зеркало из https://github.com/mozilla/kaldi.git
Added extra argument to a couple of matrix-library functions.
git-svn-id: https://svn.code.sf.net/p/kaldi/code/trunk@1363 5e6a8d80-dfce-4ca6-a32a-6e07a63d50c8
This commit is contained in:
Dan Povey
Родитель
45e4d2f0a0
Коммит
8163148b37
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@ -310,9 +310,9 @@ void CuVector<Real>::AddRowSumMat(Real alpha, const CuMatrix<Real> &mat, Real be
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#endif
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{
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Vector<Real> tmp(mat.NumCols());
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tmp.AddRowSumMat(mat.Mat());
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tmp.AddRowSumMat(1.0, mat.Mat());
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if(beta != 1.0) vec_.Scale(beta);
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vec_.AddVec(alpha,tmp);
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vec_.AddVec(alpha, tmp);
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}
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}
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@ -360,7 +360,7 @@ void CuVector<Real>::AddColSumMat(Real alpha, const CuMatrix<Real> &mat, Real be
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#endif
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{
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Vector<Real> tmp(mat.NumRows());
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tmp.AddColSumMat(mat.Mat());
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tmp.AddColSumMat(1.0, mat.Mat());
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if(beta != 1.0) vec_.Scale(beta);
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vec_.AddVec(alpha,tmp);
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}
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@ -153,7 +153,7 @@ int main(int argc, char *argv[]) {
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}
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if (subtract_mean) {
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Vector<BaseFloat> mean(features.NumCols());
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mean.AddRowSumMat(features);
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mean.AddRowSumMat(1.0, features);
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mean.Scale(1.0 / features.NumRows());
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for (int32 i = 0; i < features.NumRows(); i++)
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features.Row(i).AddVec(-1.0, mean);
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@ -153,7 +153,7 @@ int main(int argc, char *argv[]) {
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}
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if (subtract_mean) {
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Vector<BaseFloat> mean(features.NumCols());
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mean.AddRowSumMat(features);
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mean.AddRowSumMat(1.0, features);
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mean.Scale(1.0 / features.NumRows());
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for (int32 i = 0; i < features.NumRows(); i++)
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features.Row(i).AddVec(-1.0, mean);
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@ -153,7 +153,7 @@ int main(int argc, char *argv[]) {
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}
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if (subtract_mean) {
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Vector<BaseFloat> mean(features.NumCols());
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mean.AddRowSumMat(features);
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mean.AddRowSumMat(1.0, features);
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mean.Scale(1.0 / features.NumRows());
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for (size_t i = 0; i < features.NumRows(); i++)
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features.Row(i).AddVec(-1.0, mean);
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@ -116,7 +116,7 @@ int main(int argc, char *argv[]) {
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}
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if (subtract_mean) {
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Vector<BaseFloat> mean(features.NumCols());
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mean.AddRowSumMat(features);
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mean.AddRowSumMat(1.0, features);
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mean.Scale(1.0 / features.NumRows());
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for (int32 i = 0; i < features.NumRows(); i++)
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features.Row(i).AddVec(-1.0, mean);
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@ -52,7 +52,7 @@ int main(int argc, char *argv[]) {
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continue;
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}
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Vector<BaseFloat> mean(feats.NumCols());
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mean.AddRowSumMat(feats);
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mean.AddRowSumMat(1.0, feats);
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mean.Scale(1.0 / feats.NumRows());
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for (int32 i = 0; i < feats.NumRows(); i++)
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feats.Row(i).AddVec(-1.0, mean);
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@ -309,7 +309,7 @@ UnitTestEstimateFullGmm() {
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Vector<BaseFloat> mean(dim);
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cov.AddMatMat(1.0, feats, kTrans, feats, kNoTrans, 0.0);
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cov.Scale(1.0 / feats.NumRows());
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mean.AddRowSumMat(feats);
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mean.AddRowSumMat(1.0, feats);
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mean.Scale(1.0 / feats.NumRows());
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cov.AddVecVec(-1.0, mean, mean);
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BaseFloat logdet = cov.LogDet();
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@ -763,38 +763,55 @@ void MatrixBase<double>::CopyFromTp(const TpMatrix<double> & M,
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template<typename Real>
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void MatrixBase<Real>::CopyRowsFromVec(const VectorBase<Real> &rv) {
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KALDI_ASSERT(rv.Dim() == num_rows_*num_cols_);
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if (stride_ == num_cols_) {
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// one big copy operation.
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const Real *rv_data = rv.Data();
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std::memcpy(data_, rv_data, sizeof(Real)*num_rows_*num_cols_);
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} else {
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const Real *rv_data = rv.Data();
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for (MatrixIndexT r = 0; r < num_rows_; r++) {
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Real *row_data = RowData(r);
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for (MatrixIndexT c = 0; c < num_cols_; c++) {
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row_data[c] = rv_data[c];
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if (rv.Dim() == num_rows_*num_cols_) {
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if (stride_ == num_cols_) {
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// one big copy operation.
