logistic-regression-train.cc
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// ivectorbin/logistic-regression-train.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 "base/kaldi-common.h"
#include "util/common-utils.h"
#include "ivector/logistic-regression.h"
int main(int argc, char *argv[]) {
using namespace kaldi;
typedef kaldi::int32 int32;
try {
const char *usage =
"Trains a model using Logistic Regression with L-BFGS from\n"
"a set of vectors. The class labels in <classes-rspecifier>\n"
"must be a set of integers such that there are no gaps in \n"
"its range and the smallest label must be 0.\n"
"Usage: logistic-regression-train <vector-rspecifier>\n"
"<classes-rspecifier> <model-out>\n";
ParseOptions po(usage);
bool binary = true;
LogisticRegressionConfig config;
config.Register(&po);
po.Register("binary", &binary, "Write output in binary mode");
po.Read(argc, argv);
if (po.NumArgs() != 3) {
po.PrintUsage();
exit(1);
}
std::string vector_rspecifier = po.GetArg(1),
class_rspecifier = po.GetArg(2),
model_out = po.GetArg(3);
RandomAccessBaseFloatVectorReader vector_reader(vector_rspecifier);
SequentialInt32Reader class_reader(class_rspecifier);
std::vector<int32> ys;
std::vector<std::string> utt_ids;
std::vector<Vector<BaseFloat> > vectors;
int32 num_utt_done = 0, num_utt_err = 0;
int32 num_classes = 0;
for (; !class_reader.Done(); class_reader.Next()) {
std::string utt = class_reader.Key();
int32 class_label = class_reader.Value();
if (!vector_reader.HasKey(utt)) {
KALDI_WARN << "No vector for utterance " << utt;
num_utt_err++;
} else {
ys.push_back(class_label);
const Vector<BaseFloat> &vector = vector_reader.Value(utt);
vectors.push_back(vector);
// Since there are no gaps in the class labels and we
// start at 0, the largest label is the number of the
// of the classes - 1.
if (class_label > num_classes) {
num_classes = class_label;
}
num_utt_done++;
}
}
// Since the largest label is 1 minus the number of
// classes.
num_classes += 1;
KALDI_LOG << "Retrieved " << num_utt_done << " vectors with "
<< num_utt_err << " missing. "
<< "There were " << num_classes << " class labels.";
if (num_utt_done == 0)
KALDI_ERR << "No vectors processed. Unable to train.";
Matrix<BaseFloat> xs(vectors.size(), vectors[0].Dim());
for (int i = 0; i < vectors.size(); i++) {
xs.Row(i).CopyFromVec(vectors[i]);
}
vectors.clear();
LogisticRegression classifier = LogisticRegression();
classifier.Train(xs, ys, config);
WriteKaldiObject(classifier, model_out, binary);
return 0;
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}