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LdaEstimate Class Reference

Class for computing linear discriminant analysis (LDA) transform. More...

#include <lda-estimate.h>

Inheritance diagram for LdaEstimate:
Collaboration diagram for LdaEstimate:

Public Member Functions

 LdaEstimate ()
 
void Init (int32 num_classes, int32 dimension)
 Allocates memory for accumulators. More...
 
int32 NumClasses () const
 Returns the number of classes. More...
 
int32 Dim () const
 Returns the dimensionality of the feature vectors. More...
 
void ZeroAccumulators ()
 Sets all accumulators to zero. More...
 
void Scale (BaseFloat f)
 Scales all accumulators. More...
 
double TotCount ()
 Return total count of the data. More...
 
void Accumulate (const VectorBase< BaseFloat > &data, int32 class_id, BaseFloat weight=1.0)
 Accumulates data. More...
 
void Estimate (const LdaEstimateOptions &opts, Matrix< BaseFloat > *M, Matrix< BaseFloat > *Mfull=NULL) const
 Estimates the LDA transform matrix m. More...
 
void Read (std::istream &in_stream, bool binary, bool add)
 
void Write (std::ostream &out_stream, bool binary) const
 

Protected Member Functions

void GetStats (SpMatrix< double > *total_covar, SpMatrix< double > *between_covar, Vector< double > *total_mean, double *sum) const
 Extract a more processed form of the stats. More...
 
LdaEstimateoperator= (const LdaEstimate &other)
 

Static Protected Member Functions

static void AddMeanOffset (const VectorBase< double > &total_mean, Matrix< BaseFloat > *projection)
 This function modifies the LDA matrix so that it also subtracts the mean feature value. More...
 

Protected Attributes

Vector< double > zero_acc_
 
Matrix< double > first_acc_
 
SpMatrix< double > total_second_acc_
 

Detailed Description

Class for computing linear discriminant analysis (LDA) transform.

C.f. Linear Discriminant Analysis (LDA) transforms.

Definition at line 57 of file lda-estimate.h.

Constructor & Destructor Documentation

LdaEstimate ( )
inline

Definition at line 59 of file lda-estimate.h.

59 {}

Member Function Documentation

void Accumulate ( const VectorBase< BaseFloat > &  data,
int32  class_id,
BaseFloat  weight = 1.0 
)

Accumulates data.

Definition at line 45 of file lda-estimate.cc.

References SpMatrix< Real >::AddVec2(), VectorBase< Real >::Dim(), LdaEstimate::Dim(), LdaEstimate::first_acc_, KALDI_ASSERT, LdaEstimate::NumClasses(), MatrixBase< Real >::Row(), LdaEstimate::total_second_acc_, and LdaEstimate::zero_acc_.

Referenced by NnetLdaStatsAccumulator::AccStatsFromOutput(), main(), and UnitTestEstimateLda().

46  {
47  KALDI_ASSERT(class_id >= 0);
48  KALDI_ASSERT(class_id < NumClasses() && data.Dim() == Dim());
49 
50  Vector<double> data_d(data);
51 
52  zero_acc_(class_id) += weight;
53  first_acc_.Row(class_id).AddVec(weight, data_d);
54  total_second_acc_.AddVec2(weight, data_d);
55 }
int32 NumClasses() const
Returns the number of classes.
Definition: lda-estimate.h:64
Matrix< double > first_acc_
Definition: lda-estimate.h:94
Vector< double > zero_acc_
Definition: lda-estimate.h:93
const SubVector< Real > Row(MatrixIndexT i) const
Return specific row of matrix [const].
Definition: kaldi-matrix.h:182
void AddVec2(const Real alpha, const VectorBase< OtherReal > &v)
rank-one update, this <– this + alpha v v'
Definition: sp-matrix.cc:946
#define KALDI_ASSERT(cond)
Definition: kaldi-error.h:169
SpMatrix< double > total_second_acc_
Definition: lda-estimate.h:95
int32 Dim() const
Returns the dimensionality of the feature vectors.
Definition: lda-estimate.h:66
MatrixIndexT Dim() const
Returns the dimension of the vector.
Definition: kaldi-vector.h:62
void AddMeanOffset ( const VectorBase< double > &  total_mean,
Matrix< BaseFloat > *  projection 
)
staticprotected

This function modifies the LDA matrix so that it also subtracts the mean feature value.

