//Standard
void plotNodesAsSpheres(const Eigen::MatrixXf centers, const Eigen::MatrixXf colors, const Eigen::VectorXf& pVis, const Eigen::MatrixXf& stdev, PlotSpheres::Ptr spheres) {
int K = centers.rows();
assert(centers.cols() == 3);
assert(colors.rows() == K);
assert(colors.cols() == 3);
assert(pVis.size() == K);
assert(stdev.rows() == K);
MatrixXf rgba(colors.rows(), 4);
rgba << colors.rowwise().reverse(), pVis;
VectorXf sizes = stdev.rowwise().mean()/4.0;
spheres->plot(util::toVec3Array(centers), util::toVec4Array(rgba), toVec(sizes));
}
int run() {
// store cooccurrence in Eigen sparse matrix object
REDSVD::SMatrixXf A;
const int ncontext = read_cooccurrence(c_cooc_file_name, A, verbose);
// read U matrix from file
Eigen::MatrixXf V;
read_eigen_truncated_matrix(c_input_file_V_name, V, dim);
// read S matrix from file
Eigen::VectorXf S;
read_eigen_vector(c_input_file_S_name, S, dim, 1.0-eig);
// checking the dimensions
if (V.rows() != ncontext){
throw std::runtime_error("size mismatch between projection V matrix and the number of context words!!");
}
// starting projection
if (verbose) fprintf(stderr, "Running the projection...");
const double start = REDSVD::Util::getSec();
Eigen::MatrixXf embeddings = A * V * S.asDiagonal().inverse();
if (norm) embeddings.rowwise().normalize();
if (verbose) fprintf(stderr, "done in %.2f.\n",REDSVD::Util::getSec() - start);
// write out embeddings
const char *c_output_name = get_full_path(c_cooc_dir_name, c_output_file_name);
if (verbose) fprintf(stderr, "writing infered word embeddings in %s\n", c_output_name);
write_eigen_matrix(c_output_name, embeddings);
free((char*)c_output_name);
return 0;
}
/* return word nearest neighbors in the embedding space */
void getnn(FILE* fout, Eigen::MatrixXf m, const int idx){
// find nearest neighbour
Eigen::VectorXf dist = (m.rowwise() - m.row(idx)).rowwise().squaredNorm();
std::vector<int> sortidx = REDSVD::Util::ascending_order(dist);
for (int i=1;i<top;i++){
fprintf(fout, "%s, ", tokename[sortidx[i]]);
}
fprintf(fout, "%s\n", tokename[sortidx[top]]);
}
float
computeHistogramIntersection (const Eigen::VectorXf &histA, const Eigen::VectorXf &histB)
{
Eigen::MatrixXf histAB (histA.rows(), 2);
histAB.col(0) = histA;
histAB.col(1) = histB;
Eigen::VectorXf minv = histAB.rowwise().minCoeff();
return minv.sum();
}
void calcMeanAndCovarWeighedVectorized(const Eigen::MatrixXf &input, const Eigen::VectorXd &inputWeights, Eigen::MatrixXf &out_covMat, Eigen::VectorXf &out_mean,Eigen::MatrixXf &temp)
{
out_mean=input.col(0); //to resize
out_mean.setZero();
double wSumInv=1.0/inputWeights.sum();
for (int k=0;k<inputWeights.size();k++){
double w=inputWeights[k];
out_mean+=input.col(k)*(float)(w*wSumInv);
}
out_mean = input.rowwise().mean();
temp = (input.colwise() - out_mean);
for (int k=0;k<inputWeights.size();k++){
temp.col(k) *= (float)(sqrt(inputWeights(k)*wSumInv)); //using square roots, as we only want the normalized weights to be included once for each result element in the multiplication below
}
out_covMat = temp*temp.transpose();
}
int TestCovariate(Matrix& Xnull, Matrix& Y, Matrix& Xcol,
const EigenMatrix& kinshipU, const EigenMatrix& kinshipS){
Eigen::MatrixXf g;
G_to_Eigen(Xcol, &g);
// store U'*G for computing AF later.
const Eigen::MatrixXf& U = kinshipU.mat;
this->ug = U.transpose() * g;
Eigen::RowVectorXf g_mean = g.colwise().mean();
g = g.rowwise() - g_mean;
double gTg = g.array().square().sum();
double t_new = (g.array() * this->transformedY.array()).sum();
t_new = t_new * t_new / gTg;
double t_score = t_new / this->gamma;
this->betaG = (g.transpose() * this->transformedY).sum() / gTg / this->gamma;
this->betaGVar = this->ySigmaY / gTg / this->gamma;
this->pvalue = gsl_cdf_chisq_Q(t_score, 1.0);
return 0;
}
Eigen::MatrixXf centerMatrix(const Eigen::MatrixXf& x) {
return x.rowwise() - x.colwise().mean();
}
