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Use 2D normal distribution for gesture. 

Bug: 13799846
Bug: 10701902
Bug: 9505397

Change-Id: I6c3f84f035f2310f2f7dfec4432ebdb6e50d5df0
main
Keisuke Kuroyanagi 2014-04-09 15:27:25 +09:00
parent e3d57ae792
commit 26c806620c
8 changed files with 184 additions and 77 deletions
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1
native/jni/NativeFileList.mk
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@ -101,4 +101,5 @@ LATIN_IME_CORE_SRC_FILES := \
LATIN_IME_CORE_TEST_FILES := \
defines_test.cpp \
suggest/core/layout/normal_distribution_2d_test.cpp \
utils/autocorrection_threshold_utils_test.cpp

59
native/jni/src/suggest/core/layout/normal_distribution_2d.h Normal file
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@ -0,0 +1,59 @@
/*
* Copyright (C) 2014 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef LATINIME_NORMAL_DISTRIBUTION_2D_H
#define LATINIME_NORMAL_DISTRIBUTION_2D_H
#include <cmath>
#include "defines.h"
#include "suggest/core/layout/geometry_utils.h"
#include "suggest/core/layout/normal_distribution.h"
namespace latinime {
// Normal distribution on a 2D plane. The covariance is always zero, but the distribution can be
// rotated.
class NormalDistribution2D {
public:
NormalDistribution2D(const float uX, const float sigmaX, const float uY, const float sigmaY,
const float theta)
: mXDistribution(0.0f, sigmaX), mYDistribution(0.0f, sigmaY), mUX(uX), mUY(uY),
mSinTheta(sinf(theta)), mCosTheta(cosf(theta)) {}
float getProbabilityDensity(const float x, const float y) const {
// Shift
const float shiftedX = x - mUX;
const float shiftedY = y - mUY;
// Rotate
const float rotatedShiftedX = mCosTheta * shiftedX + mSinTheta * shiftedY;
const float rotatedShiftedY = -mSinTheta * shiftedX + mCosTheta * shiftedY;
return mXDistribution.getProbabilityDensity(rotatedShiftedX)
* mYDistribution.getProbabilityDensity(rotatedShiftedY);
}
private:
DISALLOW_IMPLICIT_CONSTRUCTORS(NormalDistribution2D);
const NormalDistribution mXDistribution;
const NormalDistribution mYDistribution;
const float mUX;
const float mUY;
const float mSinTheta;
const float mCosTheta;
};
} // namespace latinime
#endif // LATINIME_NORMAL_DISTRIBUTION_2D_H

8
native/jni/src/suggest/core/layout/proximity_info_params.cpp
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@ -76,8 +76,12 @@ const float ProximityInfoParams::MAX_SPEEDxANGLE_RATE_FOR_STANDARD_DEVIATION = 0
const float ProximityInfoParams::SPEEDxNEAREST_WEIGHT_FOR_STANDARD_DEVIATION = 0.5f;
const float ProximityInfoParams::MAX_SPEEDxNEAREST_RATE_FOR_STANDARD_DEVIATION = 0.15f;
const float ProximityInfoParams::MIN_STANDARD_DEVIATION = 0.37f;
const float ProximityInfoParams::PREV_DISTANCE_WEIGHT = 0.5f;
const float ProximityInfoParams::NEXT_DISTANCE_WEIGHT = 0.6f;
const float ProximityInfoParams::STANDARD_DEVIATION_X_WEIGHT_FOR_FIRST = 1.25f;
const float ProximityInfoParams::STANDARD_DEVIATION_Y_WEIGHT_FOR_FIRST = 0.85f;
const float ProximityInfoParams::STANDARD_DEVIATION_X_WEIGHT_FOR_LAST = 1.4f;
const float ProximityInfoParams::STANDARD_DEVIATION_Y_WEIGHT_FOR_LAST = 0.95f;
const float ProximityInfoParams::STANDARD_DEVIATION_X_WEIGHT = 1.1f;
const float ProximityInfoParams::STANDARD_DEVIATION_Y_WEIGHT = 0.95f;
// Used by ProximityInfoStateUtils::suppressCharProbabilities()
const float ProximityInfoParams::SUPPRESSION_LENGTH_WEIGHT = 1.5f;