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const Real *rv_data = rv.Data();
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std::memcpy(data_, rv_data, sizeof(Real)*num_rows_*num_cols_);
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} else {
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const Real *rv_data = rv.Data();
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for (MatrixIndexT r = 0; r < num_rows_; r++) {
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Real *row_data = RowData(r);
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for (MatrixIndexT c = 0; c < num_cols_; c++) {
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row_data[c] = rv_data[c];
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}
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rv_data += num_cols_;
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}
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rv_data += num_cols_;
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}
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} else if (rv.Dim() == num_cols_) {
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const Real *rv_data = rv.Data();
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for (MatrixIndexT r = 0; r < num_rows_; r++)
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std::memcpy(RowData(r), rv_data, sizeof(Real)*num_cols_);
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} else {
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KALDI_ERR << "Wrong sized arguments";
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}
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}
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template<typename Real>
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template<typename OtherReal>
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void MatrixBase<Real>::CopyRowsFromVec(const VectorBase<OtherReal> &rv) {
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KALDI_ASSERT(rv.Dim() == num_rows_*num_cols_);
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const OtherReal *rv_data = rv.Data();
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for (MatrixIndexT r = 0; r < num_rows_; r++) {
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Real *row_data = RowData(r);
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for (MatrixIndexT c = 0; c < num_cols_; c++) {
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row_data[c] = static_cast<Real>(rv_data[c]);
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if (rv.Dim() == num_rows_*num_cols_) {
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const OtherReal *rv_data = rv.Data();
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for (MatrixIndexT r = 0; r < num_rows_; r++) {
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Real *row_data = RowData(r);
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for (MatrixIndexT c = 0; c < num_cols_; c++) {
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row_data[c] = static_cast<Real>(rv_data[c]);
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}
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rv_data += num_cols_;
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}
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rv_data += num_cols_;
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} else if (rv.Dim() == num_cols_) {
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const OtherReal *rv_data = rv.Data();
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Real *first_row_data = RowData(0);
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for (MatrixIndexT c = 0; c < num_cols_; c++)
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first_row_data[c] = rv_data[c];
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for (MatrixIndexT r = 1; r < num_rows_; r++)
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std::memcpy(RowData(r), first_row_data, sizeof(Real)*num_cols_);
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} else {
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KALDI_ERR << "Wrong sized arguments.";
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}
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}
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template
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void MatrixBase<float>::CopyRowsFromVec(const VectorBase<double> &rv);
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template
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@ -802,17 +819,28 @@ void MatrixBase<double>::CopyRowsFromVec(const VectorBase<float> &rv);
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template<typename Real>
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void MatrixBase<Real>::CopyColsFromVec(const VectorBase<Real> &rv) {
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KALDI_ASSERT(rv.Dim() == num_rows_*num_cols_);
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if (rv.Dim() == num_rows_*num_cols_) {
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const Real *v_inc_data = rv.Data();
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Real *m_inc_data = data_;
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const Real *v_inc_data = rv.Data();
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Real *m_inc_data = data_;
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for (MatrixIndexT c = 0; c < num_cols_; c++) {
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for (MatrixIndexT r = 0; r < num_rows_; r++) {
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m_inc_data[r * stride_] = v_inc_data[r];
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for (MatrixIndexT c = 0; c < num_cols_; c++) {
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for (MatrixIndexT r = 0; r < num_rows_; r++) {
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m_inc_data[r * stride_] = v_inc_data[r];
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}
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v_inc_data += num_rows_;
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m_inc_data ++;
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}
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v_inc_data += num_rows_;
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m_inc_data ++;
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} else if (rv.Dim() == num_rows_) {
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const Real *v_inc_data = rv.Data();
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Real *m_inc_data = data_;
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for (MatrixIndexT r = 0; r < num_rows_; r++) {
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BaseFloat value = *(v_inc_data++);
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for (MatrixIndexT c = 0; c < num_cols_; c++)
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m_inc_data[c] = value;
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m_inc_data += stride_;
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}
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} else {
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KALDI_ERR << "Wrong size of arguments.";
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}
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}
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@ -130,14 +130,17 @@ class MatrixBase {
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MatrixTransposeType Trans = kNoTrans);
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/// Inverse of vec() operator. Copies vector into matrix, row-by-row.
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/// Note that rv.Dim() must equal NumRows()*NumCols().
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/// Note that rv.Dim() must either equal NumRows()*NumCols() or
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/// NumCols()-- this has two modes of operation.
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void CopyRowsFromVec(const VectorBase<Real> &v);
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template<typename OtherReal>
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void CopyRowsFromVec(const VectorBase<OtherReal> &v);
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/// Copies vector into matrix, column-by-column.
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/// Note that rv.Dim() must equal NumRows()*NumCols().
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/// Note that rv.Dim() must either equal NumRows()*NumCols() or NumRows();
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/// this has two modes of operation.
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void CopyColsFromVec(const VectorBase<Real> &v);
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/// Copy vector into specific column of matrix.
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void CopyColFromVec(const VectorBase<Real> &v, const MatrixIndexT col);
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/// Copy vector into specific row of matrix.
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@ -745,6 +748,12 @@ std::istream & operator >> (std::istream & In, MatrixBase<Real> & M);
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template<typename Real>
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std::istream & operator >> (std::istream & In, Matrix<Real> & M);
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template<class Real>
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bool SameDim(const MatrixBase<Real> &M, const MatrixBase<Real> &N) {
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return (M.NumRows() == N.NumRows() && M.NumCols() == N.NumCols());
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}
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/// @} end of \addtogroup matrix_funcs_io
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@ -551,7 +551,7 @@ Real VectorBase<Real>::SumLog() const {
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}
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template<typename Real>
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void VectorBase<Real>::AddRowSumMat(const MatrixBase<Real> &rM) {
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void VectorBase<Real>::AddRowSumMat(Real alpha, const MatrixBase<Real> &rM) {
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// note the double accumulator
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double sum;
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KALDI_ASSERT(dim_ == rM.NumCols());
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@ -560,12 +560,12 @@ void VectorBase<Real>::AddRowSumMat(const MatrixBase<Real> &rM) {
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for (MatrixIndexT j = 0; j < rM.NumRows(); j++) {
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sum += rM(j, i);
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}
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data_[i] += sum;
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data_[i] += alpha * sum;
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}
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}
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template<typename Real>
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void VectorBase<Real>::AddColSumMat(const MatrixBase<Real> &rM) {
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void VectorBase<Real>::AddColSumMat(Real alpha, const MatrixBase<Real> &rM) {
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// note the double accumulator
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double sum;
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KALDI_ASSERT(dim_ == rM.NumRows());
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@ -574,7 +574,7 @@ void VectorBase<Real>::AddColSumMat(const MatrixBase<Real> &rM) {
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for (MatrixIndexT j = 0; j < rM.NumCols(); j++) {
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sum += rM(i, j);
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}
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data_[i] += sum;
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data_[i] += alpha * sum;
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}
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}
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@ -242,11 +242,11 @@ class VectorBase {
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/// negative.
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Real SumLog() const;
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/// Adds sum of the rows of M to existing contents.
