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/*
* Copyright (C) 2012 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 LEARNING_JNI_STOCHASTIC_LINEAR_RANKER_H
#define LEAENING_JNI_STOCHASTIC_LINEAR_RANKER_H
#include <jni.h>
#ifdef __cplusplus
extern "C" {
#endif
/* Counts the number of learning iterations. */
const char * ITR_NUM = "IterationNumber";
/* The maximum norm of the weight vector. If norm of weights are larger than NormConstraint
they will be reprojected using RegularizationType to satisfy this constraint. */
const char * NORM_CONSTRAINT = "NormConstraint";
/* Ddetermines type of the regularization used in learning.
This regularization can be based on different norms.
Options: "L0", "L1", "L2", "L1L2", "L1LInf".
Default : LINEAR */
const char * REG_TYPE = "RegularizationType";
/* Lambda is a factor that is multiplied with the step size in learning. This can be used
to change the step size.
Default : 1.0 */
const char * LAMBDA = "Lambda";
/* This parameter determines the update type in learning process.
Options: "FULL_CS" , "CLIP_CS", "REG_CS", "SL", "ADAPTIVE_REG"
Default : "SL" */
const char * UPDATE_TYPE = "UpdateType";
/* Options: "CONST", "INV_LINEAR", "INV_QUADRATIC", "INV_SQRT"
Default: "INV_LINEAR". */
const char * ADAPT_MODE = "AdaptationMode";
/* Three differnt kernels are supported: Linear "LINEAR", Polynomial "POLY", and RBF "RBF"
Default : "LINEAR" */
const char * KERNEL_TYPE = "KernelType";
/* Kernel param is kernel-specific. In case of polynomial kernel, it is the degree of the
polynomial. In case of RBF kernel, it implies the sigma parameter. In case of linear
kernel, it is not used. */
const char * KERNEL_PARAM = "KernelParameter";
/* Kernel gain is typically a multiplicative factor to the dot product while calculating
the kernel function. In most use cases, gain should be set to 1.0. */
const char * KERNEL_GAIN = "KernelGain";
/* Kernel bias is typically an additive factors to the dot product while calculating
the kernel function. In most use cases, bias should be set to 0.0. */
const char * KERNEL_BIAS = "KernelBias";
/* This parameter determines the type of loss function to minimize.
Options : "PAIRWISE", "RECIPROCAL_RANK"
Default : "PAIRWISE" */
const char * LOSS_TYPE = "LossType";
/* The minimum percent of training pairs that are used in training.
Default : "0.1" */
const char * ACC_PROB = "AcceptaceProbability";
/* The code averages out gradient updates for MinimumBatchSize samples
before performing an iteration of the algorithm. */
const char * MIN_BATCH_SIZE = "MinimumBatchSize";
/* Specifies the number of non-zero entries allowed in a gradient.
Default is -1 which means we take the gradient as given by data without
adding any new constraints. positive number is treated as an L0 constraint */
const char * GRAD_L0_NORM = "GradientL0Nrom";
const char * REG_TYPE_L0 = "L0";
const char * REG_TYPE_L1 = "L1";
const char * REG_TYPE_L2 = "L2";
const char * REG_TYPE_L1L2 = "L1L2";
const char * REG_TYPE_L1LInf = "L1LInf";
const char * UPDATE_TYPE_FULL_CS = "FULL_CS";
const char * UPDATE_TYPE_CLIP_CS = "CLIP_CS";
const char * UPDATE_TYPE_REG_CS = "REG_CS";
const char * UPDATE_TYPE_SL = "SL";
const char * UPDATE_TYPE_ADAPTIVE_REG = "ADAPTIVE_REG";
const char * ADAPT_MODE_CONST = "CONST";
const char * ADAPT_MODE_INV_LINEAR = "INV_LINEAR";
const char * ADAPT_MODE_INV_QUADRATIC = "INV_QUADRATIC";
const char * ADAPT_MODE_INV_SQRT = "INV_SQRT";
const char * KERNEL_TYPE_LINEAR = "LINEAR";
const char * KERNEL_TYPE_POLY = "POLY";
const char * KERNEL_TYPE_RBF = "RBF";
const char * LOSS_TYPE_PAIRWISE = "PAIRWISE";
const char * LOSS_TYPE_RECIPROCAL_RANK = "RECIPROCAL_RANK";
JNIEXPORT jlong JNICALL
Java_android_bordeaux_learning_StochasticLinearRanker_initNativeClassifier(
JNIEnv* env,
jobject thiz);
JNIEXPORT jboolean JNICALL
Java_android_bordeaux_learning_StochasticLinearRanker_deleteNativeClassifier(
JNIEnv* env,
jobject thiz,
jlong paPtr);
JNIEXPORT jboolean JNICALL
Java_android_bordeaux_learning_StochasticLinearRanker_nativeUpdateClassifier(
JNIEnv* env,
jobject thiz,
jobjectArray key_array_positive,
jfloatArray value_array_positive,
jobjectArray key_array_negative,
jfloatArray value_array_negative,
jlong paPtr);
JNIEXPORT jfloat JNICALL
Java_android_bordeaux_learning_StochasticLinearRanker_nativeScoreSample(
JNIEnv* env,
jobject thiz,
jobjectArray key_array,
jfloatArray value_array,
jlong paPtr);
JNIEXPORT void JNICALL
Java_android_bordeaux_learning_StochasticLinearRanker_nativeGetWeightClassifier(
JNIEnv* env,
jobject thiz,
jobjectArray key_array_weight,
jfloatArray value_array_weight,
jfloat normalizer,
jlong paPtr);
JNIEXPORT void JNICALL
Java_android_bordeaux_learning_StochasticLinearRanker_nativeGetParameterClassifier(
JNIEnv* env,
jobject thiz,
jobjectArray key_array_param,
jobjectArray value_array_param,
jlong paPtr);
JNIEXPORT jint JNICALL
Java_android_bordeaux_learning_StochasticLinearRanker_nativeGetLengthClassifier(
JNIEnv* env,
jobject thiz,
jlong paPtr);
JNIEXPORT jboolean JNICALL
Java_android_bordeaux_learning_StochasticLinearRanker_nativeSetWeightClassifier(
JNIEnv* env,
jobject thiz,
jobjectArray key_array_model,
jfloatArray value_array_model,
jfloat normalizer_model,
jlong paPtr);
JNIEXPORT jboolean JNICALL
Java_android_bordeaux_learning_StochasticLinearRanker_nativeSetParameterClassifier(
JNIEnv* env,
jobject thiz,
jstring key,
jstring value,
jlong paPtr);
#ifdef __cplusplus
}
#endif
#endif /* ANDROID_LERNING_JNI_STOCHASTIC_LINEAR_RANKER_H */
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