Reorder suggestions result according to auto correction threshold (db1939db) · Commits · e / os / android_packages_inputmethods_LatinIME

java/src/com/android/inputmethod/latin/Suggest.java

+2 −3

Original line number	Diff line number	Diff line
		@@ -420,8 +420,6 @@ public class Suggest implements Dictionary.WordCallback {
		final String typedWord, final ArrayList<SuggestedWordInfo> suggestions) {
		final SuggestedWordInfo typedWordInfo = suggestions.get(0);
		typedWordInfo.setDebugString("+");
		double normalizedScore = BinaryDictionary.calcNormalizedScore(
		typedWord, typedWordInfo.toString(), typedWordInfo.mScore);
		final int suggestionsSize = suggestions.size();
		final ArrayList<SuggestedWordInfo> suggestionsList =
		new ArrayList<SuggestedWordInfo>(suggestionsSize);
		@@ -430,10 +428,11 @@ public class Suggest implements Dictionary.WordCallback {
		// than i because we added the typed word to mSuggestions without touching mScores.
		for (int i = 0; i < suggestionsSize - 1; ++i) {
		final SuggestedWordInfo cur = suggestions.get(i + 1);
		final double normalizedScore = BinaryDictionary.calcNormalizedScore(
		typedWord, cur.toString(), cur.mScore);
		final String scoreInfoString;
		if (normalizedScore > 0) {
		scoreInfoString = String.format("%d (%4.2f)", cur.mScore, normalizedScore);
		normalizedScore = 0.0;
		} else {
		scoreInfoString = Integer.toString(cur.mScore);
		}

native/jni/src/unigram_dictionary.cpp

+2 −1

Original line number	Diff line number	Diff line
		@@ -208,7 +208,8 @@ int UnigramDictionary::getSuggestions(ProximityInfo *proximityInfo,
		AKLOGI("Max normalized score = %f", ns);
		}
		const int suggestedWordsCount =
		queuePool->getMasterQueue()->outputSuggestions(frequencies, outWords);
		queuePool->getMasterQueue()->outputSuggestions(
		proximityInfo->getPrimaryInputWord(), codesSize, frequencies, outWords);

		if (DEBUG_DICT) {
		double ns = queuePool->getMasterQueue()->getHighestNormalizedScore(

native/jni/src/words_priority_queue.h

+56 −21

Original line number	Diff line number	Diff line
		@@ -92,10 +92,12 @@ class WordsPriorityQueue {
		return sw;
		}

		int outputSuggestions(int frequencies, unsigned short outputChars) {
		int outputSuggestions(const unsigned short* before, const int beforeLength,
		int frequencies, unsigned short outputChars) {
		mHighestSuggestedWord = 0;
		const unsigned int size = min(
		MAX_WORDS, static_cast<unsigned int>(mSuggestions.size()));
		SuggestedWord* swBuffer[size];
		int index = size - 1;
		while (!mSuggestions.empty() && index >= 0) {
		SuggestedWord* sw = mSuggestions.top();
		@@ -103,17 +105,45 @@ class WordsPriorityQueue {
		AKLOGI("dump word. %d", sw->mScore);
		DUMP_WORD(sw->mWord, sw->mWordLength);
		}
		swBuffer[index] = sw;
		mSuggestions.pop();
		--index;
		}
		if (size >= 2) {
		SuggestedWord* nsMaxSw = 0;
		unsigned int maxIndex = 0;
		double maxNs = 0;
		for (unsigned int i = 0; i < size; ++i) {
		SuggestedWord* tempSw = swBuffer[i];
		if (!tempSw) {
		continue;
		}
		const double tempNs = getNormalizedScore(tempSw, before, beforeLength, 0, 0, 0);
		if (tempNs >= maxNs) {
		maxNs = tempNs;
		maxIndex = i;
		nsMaxSw = tempSw;
		}
		}
		if (maxIndex > 0 && nsMaxSw) {
		memmove(&swBuffer[1], &swBuffer[0], maxIndex * sizeof(SuggestedWord*));
		swBuffer[0] = nsMaxSw;
		}
		}
		for (unsigned int i = 0; i < size; ++i) {
		SuggestedWord* sw = swBuffer[i];
		if (!sw) {
		AKLOGE("SuggestedWord is null %d", i);
		continue;
		}
		const unsigned int wordLength = sw->mWordLength;
		char* targetAdr = (char*) outputChars
		+ (index) * MAX_WORD_LENGTH * sizeof(short);
		frequencies[index] = sw->mScore;
		char* targetAdr = (char) outputChars + i MAX_WORD_LENGTH * sizeof(short);
		frequencies[i] = sw->mScore;
		memcpy(targetAdr, sw->mWord, (wordLength) * sizeof(short));
		if (wordLength < MAX_WORD_LENGTH) {
		((unsigned short*) targetAdr)[wordLength] = 0;
		}
		sw->mUsed = false;
		mSuggestions.pop();
		--index;
		}
		return size;
		}
		@@ -147,21 +177,8 @@ class WordsPriorityQueue {
		if (!mHighestSuggestedWord) {
		return 0.0;
		}
		SuggestedWord* sw = mHighestSuggestedWord;
		const int score = sw->mScore;
		unsigned short* word = sw->mWord;
		const int wordLength = sw->mWordLength;
		if (outScore) {
		*outScore = score;
		}
		if (outWord) {
		*outWord = word;
		}
		if (outLength) {
		*outLength = wordLength;
		}
		return Correction::RankingAlgorithm::calcNormalizedScore(
		before, beforeLength, word, wordLength, score);
		return getNormalizedScore(
		mHighestSuggestedWord, before, beforeLength, outWord, outScore, outLength);
		}

		private:
		@@ -182,6 +199,24 @@ class WordsPriorityQueue {
		return 0;
		}

		static double getNormalizedScore(SuggestedWord* sw, const unsigned short* before,
		const int beforeLength, unsigned short** outWord, int outScore, int outLength) {
		const int score = sw->mScore;
		unsigned short* word = sw->mWord;
		const int wordLength = sw->mWordLength;
		if (outScore) {
		*outScore = score;
		}
		if (outWord) {
		*outWord = word;
		}
		if (outLength) {
		*outLength = wordLength;
		}
		return Correction::RankingAlgorithm::calcNormalizedScore(
		before, beforeLength, word, wordLength, score);
		}

		typedef std::priority_queue<SuggestedWord, std::vector<SuggestedWord>,
		wordComparator> Suggestions;
		Suggestions mSuggestions;