Definition at line 166 of file lda-estimate.cc.

References VectorBase< Real >::AddMatVec(), MatrixBase< Real >::CopyColFromVec(), kaldi::kCopyData, kaldi::kNoTrans, MatrixBase< Real >::NumCols(), MatrixBase< Real >::NumRows(), and Matrix< Real >::Resize().

Referenced by LdaEstimate::Estimate(), and FeatureTransformEstimate::EstimateInternal().

167  {
168  Vector<BaseFloat> mean(mean_dbl);
169  Vector<BaseFloat> neg_projected_mean(projection->NumRows());
170  // the negative
171  neg_projected_mean.AddMatVec(-1.0, *projection, kNoTrans, mean, 0.0);
172  projection->Resize(projection->NumRows(),
173  projection->NumCols() + 1,
174  kCopyData);
175  projection->CopyColFromVec(neg_projected_mean, projection->NumCols() - 1);
176 }
void CopyColFromVec(const VectorBase< Real > &v, const MatrixIndexT col)
Copy vector into specific column of matrix.
MatrixIndexT NumRows() const
Returns number of rows (or zero for emtpy matrix).
Definition: kaldi-matrix.h:58
MatrixIndexT NumCols() const
Returns number of columns (or zero for emtpy matrix).
Definition: kaldi-matrix.h:61
void Resize(const MatrixIndexT r, const MatrixIndexT c, MatrixResizeType resize_type=kSetZero, MatrixStrideType stride_type=kDefaultStride)
Sets matrix to a specified size (zero is OK as long as both r and c are zero).
int32 Dim ( ) const
inline

Returns the dimensionality of the feature vectors.

Definition at line 66 of file lda-estimate.h.

References LdaEstimate::first_acc_, and MatrixBase< Real >::NumCols().

Referenced by NnetLdaStatsAccumulator::AccStatsFromOutput(), LdaEstimate::Accumulate(), LdaEstimate::Estimate(), FeatureTransformEstimateMulti::Estimate(), FeatureTransformEstimateMulti::EstimateTransformPart(), LdaEstimate::GetStats(), main(), LdaEstimate::Read(), test_io(), and LdaEstimate::Write().

66 { return first_acc_.NumCols(); }
Matrix< double > first_acc_
Definition: lda-estimate.h:94
MatrixIndexT NumCols() const
Returns number of columns (or zero for emtpy matrix).
Definition: kaldi-matrix.h:61
void Estimate ( const LdaEstimateOptions opts,
Matrix< BaseFloat > *  M,
Matrix< BaseFloat > *  Mfull = NULL 
) const

Estimates the LDA transform matrix m.

If Mfull != NULL, it also outputs the full matrix (without dimensionality reduction), which is useful for some purposes. If opts.remove_offset == true, it will output both matrices with an extra column which corresponds to mean-offset removal (the matrix should be multiplied by the feature with a 1 appended to give the correct result, as with other Kaldi transforms.) The "remove_offset" argument is new and should be set to false for back compatibility.

Definition at line 85 of file lda-estimate.cc.

References SpMatrix< Real >::AddMat2Sp(), MatrixBase< Real >::AddMatMat(), LdaEstimate::AddMeanOffset(), SpMatrix< Real >::AddSp(), LdaEstimateOptions::allow_large_dim, TpMatrix< Real >::Cholesky(), MatrixBase< Real >::CopyFromMat(), count, LdaEstimateOptions::dim, LdaEstimate::Dim(), LdaEstimate::GetStats(), rnnlm::i, MatrixBase< Real >::Invert(), KALDI_ASSERT, KALDI_LOG, kaldi::kNoTrans, kaldi::kTrans, LdaEstimate::NumClasses(), PackedMatrix< Real >::NumRows(), MatrixBase< Real >::Range(), LdaEstimateOptions::remove_offset, Matrix< Real >::Resize(), MatrixBase< Real >::Row(), kaldi::SortSvd(), MatrixBase< Real >::Svd(), SpMatrix< Real >::Trace(), and LdaEstimateOptions::within_class_factor.

