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src/ivector/logistic-regression.h
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// ivector/logistic-regression.h
// Copyright 2014 David Snyder
// See ../../COPYING for clarification regarding multiple authors
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.
#ifndef KALDI_IVECTOR_LOGISTIC_REGRESSION_H_
#define KALDI_IVECTOR_LOGISTIC_REGRESSION_H_
#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "matrix/matrix-lib.h"
#include <numeric>
namespace kaldi {
struct LogisticRegressionConfig {
int32 max_steps,
mix_up;
double normalizer,
power;
LogisticRegressionConfig(): max_steps(20), mix_up(0),
normalizer(0.0025), power(0.15){ }
void Register(OptionsItf *opts) {
opts->Register("max-steps", &max_steps,
"Maximum steps in L-BFGS.");
opts->Register("normalizer", &normalizer,
"Coefficient for L2 regularization.");
opts->Register("mix-up", &mix_up,
"Target number of mixture components to create, "
"if supplied.");
opts->Register("power", &power,
"Power rule for determining the number of mixtures "
"to create.");
}
};
class LogisticRegression {
public:
// xs and ys are the training data. Each row of xs is a vector
// corresponding to the class label in the same row of ys.
void Train(const Matrix<BaseFloat> &xs, const std::vector<int32> &ys,
const LogisticRegressionConfig &conf);
// Calculates the log posterior of the class label given the input xs.
// The rows of log_posteriors corresponds to the rows of xs: the
// individual data points to be evaluated. The columns of
// log_posteriors are the integer class labels.
void GetLogPosteriors(const Matrix<BaseFloat> &xs,
Matrix<BaseFloat> *log_posteriors);
// Calculates the log posterior of the class label given the input x.
// The indices of log_posteriors are the class labels.
void GetLogPosteriors(const Vector<BaseFloat> &x,
Vector<BaseFloat> *log_posteriors);
void Write(std::ostream &os, bool binary) const;
void Read(std::istream &is, bool binary);
void ScalePriors(const Vector<BaseFloat> &prior_scales);
protected:
void friend UnitTestTrain();
void friend UnitTestPosteriors();
private:
// Performs a step in the L-BFGS. This is mostly used internally
// By Train() and for testing.
BaseFloat DoStep(const Matrix<BaseFloat> &xs,
Matrix<BaseFloat> *xw, const std::vector<int32> &ys,
OptimizeLbfgs<BaseFloat> *lbfgs,
BaseFloat normalizer);
void TrainParameters(const Matrix<BaseFloat> &xs,
const std::vector<int32> &ys,
const LogisticRegressionConfig &conf,
Matrix<BaseFloat> *xw);
// Creates the mixture components. Uses conf.mix_up, conf.power,
// the occupancy of ys and GetSplitTargets() to determin the number
// of mixture components for each weight index.
void MixUp(const std::vector<int32> &ys, const int32 &num_classes,
const LogisticRegressionConfig &conf);
// Returns the objective function given the training data, xs, ys.
// The gradient is also calculated, and returned in grad. Uses
// L2 regularization.
BaseFloat GetObjfAndGrad(const Matrix<BaseFloat> &xs,
const std::vector<int32> &ys,
const Matrix<BaseFloat> &xw,
Matrix<BaseFloat> *grad,
BaseFloat normalizer);
// Sets the weights and class map. This is generally used for testing.
void SetWeights(const Matrix<BaseFloat> &weights,
const std::vector<int32> classes);
// Before mixture components or added, or if mix_up <= num_classes
// each row of weights_ corresponds to a class label.
// If mix_up > num_classes and after MixUp() is called the rows
// correspond to the mixture components. In either case each column
// corresponds to a feature in the input vectors (and the last column
// is an offset).
Matrix<BaseFloat> weights_;
// Maps from the row of weights_ to the class. Normally the
// identity mapping, but may not be for multi-mixture logistic
// regression.
std::vector<int32> class_;
};
}
#endif
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