dimension 0 = %d (expected %ld)\n", olocal.extent(0), global.extent(0));
throw std::exception();
}
}
// Copy out data, translating to local coordinates
for (int j=0; j < global.extent(0); ++j) {
int lindex[this->num_local_indices];
this->global_to_local(global(j), lindex);
for (int i=0; i<this->num_local_indices; ++i)
olocal[i](j) = lindex[i];
}
}
void GmresWrapper<Rank>::ApplyOperator(blitz::Array<cplx, 1> &input, blitz::Array<cplx, 1> &output)
{
if (Psi == 0)
{
throw std::runtime_error("Psi is 0");
}
if (TempPsi == 0)
{
throw std::runtime_error("TempPsi is 0");
}
//Map the 1d vectors to a a blitz array of correct shape
DataVector shape = Psi->GetData().shape();
DataVector stride = Psi->GetData().stride();
DataArray inData(input.data(), shape, stride, blitz::neverDeleteData);
DataArray inData2(input.data(), shape, stride, blitz::neverDeleteData);
DataArray outData(output.data(), shape, stride, blitz::neverDeleteData);
DataArray outData2(output.data(), shape, stride, blitz::neverDeleteData);
outData = 0;
//Set psi and tempPsi to point to correct vectorsbuffers
DataArray oldData = Psi->GetData();
DataArray oldTempData = TempPsi->GetData();
Psi->SetData(inData);
TempPsi->SetData(outData);
OperatorCallback(Psi, TempPsi);
//Restore the former buffers
Psi->SetData(oldData);
TempPsi->SetData(oldTempData);
}
void mouse( int button, int state, int x, int y)
{
//#define CLICK_SAVE_COLS
#ifdef CLICK_SAVE_COLS
int kw = 16;
int xmin, xmax, ymin, ymax;
xmin = std::max( 1, x - kw);
ymin = std::max( 1, y - kw);
xmax = std::min( x + kw, image.cols() - 1);
ymax = std::min( y + kw, image.rows() - 1);
std::ofstream of( "cluster_dat.hpp");
if( !of)
return;
of << "const int num_pts = " << (xmax - xmin) * (ymax - ymin) * 4 << ";\n";
of << "float dat[num_pts] = {\n";
for( int j=ymin;j<ymax;++j)
{
for( int i=xmin;i<xmax;++i)
{
Imath::Color3f col = image( j, i);
of << col.x << ", " << col.y << ", " << col.z << ", 1.0, \n";
}
}
of << "};\n";
#endif
}
double bob::learn::em::GMMMachine::logLikelihood(const blitz::Array<double, 1> &x,
blitz::Array<double,1> &log_weighted_gaussian_likelihoods) const
{
// Check dimension
bob::core::array::assertSameDimensionLength(log_weighted_gaussian_likelihoods.extent(0), m_n_gaussians);
bob::core::array::assertSameDimensionLength(x.extent(0), m_n_inputs);
return logLikelihood_(x,log_weighted_gaussian_likelihoods);
}
inline double Inverse_Matrix_with_lapack_and_Return_Determinant (blitz::Array<double,2> & M, std::string message="")
{
assert (M.rows() ==M.cols());
int n = M.rows();
if (n==0) return 1;
ref<double,2> refM(M,false);//do not check the ordering since inverse and transposition commutes
double * p = refM;
return Inverse_Matrix_with_lapack_and_Return_Determinant(p,n,n,message);
}
void IceModel_Decode::run_timestep(double time_s,
blitz::Array<int,1> const &indices,
std::map<IceField, blitz::Array<double,1>> const &vals2)
{
printf("BEGIN IceModel_Decode::run_timestep(%f) size=%ld\n", time_s, indices.size());
std::map<IceField, blitz::Array<double,1>> vals2d; /// Decoded fields
// Loop through the fields we require
std::set<IceField> fields;
get_required_fields(fields);
for (auto field = fields.begin(); field != fields.end(); ++field) {
printf("Looking for required field %s\n", field->str());
// Look up the field we require
auto ii = vals2.find(*field);
if (ii == vals2.end()) {
fprintf(stderr, "Cannot find required ice field = %s\n", field->str());
throw std::exception();
}
blitz::Array<double,1> vals(ii->second);
// Decode the field!