Referenced by main(), test_io(), and UnitTestEstimateLda().

87  {
88  int32 target_dim = opts.dim;
89  KALDI_ASSERT(target_dim > 0);
90  // between-class covar is of most rank C-1
91  KALDI_ASSERT(target_dim <= Dim() && (target_dim < NumClasses() || opts.allow_large_dim));
92  int32 dim = Dim();
93 
94  double count;
95  SpMatrix<double> total_covar, bc_covar;
96  Vector<double> total_mean;
97  GetStats(&total_covar, &bc_covar, &total_mean, &count);
98 
99  // within-class covariance
100  SpMatrix<double> wc_covar(total_covar);
101  wc_covar.AddSp(-1.0, bc_covar);
102  TpMatrix<double> wc_covar_sqrt(dim);
103  try {
104  wc_covar_sqrt.Cholesky(wc_covar);
105  } catch (...) {
106  BaseFloat smooth = 1.0e-03 * wc_covar.Trace() / wc_covar.NumRows();
107  KALDI_LOG << "Cholesky failed (possibly not +ve definite), so adding " << smooth
108  << " to diagonal and trying again.\n";
109  for (int32 i = 0; i < wc_covar.NumRows(); i++)
110  wc_covar(i, i) += smooth;
111  wc_covar_sqrt.Cholesky(wc_covar);
112  }
113  Matrix<double> wc_covar_sqrt_mat(wc_covar_sqrt);
114  // copy wc_covar_sqrt to Matrix, because it facilitates further use
115  wc_covar_sqrt_mat.Invert();
116 
117  SpMatrix<double> tmp_sp(dim);
118  tmp_sp.AddMat2Sp(1.0, wc_covar_sqrt_mat, kNoTrans, bc_covar, 0.0);
119  Matrix<double> tmp_mat(tmp_sp);
120 
121  Matrix<double> svd_u(dim, dim), svd_vt(dim, dim);
122  Vector<double> svd_d(dim);
123  tmp_mat.Svd(&svd_d, &svd_u, &svd_vt);
124  SortSvd(&svd_d, &svd_u);
125 
126  KALDI_LOG << "Data count is " << count;
127  KALDI_LOG << "LDA singular values are " << svd_d;
128 
129  KALDI_LOG << "Sum of all singular values is " << svd_d.Sum();
130  KALDI_LOG << "Sum of selected singular values is " <<
131  SubVector<double>(svd_d, 0, target_dim).Sum();
132 
133  Matrix<double> lda_mat(dim, dim);
134  lda_mat.AddMatMat(1.0, svd_u, kTrans, wc_covar_sqrt_mat, kNoTrans, 0.0);
135 
136  // finally, copy first target_dim rows to m
137  m->Resize(target_dim, dim);
138  m->CopyFromMat(lda_mat.Range(0, target_dim, 0, dim));
139 
140  if (mfull != NULL) {
141  mfull->Resize(dim, dim);
142  mfull->CopyFromMat(lda_mat);
143  }
144 
145  if (opts.within_class_factor != 1.0) { // This is not the normal code path;
146  // it's intended for use in neural net inputs.
147  for (int32 i = 0; i < svd_d.Dim(); i++) {
148  BaseFloat old_var = 1.0 + svd_d(i), // the total variance of that dim..
149  new_var = opts.within_class_factor + svd_d(i), // the variance we want..
150  scale = sqrt(new_var / old_var);
151  if (i < m->NumRows())
152  m->Row(i).Scale(scale);
153  if (mfull != NULL)
154  mfull->Row(i).Scale(scale);
155  }
156  }
157 
158  if (opts.remove_offset) {
159  AddMeanOffset(total_mean, m);
160  if (mfull != NULL)
161  AddMeanOffset(total_mean, mfull);
162  }
163 }
int32 NumClasses() const
Returns the number of classes.
Definition: lda-estimate.h:64
const size_t count
float BaseFloat
Definition: kaldi-types.h:29
void GetStats(SpMatrix< double > *total_covar, SpMatrix< double > *between_covar, Vector< double > *total_mean, double *sum) const
Extract a more processed form of the stats.
Definition: lda-estimate.cc:57
static void AddMeanOffset(const VectorBase< double > &total_mean, Matrix< BaseFloat > *projection)
This function modifies the LDA matrix so that it also subtracts the mean feature value.
#define KALDI_ASSERT(cond)
Definition: kaldi-error.h:169
int32 Dim() const
Returns the dimensionality of the feature vectors.
Definition: lda-estimate.h:66
#define KALDI_LOG
Definition: kaldi-error.h:133
void SortSvd(VectorBase< Real > *s, MatrixBase< Real > *U, MatrixBase< Real > *Vt, bool sort_on_absolute_value)
Function to ensure that SVD is sorted.
void GetStats ( SpMatrix< double > *  total_covar,
SpMatrix< double > *  between_covar,
Vector< double > *  total_mean,
double *  sum 
) const
protected