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void AddRowSumMat(const MatrixBase<Real>& M);
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/// Adds sum of the rows of M to existing contents, times alpha.
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void AddRowSumMat(Real alpha, const MatrixBase<Real>& M);
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/// Adds sum of the columns of M to existing contents.
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void AddColSumMat(const MatrixBase<Real>& M);
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void AddColSumMat(Real alpha, const MatrixBase<Real>& M);
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/// Returns log(sum(exp())) without exp overflow
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/// If prune > 0.0, ignores terms less than the max - prune.
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@ -67,16 +67,16 @@ start:
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for (MatrixIndexT i = 0;i < M->NumRows();i++)
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for (MatrixIndexT j = 0;j < M->NumCols();j++)
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(*M)(i, j) = RandGauss();
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if (M->NumRows() != 0 && M->Cond() > 100) {
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printf("Condition number of random matrix large %f, trying again (this is normal)\n",
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(float) M->Cond());
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goto start;
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}
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if (M->NumRows() != 0 && M->Cond() > 100) {
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printf("Condition number of random matrix large %f, trying again (this is normal)\n",
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(float) M->Cond());
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goto start;
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}
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}
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template<class Real> static void InitRand(SpMatrix<Real> *M) {
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start:
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start:
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for (MatrixIndexT i = 0;i < M->NumRows();i++)
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for (MatrixIndexT j = 0;j<=i;j++)
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(*M)(i, j) = RandGauss();
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@ -115,14 +115,14 @@ static bool ApproxEqual(const SpMatrix<Real> &A,
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}
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/* was:
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template<class Real>
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bool ApproxEqual(SpMatrix<Real> &A, SpMatrix<Real> &B, float tol = 0.001) {
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KALDI_ASSERT(A.NumRows() == B.NumRows()&&A.NumCols() == B.NumCols());
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for (MatrixIndexT i = 0;i < A.NumRows();i++)
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for (MatrixIndexT j = 0;j<=i;j++)
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if (std::abs(A(i, j)-B(i, j)) > tol*std::max(1.0, (double) (std::abs(A(i, j))+std::abs(B(i, j))))) return false;
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return true;
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}
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template<class Real>
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bool ApproxEqual(SpMatrix<Real> &A, SpMatrix<Real> &B, float tol = 0.001) {
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KALDI_ASSERT(A.NumRows() == B.NumRows()&&A.NumCols() == B.NumCols());
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for (MatrixIndexT i = 0;i < A.NumRows();i++)
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for (MatrixIndexT j = 0;j<=i;j++)
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if (std::abs(A(i, j)-B(i, j)) > tol*std::max(1.0, (double) (std::abs(A(i, j))+std::abs(B(i, j))))) return false;
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return true;
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}
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*/
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template<class Real> static void AssertEqual(Vector<Real> &A, Vector<Real> &B, float tol = 0.001) {
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@ -209,6 +209,27 @@ static void UnitTestSpAddVec() {
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}
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}
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template<class Real> static void UnitTestCopyRowsAndCols() {
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// Test other mode of CopyRowsFromVec, and CopyColsFromVec,
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// where vector is duplicated.
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for (int32 i = 0; i < 30; i++) {
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int32 dimM = 1 + rand() % 5, dimN = 1 + rand() % 5;
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Vector<float> w(dimN); // test cross-type version of
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// CopyRowsFromVec.
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Vector<Real> v(dimM);
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Matrix<Real> M(dimM, dimN), N(dimM, dimN);
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InitRand(&v);
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InitRand(&w);
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M.CopyColsFromVec(v);
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N.CopyRowsFromVec(w);
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for (int32 r = 0; r < dimM; r++) {
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for (int32 c = 0; c < dimN; c++) {
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KALDI_ASSERT(M(r, c) == v(r));
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KALDI_ASSERT(N(r, c) == w(c));
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}
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}
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}
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}
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template<class Real> static void UnitTestSpliceRows() {
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@ -291,10 +312,10 @@ template<class Real> static void UnitTestRemoveRow() {
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Vector<Real> N(V);
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N.RemoveElement(i);
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for (MatrixIndexT j = 0;j < i;j++) {
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KALDI_ASSERT(V(j) == N(j));
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KALDI_ASSERT(V(j) == N(j));
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}
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for (MatrixIndexT j = i+1;j < dimM;j++) {
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KALDI_ASSERT(V(j) == N(j-1));
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KALDI_ASSERT(V(j) == N(j-1));
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}
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}
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@ -354,8 +375,10 @@ static void UnitTestSimpleForVec() { // testing some simple operaters on vector
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Matrix<Real> M(dimM, dimN);
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InitRand(&M);
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Vector<Real> Vr(dimN), Vc(dimM);
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Vr.AddRowSumMat(M);
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Vc.AddColSumMat(M);
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Vr.AddRowSumMat(0.5, M);
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Vc.AddColSumMat(0.5, M);
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Vr.Scale(2.0);
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Vc.Scale(2.0);
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Vector<Real> V2r(dimN), V2c(dimM);
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for (MatrixIndexT k = 0; k < dimM; k++) {
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@ -511,7 +534,7 @@ static void UnitTestSimpleForMat() { // test some simple operates on all kinds
|
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y.Cholesky(x);
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std::cout << "Matrix y is a lower triangular Cholesky decomposition of x:"
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<< '\n';
|
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<< '\n';
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std::cout << y << '\n';
|
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// test sp-matrix's LogPosDefDet() function
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@ -825,13 +848,13 @@ template<class Real> static void UnitTestSherman() {
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Matrix<Real> tt2(dimK, dimK);
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tt2.AddMatMat(1.0, V, kTrans, tt1, kNoTrans, 0.0);
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for (MatrixIndexT i = 0;i < dimK;i++)
|
||||
for (MatrixIndexT j = 0;j < dimK;j++)
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||||
{
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if (i == j)
|
||||
I(i, j) = 1.0;
|
||||
else
|
||||
I(i, j) = 0.0;
|
||||
}
|
||||
for (MatrixIndexT j = 0;j < dimK;j++)
|
||||
{
|
||||
if (i == j)
|
||||
I(i, j) = 1.0;
|
||||
else
|
||||
I(i, j) = 0.0;
|
||||
}
|
||||
tt2.AddMat(1.0, I); // I = identity
|
||||
tt2.Invert();
|
||||
|
||||
|
|
@ -975,25 +998,25 @@ template<class Real> static void UnitTestSvdNodestroy() {
|
|||
|
||||
|
||||
/*
|
||||
template<class Real> static void UnitTestSvdVariants() { // just make sure it doesn't crash if we call it but don't want left or right singular vectors. there are KALDI_ASSERTs inside the Svd.