blitz::Array<double,1> valsd(ndata());
valsd = nan;
int n = indices.size();
for (int i=0; i < n; ++i) {
int ix = indices(i);
// Do our own bounds checking!
if (ix < 0 || ix >= ndata()) {
fprintf(stderr, "IceModel: index %d out of range [0, %d)\n", ix, ndata());
throw std::exception();
}
#if 0
// Sanity check for NaN coming through
if (std::isnan(vals(i))) {
fprintf(stderr, "IceModel::decode: vals[%d] (index=%d) is NaN!\n", i, ix);
throw std::exception();
}
#endif
// Add this value to existing field
double &oval = valsd(ix);
if (std::isnan(oval)) oval = vals(i);
else oval += vals(i);
}
// Store decoded field in our output
vals2d.insert(std::make_pair(*field, valsd));
printf("Done decoding required field, %s\n", field->str());
}
// Pass decoded fields on to subclass
run_decoded(time_s, vals2d);
printf("END IceModel_Decode::run_timestep(%ld)\n", time_s);
}
blitz::Array<double,1> bob::example::library::reverse (const blitz::Array<double,1>& array){
// create new array in the desired shape
blitz::Array<double,1> retval(array.shape());
// copy data
for (int i = 0, j = array.extent(0)-1; i < array.extent(0); ++i, --j){
retval(j) = array(i);
}
// return the copied data
return retval;
}
void Machine::setInputDivision (const blitz::Array<double,1>& v) {
if (m_weight.extent(0) != v.extent(0)) {
boost::format m("mismatch on the input division shape: expected a vector of size %d, but you input one with size = %d instead");
m % m_weight.extent(0) % v.extent(0);
throw std::runtime_error(m.str());
}
m_input_div.reference(bob::core::array::ccopy(v));
}
void Machine::setBiases (const blitz::Array<double,1>& bias) {
if (m_weight.extent(1) != bias.extent(0)) {
boost::format m("mismatch on the bias shape: expected a vector of size %d, but you input one with size = %d instead");
m % m_weight.extent(1) % bias.extent(0);
throw std::runtime_error(m.str());
}
m_bias.reference(bob::core::array::ccopy(bias));
}
Mat naiveBlitzToCvMat(blitz::Array<float, 2> a, float multiplier){
int i, j;
Mat b = Mat(a.rows(), a.cols(), CV_32FC1);
for (i=0; i< a.rows(); i++){
for(j=0; j<a.cols(); j++){
b.at<float>(i,j) = a(i, j) * multiplier;
}
}
return b;
}
void binary_save(const blitz::Array<T,n> & M,std::string filename)
{
T z;assert(M.isStorageContiguous());
std::stringstream f;M.dumpStructureInformation(f);
std::ofstream out(filename.c_str(),std::ios::binary);
std::string s = f.str();int i=int(s.length())+1;char c='1';
out.write( (char*) &i,sizeof(i));
out.write( (s.c_str()) ,i*sizeof(c));
out.write( (char*)M.dataFirst(),M.numElements()*sizeof(z));
}
void convert2rgb( blitz::Array<Imath::Color3f,2>& img)
{
vigra::BasicImageView<vigra::TinyVector<float,3> > img_view( (vigra::TinyVector<float,3> *) img.dataFirst(), img.cols(), img.rows());
for( int j=0;j<img.rows();++j)
for( int i=0;i<img.cols();++i)
img( j, i) *= lab_scale;
vigra::transformImage( vigra::srcImageRange( img_view), vigra::destImage( img_view), vigra::Lab2RGBFunctor<float>( 1.0f));
}
inline void for_cons(const blitz::Array<T,n> & M, bool check_order){
need_copy = (!(M.isStorageContiguous()));
if (check_order) for (int i=0; i<n;i++) need_copy = (need_copy || (M.ordering(i)!=i));
#ifdef DEBUG_REF_WARNING
if (need_copy) std::cout<<"WARNING : REF : COPY NEEDED. Performance will be degraded"<<std::endl;
#endif
Mref = (blitz::Array<T,n> *)&M;
// The copy has the same shape but is ordered like a fortran array
if (need_copy) {Mcopy.resize(M.shape());Mcopy=M;}
}
void binary_load(blitz::Array<T,n> & M,std::string filename)
{
assert(M.isStorageContiguous());T z;
std::ifstream out(filename.c_str(),std::ios::binary);