Extract a more processed form of the stats.

Definition at line 57 of file lda-estimate.cc.

References VectorBase< Real >::AddRowSumMat(), SpMatrix< Real >::AddVec2(), SpMatrix< Real >::CopyFromSp(), VectorBase< Real >::CopyRowFromMat(), LdaEstimate::Dim(), LdaEstimate::first_acc_, LdaEstimate::NumClasses(), SpMatrix< Real >::Resize(), Vector< Real >::Resize(), PackedMatrix< Real >::Scale(), VectorBase< Real >::Scale(), VectorBase< Real >::Sum(), LdaEstimate::total_second_acc_, and LdaEstimate::zero_acc_.

Referenced by LdaEstimate::Estimate(), FeatureTransformEstimate::Estimate(), and FeatureTransformEstimateMulti::Estimate().

60  {
61  int32 num_class = NumClasses(), dim = Dim();
62  double sum = zero_acc_.Sum();
63  *tot_count = sum;
64  total_covar->Resize(dim);
65  total_covar->CopyFromSp(total_second_acc_);
66  total_mean->Resize(dim);
67  total_mean->AddRowSumMat(1.0, first_acc_);
68  total_mean->Scale(1.0 / sum);
69  total_covar->Scale(1.0 / sum);
70  total_covar->AddVec2(-1.0, *total_mean);
71 
72  between_covar->Resize(dim);
73  Vector<double> class_mean(dim);
74  for (int32 c = 0; c < num_class; c++) {
75  if (zero_acc_(c) != 0.0) {
76  class_mean.CopyRowFromMat(first_acc_, c);
77  class_mean.Scale(1.0 / zero_acc_(c));
78  between_covar->AddVec2(zero_acc_(c) / sum, class_mean);
79  }
80  }
81  between_covar->AddVec2(-1.0, *total_mean);
82 }
void AddRowSumMat(Real alpha, const MatrixBase< Real > &M, Real beta=1.0)
Does *this = alpha * (sum of rows of M) + beta * *this.
int32 NumClasses() const
Returns the number of classes.
Definition: lda-estimate.h:64
Matrix< double > first_acc_
Definition: lda-estimate.h:94
void Scale(Real c)
Real Sum() const
Returns sum of the elements.
void Resize(MatrixIndexT length, MatrixResizeType resize_type=kSetZero)
Set vector to a specified size (can be zero).
Vector< double > zero_acc_
Definition: lda-estimate.h:93
void CopyFromSp(const SpMatrix< Real > &other)
Definition: sp-matrix.h:85
void Resize(MatrixIndexT nRows, MatrixResizeType resize_type=kSetZero)
Definition: sp-matrix.h:81
void Scale(Real alpha)
Multiplies all elements by this constant.
void AddVec2(const Real alpha, const VectorBase< OtherReal > &v)
rank-one update, this <– this + alpha v v'
Definition: sp-matrix.cc:946
SpMatrix< double > total_second_acc_
Definition: lda-estimate.h:95
int32 Dim() const
Returns the dimensionality of the feature vectors.
Definition: lda-estimate.h:66
void Init ( int32  num_classes,
int32  dimension 
)

Allocates memory for accumulators.

