am 9465819c: Merge "Add BinaryDictionary.getBigramProbabilityNative()."

* commit '9465819cf6f2e6c2074daaae60c5efc0c170185e':
  Add BinaryDictionary.getBigramProbabilityNative().
This commit is contained in:
Keisuke Kuroyanagi 2013-09-17 21:12:09 -07:00 committed by Android Git Automerger
commit bb11d26649
7 changed files with 99 additions and 32 deletions

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@ -109,7 +109,7 @@ public final class BinaryDictionary extends Dictionary {
private static native void flushWithGCNative(long dict, String filePath);
private static native void closeNative(long dict);
private static native int getProbabilityNative(long dict, int[] word);
private static native boolean isValidBigramNative(long dict, int[] word0, int[] word1);
private static native int getBigramProbabilityNative(long dict, int[] word0, int[] word1);
private static native int getSuggestionsNative(long dict, long proximityInfo,
long traverseSession, int[] xCoordinates, int[] yCoordinates, int[] times,
int[] pointerIds, int[] inputCodePoints, int inputSize, int commitPoint,
@ -122,6 +122,8 @@ public final class BinaryDictionary extends Dictionary {
private static native void addBigramWordsNative(long dict, int[] word0, int[] word1,
int probability);
private static native void removeBigramWordsNative(long dict, int[] word0, int[] word1);
private static native int calculateProbabilityNative(long dict, int unigramProbability,
int bigramProbability);
// TODO: Move native dict into session
private final void loadDictionary(final String path, final long startOffset,
@ -219,12 +221,12 @@ public final class BinaryDictionary extends Dictionary {
@Override
public boolean isValidWord(final String word) {
return getFrequency(word) >= 0;
return getFrequency(word) != NOT_A_PROBABILITY;
}
@Override
public int getFrequency(final String word) {
if (word == null) return -1;
if (word == null) return NOT_A_PROBABILITY;
int[] codePoints = StringUtils.toCodePointArray(word);
return getProbabilityNative(mNativeDict, codePoints);
}
@ -232,10 +234,14 @@ public final class BinaryDictionary extends Dictionary {
// TODO: Add a batch process version (isValidBigramMultiple?) to avoid excessive numbers of jni
// calls when checking for changes in an entire dictionary.
public boolean isValidBigram(final String word0, final String word1) {
if (TextUtils.isEmpty(word0) || TextUtils.isEmpty(word1)) return false;
return getBigramProbability(word0, word1) != NOT_A_PROBABILITY;
}
public int getBigramProbability(final String word0, final String word1) {
if (TextUtils.isEmpty(word0) || TextUtils.isEmpty(word1)) return NOT_A_PROBABILITY;
final int[] codePoints0 = StringUtils.toCodePointArray(word0);
final int[] codePoints1 = StringUtils.toCodePointArray(word1);
return isValidBigramNative(mNativeDict, codePoints0, codePoints1);
return getBigramProbabilityNative(mNativeDict, codePoints0, codePoints1);
}
// Add a unigram entry to binary dictionary in native code.
@ -285,6 +291,12 @@ public final class BinaryDictionary extends Dictionary {
return needsToRunGCNative(mNativeDict);
}
@UsedForTesting
public int calculateProbability(final int unigramProbability, final int bigramProbability) {
if (!isValidDictionary()) return NOT_A_PROBABILITY;
return calculateProbabilityNative(mNativeDict, unigramProbability, bigramProbability);
}
@Override
public boolean shouldAutoCommit(final SuggestedWordInfo candidate) {
// TODO: actually use the confidence rather than use this completely broken heuristic