int i;char c='1';
out.read( (char*) &i,sizeof(i));
char *st = new char[i+1];
out.read( st ,i*sizeof(c)); std::string s(st);
std::stringstream f; M.dumpStructureInformation(f);
if (f.str() != s) FATAL("Can not load binary : array do not conform. Structure (file vs array)"<<s<<"----"<<f.str());
out.read( (char*)M.dataFirst(),M.numElements()*sizeof(z));
}
Machine::Machine(const blitz::Array<double,2>& weight)
: m_input_sub(weight.extent(0)),
m_input_div(weight.extent(0)),
m_bias(weight.extent(1)),
m_activation(boost::make_shared<bob::learn::activation::IdentityActivation>()),
m_buffer(weight.extent(0))
{
m_input_sub = 0.0;
m_input_div = 1.0;
m_bias = 0.0;
m_weight.reference(bob::core::array::ccopy(weight));
}
double bob::learn::em::GMMMachine::logLikelihood(const blitz::Array<double, 2> &x) const {
// Check dimension
bob::core::array::assertSameDimensionLength(x.extent(1), m_n_inputs);
// Call the other logLikelihood_ (overloaded) function
double sum_ll = 0;
for (int i=0; i<x.extent(0); i++)
sum_ll+= logLikelihood_(x(i,blitz::Range::all()));
return sum_ll/x.extent(0);
}
void check_dimensions(
std::string const &vname,
blitz::Array<T, rank> const &arr,
std::vector<int> const &dims)
{
for (int i=0; i<rank; ++i) {
if (dims[i] >= 0 && arr.extent(i) != dims[i]) {
fprintf(stderr,
"Error in %s: expected dimension #%d = %d (is %d instead)\n",
vname.c_str(), i, dims[i], arr.extent(i));
throw std::exception();
}
}
}
void Machine::forward (const blitz::Array<double,1>& input, blitz::Array<double,1>& output) const {
if (m_weight.extent(0) != input.extent(0)) { //checks input dimension
boost::format m("mismatch on the input dimension: expected a vector of size %d, but you input one with size = %d instead");
m % m_weight.extent(0) % input.extent(0);
throw std::runtime_error(m.str());
}
if (m_weight.extent(1) != output.extent(0)) { //checks output dimension
boost::format m("mismatch on the output dimension: expected a vector of size %d, but you input one with size = %d instead");
m % m_weight.extent(1) % output.extent(0);
throw std::runtime_error(m.str());
}
forward_(input, output);
}
void Machine::setWeights (const blitz::Array<double,2>& weight) {
if (weight.extent(0) != m_input_sub.extent(0)) { //checks 1st dimension
boost::format m("mismatch on the weight shape (number of rows): expected a weight matrix with %d row(s), but you input one with %d row(s) instead");
m % m_input_sub.extent(0) % weight.extent(0);
throw std::runtime_error(m.str());
}
if (weight.extent(1) != m_bias.extent(0)) { //checks 2nd dimension
boost::format m("mismatch on the weight shape (number of columns): expected a weight matrix with %d column(s), but you input one with %d column(s) instead");
m % m_bias.extent(0) % weight.extent(1);
throw std::runtime_error(m.str());
}
m_weight.reference(bob::core::array::ccopy(weight));
}
double bob::math::det_(const blitz::Array<double,2>& A)
{
// Size variable
int N = A.extent(0);
// Perform an LU decomposition
blitz::Array<double,2> L(N,N);
blitz::Array<double,2> U(N,N);
blitz::Array<double,2> P(N,N);
math::lu(A, L, U, P);
// Compute the determinant of A = det(P*L)*PI(diag(U))
// where det(P*L) = +- 1 (Number of permutation in P)
// and PI(diag(U)) is the product of the diagonal elements of U
blitz::Array<double,2> Lperm(N,N);
math::prod(P,L,Lperm);
int s = 1;
double Udiag=1.;
for (int i=0; i<N; ++i)
{
for (int j=i+1; j<N; ++j)
if (P(i,j) > 0)
{
s = -s;
break;
}
Udiag *= U(i,i);
}
return s*Udiag;
}
double bob::learn::em::GMMMachine::logLikelihood(const blitz::Array<double, 1> &x) const {