|
||||
#ifndef HAVE_ATLAS
|
||||
template<class Real> static void UnitTestSvdVariants() { // just make sure it doesn't crash if we call it but don't want left or right singular vectors. there are KALDI_ASSERTs inside the Svd.
|
||||
#ifndef HAVE_ATLAS
|
||||
int Base = 10, Rand = 5, Iter = 25;
|
||||
for (int iter = 0;iter < Iter;iter++) {
|
||||
MatrixIndexT dimM = Base + rand() % Rand, dimN = Base + rand() % Rand;
|
||||
// if (dimM<dimN) std::swap(dimM, dimN); // M>=N.
|
||||
Matrix<Real> M(dimM, dimN);
|
||||
Matrix<Real> U(dimM, dimM), Vt(dimN, dimN); Vector<Real> v(std::min(dimM, dimN));
|
||||
Matrix<Real> Utmp(dimM, 1); Matrix<Real> Vttmp(1, dimN);
|
||||
InitRand(&M);
|
||||
M.Svd(v, U, Vttmp, "A", "N");
|
||||
M.Svd(v, Utmp, Vt, "N", "A");
|
||||
Matrix<Real> U2(dimM, dimM), Vt2(dimN, dimN); Vector<Real> v2(std::min(dimM, dimN));
|
||||
M.Svd(v, U2, Vt2, "A", "A");
|
||||
AssertEqual(U, U2); AssertEqual(Vt, Vt2);
|
||||
MatrixIndexT dimM = Base + rand() % Rand, dimN = Base + rand() % Rand;
|
||||
// if (dimM<dimN) std::swap(dimM, dimN); // M>=N.
|
||||
Matrix<Real> M(dimM, dimN);
|
||||
Matrix<Real> U(dimM, dimM), Vt(dimN, dimN); Vector<Real> v(std::min(dimM, dimN));
|
||||
Matrix<Real> Utmp(dimM, 1); Matrix<Real> Vttmp(1, dimN);
|
||||
InitRand(&M);
|
||||
M.Svd(v, U, Vttmp, "A", "N");
|
||||
M.Svd(v, Utmp, Vt, "N", "A");
|
||||
Matrix<Real> U2(dimM, dimM), Vt2(dimN, dimN); Vector<Real> v2(std::min(dimM, dimN));
|
||||
M.Svd(v, U2, Vt2, "A", "A");
|
||||
AssertEqual(U, U2); AssertEqual(Vt, Vt2);
|
||||
|
||||
}
|
||||
#endif
|
||||
}*/
|
||||
#endif
|
||||
}*/
|
||||
|
||||
template<class Real> static void UnitTestSvdJustvec() { // Making sure gives same answer if we get just the vector, not the eigs.
|
||||
int Base = 10, Rand = 5, Iter = 25;
|
||||
|
|
@ -1034,9 +1057,9 @@ template<class Real> static void UnitTestEig() {
|
|||
for (int iter = 0;iter < 5;iter++) {
|
||||
MatrixIndexT dimM = 1 + iter;
|
||||
/* if (iter < 10)
|
||||
dimM = 1 + rand() % 6;
|
||||
else
|
||||
dimM = 5 + rand()%10; */
|
||||
dimM = 1 + rand() % 6;
|
||||
else
|
||||
dimM = 5 + rand()%10; */
|
||||
Matrix<Real> M(dimM, dimM);
|
||||
InitRand(&M);
|
||||
Matrix<Real> P(dimM, dimM);
|
||||
|
|
@ -1392,21 +1415,21 @@ static void UnitTestTransposeScatter() {
|
|||
|
||||
for (MatrixIndexT i = 0;i < Ap.NumRows();i++) {
|
||||
for (MatrixIndexT j = 0; j<=i; j++) {
|
||||
Ap(i, j) = RandGauss();
|
||||
Ap(i, j) = RandGauss();
|
||||
}
|
||||
}
|
||||
for (MatrixIndexT i = 0;i < M.NumRows();i++) {
|
||||
for (MatrixIndexT j = 0; j < M.NumCols(); j++) {
|
||||
M(i, j) = RandGauss();
|
||||
M(i, j) = RandGauss();
|
||||
}
|
||||
}
|
||||
/*
|
||||
std::stringstream ss("1 2 3");
|
||||
ss >> Ap;
|
||||
ss.clear();
|
||||
ss.str("5 6 7 8 9 10");
|
||||
ss >> M;
|
||||
*/
|
||||
/*
|
||||
std::stringstream ss("1 2 3");
|
||||
ss >> Ap;
|
||||
ss.clear();
|
||||
ss.str("5 6 7 8 9 10");
|
||||
ss >> M;
|
||||
*/
|
||||
|
||||
Af.CopyFromSp(Ap);
|
||||
A_MT.AddMatMat(1.0, Af, kNoTrans, M, kTrans, 0.0);
|
||||
|
|
@ -1414,9 +1437,9 @@ static void UnitTestTransposeScatter() {
|
|||
Op.AddMat2Sp(1.0, M, kNoTrans, Ap, 0.0);
|
||||
|
||||
|
||||
// std::cout << "A" << '\n' << Af << '\n';
|
||||
// std::cout << "M" << '\n' << M << '\n';
|
||||
// std::cout << "Op" << '\n' << Op << '\n';
|
||||
// std::cout << "A" << '\n' << Af << '\n';
|
||||
// std::cout << "M" << '\n' << M << '\n';
|
||||
// std::cout << "Op" << '\n' << Op << '\n';
|
||||
|
||||
for (MatrixIndexT i = 0; i < dimO; i++) {
|
||||
for (MatrixIndexT j = 0; j<=i; j++) {
|
||||
|
|
@ -1429,8 +1452,8 @@ static void UnitTestTransposeScatter() {
|
|||
Af.AddMatMat(1.0, M, kTrans, A_MT, kNoTrans, 1.0);
|
||||
Ap.AddMat2Sp(1.0, M, kTrans, Op, 1.0);
|
||||
|
||||
// std::cout << "Ap" << '\n' << Ap << '\n';
|
||||
// std::cout << "Af" << '\n' << Af << '\n';
|
||||
// std::cout << "Ap" << '\n' << Ap << '\n';
|
||||
// std::cout << "Af" << '\n' << Af << '\n';
|
||||
|
||||
for (MatrixIndexT i = 0; i < dimA; i++) {
|
||||
for (MatrixIndexT j = 0; j<=i; j++) {
|
||||
|
|
@ -1671,7 +1694,7 @@ template<class Real> static void UnitTestSimple() {
|
|||
S.CopyFromMat(N);
|
||||
KALDI_ASSERT(S.IsZero());
|
||||
KALDI_ASSERT(S.IsDiagonal());
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -1885,14 +1908,14 @@ template<class Real> static void UnitTestRange() { // Testing SubMatrix class.