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@ -188,8 +188,8 @@ static jint latinime_BinaryDictionary_getProbability(JNIEnv *env, jclass clazz,
return dictionary->getProbability(codePoints, wordLength);
}
static jboolean latinime_BinaryDictionary_isValidBigram(JNIEnv *env, jclass clazz, jlong dict,
jintArray word0, jintArray word1) {
static jint latinime_BinaryDictionary_getBigramProbability(JNIEnv *env, jclass clazz,
jlong dict, jintArray word0, jintArray word1) {
Dictionary *dictionary = reinterpret_cast<Dictionary *>(dict);
if (!dictionary) return JNI_FALSE;
const jsize word0Length = env->GetArrayLength(word0);
@ -198,7 +198,8 @@ static jboolean latinime_BinaryDictionary_isValidBigram(JNIEnv *env, jclass claz
int word1CodePoints[word1Length];
env->GetIntArrayRegion(word0, 0, word0Length, word0CodePoints);
env->GetIntArrayRegion(word1, 0, word1Length, word1CodePoints);
return dictionary->isValidBigram(word0CodePoints, word0Length, word1CodePoints, word1Length);
return dictionary->getBigramProbability(word0CodePoints, word0Length, word1CodePoints,
word1Length);
}
static jfloat latinime_BinaryDictionary_calcNormalizedScore(JNIEnv *env, jclass clazz,
@ -269,6 +270,16 @@ static void latinime_BinaryDictionary_removeBigramWords(JNIEnv *env, jclass claz
word1Length);
}
static int latinime_BinaryDictionary_calculateProbabilityNative(JNIEnv *env, jclass clazz,
jlong dict, jint unigramProbability, jint bigramProbability) {
Dictionary *dictionary = reinterpret_cast<Dictionary *>(dict);
if (!dictionary) {
return NOT_A_PROBABILITY;
}
return dictionary->getDictionaryStructurePolicy()->getProbability(unigramProbability,
bigramProbability);
}
static const JNINativeMethod sMethods[] = {
{
const_cast<char *>("openNative"),
@ -306,9 +317,9 @@ static const JNINativeMethod sMethods[] = {
reinterpret_cast<void *>(latinime_BinaryDictionary_getProbability)
},
{
const_cast<char *>("isValidBigramNative"),
const_cast<char *>("(J[I[I)Z"),
reinterpret_cast<void *>(latinime_BinaryDictionary_isValidBigram)
const_cast<char *>("getBigramProbabilityNative"),
const_cast<char *>("(J[I[I)I"),
reinterpret_cast<void *>(latinime_BinaryDictionary_getBigramProbability)
},
{
const_cast<char *>("calcNormalizedScoreNative"),
@ -334,6 +345,11 @@ static const JNINativeMethod sMethods[] = {
const_cast<char *>("removeBigramWordsNative"),
const_cast<char *>("(J[I[I)V"),
reinterpret_cast<void *>(latinime_BinaryDictionary_removeBigramWords)
},
{
const_cast<char *>("calculateProbabilityNative"),
const_cast<char *>("(JII)I"),
reinterpret_cast<void *>(latinime_BinaryDictionary_calculateProbabilityNative)
}
};

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@ -150,24 +150,26 @@ int BigramDictionary::getBigramListPositionForWord(const int *prevWord, const in
return mDictionaryStructurePolicy->getBigramsPositionOfNode(pos);
}
bool BigramDictionary::isValidBigram(const int *word0, int length0, const int *word1,
int BigramDictionary::getBigramProbability(const int *word0, int length0, const int *word1,
int length1) const {
int pos = getBigramListPositionForWord(word0, length0, false /* forceLowerCaseSearch */);
// getBigramListPositionForWord returns 0 if this word isn't in the dictionary or has no bigrams
if (NOT_A_DICT_POS == pos) return false;
if (NOT_A_DICT_POS == pos) return NOT_A_PROBABILITY;
int nextWordPos = mDictionaryStructurePolicy->getTerminalNodePositionOfWord(word1, length1,
false /* forceLowerCaseSearch */);
if (NOT_A_DICT_POS == nextWordPos) return false;
if (NOT_A_DICT_POS == nextWordPos) return NOT_A_PROBABILITY;
BinaryDictionaryBigramsIterator bigramsIt(
mDictionaryStructurePolicy->getBigramsStructurePolicy(), pos);
while (bigramsIt.hasNext()) {
bigramsIt.next();
if (bigramsIt.getBigramPos() == nextWordPos) {
return true;
return mDictionaryStructurePolicy->getProbability(
mDictionaryStructurePolicy->getUnigramProbabilityOfPtNode(nextWordPos),
bigramsIt.getProbability());
}
}
return false;
return NOT_A_PROBABILITY;
}
// TODO: Move functions related to bigram to here

