List of linearToLog() Examples

Examples of linearToLog()

edu.cmu.sphinx.util.LogMath.linearToLog()
Converts the value from linear scale to log scale. The log scale numbers are limited by the range of the type float. The linear scale numbers can be any double value. @param linearValue the value to be converted to log scale @return the value in log scale @throws IllegalArgumentException

Examples of edu.cmu.sphinx.util.LogMath.linearToLog()


    // get the rest of the configuration data
    LogMath logMath = LogMath.getLogMath();
    logWordInsertionProbability = logMath.linearToLog(ps
        .getDouble(PROP_WORD_INSERTION_PROBABILITY));
    logSilenceInsertionProbability = logMath.linearToLog(ps
        .getDouble(PROP_SILENCE_INSERTION_PROBABILITY));
    logUnitInsertionProbability = logMath.linearToLog(ps
        .getDouble(PROP_UNIT_INSERTION_PROBABILITY));
    logFillerInsertionProbability = logMath.linearToLog(ps
        .getDouble(PROP_FILLER_INSERTION_PROBABILITY));

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Examples of edu.cmu.sphinx.util.LogMath.linearToLog()

    LogMath logMath = LogMath.getLogMath();
    logWordInsertionProbability = logMath.linearToLog(ps
        .getDouble(PROP_WORD_INSERTION_PROBABILITY));
    logSilenceInsertionProbability = logMath.linearToLog(ps
        .getDouble(PROP_SILENCE_INSERTION_PROBABILITY));
    logUnitInsertionProbability = logMath.linearToLog(ps
        .getDouble(PROP_UNIT_INSERTION_PROBABILITY));
    logFillerInsertionProbability = logMath.linearToLog(ps
        .getDouble(PROP_FILLER_INSERTION_PROBABILITY));
    languageWeight = ps.getFloat(Linguist.PROP_LANGUAGE_WEIGHT);
    addOutOfGrammarBranch = ps.getBoolean(ADD_OUT_OF_GRAMMAR_BRANCH);

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Examples of edu.cmu.sphinx.util.LogMath.linearToLog()

        .getDouble(PROP_WORD_INSERTION_PROBABILITY));
    logSilenceInsertionProbability = logMath.linearToLog(ps
        .getDouble(PROP_SILENCE_INSERTION_PROBABILITY));
    logUnitInsertionProbability = logMath.linearToLog(ps
        .getDouble(PROP_UNIT_INSERTION_PROBABILITY));
    logFillerInsertionProbability = logMath.linearToLog(ps
        .getDouble(PROP_FILLER_INSERTION_PROBABILITY));
    languageWeight = ps.getFloat(Linguist.PROP_LANGUAGE_WEIGHT);
    addOutOfGrammarBranch = ps.getBoolean(ADD_OUT_OF_GRAMMAR_BRANCH);
    logOutOfGrammarBranchProbability = logMath.linearToLog(ps
        .getDouble(OUT_OF_GRAMMAR_PROBABILITY));

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Examples of edu.cmu.sphinx.util.LogMath.linearToLog()

        .getDouble(PROP_UNIT_INSERTION_PROBABILITY));
    logFillerInsertionProbability = logMath.linearToLog(ps
        .getDouble(PROP_FILLER_INSERTION_PROBABILITY));
    languageWeight = ps.getFloat(Linguist.PROP_LANGUAGE_WEIGHT);
    addOutOfGrammarBranch = ps.getBoolean(ADD_OUT_OF_GRAMMAR_BRANCH);
    logOutOfGrammarBranchProbability = logMath.linearToLog(ps
        .getDouble(OUT_OF_GRAMMAR_PROBABILITY));


    logPhoneInsertionProbability = logMath.linearToLog(ps
        .getDouble(PHONE_INSERTION_PROBABILITY));
    if (addOutOfGrammarBranch) {