|
|||
KALDI_ASSERT(sub.Sum() == V.Range(lenStart, lenEnd-lenStart).Sum());
|
||||
|
||||
for (MatrixIndexT i = lenStart;i < lenEnd;i++)
|
||||
KALDI_ASSERT(V(i) == sub(i-lenStart));
|
||||
KALDI_ASSERT(V(i) == sub(i-lenStart));
|
||||
|
||||
InitRand(&sub);
|
||||
|
||||
KALDI_ASSERT(sub.Sum() == V.Range(lenStart, lenEnd-lenStart).Sum());
|
||||
|
||||
for (MatrixIndexT i = lenStart;i < lenEnd;i++)
|
||||
KALDI_ASSERT(V(i) == sub(i-lenStart));
|
||||
KALDI_ASSERT(V(i) == sub(i-lenStart));
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -2047,7 +2070,7 @@ template<class Real> static void UnitTestSolve() {
|
|||
SolveQuadraticProblem(H, g, &x2);
|
||||
#endif
|
||||
KALDI_ASSERT(VecVec(x2, g) -0.5* VecSpVec(x2, H, x2) >=
|
||||
VecVec(x, g) -0.5* VecSpVec(x, H, x));
|
||||
VecVec(x, g) -0.5* VecSpVec(x, H, x));
|
||||
// Check objf not decreased.
|
||||
}
|
||||
|
||||
|
|
@ -2086,16 +2109,16 @@ template<class Real> static void UnitTestSolve() {
|
|||
M3.AddMatSp(1.0, Y, kNoTrans, Qinv, 0.0);
|
||||
if (Q.Cond() < 1000.0) {
|
||||
AssertEqual(M2, M3); // This equality only holds if SigmaInv full-rank,
|
||||
// which is overwhelmingly likely if dimO > dimM
|
||||
// which is overwhelmingly likely if dimO > dimM
|
||||
}
|
||||
|
||||
{
|
||||
Real a1 = TraceMatSpMat(M2, kTrans, SigmaInv, Y, kNoTrans),
|
||||
a2 = TraceMatSpMatSp(M2, kNoTrans, Q, M2, kTrans, SigmaInv),
|
||||
b1 = TraceMatSpMat(M, kTrans, SigmaInv, Y, kNoTrans),
|
||||
b2 = TraceMatSpMatSp(M, kNoTrans, Q, M, kTrans, SigmaInv),
|
||||
a3 = a1 - 0.5 * a2,
|
||||
b3 = b1 - 0.5 * b2;
|
||||
a2 = TraceMatSpMatSp(M2, kNoTrans, Q, M2, kTrans, SigmaInv),
|
||||
b1 = TraceMatSpMat(M, kTrans, SigmaInv, Y, kNoTrans),
|
||||
b2 = TraceMatSpMatSp(M, kNoTrans, Q, M, kTrans, SigmaInv),
|
||||
a3 = a1 - 0.5 * a2,
|
||||
b3 = b1 - 0.5 * b2;
|
||||
KALDI_ASSERT(a3 >= b3);
|
||||
// KALDI_LOG << "a3 = " << a3 << ", b3 = " << b3 << ", c3 = " << c3;
|
||||
} // Check objf not decreased.
|
||||
|
|
@ -2114,8 +2137,8 @@ template<class Real> static void UnitTestSolve() {
|
|||
Matrix<Real> M(dimO, dimM), G(dimO, dimM);
|
||||
M.SetRandn();
|
||||
G.SetRandn();
|
||||
// InitRand(&M);
|
||||
// InitRand(&G);
|
||||
// InitRand(&M);
|
||||
// InitRand(&G);
|
||||
|
||||
Matrix<Real> M2(M);
|
||||
|
||||
|
|
@ -2128,13 +2151,13 @@ template<class Real> static void UnitTestSolve() {
|
|||
|
||||
{
|
||||
Real a1 = TraceMatMat(M2, G, kTrans),
|
||||
a2 = TraceMatSpMatSp(M2, kNoTrans, Q1, M2, kTrans, P1),
|
||||
a3 = TraceMatSpMatSp(M2, kNoTrans, Q2, M2, kTrans, P2),
|
||||
b1 = TraceMatMat(M, G, kTrans),
|
||||
b2 = TraceMatSpMatSp(M, kNoTrans, Q1, M, kTrans, P1),
|
||||
b3 = TraceMatSpMatSp(M, kNoTrans, Q2, M, kTrans, P2),
|
||||
a4 = a1 - 0.5 * a2 - 0.5 * a3,
|
||||
b4 = b1 - 0.5 * b2 - 0.5 * b3;
|
||||
a2 = TraceMatSpMatSp(M2, kNoTrans, Q1, M2, kTrans, P1),
|
||||
a3 = TraceMatSpMatSp(M2, kNoTrans, Q2, M2, kTrans, P2),
|
||||
b1 = TraceMatMat(M, G, kTrans),
|
||||
b2 = TraceMatSpMatSp(M, kNoTrans, Q1, M, kTrans, P1),
|
||||
b3 = TraceMatSpMatSp(M, kNoTrans, Q2, M, kTrans, P2),
|
||||
a4 = a1 - 0.5 * a2 - 0.5 * a3,
|
||||
b4 = b1 - 0.5 * b2 - 0.5 * b3;
|
||||
KALDI_LOG << "a4 = " << a4 << ", b4 = " << b4;
|
||||
KALDI_ASSERT(a4 >= b4);
|
||||
} // Check objf not decreased.