Definition at line 26 of file lda-estimate.cc.

References LdaEstimate::first_acc_, SpMatrix< Real >::Resize(), Vector< Real >::Resize(), Matrix< Real >::Resize(), LdaEstimate::total_second_acc_, and LdaEstimate::zero_acc_.

Referenced by NnetLdaStatsAccumulator::AccStatsFromOutput(), main(), LdaEstimate::Read(), test_io(), and UnitTestEstimateLda().

26  {
27  zero_acc_.Resize(num_classes);
28  first_acc_.Resize(num_classes, dimension);
29  total_second_acc_.Resize(dimension);
30 }
Matrix< double > first_acc_
Definition: lda-estimate.h:94
void Resize(MatrixIndexT length, MatrixResizeType resize_type=kSetZero)
Set vector to a specified size (can be zero).
Vector< double > zero_acc_
Definition: lda-estimate.h:93
void Resize(MatrixIndexT nRows, MatrixResizeType resize_type=kSetZero)
Definition: sp-matrix.h:81
SpMatrix< double > total_second_acc_
Definition: lda-estimate.h:95
void Resize(const MatrixIndexT r, const MatrixIndexT c, MatrixResizeType resize_type=kSetZero, MatrixStrideType stride_type=kDefaultStride)
Sets matrix to a specified size (zero is OK as long as both r and c are zero).
int32 NumClasses ( ) const
inline

Returns the number of classes.

Definition at line 64 of file lda-estimate.h.

References LdaEstimate::first_acc_, and MatrixBase< Real >::NumRows().

Referenced by LdaEstimate::Accumulate(), LdaEstimate::Estimate(), LdaEstimate::GetStats(), LdaEstimate::Read(), test_io(), and LdaEstimate::Write().

64 { return first_acc_.NumRows(); }
Matrix< double > first_acc_
Definition: lda-estimate.h:94
MatrixIndexT NumRows() const
Returns number of rows (or zero for emtpy matrix).
Definition: kaldi-matrix.h:58
LdaEstimate& operator= ( const LdaEstimate other)
protected
void Read ( std::istream &  in_stream,
bool  binary,
bool  add 
)

Definition at line 180 of file lda-estimate.cc.

References MatrixBase< Real >::AddMat(), SpMatrix< Real >::AddSp(), VectorBase< Real >::AddVec(), SpMatrix< Real >::AddVec2(), LdaEstimate::Dim(), kaldi::ExpectToken(), LdaEstimate::first_acc_, LdaEstimate::Init(), KALDI_ERR, LdaEstimate::NumClasses(), PackedMatrix< Real >::Read(), Vector< Real >::Read(), Matrix< Real >::Read(), kaldi::ReadBasicType(), kaldi::ReadToken(), MatrixBase< Real >::Row(), VectorBase< Real >::SetZero(), PackedMatrix< Real >::SetZero(), MatrixBase< Real >::SetZero(), LdaEstimate::total_second_acc_, and LdaEstimate::zero_acc_.

Referenced by main(), and test_io().