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Examples of edu.cmu.sphinx.util.LogMath.linearToLog()

    languageWeight = ps.getFloat(Linguist.PROP_LANGUAGE_WEIGHT);
    addOutOfGrammarBranch = ps.getBoolean(ADD_OUT_OF_GRAMMAR_BRANCH);
    logOutOfGrammarBranchProbability = logMath.linearToLog(ps
        .getDouble(OUT_OF_GRAMMAR_PROBABILITY));


    logPhoneInsertionProbability = logMath.linearToLog(ps
        .getDouble(PHONE_INSERTION_PROBABILITY));
    if (addOutOfGrammarBranch) {
      phoneLoopAcousticModel = (AcousticModel) ps
          .getComponent(PHONE_LOOP_ACOUSTIC_MODEL);
    }

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Examples of edu.cmu.sphinx.util.LogMath.linearToLog()

            uniprobs.put(e.getKey(),
                         (float) e.getValue() * deflate / wordCount);
        }


        LogMath lmath = LogMath.getLogMath();
        float logUnigramWeight = lmath.linearToLog(unigramWeight);
        float invLogUnigramWeight = lmath.linearToLog(1 - unigramWeight);
        float logUniformProb = -lmath.linearToLog(uniprobs.size());


        Set<WordSequence> sorted1grams = new TreeSet<WordSequence>(unigrams.keySet());
        Iterator<WordSequence> iter =

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Examples of edu.cmu.sphinx.util.LogMath.linearToLog()

                         (float) e.getValue() * deflate / wordCount);
        }


        LogMath lmath = LogMath.getLogMath();
        float logUnigramWeight = lmath.linearToLog(unigramWeight);
        float invLogUnigramWeight = lmath.linearToLog(1 - unigramWeight);
        float logUniformProb = -lmath.linearToLog(uniprobs.size());


        Set<WordSequence> sorted1grams = new TreeSet<WordSequence>(unigrams.keySet());
        Iterator<WordSequence> iter =
                new TreeSet<WordSequence>(bigrams.keySet()).iterator();

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Examples of edu.cmu.sphinx.util.LogMath.linearToLog()

        }


        LogMath lmath = LogMath.getLogMath();
        float logUnigramWeight = lmath.linearToLog(unigramWeight);
        float invLogUnigramWeight = lmath.linearToLog(1 - unigramWeight);
        float logUniformProb = -lmath.linearToLog(uniprobs.size());


        Set<WordSequence> sorted1grams = new TreeSet<WordSequence>(unigrams.keySet());
        Iterator<WordSequence> iter =
                new TreeSet<WordSequence>(bigrams.keySet()).iterator();
        WordSequence ws = iter.hasNext() ? iter.next() : null;

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Examples of edu.cmu.sphinx.util.LogMath.linearToLog()

        Set<WordSequence> sorted1grams = new TreeSet<WordSequence>(unigrams.keySet());
        Iterator<WordSequence> iter =
                new TreeSet<WordSequence>(bigrams.keySet()).iterator();
        WordSequence ws = iter.hasNext() ? iter.next() : null;
        for (WordSequence unigram : sorted1grams) {
            float p = lmath.linearToLog(uniprobs.get(unigram));
            p += logUnigramWeight;
            p = lmath.addAsLinear(p, logUniformProb + invLogUnigramWeight);
            logProbs.put(unigram, p);


            float sum = 0.f;

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Examples of edu.cmu.sphinx.util.LogMath.linearToLog()

                    sum += uniprobs.get(ws.getNewest());
                }
                ws = iter.hasNext() ? iter.next() : null;
            }


            logBackoffs.put(unigram, lmath.linearToLog(discount / (1 - sum)));
        }


        Map<WordSequence, Float> biprobs = new HashMap<WordSequence, Float>();
        for (Map.Entry<WordSequence, Integer> entry : bigrams.entrySet()) {
            int unigramCount = unigrams.get(entry.getKey().getOldest());

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