|
||||
|
|
@ -2205,15 +2228,15 @@ template<class Real> static void UnitTestTrace() {
|
|||
ABC.AddMatMat(1.0, A, kNoTrans, BC, kNoTrans, 0.0);
|
||||
|
||||
Real
|
||||
t1 = TraceMat(ABC),
|
||||
t2 = ABC.Trace(),
|
||||
t3 = TraceMatMat(A, BC),
|
||||
t4 = TraceMatMat(AT, BC, kTrans),
|
||||
t5 = TraceMatMat(BC, AT, kTrans),
|
||||
t6 = TraceMatMatMat(A, kNoTrans, B, kNoTrans, C, kNoTrans),
|
||||
t7 = TraceMatMatMat(AT, kTrans, B, kNoTrans, C, kNoTrans),
|
||||
t8 = TraceMatMatMat(AT, kTrans, BT, kTrans, C, kNoTrans),
|
||||
t9 = TraceMatMatMat(AT, kTrans, BT, kTrans, CT, kTrans);
|
||||
t1 = TraceMat(ABC),
|
||||
t2 = ABC.Trace(),
|
||||
t3 = TraceMatMat(A, BC),
|
||||
t4 = TraceMatMat(AT, BC, kTrans),
|
||||
t5 = TraceMatMat(BC, AT, kTrans),
|
||||
t6 = TraceMatMatMat(A, kNoTrans, B, kNoTrans, C, kNoTrans),
|
||||
t7 = TraceMatMatMat(AT, kTrans, B, kNoTrans, C, kNoTrans),
|
||||
t8 = TraceMatMatMat(AT, kTrans, BT, kTrans, C, kNoTrans),
|
||||
t9 = TraceMatMatMat(AT, kTrans, BT, kTrans, CT, kTrans);
|
||||
|
||||
Matrix<Real> ABC1(dimM, dimP); // tests AddMatMatMat.
|
||||
ABC1.AddMatMatMat(1.0, A, kNoTrans, B, kNoTrans, C, kNoTrans, 0.0);
|
||||
|
|
@ -2228,8 +2251,8 @@ template<class Real> static void UnitTestTrace() {
|
|||
|
||||
Real tol = 0.001;
|
||||
KALDI_ASSERT((std::abs(t1-t2) < tol) && (std::abs(t2-t3) < tol) && (std::abs(t3-t4) < tol)
|
||||
&& (std::abs(t4-t5) < tol) && (std::abs(t5-t6) < tol) && (std::abs(t6-t7) < tol)
|
||||
&& (std::abs(t7-t8) < tol) && (std::abs(t8-t9) < tol));
|
||||
&& (std::abs(t4-t5) < tol) && (std::abs(t5-t6) < tol) && (std::abs(t6-t7) < tol)
|
||||
&& (std::abs(t7-t8) < tol) && (std::abs(t8-t9) < tol));
|
||||
}
|
||||
|
||||
for (MatrixIndexT i = 0;i < 5;i++) {
|
||||
|
|
@ -2833,7 +2856,7 @@ template<class Real> static void UnitTestCompressedMatrix() {
|
|||
for (int i = 0; i < num_rows; i++) {
|
||||
Vector<Real> V(num_cols);
|
||||
cmat.CopyRowToVec(i, &V); // get row.
|
||||
for (MatrixIndexT k = 0;k < num_cols;k++) {
|
||||
for (MatrixIndexT k = 0; k < num_cols; k++) {
|
||||
AssertEqual(M2(i, k), V(k));
|
||||
}
|
||||
}
|
||||
|
|
@ -2850,10 +2873,10 @@ template<class Real> static void UnitTestCompressedMatrix() {
|
|||
//test of getting a submatrix
|
||||
if(num_rows != 0 && num_cols != 0){
|
||||
int32 sub_row_offset = (num_rows == 1 ? 0 : rand() % (num_rows-1)),
|
||||
sub_col_offset = (num_cols == 1 ? 0 : rand() % (num_cols-1));
|
||||
// to make sure we don't mod by zero
|
||||
sub_col_offset = (num_cols == 1 ? 0 : rand() % (num_cols-1));
|
||||
// to make sure we don't mod by zero
|
||||
int32 num_subrows = rand() % (num_rows-sub_row_offset),
|
||||
num_subcols = rand() % (num_cols-sub_col_offset);
|
||||
num_subcols = rand() % (num_cols-sub_col_offset);
|
||||
if(num_subrows == 0 || num_subcols == 0){ // in case we randomized to
|
||||
// empty matrix, at least make it correct
|
||||
num_subrows = 0;
|
||||
|
|
@ -2926,7 +2949,7 @@ template<class Real> static void MatrixUnitTest() {
|
|||
UnitTestSplitRadixComplexFft2<Real>();
|
||||
UnitTestDct<Real>();
|
||||
UnitTestRealFft<Real>();
|
||||
KALDI_LOG << " Point C";
|
||||
KALDI_LOG << " Point C";
|
||||
UnitTestSplitRadixRealFft<Real>();
|
||||
UnitTestSvd<Real>();
|
||||
UnitTestSvdNodestroy<Real>();
|
||||
|
|
@ -2934,14 +2957,14 @@ template<class Real> static void MatrixUnitTest() {
|
|||
UnitTestSpAddVec<Real, float>();
|
||||
UnitTestSpAddVec<Real, double>();
|
||||
UnitTestSpInvert<Real>();
|
||||
KALDI_LOG << " Point D";
|
||||
KALDI_LOG << " Point D";
|
||||
UnitTestTpInvert<Real>();
|
||||
UnitTestIo<Real>();
|
||||
UnitTestIoCross<Real>();
|
||||
UnitTestHtkIo<Real>();
|
||||
UnitTestScale<Real>();