180  {
181  int32 num_classes, dim;
182  std::string token;
183 
184  ExpectToken(in_stream, binary, "<LDAACCS>");
185  ExpectToken(in_stream, binary, "<VECSIZE>");
186  ReadBasicType(in_stream, binary, &dim);
187  ExpectToken(in_stream, binary, "<NUMCLASSES>");
188  ReadBasicType(in_stream, binary, &num_classes);
189 
190  if (add) {
191  if (NumClasses() != 0 || Dim() != 0) {
192  if (num_classes != NumClasses() || dim != Dim()) {
193  KALDI_ERR <<"LdaEstimate::Read, dimension or classes count mismatch, "
194  <<(NumClasses()) << ", " <<(Dim()) << ", "
195  << " vs. " <<(num_classes) << ", " << (dim);
196  }
197  } else {
198  Init(num_classes, dim);
199  }
200  } else {
201  Init(num_classes, dim);
202  }
203 
204  // these are needed for demangling the variances.
205  Vector<double> tmp_zero_acc;
206  Matrix<double> tmp_first_acc;
207  SpMatrix<double> tmp_sec_acc;
208 
209  ReadToken(in_stream, binary, &token);
210  while (token != "</LDAACCS>") {
211  if (token == "<ZERO_ACCS>") {
212  tmp_zero_acc.Read(in_stream, binary, false);
213  if (!add) zero_acc_.SetZero();
214  zero_acc_.AddVec(1.0, tmp_zero_acc);
215  // zero_acc_.Read(in_stream, binary, add);
216  } else if (token == "<FIRST_ACCS>") {
217  tmp_first_acc.Read(in_stream, binary, false);
218  if (!add) first_acc_.SetZero();
219  first_acc_.AddMat(1.0, tmp_first_acc);
220  // first_acc_.Read(in_stream, binary, add);
221  } else if (token == "<SECOND_ACCS>") {
222  tmp_sec_acc.Read(in_stream, binary, false);
223  for (int32 c = 0; c < static_cast<int32>(NumClasses()); c++) {
224  if (tmp_zero_acc(c) != 0)
225  tmp_sec_acc.AddVec2(1.0 / tmp_zero_acc(c), tmp_first_acc.Row(c));
226  }
227  if (!add) total_second_acc_.SetZero();
228  total_second_acc_.AddSp(1.0, tmp_sec_acc);
229  // total_second_acc_.Read(in_stream, binary, add);
230  } else {
231  KALDI_ERR << "Unexpected token '" << token << "' in file ";
232  }
233  ReadToken(in_stream, binary, &token);
234  }
235 }
void AddSp(const Real alpha, const SpMatrix< Real > &Ma)
Definition: sp-matrix.h:211
int32 NumClasses() const
Returns the number of classes.
Definition: lda-estimate.h:64
void ReadBasicType(std::istream &is, bool binary, T *t)
ReadBasicType is the name of the read function for bool, integer types, and floating-point types...
Definition: io-funcs-inl.h:55
Matrix< double > first_acc_
Definition: lda-estimate.h:94
void AddMat(const Real alpha, const MatrixBase< Real > &M, MatrixTransposeType transA=kNoTrans)
*this += alpha * M [or M^T]
void ReadToken(std::istream &is, bool binary, std::string *str)
ReadToken gets the next token and puts it in str (exception on failure).
Definition: io-funcs.cc:154
void Init(int32 num_classes, int32 dimension)
Allocates memory for accumulators.
Definition: lda-estimate.cc:26
Vector< double > zero_acc_
Definition: lda-estimate.h:93
void ExpectToken(std::istream &is, bool binary, const char *token)
ExpectToken tries to read in the given token, and throws an exception on failure. ...
Definition: io-funcs.cc:188
#define KALDI_ERR
Definition: kaldi-error.h:127
void SetZero()
Sets matrix to zero.
SpMatrix< double > total_second_acc_
Definition: lda-estimate.h:95
int32 Dim() const
Returns the dimensionality of the feature vectors.
Definition: lda-estimate.h:66
void SetZero()
Set vector to all zeros.
void AddVec(const Real alpha, const VectorBase< OtherReal > &v)
Add vector : *this = *this + alpha * rv (with casting between floats and doubles) ...
void Scale ( BaseFloat  f)

Scales all accumulators.

Definition at line 38 of file lda-estimate.cc.

References rnnlm::d, LdaEstimate::first_acc_, PackedMatrix< Real >::Scale(), MatrixBase< Real >::Scale(), VectorBase< Real >::Scale(), LdaEstimate::total_second_acc_, and LdaEstimate::zero_acc_.

Referenced by test_io().

38  {
39  double d = static_cast<double>(f);
40  zero_acc_.Scale(d);
41  first_acc_.Scale(d);
43 }
Matrix< double > first_acc_
Definition: lda-estimate.h:94
void Scale(Real c)
Vector< double > zero_acc_
Definition: lda-estimate.h:93
void Scale(Real alpha)
Multiply each element with a scalar value.
void Scale(Real alpha)
Multiplies all elements by this constant.
SpMatrix< double > total_second_acc_
Definition: lda-estimate.h:95
double TotCount ( )
inline

Return total count of the data.

Definition at line 72 of file lda-estimate.h.