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@ -29,7 +29,7 @@ class BigramDictionary {
int getPredictions(const int *word, int length, int *outBigramCodePoints,
int *outBigramProbability, int *outputTypes) const;
bool isValidBigram(const int *word1, int length1, const int *word2, int length2) const;
int getBigramProbability(const int *word1, int length1, const int *word2, int length2) const;
~BigramDictionary();
private:

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@ -93,8 +93,9 @@ int Dictionary::getProbability(const int *word, int length) const {
return getDictionaryStructurePolicy()->getUnigramProbabilityOfPtNode(pos);
}
bool Dictionary::isValidBigram(const int *word0, int length0, const int *word1, int length1) const {
return mBigramDictionary->isValidBigram(word0, length0, word1, length1);
int Dictionary::getBigramProbability(const int *word0, int length0, const int *word1,
int length1) const {
return mBigramDictionary->getBigramProbability(word0, length0, word1, length1);
}
void Dictionary::addUnigramWord(const int *const word, const int length, const int probability) {

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@ -67,7 +67,7 @@ class Dictionary {
int getProbability(const int *word, int length) const;
bool isValidBigram(const int *word0, int length0, const int *word1, int length1) const;
int getBigramProbability(const int *word0, int length0, const int *word1, int length1) const;
void addUnigramWord(const int *const word, const int length, const int probability);