|
||||
UnitTestTrace<Real>();
|
||||
KALDI_LOG << " Point E";
|
||||
KALDI_LOG << " Point E";
|
||||
CholeskyUnitTestTr<Real>();
|
||||
UnitTestAxpy<Real>();
|
||||
UnitTestSimple<Real>();
|
||||
|
|
@ -2954,7 +2977,7 @@ template<class Real> static void MatrixUnitTest() {
|
|||
// UnitTestSvdVariants<Real>();
|
||||
UnitTestPower<Real>();
|
||||
UnitTestDeterminant<Real>();
|
||||
KALDI_LOG << " Point F";
|
||||
KALDI_LOG << " Point F";
|
||||
UnitTestDeterminantSign<Real>();
|
||||
UnitTestSger<Real>();
|
||||
UnitTestAddOuterProductPlusMinus<Real>();
|
||||
|
|
@ -2965,13 +2988,14 @@ template<class Real> static void MatrixUnitTest() {
|
|||
UnitTestSherman<Real>();
|
||||
UnitTestSpVec<Real>();
|
||||
UnitTestLimitCondInvert<Real>();
|
||||
KALDI_LOG << " Point G";
|
||||
KALDI_LOG << " Point G";
|
||||
UnitTestFloorChol<Real>();
|
||||
UnitTestFloorUnit<Real>();
|
||||
UnitTestLimitCond<Real>();
|
||||
UnitTestMat2Vec<Real>();
|
||||
UnitTestSpLogExp<Real>();
|
||||
KALDI_LOG << " Point H";
|
||||
KALDI_LOG << " Point H";
|
||||
UnitTestCopyRowsAndCols<Real>();
|
||||
UnitTestSpliceRows<Real>();
|
||||
UnitTestAddSp<Real>();
|
||||
UnitTestRemoveRow<Real>();
|
||||
|
|
@ -2982,12 +3006,12 @@ template<class Real> static void MatrixUnitTest() {
|
|||
UnitTestSimpleForMat<Real>();
|
||||
UnitTestNorm<Real>();
|
||||
UnitTestMul<Real>();
|
||||
KALDI_LOG << " Point I";
|
||||
KALDI_LOG << " Point I";
|
||||
UnitTestSolve<Real>();
|
||||
UnitTestMaxMin<Real>();
|
||||
UnitTestInnerProd<Real>();
|
||||
UnitTestScaleDiag<Real>();
|
||||
KALDI_LOG << " Point J";
|
||||
KALDI_LOG << " Point J";
|
||||
UnitTestTraceSpSpLower<Real>();
|
||||
UnitTestTranspose<Real>();
|
||||
UnitTestAddVecToRows<Real>();
|
||||
|
|
|
|||
|
|
@ -72,7 +72,7 @@ class BiasedLinearity : public UpdatableComponent {
|
|||
// compute gradient
|
||||
linearity_corr_.AddMatMat(1.0, err, kTrans, input, kNoTrans, momentum_);
|
||||
bias_corr_.Scale(momentum_);
|
||||
bias_corr_.AddRowSumMat(err);
|
||||
bias_corr_.AddRowSumMat(1.0, err);
|
||||
// l2 regularization
|
||||
if (l2_penalty_ != 0.0) {
|
||||
linearity_.AddMat(-learn_rate_*l2_penalty_*input.NumRows(), linearity_);
|
||||
|
|
|
|||
|
|
@ -128,7 +128,7 @@ void Rnnlm::Backpropagate(const MatrixBase<BaseFloat> &in_err) {
|
|||
// update layer2
|
||||
W2_.AddMatMat(-learn_rate_, h2_, kTrans, in_err, kNoTrans, 1.0);
|
||||
b2_corr_.SetZero();
|
||||
b2_corr_.AddRowSumMat(in_err);
|
||||
b2_corr_.AddRowSumMat(1.0, in_err);
|
||||
b2_.AddVec(-learn_rate_, b2_corr_);
|
||||
|
||||
// LAYER1
|
||||
|
|
@ -136,7 +136,7 @@ void Rnnlm::Backpropagate(const MatrixBase<BaseFloat> &in_err) {
|
|||
U1_corr_.SetZero();
|
||||
b1_corr_.SetZero();
|
||||
// accumulate gradient for layer1
|
||||
b1_corr_.AddRowSumMat(e2_);
|
||||
b1_corr_.AddRowSumMat(1.0, e2_);
|
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for(int32 r=0; r<e2_.NumRows();r++) {
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V1_corr_.Row(in_seq_[r]-1).AddVec(1.0, e2_.Row(r));
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}
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@ -174,7 +174,7 @@ void Rnnlm::Backpropagate(const MatrixBase<BaseFloat> &in_err) {
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}
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// accumulate graidient
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b1_corr_.AddRowSumMat(E);
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b1_corr_.AddRowSumMat(1.0, E);
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for(int32 r=0; r<E.NumRows();r++) {
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// :TODO: IS IT CORRECT?
