/usr/include/ngram/ngram-bayes-model-merge.h is in libngram-dev 1.3.0-1.
This file is owned by root:root, with mode 0o644.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | // 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
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
// Copyright 2005-2016 Brian Roark and Google, Inc.
// Implements 'full' bayes model merging.
#ifndef NGRAM_NGRAM_BAYES_MODEL_MERGE_H_
#define NGRAM_NGRAM_BAYES_MODEL_MERGE_H_
#include <cmath>
#include <fst/fst.h>
#include <ngram/ngram-merge.h>
namespace ngram {
class NGramBayesModelMerge : public NGramMerge<StdArc> {
public:
typedef StdArc::StateId StateId;
typedef StdArc::Label Label;
// Constructs an NGramBayesModelMerge object consisting of ngram model
// to be merged.
// Ownership of FST is retained by the caller.
explicit NGramBayesModelMerge(StdMutableFst *infst1, Label backoff_label = 0,
double norm_eps = kNormEps)
: NGramMerge<StdArc>(infst1, backoff_label, norm_eps, true) {
if (!CheckNormalization()) {
NGRAMERROR() << "NGramBayesModelMerge: Model 1 must be normalized to"
<< " use smoothing in merging";
NGramModel::SetError();
}
}
// Perform smooth-model merge with n-gram model specified by the FST argument
// and mixing weights alpha and beta. Resultant model will be normalized.
void MergeNGramModels(const StdFst &infst2, double alpha, double beta) {
NGramModel<StdArc> mod2(infst2);
if (!mod2.CheckNormalization()) {
NGRAMERROR() << "NGramBayesModelMerge: Model 2 must be normalized to"
<< " use smoothing in merging";
NGramModel::SetError();
return;
}
alpha_ = -log(alpha);
beta_ = -log(beta);
state_alpha_.clear();
if (!NGramMerge<StdArc>::MergeNGramModels(infst2, true)) {
NGRAMERROR() << "NGramBayesModelMerge: Model merging failed";
NGramModel::SetError();
}
}
protected:
// Specifies resultant weight when combining a weight from each FST
StdArc::Weight MergeWeights(StateId s1, StateId s2, Label label,
StdArc::Weight w1, StdArc::Weight w2,
bool in_fst1, bool in_fst2) const override {
if (label == BackoffLabel()) { // don't modify (needed) backoff weights
return in_fst1 ? w1.Value() : w2.Value();
} else {
StateId st = in_fst1 ? s1 : ExactMap2To1(s2);
double alpha = StateAlpha(st);
double beta = NegLogDiff(0.0, alpha);
return NegLogSum(w1.Value() + alpha, w2.Value() + beta);
}
}
private:
// normalized state weight to scale model ngram1
double StateAlpha(StateId st) const {
while (st >= state_alpha_.size()) state_alpha_.push_back(-1.0);
if (state_alpha_[st] < 0.0) {
const vector<Label> &ngram = StateNGram(st);
// -log p(h|k), k=1,2
double w1 = ScalarValue(GetNGramCost(ngram));
double w2 = ScalarValue(NGram2().GetNGramCost(ngram));
// p(k|h) = p(h|k) p(k) / sum_k' p(h|k') p(k')
state_alpha_[st] = w1 + alpha_;
// Only normalize non-infinite cost (to avoid potential NaN issues).
// If state_alpha_[st] = inf (i.e., p = 0), then normalized is also inf.
if (state_alpha_[st] < StdArc::Weight::Zero().Value())
state_alpha_[st] -= NegLogSum(w1 + alpha_, w2 + beta_);
}
return state_alpha_[st];
}
double alpha_; // global weight to scale model ngram1
double beta_; // global weight to scale model ngram2
// stored normalized state weight to scale model ngram1
mutable vector<double> state_alpha_;
DISALLOW_COPY_AND_ASSIGN(NGramBayesModelMerge);
};
} // namespace ngram
#endif // NGRAM_NGRAM_BAYES_MODEL_MERGE_H_
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