9
native/jni/src/suggest/core/layout/proximity_info_params.h
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@ -78,8 +78,13 @@ class ProximityInfoParams {
static const float SPEEDxNEAREST_WEIGHT_FOR_STANDARD_DEVIATION;
static const float MAX_SPEEDxNEAREST_RATE_FOR_STANDARD_DEVIATION;
static const float MIN_STANDARD_DEVIATION;
static const float PREV_DISTANCE_WEIGHT;
static const float NEXT_DISTANCE_WEIGHT;
// X means gesture's direction. Y means gesture's orthogonal direction.
static const float STANDARD_DEVIATION_X_WEIGHT_FOR_FIRST;
static const float STANDARD_DEVIATION_Y_WEIGHT_FOR_FIRST;
static const float STANDARD_DEVIATION_X_WEIGHT_FOR_LAST;
static const float STANDARD_DEVIATION_Y_WEIGHT_FOR_LAST;
static const float STANDARD_DEVIATION_X_WEIGHT;
static const float STANDARD_DEVIATION_Y_WEIGHT;
// Used by ProximityInfoStateUtils::suppressCharProbabilities()
static const float SUPPRESSION_LENGTH_WEIGHT;

2
native/jni/src/suggest/core/layout/proximity_info_state.cpp
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@ -134,7 +134,7 @@ void ProximityInfoState::initInputParams(const int pointerId, const float maxPoi
mProximityInfo->getKeyCount(), lastSavedInputSize, mSampledInputSize,
&mSampledInputXs, &mSampledInputYs, &mSpeedRates, &mSampledLengthCache,
&mSampledNormalizedSquaredLengthCache, &mSampledNearKeySets,
&mCharProbabilities);
mProximityInfo, &mCharProbabilities);
ProximityInfoStateUtils::updateSampledSearchKeySets(mProximityInfo,
mSampledInputSize, lastSavedInputSize, &mSampledLengthCache,
&mSampledNearKeySets, &mSampledSearchKeySets,

