Donate to e Foundation | Murena handsets with /e/OS | Own a part of Murena! Learn more

Commit 2fa6d02a authored by Keisuke Kuroyanagi's avatar Keisuke Kuroyanagi Committed by Android Git Automerger
Browse files

am 627b0107: Merge "Support decaying dict in getWordProbability()." 

* commit '627b0107':
  Support decaying dict in getWordProbability().
parents 1c132b61 627b0107

native/jni/src/suggest/policyimpl/dictionary/structure/v4/content/language_model_dict_content.cpp

+8 −7
Original line number Diff line number Diff line
@@ -39,7 +39,7 @@ bool LanguageModelDictContent::runGC( 
}



int LanguageModelDictContent::getWordProbability(const WordIdArrayView prevWordIds,

        const int wordId) const {

        const int wordId, const HeaderPolicy *const headerPolicy) const {

    int bitmapEntryIndices[MAX_PREV_WORD_COUNT_FOR_N_GRAM + 1];

    bitmapEntryIndices[0] = mTrieMap.getRootBitmapEntryIndex();

    int maxLevel = 0;
@@ -58,14 +58,15 @@ int LanguageModelDictContent::getWordProbability(const WordIdArrayView prevWordI 
        if (!result.mIsValid) {

            continue;

        }

        const int probability =

                ProbabilityEntry::decode(result.mValue, mHasHistoricalInfo).getProbability();

        const ProbabilityEntry probabilityEntry =

                ProbabilityEntry::decode(result.mValue, mHasHistoricalInfo);

        if (mHasHistoricalInfo) {

            return std::min(

                    probability + ForgettingCurveUtils::getProbabilityBiasForNgram(i + 1 /* n */),

                    MAX_PROBABILITY);

            const int probability = ForgettingCurveUtils::decodeProbability(

                    probabilityEntry.getHistoricalInfo(), headerPolicy)

                            + ForgettingCurveUtils::getProbabilityBiasForNgram(i + 1 /* n */);

            return std::min(probability, MAX_PROBABILITY);

        } else {

            return probability;

            return probabilityEntry.getProbability();

        }

    }

    // Cannot find the word.

native/jni/src/suggest/policyimpl/dictionary/structure/v4/content/language_model_dict_content.h

+2 −1
Original line number Diff line number Diff line
@@ -128,7 +128,8 @@ class LanguageModelDictContent { 
            const LanguageModelDictContent *const originalContent,

            int *const outNgramCount);



    int getWordProbability(const WordIdArrayView prevWordIds, const int wordId) const;

    int getWordProbability(const WordIdArrayView prevWordIds, const int wordId,

            const HeaderPolicy *const headerPolicy) const;



    ProbabilityEntry getProbabilityEntry(const int wordId) const {

        return getNgramProbabilityEntry(WordIdArrayView(), wordId);

native/jni/src/suggest/policyimpl/dictionary/structure/v4/ver4_patricia_trie_policy.cpp

+4 −3
Original line number Diff line number Diff line
@@ -121,9 +121,10 @@ const WordAttributes Ver4PatriciaTriePolicy::getWordAttributesInContext( 
            mBuffers->getTerminalPositionLookupTable()->getTerminalPtNodePosition(wordId);

    const PtNodeParams ptNodeParams = mNodeReader.fetchPtNodeParamsInBufferFromPtNodePos(ptNodePos);

    // TODO: Support n-gram.

    return WordAttributes(mBuffers->getLanguageModelDictContent()->getWordProbability(

            prevWordIds.limit(1 /* maxSize */), wordId), ptNodeParams.isBlacklisted(),

            ptNodeParams.isNotAWord(), ptNodeParams.getProbability() == 0);

    const int probability = mBuffers->getLanguageModelDictContent()->getWordProbability(

            prevWordIds.limit(1 /* maxSize */), wordId, mHeaderPolicy);

    return WordAttributes(probability, ptNodeParams.isBlacklisted(), ptNodeParams.isNotAWord(),

            probability == 0);

}



int Ver4PatriciaTriePolicy::getProbabilityOfWord(const WordIdArrayView prevWordIds,

native/jni/tests/suggest/policyimpl/dictionary/structure/v4/content/language_model_dict_content_test.cpp

+4 −2
Original line number Diff line number Diff line
@@ -107,13 +107,15 @@ TEST(LanguageModelDictContentTest, TestGetWordProbability) { 
    languageModelDictContent.setProbabilityEntry(prevWordIds[0], &probabilityEntry);

    languageModelDictContent.setNgramProbabilityEntry(prevWordIds.limit(1), wordId,

            &bigramProbabilityEntry);

    EXPECT_EQ(bigramProbability, languageModelDictContent.getWordProbability(prevWordIds, wordId));

    EXPECT_EQ(bigramProbability, languageModelDictContent.getWordProbability(prevWordIds, wordId,

            nullptr /* headerPolicy */));

    const ProbabilityEntry trigramProbabilityEntry(flag, trigramProbability);

    languageModelDictContent.setNgramProbabilityEntry(prevWordIds.limit(1),

            prevWordIds[1], &probabilityEntry);

    languageModelDictContent.setNgramProbabilityEntry(prevWordIds.limit(2), wordId,

            &trigramProbabilityEntry);

    EXPECT_EQ(trigramProbability, languageModelDictContent.getWordProbability(prevWordIds, wordId));

    EXPECT_EQ(trigramProbability, languageModelDictContent.getWordProbability(prevWordIds, wordId,

            nullptr /* headerPolicy */));

}



}  // namespace