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src/ivector/logistic-regression-test.cc
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// ivector/logistic-regression-test.cc
// 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.
#include <time.h>
#include "ivector/logistic-regression.h"
namespace kaldi {
void UnitTestPosteriors() {
int32 n_features = Rand() % 600 + 10,
n_xs = Rand() % 200 + 100,
n_labels = Rand() % 20 + 10;
LogisticRegressionConfig conf;
conf.max_steps = 20;
conf.normalizer = 0.001;
Matrix<BaseFloat> xs(n_xs, n_features);
xs.SetRandn();
Matrix<BaseFloat> weights(n_labels, n_features + 1);
weights.SetRandn();
LogisticRegression classifier = LogisticRegression();
std::vector<int32> classes;
for (int32 i = 0; i < weights.NumRows(); i++) {
classes.push_back(i);
}
classifier.SetWeights(weights, classes);
// Get posteriors for the xs using batch and serial methods.
Matrix<BaseFloat> batch_log_posteriors;
classifier.GetLogPosteriors(xs, &batch_log_posteriors);
Matrix<BaseFloat> log_posteriors(n_xs, n_labels);
for (int32 i = 0; i < n_xs; i++) {
Vector<BaseFloat> x(n_features);
x.CopyRowFromMat(xs, i);
Vector<BaseFloat> log_post;
classifier.GetLogPosteriors(x, &log_post);
// Verify that sum_y p(y|x) = 1.0.
Vector<BaseFloat> post(log_post);
post.ApplyExp();
KALDI_ASSERT(ApproxEqual(post.Sum(), 1.0));
log_posteriors.Row(i).CopyFromVec(log_post);
}
// Verify equivalence of batch and serial methods.
float tolerance = 0.01;
KALDI_ASSERT(log_posteriors.ApproxEqual(batch_log_posteriors, tolerance));
}
void UnitTestTrain() {
int32 n_features = Rand() % 600 + 10,
n_xs = Rand() % 200 + 100,
n_labels = Rand() % 20 + 10;
double normalizer = 0.01;
Matrix<BaseFloat> xs(n_xs, n_features);
xs.SetRandn();
std::vector<int32> ys;
for (int32 i = 0; i < n_xs; i++) {
ys.push_back(Rand() % n_labels);
}
LogisticRegressionConfig conf;
conf.max_steps = 20;
conf.normalizer = normalizer;
// Train the classifier
LogisticRegression classifier = LogisticRegression();
classifier.Train(xs, ys, conf);
// Internally in LogisticRegression we add an additional element to
// the x vectors: a 1.0 which handles the prior.
Matrix<BaseFloat> xs_with_prior(n_xs, n_features + 1);
for (int32 i = 0; i < n_xs; i++) {
xs_with_prior(i, n_features) = 1.0;
}
SubMatrix<BaseFloat> sub_xs(xs_with_prior, 0, n_xs, 0, n_features);
sub_xs.CopyFromMat(xs);
Matrix<BaseFloat> xw(n_xs, n_labels);
xw.AddMatMat(1.0, xs_with_prior, kNoTrans, classifier.weights_,
kTrans, 0.0);
Matrix<BaseFloat> grad(classifier.weights_.NumRows(),
classifier.weights_.NumCols());
double objf_trained = classifier.GetObjfAndGrad(xs_with_prior,
ys, xw, &grad, normalizer);
// Calculate objective function using a random weight matrix.
Matrix<BaseFloat> xw_rand(n_xs, n_labels);
Matrix<BaseFloat> weights_rand(classifier.weights_);
weights_rand.SetRandn();
xw.AddMatMat(1.0, xs_with_prior, kNoTrans, weights_rand,
kTrans, 0.0);
// Verify that the objective function after training is better
// than the objective function with a random weight matrix.
double objf_rand_w = classifier.GetObjfAndGrad(xs_with_prior, ys,
xw_rand, &grad, normalizer);
KALDI_ASSERT(objf_trained > objf_rand_w);
KALDI_ASSERT(objf_trained > Log(1.0 / n_xs));
}
}
int main() {
using namespace kaldi;
srand (time(NULL));
UnitTestTrain();
UnitTestPosteriors();
return 0;
}
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