113
native/jni/src/suggest/core/layout/proximity_info_state_utils.cpp
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@ -24,7 +24,7 @@
#include "defines.h"
#include "suggest/core/layout/geometry_utils.h"
#include "suggest/core/layout/normal_distribution.h"
#include "suggest/core/layout/normal_distribution_2d.h"
#include "suggest/core/layout/proximity_info.h"
#include "suggest/core/layout/proximity_info_params.h"
@ -627,6 +627,7 @@ namespace latinime {
const std::vector<int> *const sampledLengthCache,
const std::vector<float> *const sampledNormalizedSquaredLengthCache,
std::vector<NearKeycodesSet> *sampledNearKeySets,
const ProximityInfo *const proximityInfo,
std::vector<hash_map_compat<int, float> > *charProbabilities) {
charProbabilities->resize(sampledInputSize);
// Calculates probabilities of using a point as a correlated point with the character
@ -709,89 +710,57 @@ namespace latinime {
// (1.0f - skipProbability).
const float inputCharProbability = 1.0f - skipProbability;
const float speedxAngleRate = std::min(speedRate * currentAngle / M_PI_F
const float speedMultipliedByAngleRate = std::min(speedRate * currentAngle / M_PI_F
* ProximityInfoParams::SPEEDxANGLE_WEIGHT_FOR_STANDARD_DEVIATION,
ProximityInfoParams::MAX_SPEEDxANGLE_RATE_FOR_STANDARD_DEVIATION);
const float speedxNearestKeyDistanceRate = std::min(speedRate * nearestKeyDistance
* ProximityInfoParams::SPEEDxNEAREST_WEIGHT_FOR_STANDARD_DEVIATION,
ProximityInfoParams::MAX_SPEEDxNEAREST_RATE_FOR_STANDARD_DEVIATION);
const float sigma = speedxAngleRate + speedxNearestKeyDistanceRate
+ ProximityInfoParams::MIN_STANDARD_DEVIATION;
NormalDistribution distribution(
ProximityInfoParams::CENTER_VALUE_OF_NORMALIZED_DISTRIBUTION, sigma);
const float speedMultipliedByNearestKeyDistanceRate = std::min(
speedRate * nearestKeyDistance
* ProximityInfoParams::SPEEDxNEAREST_WEIGHT_FOR_STANDARD_DEVIATION,
ProximityInfoParams::MAX_SPEEDxNEAREST_RATE_FOR_STANDARD_DEVIATION);
const float sigma = (speedMultipliedByAngleRate + speedMultipliedByNearestKeyDistanceRate
+ ProximityInfoParams::MIN_STANDARD_DEVIATION) * mostCommonKeyWidth;
float theta = 0.0f;
// TODO: Use different metrics to compute sigmas.
float sigmaX = sigma;
float sigmaY = sigma;
if (i == 0 && i != sampledInputSize - 1) {
// First point
theta = getDirection(sampledInputXs, sampledInputYs, i + 1, i);
sigmaX *= ProximityInfoParams::STANDARD_DEVIATION_X_WEIGHT_FOR_FIRST;
sigmaY *= ProximityInfoParams::STANDARD_DEVIATION_Y_WEIGHT_FOR_FIRST;
} else {
if (i == sampledInputSize - 1) {
// Last point
sigmaX *= ProximityInfoParams::STANDARD_DEVIATION_X_WEIGHT_FOR_LAST;
sigmaY *= ProximityInfoParams::STANDARD_DEVIATION_Y_WEIGHT_FOR_LAST;
} else {
sigmaX *= ProximityInfoParams::STANDARD_DEVIATION_X_WEIGHT;
sigmaY *= ProximityInfoParams::STANDARD_DEVIATION_Y_WEIGHT;
}
theta = getDirection(sampledInputXs, sampledInputYs, i, i - 1);
}
NormalDistribution2D distribution((*sampledInputXs)[i], sigmaX, (*sampledInputYs)[i],
sigmaY, theta);
// Summing up probability densities of all near keys.
float sumOfProbabilityDensities = 0.0f;
for (int j = 0; j < keyCount; ++j) {
if ((*sampledNearKeySets)[i].test(j)) {
float distance = sqrtf(getPointToKeyByIdLength(
maxPointToKeyLength, sampledNormalizedSquaredLengthCache, keyCount, i, j));
if (i == 0 && i != sampledInputSize - 1) {
// For the first point, weighted average of distances from first point and the
// next point to the key is used as a point to key distance.
const float nextDistance = sqrtf(getPointToKeyByIdLength(
maxPointToKeyLength, sampledNormalizedSquaredLengthCache, keyCount,
i + 1, j));
if (nextDistance < distance) {
// The distance of the first point tends to bigger than continuing
// points because the first touch by the user can be sloppy.
// So we promote the first point if the distance of that point is larger
// than the distance of the next point.
distance = (distance
+ nextDistance * ProximityInfoParams::NEXT_DISTANCE_WEIGHT)
/ (1.0f + ProximityInfoParams::NEXT_DISTANCE_WEIGHT);
}
} else if (i != 0 && i == sampledInputSize - 1) {
// For the first point, weighted average of distances from last point and
// the previous point to the key is used as a point to key distance.
const float previousDistance = sqrtf(getPointToKeyByIdLength(
maxPointToKeyLength, sampledNormalizedSquaredLengthCache, keyCount,
i - 1, j));
if (previousDistance < distance) {
// The distance of the last point tends to bigger than continuing points
// because the last touch by the user can be sloppy. So we promote the
// last point if the distance of that point is larger than the distance of
// the previous point.
distance = (distance
+ previousDistance * ProximityInfoParams::PREV_DISTANCE_WEIGHT)
/ (1.0f + ProximityInfoParams::PREV_DISTANCE_WEIGHT);
}
}
// TODO: Promote the first point when the extended line from the next input is near
// from a key. Also, promote the last point as well.
sumOfProbabilityDensities += distribution.getProbabilityDensity(distance);
sumOfProbabilityDensities += distribution.getProbabilityDensity(
proximityInfo->getKeyCenterXOfKeyIdG(j,
NOT_A_COORDINATE /* referencePointX */, true /* isGeometric */),
proximityInfo->getKeyCenterYOfKeyIdG(j,
NOT_A_COORDINATE /* referencePointY */, true /* isGeometric */));
}
}
// Split the probability of an input point to keys that are close to the input point.
for (int j = 0; j < keyCount; ++j) {
if ((*sampledNearKeySets)[i].test(j)) {
float distance = sqrtf(getPointToKeyByIdLength(
maxPointToKeyLength, sampledNormalizedSquaredLengthCache, keyCount, i, j));
if (i == 0 && i != sampledInputSize - 1) {
// For the first point, weighted average of distances from the first point and
// the next point to the key is used as a point to key distance.
const float prevDistance = sqrtf(getPointToKeyByIdLength(
maxPointToKeyLength, sampledNormalizedSquaredLengthCache, keyCount,
i + 1, j));
if (prevDistance < distance) {
distance = (distance
+ prevDistance * ProximityInfoParams::NEXT_DISTANCE_WEIGHT)
/ (1.0f + ProximityInfoParams::NEXT_DISTANCE_WEIGHT);
}
} else if (i != 0 && i == sampledInputSize - 1) {
// For the first point, weighted average of distances from last point and
// the previous point to the key is used as a point to key distance.
const float prevDistance = sqrtf(getPointToKeyByIdLength(
maxPointToKeyLength, sampledNormalizedSquaredLengthCache, keyCount,
i - 1, j));
if (prevDistance < distance) {
distance = (distance
+ prevDistance * ProximityInfoParams::PREV_DISTANCE_WEIGHT)
/ (1.0f + ProximityInfoParams::PREV_DISTANCE_WEIGHT);
}
}
const float probabilityDensity = distribution.getProbabilityDensity(distance);
const float probabilityDensity = distribution.getProbabilityDensity(
proximityInfo->getKeyCenterXOfKeyIdG(j,
NOT_A_COORDINATE /* referencePointX */, true /* isGeometric */),
proximityInfo->getKeyCenterYOfKeyIdG(j,
NOT_A_COORDINATE /* referencePointY */, true /* isGeometric */));
const float probability = inputCharProbability * probabilityDensity
/ sumOfProbabilityDensities;
(*charProbabilities)[i][j] = probability;

