1 files changed, 0 insertions, 193 deletions
diff --git a/bordeaux/learning/stochastic_linear_ranker/java/android/bordeaux/learning/StochasticLinearRanker.java b/bordeaux/learning/stochastic_linear_ranker/java/android/bordeaux/learning/StochasticLinearRanker.java
deleted file mode 100644
index 59f32a955..000000000
--- a/bordeaux/learning/stochastic_linear_ranker/java/android/bordeaux/learning/StochasticLinearRanker.java
+++ /dev/null
@@ -1,193 +0,0 @@
-/*
- * Copyright (C) 2011 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.
- */
-
-
-package android.bordeaux.learning;
-
-import android.util.Log;
-
-import java.io.Serializable;
-import java.util.List;
-import java.util.Arrays;
-import java.util.ArrayList;
-import java.util.HashMap;
-import java.util.Map;
-
-/**
- * Stochastic Linear Ranker, learns how to rank a sample. The learned rank score
- * can be used to compare samples.
- * This java class wraps the native StochasticLinearRanker class.
- * To update the ranker, call updateClassifier with two samples, with the first
- * one having higher rank than the second one.
- * To get the rank score of the sample call scoreSample.
- *  TODO: adding more interfaces for changing the learning parameters
- */
-public class StochasticLinearRanker {
-    String TAG = "StochasticLinearRanker";
-    public static int VAR_NUM = 14;
-    static public class Model implements Serializable {
-        public HashMap<String, Float> weights = new HashMap<String, Float>();
-        public float weightNormalizer = 1;
-        public HashMap<String, String> parameters = new HashMap<String, String>();
-    }
-
-    /**
-     * Initializing a ranker
-     */
-    public StochasticLinearRanker() {
-        mNativeClassifier = initNativeClassifier();
-    }
-
-    /**
-     * Reset the ranker
-     */
-    public void resetRanker(){
-        deleteNativeClassifier(mNativeClassifier);
-        mNativeClassifier = initNativeClassifier();
-    }
-
-    /**
-     * Train the ranker with a pair of samples. A sample,  a pair of arrays of
-     * keys and values. The first sample should have higher rank than the second
-     * one.
-     */
-    public boolean updateClassifier(String[] keys_positive,
-                                    float[] values_positive,
-                                    String[] keys_negative,
-                                    float[] values_negative) {
-        return nativeUpdateClassifier(keys_positive, values_positive,
-                                      keys_negative, values_negative,
-                                      mNativeClassifier);
-    }
-
-    /**
-     * Get the rank score of the sample, a sample is a list of key, value pairs.
-     */
-    public float scoreSample(String[] keys, float[] values) {
-        return nativeScoreSample(keys, values, mNativeClassifier);
-    }
-
-    /**
-     * Get the current model and parameters of ranker
-     */
-    public Model getUModel(){
-        Model slrModel = new Model();
-        int len = nativeGetLengthClassifier(mNativeClassifier);
-        String[] wKeys = new String[len];
-        float[] wValues = new float[len];
-        float wNormalizer = 1;
-        nativeGetWeightClassifier(wKeys, wValues, wNormalizer, mNativeClassifier);
-        slrModel.weightNormalizer = wNormalizer;
-        for (int  i=0; i< wKeys.length ; i++)
-            slrModel.weights.put(wKeys[i], wValues[i]);
-
-        String[] paramKeys = new String[VAR_NUM];
-        String[] paramValues = new String[VAR_NUM];
-        nativeGetParameterClassifier(paramKeys, paramValues, mNativeClassifier);
-        for (int  i=0; i< paramKeys.length ; i++)
-            slrModel.parameters.put(paramKeys[i], paramValues[i]);
-        return slrModel;
-    }
-
-    /**
-     * load the given model and parameters to the ranker
-     */
-    public boolean loadModel(Model model) {
-        String[] wKeys = new String[model.weights.size()];
-        float[] wValues = new float[model.weights.size()];
-        int i = 0 ;
-        for (Map.Entry<String, Float> e : model.weights.entrySet()){
-            wKeys[i] = e.getKey();
-            wValues[i] = e.getValue();
-            i++;
-        }
-        boolean res = setModelWeights(wKeys, wValues, model.weightNormalizer);
-        if (!res)
-            return false;
-
-        for (Map.Entry<String, String> e : model.parameters.entrySet()){
-            res = setModelParameter(e.getKey(), e.getValue());
-            if (!res)
-                return false;
-        }
-        return res;
-    }
-
-    public boolean setModelWeights(String[] keys, float [] values, float normalizer){
-        return nativeSetWeightClassifier(keys, values, normalizer, mNativeClassifier);
-    }
-
-    public boolean setModelParameter(String key, String value){
-        boolean res = nativeSetParameterClassifier(key, value, mNativeClassifier);
-        return res;
-    }
-
-    /**
-     * Print a model for debugging
-     */
-    public void print(Model model){
-        String Sw = "";
-        String Sp = "";
-        for (Map.Entry<String, Float> e : model.weights.entrySet())
-            Sw = Sw + "<" + e.getKey() + "," + e.getValue() + "> ";
-        for (Map.Entry<String, String> e : model.parameters.entrySet())
-            Sp = Sp + "<" + e.getKey() + "," + e.getValue() + "> ";
-        Log.i(TAG, "Weights are " + Sw);
-        Log.i(TAG, "Normalizer is " + model.weightNormalizer);
-        Log.i(TAG, "Parameters are " + Sp);
-    }
-
-    @Override
-    protected void finalize() throws Throwable {
-        deleteNativeClassifier(mNativeClassifier);
-    }
-
-    static {
-        System.loadLibrary("bordeaux");
-    }
-
-    private long mNativeClassifier;
-
-    /*
-     * The following methods are the java stubs for the jni implementations.
-     */
-    private native long initNativeClassifier();
-
-    private native void deleteNativeClassifier(long classifierPtr);
-
-    private native boolean nativeUpdateClassifier(
-            String[] keys_positive,
-            float[] values_positive,
-            String[] keys_negative,
-            float[] values_negative,
-            long classifierPtr);
-
-    private native float nativeScoreSample(String[] keys, float[] values, long classifierPtr);
-
-    private native void nativeGetWeightClassifier(String [] keys, float[] values, float normalizer,
-                                                  long classifierPtr);
-
-    private native void nativeGetParameterClassifier(String [] keys, String[] values,
-                                                  long classifierPtr);
-
-    private native int nativeGetLengthClassifier(long classifierPtr);
-
-    private native boolean nativeSetWeightClassifier(String [] keys, float[] values,
-                                                     float normalizer, long classifierPtr);
-
-    private native boolean nativeSetParameterClassifier(String key, String value,
-                                                        long classifierPtr);
-}