Examples of edu.cmu.sphinx.util.LogMath.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


    // 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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    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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        .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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        .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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    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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            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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                         (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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        }


        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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        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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                    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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