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// V1_corr_.Row(in_seq_[r+step]).AddVec(1.0,E.Row(r));
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|
|
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|
@ -273,8 +273,8 @@ double EbwAmSgmmUpdater::UpdateM(const MleAmSgmmAccs &num_accs,
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||||
Vector<double> num_count_vec(I), den_count_vec(I), impr_vec(I);
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for (int32 j = 0; j < num_accs.num_states_; j++) {
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num_count_vec.AddRowSumMat(num_accs.gamma_[j]);
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den_count_vec.AddRowSumMat(den_accs.gamma_[j]);
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num_count_vec.AddRowSumMat(1.0, num_accs.gamma_[j]);
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den_count_vec.AddRowSumMat(1.0, den_accs.gamma_[j]);
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}
|
||||
|
||||
for (int32 i = 0; i < I; i++) {
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||||
|
|
@ -363,8 +363,8 @@ double EbwAmSgmmUpdater::UpdateWParallel(const MleAmSgmmAccs &num_accs,
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|
||||
Vector<double> num_count_vec(I), den_count_vec(I), impr_vec(I);
|
||||
for (int32 j = 0; j < num_accs.num_states_; j++) {
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||||
num_count_vec.AddRowSumMat(num_accs.gamma_[j]);
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||||
den_count_vec.AddRowSumMat(den_accs.gamma_[j]);
|
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num_count_vec.AddRowSumMat(1.0, num_accs.gamma_[j]);
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||||
den_count_vec.AddRowSumMat(1.0, den_accs.gamma_[j]);
|
||||
}
|
||||
|
||||
// Get the F_i and g_i quantities-- this is done in parallel (multi-core),
|
||||
|
|
@ -448,8 +448,8 @@ double EbwAmSgmmUpdater::UpdateN(const MleAmSgmmAccs &num_accs,
|
|||
|
||||
Vector<double> num_count_vec(I), den_count_vec(I), impr_vec(I);
|
||||
for (int32 j = 0; j < num_accs.num_states_; j++) {
|
||||
num_count_vec.AddRowSumMat(num_accs.gamma_[j]);
|
||||
den_count_vec.AddRowSumMat(den_accs.gamma_[j]);
|
||||
num_count_vec.AddRowSumMat(1.0, num_accs.gamma_[j]);
|
||||
den_count_vec.AddRowSumMat(1.0, den_accs.gamma_[j]);
|
||||
}
|
||||
|
||||
for (int32 i = 0; i < I; i++) {
|
||||
|
|
@ -522,8 +522,8 @@ double EbwAmSgmmUpdater::UpdateVars(const MleAmSgmmAccs &num_accs,
|
|||
|
||||
Vector<double> num_count_vec(I), den_count_vec(I), impr_vec(I);
|
||||
for (int32 j = 0; j < num_accs.num_states_; j++) {
|
||||
num_count_vec.AddRowSumMat(num_accs.gamma_[j]);
|
||||
den_count_vec.AddRowSumMat(den_accs.gamma_[j]);
|
||||
num_count_vec.AddRowSumMat(1.0, num_accs.gamma_[j]);
|
||||
den_count_vec.AddRowSumMat(1.0, den_accs.gamma_[j]);
|
||||
}
|
||||
|
||||
for (int32 i = 0; i < I; i++) {
|
||||
|
|
|
|||
|
|
@ -538,7 +538,7 @@ BaseFloat AmSgmm2::LogLikelihood(const Sgmm2PerFrameDerivedVars &per_frame_vars,
|
|||
substate_cache.remaining_log_like = max;
|
||||
int32 num_substates = loglikes.NumCols();
|
||||
substate_cache.likes.Resize(num_substates); // zeroes it.
|
||||
substate_cache.likes.AddRowSumMat(loglikes); // add likelihoods [not in log!] for
|
||||
substate_cache.likes.AddRowSumMat(1.0, loglikes); // add likelihoods [not in log!] for
|
||||
// each column [i.e. summing over the rows], so we get the sum for
|
||||
// each substate index. You have to multiply by exp(remaining_log_like)
|
||||
// to get a real likelihood.
|
||||
|
|
@ -613,7 +613,7 @@ void AmSgmm2::SplitSubstatesInGroup(const Vector<BaseFloat> &pdf_occupancies,
|
|||
int32 split_m; // substate to split.
|
||||
{
|
||||
Vector<BaseFloat> substate_count(tgt_M);
|
||||
substate_count.AddRowSumMat(c_j);
|
||||
substate_count.AddRowSumMat(1.0, c_j);
|
||||
BaseFloat *data = substate_count.Data();
|
||||
split_m = std::max_element(data, data+cur_M) - data;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -50,10 +50,10 @@ void EbwAmSgmm2Updater::Update(const MleAmSgmm2Accs &num_accs,
|
|||
|
||||
Vector<double> gamma_num(num_accs.num_gaussians_);
|
||||
for (int32 j1 = 0; j1 < num_accs.num_groups_; j1++)
|
||||
gamma_num.AddRowSumMat(num_accs.gamma_[j1]);
|
||||
gamma_num.AddRowSumMat(1.0, num_accs.gamma_[j1]);
|
||||
Vector<double> gamma_den(den_accs.num_gaussians_);
|
||||
for (int32 j1 = 0; j1 < den_accs.num_groups_; j1++)
|
||||
gamma_den.AddRowSumMat(den_accs.gamma_[j1]);
|
||||
gamma_den.AddRowSumMat(1.0, den_accs.gamma_[j1]);
|
||||
|
||||
BaseFloat tot_impr = 0.0;
|
||||
|
||||
|
|
|
|||
|
|
@ -617,7 +617,7 @@ void MleAmSgmm2Updater::Update(const MleAmSgmm2Accs &accs,
|
|||
|
||||
Vector<double> gamma_i(accs.num_gaussians_);
|
||||
for (int32 j1 = 0; j1 < accs.num_groups_; j1++)
|
||||
gamma_i.AddRowSumMat(accs.gamma_[j1]); // add sum of rows of
|
||||
gamma_i.AddRowSumMat(1.0, accs.gamma_[j1]); // add sum of rows of
|
||||
// accs.gamma_[j1], to gamma_i.
|
||||
|
||||
if (flags & kSgmmPhoneProjections)
|
||||
|
|
|
|||
|
|
@ -63,7 +63,7 @@ void LdaEstimate::Estimate(int32 target_dim,
|
|||
// total covariance
|
||||
double sum = zero_acc_.Sum();
|
||||
Vector<double> total_mean(dim);
|
||||
total_mean.AddRowSumMat(first_acc_);
|
||||
total_mean.AddRowSumMat(1.0, first_acc_);
|
||||
total_mean.Scale(1/sum);
|
||||
SpMatrix<double> total_covar(total_second_acc_);
|
||||
total_covar.Scale(1/sum);
|
||||
|
|
|
|||
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