1 /* 2 * Copyright (C) 2012 The Android Open Source Project 3 * 4 * Licensed under the Apache License, Version 2.0 (the "License"); 5 * you may not use this file except in compliance with the License. 6 * You may obtain a copy of the License at 7 * 8 * http://www.apache.org/licenses/LICENSE-2.0 9 * 10 * Unless required by applicable law or agreed to in writing, software 11 * distributed under the License is distributed on an "AS IS" BASIS, 12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 * See the License for the specific language governing permissions and 14 * limitations under the License. 15 */ 16 17 #ifndef LEARNING_JNI_STOCHASTIC_LINEAR_RANKER_H 18 #define LEARNING_JNI_STOCHASTIC_LINEAR_RANKER_H 19 20 #include <jni.h> 21 22 #ifdef __cplusplus 23 extern "C" { 24 #endif 25 26 /* Counts the number of learning iterations. */ 27 const char * ITR_NUM = "IterationNumber"; 28 29 /* The maximum norm of the weight vector. If norm of weights are larger than NormConstraint 30 they will be reprojected using RegularizationType to satisfy this constraint. */ 31 const char * NORM_CONSTRAINT = "NormConstraint"; 32 33 /* Ddetermines type of the regularization used in learning. 34 This regularization can be based on different norms. 35 Options: "L0", "L1", "L2", "L1L2", "L1LInf". 36 Default : LINEAR */ 37 const char * REG_TYPE = "RegularizationType"; 38 39 /* Lambda is a factor that is multiplied with the step size in learning. This can be used 40 to change the step size. 41 Default : 1.0 */ 42 const char * LAMBDA = "Lambda"; 43 44 /* This parameter determines the update type in learning process. 45 Options: "FULL_CS" , "CLIP_CS", "REG_CS", "SL", "ADAPTIVE_REG" 46 Default : "SL" */ 47 const char * UPDATE_TYPE = "UpdateType"; 48 49 /* Options: "CONST", "INV_LINEAR", "INV_QUADRATIC", "INV_SQRT" 50 Default: "INV_LINEAR". */ 51 const char * ADAPT_MODE = "AdaptationMode"; 52 53 /* Three differnt kernels are supported: Linear "LINEAR", Polynomial "POLY", and RBF "RBF" 54 Default : "LINEAR" */ 55 const char * KERNEL_TYPE = "KernelType"; 56 57 /* Kernel param is kernel-specific. In case of polynomial kernel, it is the degree of the 58 polynomial. In case of RBF kernel, it implies the sigma parameter. In case of linear 59 kernel, it is not used. */ 60 const char * KERNEL_PARAM = "KernelParameter"; 61 62 /* Kernel gain is typically a multiplicative factor to the dot product while calculating 63 the kernel function. In most use cases, gain should be set to 1.0. */ 64 const char * KERNEL_GAIN = "KernelGain"; 65 66 /* Kernel bias is typically an additive factors to the dot product while calculating 67 the kernel function. In most use cases, bias should be set to 0.0. */ 68 const char * KERNEL_BIAS = "KernelBias"; 69 70 /* This parameter determines the type of loss function to minimize. 71 Options : "PAIRWISE", "RECIPROCAL_RANK" 72 Default : "PAIRWISE" */ 73 const char * LOSS_TYPE = "LossType"; 74 75 /* The minimum percent of training pairs that are used in training. 76 Default : "0.1" */ 77 const char * ACC_PROB = "AcceptaceProbability"; 78 79 /* The code averages out gradient updates for MinimumBatchSize samples 80 before performing an iteration of the algorithm. */ 81 const char * MIN_BATCH_SIZE = "MinimumBatchSize"; 82 83 /* Specifies the number of non-zero entries allowed in a gradient. 