View file

@ -151,7 +151,7 @@ public class BinaryDictionaryTests extends AndroidTestCase {
final int[] codePointSet = CodePointUtils.generateCodePointSet(codePointSetSize, random);
for (int i = 0; i < wordCount; ++i) {
final String word = CodePointUtils.generateWord(random, codePointSet);
probabilityMap.put(word, random.nextInt() & 0xFF);
probabilityMap.put(word, random.nextInt(0xFF));
}
for (String word : probabilityMap.keySet()) {
binaryDictionary.addUnigramWord(word, probabilityMap.get(word));
@ -163,8 +163,6 @@ public class BinaryDictionaryTests extends AndroidTestCase {
}
public void testAddBigramWords() {
// TODO: Add a test to check the frequency of the bigram score which uses current value
// calculated in the native code
File dictFile = null;
try {
dictFile = createEmptyDictionaryAndGetFile("TestBinaryDictionary");
@ -179,6 +177,7 @@ public class BinaryDictionaryTests extends AndroidTestCase {
final int unigramProbability = 100;
final int bigramProbability = 10;
final int updatedBigramProbability = 15;
binaryDictionary.addUnigramWord("aaa", unigramProbability);
binaryDictionary.addUnigramWord("abb", unigramProbability);
binaryDictionary.addUnigramWord("bcc", unigramProbability);
@ -187,21 +186,49 @@ public class BinaryDictionaryTests extends AndroidTestCase {
binaryDictionary.addBigramWords("abb", "aaa", bigramProbability);
binaryDictionary.addBigramWords("abb", "bcc", bigramProbability);
final int probability = binaryDictionary.calculateProbability(unigramProbability,
bigramProbability);
assertEquals(true, binaryDictionary.isValidBigram("aaa", "abb"));
assertEquals(true, binaryDictionary.isValidBigram("aaa", "bcc"));
assertEquals(true, binaryDictionary.isValidBigram("abb", "aaa"));
assertEquals(true, binaryDictionary.isValidBigram("abb", "bcc"));
assertEquals(probability, binaryDictionary.getBigramProbability("aaa", "abb"));
assertEquals(probability, binaryDictionary.getBigramProbability("aaa", "bcc"));
assertEquals(probability, binaryDictionary.getBigramProbability("abb", "aaa"));
assertEquals(probability, binaryDictionary.getBigramProbability("abb", "bcc"));
binaryDictionary.addBigramWords("aaa", "abb", updatedBigramProbability);
final int updatedProbability = binaryDictionary.calculateProbability(unigramProbability,
updatedBigramProbability);
assertEquals(updatedProbability, binaryDictionary.getBigramProbability("aaa", "abb"));
assertEquals(false, binaryDictionary.isValidBigram("bcc", "aaa"));
assertEquals(false, binaryDictionary.isValidBigram("bcc", "bbc"));
assertEquals(false, binaryDictionary.isValidBigram("aaa", "aaa"));
assertEquals(Dictionary.NOT_A_PROBABILITY,
binaryDictionary.getBigramProbability("bcc", "aaa"));
assertEquals(Dictionary.NOT_A_PROBABILITY,
binaryDictionary.getBigramProbability("bcc", "bbc"));
assertEquals(Dictionary.NOT_A_PROBABILITY,
binaryDictionary.getBigramProbability("aaa", "aaa"));
// Testing bigram link.
binaryDictionary.addUnigramWord("abcde", unigramProbability);
binaryDictionary.addUnigramWord("fghij", unigramProbability);
binaryDictionary.addBigramWords("abcde", "fghij", bigramProbability);
binaryDictionary.addUnigramWord("fgh", unigramProbability);
binaryDictionary.addUnigramWord("abc", unigramProbability);
binaryDictionary.addUnigramWord("f", unigramProbability);
assertEquals(probability, binaryDictionary.getBigramProbability("abcde", "fghij"));
assertEquals(Dictionary.NOT_A_PROBABILITY,
binaryDictionary.getBigramProbability("abcde", "fgh"));
binaryDictionary.addBigramWords("abcde", "fghij", updatedBigramProbability);
assertEquals(updatedProbability, binaryDictionary.getBigramProbability("abcde", "fghij"));
dictFile.delete();
}
public void testRandomlyAddBigramWords() {
// TODO: Add a test to check the frequency of the bigram score which uses current value
// calculated in the native code
final int wordCount = 100;
final int bigramCount = 1000;
final int codePointSetSize = 50;
@ -222,29 +249,38 @@ public class BinaryDictionaryTests extends AndroidTestCase {
// Test a word that isn't contained within the dictionary.
final Random random = new Random(seed);
final int[] codePointSet = CodePointUtils.generateCodePointSet(codePointSetSize, random);
final int unigramProbability = 100;
final int bigramProbability = 10;
final int[] unigramProbabilities = new int[wordCount];
for (int i = 0; i < wordCount; ++i) {
final String word = CodePointUtils.generateWord(random, codePointSet);
words.add(word);
final int unigramProbability = random.nextInt(0xFF);
unigramProbabilities[i] = unigramProbability;
binaryDictionary.addUnigramWord(word, unigramProbability);
}
final boolean[][] bigramRelations = new boolean[wordCount][wordCount];
final int[][] probabilities = new int[wordCount][wordCount];
for (int i = 0; i < wordCount; ++i) {
for (int j = 0; j < wordCount; ++j) {
probabilities[i][j] = Dictionary.NOT_A_PROBABILITY;
}
}
for (int i = 0; i < bigramCount; i++) {
final int word0Index = random.nextInt(wordCount);
final int word1Index = random.nextInt(wordCount);
final String word0 = words.get(word0Index);
final String word1 = words.get(word1Index);
bigramRelations[word0Index][word1Index] = true;
final int bigramProbability = random.nextInt(0xF);
probabilities[word0Index][word1Index] = binaryDictionary.calculateProbability(
unigramProbabilities[word1Index], bigramProbability);
binaryDictionary.addBigramWords(word0, word1, bigramProbability);
}
for (int i = 0; i < words.size(); i++) {
for (int j = 0; j < words.size(); j++) {
assertEquals(bigramRelations[i][j],
binaryDictionary.isValidBigram(words.get(i), words.get(j)));
assertEquals(probabilities[i][j],
binaryDictionary.getBigramProbability(words.get(i), words.get(j)));
}
}