1
native/jni/src/suggest/core/layout/proximity_info_state_utils.h
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@ -72,6 +72,7 @@ class ProximityInfoStateUtils {
const std::vector<int> *const sampledLengthCache,
const std::vector<float> *const sampledNormalizedSquaredLengthCache,
std::vector<NearKeycodesSet> *sampledNearKeySets,
const ProximityInfo *const proximityInfo,
std::vector<hash_map_compat<int, float> > *charProbabilities);
static void updateSampledSearchKeySets(const ProximityInfo *const proximityInfo,
const int sampledInputSize, const int lastSavedInputSize,

68
native/jni/tests/suggest/core/layout/normal_distribution_2d_test.cpp Normal file
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@ -0,0 +1,68 @@
/*
* Copyright (C) 2014 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "suggest/core/layout/normal_distribution_2d.h"
#include <gtest/gtest.h>
#include <vector>
namespace latinime {
namespace {
static const float ORIGIN_X = 0.0f;
static const float ORIGIN_Y = 0.0f;
static const float LARGE_STANDARD_DEVIATION = 100.0f;
static const float SMALL_STANDARD_DEVIATION = 10.0f;
static const float ZERO_RADIAN = 0.0f;
TEST(NormalDistribution2DTest, ProbabilityDensity) {
const NormalDistribution2D distribution(ORIGIN_X, LARGE_STANDARD_DEVIATION, ORIGIN_Y,
SMALL_STANDARD_DEVIATION, ZERO_RADIAN);
static const float SMALL_COORDINATE = 10.0f;
static const float LARGE_COORDINATE = 20.0f;
// The probability density of the point near the distribution center is larger than the
// probability density of the point that is far from distribution center.
EXPECT_GE(distribution.getProbabilityDensity(SMALL_COORDINATE, SMALL_COORDINATE),
distribution.getProbabilityDensity(LARGE_COORDINATE, LARGE_COORDINATE));
// The probability density of the point shifted toward the direction that has larger standard
// deviation is larger than the probability density of the point shifted towards another
// direction.
EXPECT_GE(distribution.getProbabilityDensity(LARGE_COORDINATE, SMALL_COORDINATE),
distribution.getProbabilityDensity(SMALL_COORDINATE, LARGE_COORDINATE));
}
TEST(NormalDistribution2DTest, Rotate) {
static const float COORDINATES[] = {0.0f, 10.0f, 100.0f, -20.0f};
static const float EPSILON = 0.01f;
const NormalDistribution2D distribution(ORIGIN_X, LARGE_STANDARD_DEVIATION, ORIGIN_Y,
SMALL_STANDARD_DEVIATION, ZERO_RADIAN);
const NormalDistribution2D rotatedDistribution(ORIGIN_X, LARGE_STANDARD_DEVIATION, ORIGIN_Y,
SMALL_STANDARD_DEVIATION, M_PI_4);
for (const float x : COORDINATES) {
for (const float y : COORDINATES) {
// The probability density of the rotated distribution at the point and the probability
// density of the original distribution at the rotated point are the same.
const float probabilityDensity0 = distribution.getProbabilityDensity(x, y);
const float probabilityDensity1 = rotatedDistribution.getProbabilityDensity(-y, x);
EXPECT_NEAR(probabilityDensity0, probabilityDensity1, EPSILON);
}
}
}
} // namespace
} // namespace latinime