// Check dimension
bob::core::array::assertSameDimensionLength(x.extent(0), m_n_inputs);
// Call the other logLikelihood_ (overloaded) function
// (log_weighted_gaussian_likelihoods will be discarded)
return logLikelihood_(x,m_cache_log_weighted_gaussian_likelihoods);
}
void bob::learn::em::GMMMachine::setVarianceThresholds(const blitz::Array<double, 2>& variance_thresholds) {
bob::core::array::assertSameDimensionLength(variance_thresholds.extent(0), m_n_gaussians);
bob::core::array::assertSameDimensionLength(variance_thresholds.extent(1), m_n_inputs);
for(size_t i=0; i<m_n_gaussians; ++i)
m_gaussians[i]->setVarianceThresholds(variance_thresholds(i,blitz::Range::all()));
m_cache_supervector = false;
}
double bob::math::slogdet_(const blitz::Array<double,2>& A, int& sign)
{
// Size variable
int N = A.extent(0);
// Perform an LU decomposition
blitz::Array<double,2> L(N,N);
blitz::Array<double,2> U(N,N);
blitz::Array<double,2> P(N,N);
math::lu(A, L, U, P);
// Compute the determinant of A = det(P*L)*SI(diag(U))
// where det(P*L) = +- 1 (Number of permutation in P)
// and SI(diag(log|U|)) is the sum of the logarithm of the
// diagonal elements of U
blitz::Array<double,2> Lperm(N,N);
math::prod(P,L,Lperm);
sign = 1;
double Udiag=0.;
for (int i=0; i<N; ++i)
{
for (int j=i+1; j<N; ++j)
if (P(i,j) > 0)
{
sign = -sign;
break;
}
Udiag += log(fabs(U(i,i)));
}
// Check for infinity
if ((Udiag*-1) == std::numeric_limits<double>::infinity())
sign = 0;
return Udiag;
}
void bob::learn::em::GMMMachine::setMeans(const blitz::Array<double,2> &means) {
bob::core::array::assertSameDimensionLength(means.extent(0), m_n_gaussians);
bob::core::array::assertSameDimensionLength(means.extent(1), m_n_inputs);
for(size_t i=0; i<m_n_gaussians; ++i)
m_gaussians[i]->updateMean() = means(i,blitz::Range::all());
m_cache_supervector = false;
}
void bob::learn::boosting::LUTTrainer::weightedHistogram(const blitz::Array<uint16_t,1>& features, const blitz::Array<double,1>& weights) const{
bob::core::array::assertSameShape(features, weights);
_gradientHistogram = 0.;
for (int i = features.extent(0); i--;){
_gradientHistogram((int)features(i)) += weights(i);
}
}
void bob::learn::em::GMMMachine::setVarianceSupervector(const blitz::Array<double,1> &variance_supervector) {
bob::core::array::assertSameDimensionLength(variance_supervector.extent(0), m_n_gaussians*m_n_inputs);
for(size_t i=0; i<m_n_gaussians; ++i) {
m_gaussians[i]->updateVariance() = variance_supervector(blitz::Range(i*m_n_inputs, (i+1)*m_n_inputs-1));
m_gaussians[i]->applyVarianceThresholds();
}
m_cache_supervector = false;
}
void gl_init( void)
{
glPixelStorei ( GL_UNPACK_ALIGNMENT, 1 );
glClearColor ( 0.0F, 0.0F, 0.0F, 0.0F );
glViewport ( 0, 0, image.cols(), image.rows());
// set coordinate frame for graphics in window
glMatrixMode ( GL_PROJECTION );
glLoadIdentity();
gluOrtho2D ( 0, image.cols(), image.rows(), 0);
glMatrixMode ( GL_MODELVIEW);
glLoadIdentity();
glPixelZoom( 1, -1);
}
void bob::learn::em::GMMMachine::accStatistics_(const blitz::Array<double,2>& input, bob::learn::em::GMMStats& stats) const {
// iterate over data
blitz::Range a = blitz::Range::all();
for(int i=0; i<input.extent(0); ++i) {
// Get example
blitz::Array<double,1> x(input(i, a));
// Accumulate statistics
accStatistics_(x,stats);
}
}
int32_t bob::learn::boosting::LUTTrainer::bestIndex(const blitz::Array<double,1>& array) const{
double min = std::numeric_limits<double>::max();
int32_t minIndex = -1;
for (int i = 0; i < array.extent(0); ++i){
if (array(i) < min){
min = array(i);
minIndex = i;
}
}
return minIndex;
}