84 Default is -1 which means we take the gradient as given by data without 85 adding any new constraints. positive number is treated as an L0 constraint */ 86 const char * GRAD_L0_NORM = "GradientL0Nrom"; 87 88 const char * REG_TYPE_L0 = "L0"; 89 const char * REG_TYPE_L1 = "L1"; 90 const char * REG_TYPE_L2 = "L2"; 91 const char * REG_TYPE_L1L2 = "L1L2"; 92 const char * REG_TYPE_L1LInf = "L1LInf"; 93 const char * UPDATE_TYPE_FULL_CS = "FULL_CS"; 94 const char * UPDATE_TYPE_CLIP_CS = "CLIP_CS"; 95 const char * UPDATE_TYPE_REG_CS = "REG_CS"; 96 const char * UPDATE_TYPE_SL = "SL"; 97 const char * UPDATE_TYPE_ADAPTIVE_REG = "ADAPTIVE_REG"; 98 const char * ADAPT_MODE_CONST = "CONST"; 99 const char * ADAPT_MODE_INV_LINEAR = "INV_LINEAR"; 100 const char * ADAPT_MODE_INV_QUADRATIC = "INV_QUADRATIC"; 101 const char * ADAPT_MODE_INV_SQRT = "INV_SQRT"; 102 const char * KERNEL_TYPE_LINEAR = "LINEAR"; 103 const char * KERNEL_TYPE_POLY = "POLY"; 104 const char * KERNEL_TYPE_RBF = "RBF"; 105 const char * LOSS_TYPE_PAIRWISE = "PAIRWISE"; 106 const char * LOSS_TYPE_RECIPROCAL_RANK = "RECIPROCAL_RANK"; 107 108 JNIEXPORT jlong JNICALL 109 Java_android_bordeaux_learning_StochasticLinearRanker_initNativeClassifier( 110 JNIEnv* env, 111 jobject thiz); 112 113 114 JNIEXPORT jboolean JNICALL 115 Java_android_bordeaux_learning_StochasticLinearRanker_deleteNativeClassifier( 116 JNIEnv* env, 117 jobject thiz, 118 jlong paPtr); 119 120 JNIEXPORT jboolean JNICALL 121 Java_android_bordeaux_learning_StochasticLinearRanker_nativeUpdateClassifier( 122 JNIEnv* env, 123 jobject thiz, 124 jobjectArray key_array_positive, 125 jfloatArray value_array_positive, 126 jobjectArray key_array_negative, 127 jfloatArray value_array_negative, 128 jlong paPtr); 129 130 JNIEXPORT jfloat JNICALL 131 Java_android_bordeaux_learning_StochasticLinearRanker_nativeScoreSample( 132 JNIEnv* env, 133 jobject thiz, 134 jobjectArray key_array, 135 jfloatArray value_array, 136 jlong paPtr); 137 138 JNIEXPORT void JNICALL 139 Java_android_bordeaux_learning_StochasticLinearRanker_nativeGetWeightClassifier( 140 JNIEnv* env, 141 jobject thiz, 142 jobjectArray key_array_weight, 143 jfloatArray value_array_weight, 144 jfloat normalizer, 145 jlong paPtr); 146 147 JNIEXPORT void JNICALL 148 Java_android_bordeaux_learning_StochasticLinearRanker_nativeGetParameterClassifier( 149 JNIEnv* env, 150 jobject thiz, 151 jobjectArray key_array_param, 152 jobjectArray value_array_param, 153 jlong paPtr); 154 155 JNIEXPORT jint JNICALL 156 Java_android_bordeaux_learning_StochasticLinearRanker_nativeGetLengthClassifier( 157 JNIEnv* env, 158 jobject thiz, 159 jlong paPtr); 160 161 JNIEXPORT jboolean JNICALL 162 Java_android_bordeaux_learning_StochasticLinearRanker_nativeSetWeightClassifier( 163 JNIEnv* env, 164 jobject thiz, 165 jobjectArray key_array_model, 166 jfloatArray value_array_model, 167 jfloat normalizer_model, 168 jlong paPtr); 169 170 JNIEXPORT jboolean JNICALL 171 Java_android_bordeaux_learning_StochasticLinearRanker_nativeSetParameterClassifier( 172 JNIEnv* env, 173 jobject thiz, 174 jstring key, 175 jstring value, 176 jlong paPtr); 177 178 #ifdef __cplusplus 179 } 180 #endif 181 182 #endif /* ANDROID_LEARNING_JNI_STOCHASTIC_LINEAR_RANKER_H */ 183