References VectorBase< Real >::Sum(), and LdaEstimate::zero_acc_.

Referenced by NnetLdaStatsAccumulator::WriteStats().

72 { return zero_acc_.Sum(); }
Real Sum() const
Returns sum of the elements.
Vector< double > zero_acc_
Definition: lda-estimate.h:93
void Write ( std::ostream &  out_stream,
bool  binary 
) const

Definition at line 237 of file lda-estimate.cc.

References SpMatrix< Real >::AddVec2(), LdaEstimate::Dim(), LdaEstimate::first_acc_, LdaEstimate::NumClasses(), MatrixBase< Real >::Row(), LdaEstimate::total_second_acc_, PackedMatrix< Real >::Write(), VectorBase< Real >::Write(), MatrixBase< Real >::Write(), kaldi::WriteBasicType(), kaldi::WriteToken(), and LdaEstimate::zero_acc_.

Referenced by main(), and test_io().

237  {
238  WriteToken(out_stream, binary, "<LDAACCS>");
239  WriteToken(out_stream, binary, "<VECSIZE>");
240  WriteBasicType(out_stream, binary, static_cast<int32>(Dim()));
241  WriteToken(out_stream, binary, "<NUMCLASSES>");
242  WriteBasicType(out_stream, binary, static_cast<int32>(NumClasses()));
243 
244  WriteToken(out_stream, binary, "<ZERO_ACCS>");
245  Vector<BaseFloat> zero_acc_bf(zero_acc_);
246  zero_acc_bf.Write(out_stream, binary);
247  WriteToken(out_stream, binary, "<FIRST_ACCS>");
248  Matrix<BaseFloat> first_acc_bf(first_acc_);
249  first_acc_bf.Write(out_stream, binary);
250  WriteToken(out_stream, binary, "<SECOND_ACCS>");
251  SpMatrix<double> tmp_sec_acc(total_second_acc_);
252  for (int32 c = 0; c < static_cast<int32>(NumClasses()); c++) {
253  if (zero_acc_(c) != 0)
254  tmp_sec_acc.AddVec2(-1.0 / zero_acc_(c), first_acc_.Row(c));
255  }
256  SpMatrix<BaseFloat> tmp_sec_acc_bf(tmp_sec_acc);
257  tmp_sec_acc_bf.Write(out_stream, binary);
258 
259  WriteToken(out_stream, binary, "</LDAACCS>");
260 }
int32 NumClasses() const
Returns the number of classes.
Definition: lda-estimate.h:64
Matrix< double > first_acc_
Definition: lda-estimate.h:94
Vector< double > zero_acc_
Definition: lda-estimate.h:93
const SubVector< Real > Row(MatrixIndexT i) const
Return specific row of matrix [const].
Definition: kaldi-matrix.h:182
void WriteToken(std::ostream &os, bool binary, const char *token)
The WriteToken functions are for writing nonempty sequences of non-space characters.
Definition: io-funcs.cc:134
SpMatrix< double > total_second_acc_
Definition: lda-estimate.h:95
void WriteBasicType(std::ostream &os, bool binary, T t)
WriteBasicType is the name of the write function for bool, integer types, and floating-point types...
Definition: io-funcs-inl.h:34
int32 Dim() const
Returns the dimensionality of the feature vectors.
Definition: lda-estimate.h:66
void ZeroAccumulators ( )

Sets all accumulators to zero.

Definition at line 32 of file lda-estimate.cc.

References LdaEstimate::first_acc_, VectorBase< Real >::SetZero(), PackedMatrix< Real >::SetZero(), MatrixBase< Real >::SetZero(), LdaEstimate::total_second_acc_, and LdaEstimate::zero_acc_.

Referenced by UnitTestEstimateLda().

32  {
36 }
Matrix< double > first_acc_
Definition: lda-estimate.h:94
Vector< double > zero_acc_
Definition: lda-estimate.h:93
void SetZero()
Sets matrix to zero.
SpMatrix< double > total_second_acc_
Definition: lda-estimate.h:95
void SetZero()
Set vector to all zeros.

Member Data Documentation


The documentation for this class was generated from the following files: