release-request-7fec6a93-555f-41fe-a89d-484fc3bd7083-for-git_oc-dr1-release-3983577 snap-temp-L94700000061410674android-8.0.0_r34android-8.0.0_r33android-8.0.0_r27android-8.0.0_r26android-8.0.0_r25android-8.0.0_r24android-8.0.0_r23android-8.0.0_r22android-8.0.0_r21oreo-dr3-releaseoreo-dr2-releaseoreo-dr1-release

Change-Id: If0af17c654223a00ac8c4eeadad30b1d0880bee8

68 files changed, 0 insertions, 7441 deletions

diff --git a/Android.mk b/Android.mk
deleted file mode 100644
index 0918da517..000000000
--- a/Android.mk
+++ /dev/null
@@ -1,18 +0,0 @@
-# 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.
-#
-
-TOP_LOCAL_PATH:= $(call my-dir)
-
-include $(call all-subdir-makefiles)
diff --git a/bordeaux/Android.mk b/bordeaux/Android.mk
deleted file mode 100644
index 0918da517..000000000
--- a/bordeaux/Android.mk
+++ /dev/null
@@ -1,18 +0,0 @@
-# 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.
-#
-
-TOP_LOCAL_PATH:= $(call my-dir)
-
-include $(call all-subdir-makefiles)
diff --git a/bordeaux/learning/Android.mk b/bordeaux/learning/Android.mk
deleted file mode 100644
index 562a81def..000000000
--- a/bordeaux/learning/Android.mk
+++ /dev/null
@@ -1,50 +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.
-#
-
-LEARNING_LOCAL_PATH:= $(call my-dir)
-include $(call all-subdir-makefiles)
-
-include $(CLEAR_VARS)
-
-LOCAL_MODULE_TAGS := samples tests
-
-LOCAL_MODULE := libbordeaux
-
-LOCAL_WHOLE_STATIC_LIBRARIES := libmulticlass_pa libstochastic_linear
-LOCAL_SHARED_LIBRARIES := libcutils liblog
-
-
-LOCAL_C_INCLUDES += $(LOCAL_PATH)/../native
-
-include $(BUILD_SHARED_LIBRARY)
-
-##
-# Build java lib
-##
-LOCAL_PATH:= $(LEARNING_LOCAL_PATH)
-include $(CLEAR_VARS)
-
-LOCAL_MODULE_TAGS := samples tests
-
-LOCAL_SRC_FILES := $(call all-java-files-under, multiclass_pa stochastic_linear_ranker ) \
-                   $(call all-java-files-under, predictor_histogram)
-
-LOCAL_MODULE :=  bordeaux_learners
-
-LOCAL_PROGUARD_ENABLED := disabled
-
-LOCAL_JNI_SHARED_LIBRARIES := libbordeaux
-
-include $(BUILD_STATIC_JAVA_LIBRARY)
diff --git a/bordeaux/learning/multiclass_pa/Android.mk b/bordeaux/learning/multiclass_pa/Android.mk
deleted file mode 100644
index c69f69158..000000000
--- a/bordeaux/learning/multiclass_pa/Android.mk
+++ /dev/null
@@ -1,33 +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.
-#
-
-LOCAL_PATH := $(call my-dir)
-
-##
-# Build native code
-##
-include $(CLEAR_VARS)
-
-LOCAL_MODULE_TAGS := samples tests
-
-LOCAL_MODULE := libmulticlass_pa
-
-LOCAL_SRC_FILES := native/multiclass_pa.cpp \
-                   jni/jni_multiclass_pa.cpp
-
-
-LOCAL_C_INCLUDES += $(LOCAL_PATH)/../native
-
-include $(BUILD_STATIC_LIBRARY)
diff --git a/bordeaux/learning/multiclass_pa/java/android/bordeaux/learning/MulticlassPA.java b/bordeaux/learning/multiclass_pa/java/android/bordeaux/learning/MulticlassPA.java
deleted file mode 100644
index 8a868e1cc..000000000
--- a/bordeaux/learning/multiclass_pa/java/android/bordeaux/learning/MulticlassPA.java
+++ /dev/null
@@ -1,68 +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;
-
-/**
- * Wrapper for multiclass passive aggressive classifier.
- * version 1 supports indexed sparse feature only.
- */
-public class MulticlassPA {
-
-    public MulticlassPA(int numClasses, int numDimensions, float aggressiveness) {
-      nativeClassifier = initNativeClassifier(numClasses, numDimensions,
-                                              aggressiveness);
-    }
-
-    /**
-     * Train on one example
-     */
-    public boolean sparseTrainOneExample(int[] index_array,
-                                       float[] float_array,
-                                       int target) {
-        return nativeSparseTrainOneExample(
-                index_array, float_array, target, nativeClassifier);
-    }
-
-    /**
-     * Train on one example
-     */
-    public int sparseGetClass(int[] index_array, float[] float_array) {
-        return nativeSparseGetClass(index_array, float_array, nativeClassifier);
-    }
-
-    static {
-        System.loadLibrary("bordeaux");
-    }
-
-    private long nativeClassifier;
-
-    /*
-     * Initialize native classifier
-     */
-    private native long initNativeClassifier(int num_classes, int num_dims, float aggressiveness);
-
-    private native void deleteNativeClassifier(long classPtr);
-
-    private native boolean nativeSparseTrainOneExample(int[] index_array,
-                                                     float[] float_array,
-                                                     int target, long classPtr);
-
-    private native int nativeSparseGetClass(int[] index_array,
-                                            float[] float_array,
-                                            long classPtr);
-}
diff --git a/bordeaux/learning/multiclass_pa/jni/jni_multiclass_pa.cpp b/bordeaux/learning/multiclass_pa/jni/jni_multiclass_pa.cpp
deleted file mode 100644
index 3825586ed..000000000
--- a/bordeaux/learning/multiclass_pa/jni/jni_multiclass_pa.cpp
+++ /dev/null
@@ -1,116 +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.
- */
-
-#include "jni/jni_multiclass_pa.h"
-#include "native/multiclass_pa.h"
-
-#include <vector>
-
-using learningfw::MulticlassPA;
-using std::vector;
-using std::pair;
-
-void CreateIndexValuePairs(const int* indices, const float* values,
-                           const int length, vector<pair<int, float> >* pairs) {
-  pairs->clear();
-
-  for (int i = 0; i < length; ++i) {
-    pair<int, float> new_pair(indices[i], values[i]);
-    pairs->push_back(new_pair);
-  }
-}
-
-jlong Java_android_bordeaux_learning_MulticlassPA_initNativeClassifier(JNIEnv* /* env */,
-                                                       jobject /* thiz */,
-                                                       jint num_classes,
-                                                       jint num_dims,
-                                                       jfloat aggressiveness) {
-  MulticlassPA* classifier = new MulticlassPA(num_classes,
-                                              num_dims,
-                                              aggressiveness);
-  return ((jlong) classifier);
-}
-
-
-jboolean Java_android_bordeaux_learning_MulticlassPA_deleteNativeClassifier(JNIEnv* /* env */,
-                                                             jobject /* thiz */,
-                                                             jlong paPtr) {
-  MulticlassPA* classifier = (MulticlassPA*) paPtr;
-  delete classifier;
-  return JNI_TRUE;
-}
-
-jboolean Java_android_bordeaux_learning_MulticlassPA_nativeSparseTrainOneExample(JNIEnv* env,
-                                                                  jobject /* thiz */,
-                                                                  jintArray index_array,
-                                                                  jfloatArray value_array,
-                                                                  jint target,
-                                                                  jlong paPtr) {
-  MulticlassPA* classifier = (MulticlassPA*) paPtr;
-
-  if (classifier && index_array && value_array) {
-
-    jfloat* values = env->GetFloatArrayElements(value_array, NULL);
-    jint* indices = env->GetIntArrayElements(index_array, NULL);
-    const int value_len = env->GetArrayLength(value_array);
-    const int index_len = env->GetArrayLength(index_array);
-
-    if (values && indices && value_len == index_len) {
-      vector<pair<int, float> > inputs;
-
-      CreateIndexValuePairs(indices, values, value_len, &inputs);
-      classifier->SparseTrainOneExample(inputs, target);
-      env->ReleaseIntArrayElements(index_array, indices, JNI_ABORT);
-      env->ReleaseFloatArrayElements(value_array, values, JNI_ABORT);
-
-      return JNI_TRUE;
-    }
-    env->ReleaseIntArrayElements(index_array, indices, JNI_ABORT);
-    env->ReleaseFloatArrayElements(value_array, values, JNI_ABORT);
-  }
-
-  return JNI_FALSE;
-}
-
-
-jint Java_android_bordeaux_learning_MulticlassPA_nativeSparseGetClass(JNIEnv* env,
-                                                       jobject /* thiz */,
-                                                       jintArray index_array,
-                                                       jfloatArray value_array,
-                                                       jlong paPtr) {
-
-  MulticlassPA* classifier = (MulticlassPA*) paPtr;
-
-  if (classifier && index_array && value_array) {
-
-    jfloat* values = env->GetFloatArrayElements(value_array, NULL);
-    jint* indices = env->GetIntArrayElements(index_array, NULL);
-    const int value_len = env->GetArrayLength(value_array);
-    const int index_len = env->GetArrayLength(index_array);
-
-    if (values && indices && value_len == index_len) {
-      vector<pair<int, float> > inputs;
-      CreateIndexValuePairs(indices, values, value_len, &inputs);
-      env->ReleaseIntArrayElements(index_array, indices, JNI_ABORT);
-      env->ReleaseFloatArrayElements(value_array, values, JNI_ABORT);
-      return classifier->SparseGetClass(inputs);
-    }
-    env->ReleaseIntArrayElements(index_array, indices, JNI_ABORT);
-    env->ReleaseFloatArrayElements(value_array, values, JNI_ABORT);
-  }
-
-  return -1;
-}
diff --git a/bordeaux/learning/multiclass_pa/jni/jni_multiclass_pa.h b/bordeaux/learning/multiclass_pa/jni/jni_multiclass_pa.h
deleted file mode 100644
index dd0f43d5c..000000000
--- a/bordeaux/learning/multiclass_pa/jni/jni_multiclass_pa.h
+++ /dev/null
@@ -1,59 +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.
- */
-
-#ifndef LEARNINGFW_JNI_MULTICLASS_PA_H
-#define LEARNINGFW_JNI_MULTICLASS_PA_H
-
-#include <jni.h>
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-JNIEXPORT jlong JNICALL
-Java_android_bordeaux_learning_MulticlassPA_initNativeClassifier(JNIEnv* env,
-                                                  jobject thiz,
-                                                  jint num_classes,
-                                                  jint num_dims,
-                                                  jfloat aggressiveness);
-
-
-JNIEXPORT jboolean JNICALL
-Java_android_bordeaux_learning_MulticlassPA_deleteNativeClassifier(JNIEnv* env,
-                                                    jobject thiz,
-                                                    jlong paPtr);
-
-JNIEXPORT jboolean JNICALL
-Java_android_bordeaux_learning_MulticlassPA_nativeSparseTrainOneExample(JNIEnv* env,
-                                                         jobject thiz,
-                                                         jintArray index_array,
-                                                         jfloatArray value_array,
-                                                         jint target,
-                                                         jlong paPtr);
-
-JNIEXPORT jint JNICALL
-Java_android_bordeaux_learning_MulticlassPA_nativeSparseGetClass(JNIEnv* env,
-                                                  jobject thiz,
-                                                  jintArray index_array,
-                                                  jfloatArray value_array,
-                                                  jlong paPtr);
-
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif /* ANDROID_LEARNINGFW_JNI_MULTICLASS_PA_H */
diff --git a/bordeaux/learning/multiclass_pa/native/multiclass_pa.cpp b/bordeaux/learning/multiclass_pa/native/multiclass_pa.cpp
deleted file mode 100644
index 9ee12b035..000000000
--- a/bordeaux/learning/multiclass_pa/native/multiclass_pa.cpp
+++ /dev/null
@@ -1,250 +0,0 @@
-/*
- * 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.
- */
-
-//
-// This file contains the MulticlassPA class which implements a simple
-// linear multi-class classifier based on the multi-prototype version of
-// passive aggressive.
-
-#include "native/multiclass_pa.h"
-
-#include <stdlib.h>
-
-using std::vector;
-using std::pair;
-
-namespace learningfw {
-
-float RandFloat() {
-  return static_cast<float>(rand()) / RAND_MAX;
-}
-
-MulticlassPA::MulticlassPA(int num_classes,
-                           int num_dimensions,
-                           float aggressiveness)
-    : num_classes_(num_classes),
-      num_dimensions_(num_dimensions),
-      aggressiveness_(aggressiveness) {
-  InitializeParameters();
-}
-
-MulticlassPA::~MulticlassPA() {
-}
-
-void MulticlassPA::InitializeParameters() {
-  parameters_.resize(num_classes_);
-  for (int i = 0; i < num_classes_; ++i) {
-    parameters_[i].resize(num_dimensions_);
-    for (int j = 0; j < num_dimensions_; ++j) {
-      parameters_[i][j] = 0.0;
-    }
-  }
-}
-
-int MulticlassPA::PickAClassExcept(int target) {
-  int picked;
-  do {
-    picked = static_cast<int>(RandFloat() * num_classes_);
-    //    picked = static_cast<int>(random_.RandFloat() * num_classes_);
-  } while (target == picked);
-  return picked;
-}
-
-int MulticlassPA::PickAnExample(int num_examples) {
-  return static_cast<int>(RandFloat() * num_examples);
-}
-
-float MulticlassPA::Score(const vector<float>& inputs,
-                          const vector<float>& parameters) const {
-  // CHECK_EQ(inputs.size(), parameters.size());
-  float result = 0.0;
-  for (int i = 0; i < static_cast<int>(inputs.size()); ++i) {
-    result += inputs[i] * parameters[i];
-  }
-  return result;
-}
-
-float MulticlassPA::SparseScore(const vector<pair<int, float> >& inputs,
-                                const vector<float>& parameters) const {
-  float result = 0.0;
-  for (int i = 0; i < static_cast<int>(inputs.size()); ++i) {
-    //DCHECK_GE(inputs[i].first, 0);
-    //DCHECK_LT(inputs[i].first, parameters.size());
-    result += inputs[i].second * parameters[inputs[i].first];
-  }
-  return result;
-}
-
-float MulticlassPA::L2NormSquare(const vector<float>& inputs) const {
-  float norm = 0;
-  for (int i = 0; i < static_cast<int>(inputs.size()); ++i) {
-    norm += inputs[i] * inputs[i];
-  }
-  return norm;
-}
-
-float MulticlassPA::SparseL2NormSquare(
-    const vector<pair<int, float> >& inputs) const {
-  float norm = 0;
-  for (int i = 0; i < static_cast<int>(inputs.size()); ++i) {
-    norm += inputs[i].second * inputs[i].second;
-  }
-  return norm;
-}
-
-float MulticlassPA::TrainOneExample(const vector<float>& inputs, int target) {
-  //CHECK_GE(target, 0);
-  //CHECK_LT(target, num_classes_);
-  float target_class_score = Score(inputs, parameters_[target]);
-  //  VLOG(1) << "target class " << target << " score " << target_class_score;
-  int other_class = PickAClassExcept(target);
-  float other_class_score = Score(inputs, parameters_[other_class]);
-  //  VLOG(1) << "other class " << other_class << " score " << other_class_score;
-  float loss = 1.0 - target_class_score + other_class_score;
-  if (loss > 0.0) {
-    // Compute the learning rate according to PA-I.
-    float twice_norm_square = L2NormSquare(inputs) * 2.0;
-    if (twice_norm_square == 0.0) {
-      twice_norm_square = kEpsilon;
-    }
-    float rate = loss / twice_norm_square;
-    if (rate > aggressiveness_) {
-      rate = aggressiveness_;
-    }
-    //    VLOG(1) << "loss = " << loss << " rate = " << rate;
-    // Modify the parameter vectors of the correct and wrong classes
-    for (int i = 0; i < static_cast<int>(inputs.size()); ++i) {
-      // First modify the parameter value of the correct class
-      parameters_[target][i] += rate * inputs[i];
-      // Then modify the parameter value of the wrong class
-      parameters_[other_class][i] -= rate * inputs[i];
-    }
-    return loss;
-  }
-  return 0.0;
-}
-
-float MulticlassPA::SparseTrainOneExample(
-    const vector<pair<int, float> >& inputs, int target) {
-  // CHECK_GE(target, 0);
-  // CHECK_LT(target, num_classes_);
-  float target_class_score = SparseScore(inputs, parameters_[target]);
-  //  VLOG(1) << "target class " << target << " score " << target_class_score;
-  int other_class = PickAClassExcept(target);
-  float other_class_score = SparseScore(inputs, parameters_[other_class]);
-  //  VLOG(1) << "other class " << other_class << " score " << other_class_score;
-  float loss = 1.0 - target_class_score + other_class_score;
-  if (loss > 0.0) {
-    // Compute the learning rate according to PA-I.
-    float twice_norm_square = SparseL2NormSquare(inputs) * 2.0;
-    if (twice_norm_square == 0.0) {
-      twice_norm_square = kEpsilon;
-    }
-    float rate = loss / twice_norm_square;
-    if (rate > aggressiveness_) {
-      rate = aggressiveness_;
-    }
-    //    VLOG(1) << "loss = " << loss << " rate = " << rate;
-    // Modify the parameter vectors of the correct and wrong classes
-    for (int i = 0; i < static_cast<int>(inputs.size()); ++i) {
-      // First modify the parameter value of the correct class
-      parameters_[target][inputs[i].first] += rate * inputs[i].second;
-      // Then modify the parameter value of the wrong class
-      parameters_[other_class][inputs[i].first] -= rate * inputs[i].second;
-    }
-    return loss;
-  }
-  return 0.0;
-}
-
-float MulticlassPA::Train(const vector<pair<vector<float>, int> >& data,
-                          int num_iterations) {
-  int num_examples = data.size();
-  float total_loss = 0.0;
-  for (int t = 0; t < num_iterations; ++t) {
-    int index = PickAnExample(num_examples);
-    float loss_t = TrainOneExample(data[index].first, data[index].second);
-    total_loss += loss_t;
-  }
-  return total_loss / static_cast<float>(num_iterations);
-}
-
-float MulticlassPA::SparseTrain(
-    const vector<pair<vector<pair<int, float> >, int> >& data,
-    int num_iterations) {
-  int num_examples = data.size();
-  float total_loss = 0.0;
-  for (int t = 0; t < num_iterations; ++t) {
-    int index = PickAnExample(num_examples);
-    float loss_t = SparseTrainOneExample(data[index].first, data[index].second);
-    total_loss += loss_t;
-  }
-  return total_loss / static_cast<float>(num_iterations);
-}
-
-int MulticlassPA::GetClass(const vector<float>& inputs) {
-  int best_class = -1;
-  float best_score = -10000.0;
-  // float best_score = -MathLimits<float>::kMax;
-  for (int i = 0; i < num_classes_; ++i) {
-    float score_i = Score(inputs, parameters_[i]);
-    if (score_i > best_score) {
-      best_score = score_i;
-      best_class = i;
-    }
-  }
-  return best_class;
-}
-
-int MulticlassPA::SparseGetClass(const vector<pair<int, float> >& inputs) {
-  int best_class = -1;
-  float best_score = -10000.0;
-  //float best_score = -MathLimits<float>::kMax;
-  for (int i = 0; i < num_classes_; ++i) {
-    float score_i = SparseScore(inputs, parameters_[i]);
-    if (score_i > best_score) {
-      best_score = score_i;
-      best_class = i;
-    }
-  }
-  return best_class;
-}
-
-float MulticlassPA::Test(const vector<pair<vector<float>, int> >& data) {
-  int num_examples = data.size();
-  float total_error = 0.0;
-  for (int t = 0; t < num_examples; ++t) {
-    int best_class = GetClass(data[t].first);
-    if (best_class != data[t].second) {
-      ++total_error;
-    }
-  }
-  return total_error / num_examples;
-}
-
-float MulticlassPA::SparseTest(
-    const vector<pair<vector<pair<int, float> >, int> >& data) {
-  int num_examples = data.size();
-  float total_error = 0.0;
-  for (int t = 0; t < num_examples; ++t) {
-    int best_class = SparseGetClass(data[t].first);
-    if (best_class != data[t].second) {
-      ++total_error;
-    }
-  }
-  return total_error / num_examples;
-}
-}  // namespace learningfw
diff --git a/bordeaux/learning/multiclass_pa/native/multiclass_pa.h b/bordeaux/learning/multiclass_pa/native/multiclass_pa.h
deleted file mode 100644
index c4d99440a..000000000
--- a/bordeaux/learning/multiclass_pa/native/multiclass_pa.h
+++ /dev/null
@@ -1,117 +0,0 @@
-/*
- * 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.
- */
-
-// This file contains the MulticlassPA class which implements a simple
-// linear multi-class classifier based on the multi-prototype version of
-// passive aggressive.
-
-#ifndef LEARNINGFW_MULTICLASS_PA_H_
-#define LEARNINGFW_MULTICLASS_PA_H_
-
-#include <vector>
-#include <cmath>
-
-const float kEpsilon = 1.0e-4;
-
-namespace learningfw {
-
-class MulticlassPA {
- public:
-  MulticlassPA(int num_classes,
-               int num_dimensions,
-               float aggressiveness);
-  virtual ~MulticlassPA();
-
-  // Initialize all parameters to 0.0.
-  void InitializeParameters();
-
-  // Returns a random class that is different from the target class.
-  int PickAClassExcept(int target);
-
-  // Returns a random example.
-  int PickAnExample(int num_examples);
-
-  // Computes the score of a given input vector for a given parameter
-  // vector, by computing the dot product between the two.
-  float Score(const std::vector<float>& inputs,
-              const std::vector<float>& parameters) const;
-  float SparseScore(const std::vector<std::pair<int, float> >& inputs,
-                    const std::vector<float>& parameters) const;
-
-  // Returns the square of the L2 norm.
-  float L2NormSquare(const std::vector<float>& inputs) const;
-  float SparseL2NormSquare(const std::vector<std::pair<int, float> >& inputs) const;
-
-  // Verify if the given example is correctly classified with margin with
-  // respect to a random class.  If not, then modifies the corresponding
-  // parameters using passive-aggressive.
-  virtual float TrainOneExample(const std::vector<float>& inputs, int target);
-  virtual float SparseTrainOneExample(
-      const std::vector<std::pair<int, float> >& inputs, int target);
-
-  // Iteratively train the model for num_iterations on the given dataset.
-  float Train(const std::vector<std::pair<std::vector<float>, int> >& data,
-              int num_iterations);
-  float SparseTrain(
-      const std::vector<std::pair<std::vector<std::pair<int, float> >, int> >& data,
-      int num_iterations);
-
-  // Returns the best class for a given input vector.
-  virtual int GetClass(const std::vector<float>& inputs);
-  virtual int SparseGetClass(const std::vector<std::pair<int, float> >& inputs);
-
-  // Computes the test error of a given test set on the current model.
-  float Test(const std::vector<std::pair<std::vector<float>, int> >& data);
-  float SparseTest(
-      const std::vector<std::pair<std::vector<std::pair<int, float> >, int> >& data);
-
-  // A few accessors used by the sub-classes.
-  inline float aggressiveness() const {
-    return aggressiveness_;
-  }
-
-  inline std::vector<std::vector<float> >& parameters() {
-    return parameters_;
-  }
-
-  inline std::vector<std::vector<float> >* mutable_parameters() {
-    return &parameters_;
-  }
-
-  inline int num_classes() const {
-    return num_classes_;
-  }
-
-  inline int num_dimensions() const {
-    return num_dimensions_;
-  }
-
- private:
-  // Keeps the current parameter vector.
-  std::vector<std::vector<float> > parameters_;
-
-  // The number of classes of the problem.
-  int num_classes_;
-
-  // The number of dimensions of the input vectors.
-  int num_dimensions_;
-
-  // Controls how "aggressive" training should be.
-  float aggressiveness_;
-
-};
-}  // namespace learningfw
-#endif  // LEARNINGFW_MULTICLASS_PA_H_
diff --git a/bordeaux/learning/multiclass_pa/native/util.cpp b/bordeaux/learning/multiclass_pa/native/util.cpp
deleted file mode 100644
index fc92fe49a..000000000
--- a/bordeaux/learning/multiclass_pa/native/util.cpp
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * 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.
- */
-
-// This file contains a few utilities for MulticlassPA
-
-#include "learning/multiclass_pa/util.h"
-
-#include <string>
-
-#include "learning/multiclass_pa/multiclass_pa.h"
-#include "learning/multiclass_pa/multiclass_pa_max.h"
-#include "learning/multiclass_pa/multiclass_pa_opt.h"
-#include "learning/multiclass_pa/multiclass_warp.h"
-#include "strings/strutil.h"
-
-namespace learning_multiclass_pa {
-
-MulticlassPA* CreateMulticlassPA(const string& version,
-                                 int32 num_classes,
-                                 int32 num_dimensions,
-                                 float aggressiveness) {
-  MulticlassPA* machine = NULL;
-  if (StringCaseEqual(version, "max")) {
-    machine = reinterpret_cast<MulticlassPA*>(
-      new MulticlassPAMax(num_classes, num_dimensions, aggressiveness));
-  } else if (StringCaseEqual(version, "opt")) {
-    machine = reinterpret_cast<MulticlassPA*>(
-      new MulticlassPAOpt(num_classes, num_dimensions, aggressiveness));
-  } else if (StringCaseEqual(version, "random")) {
-    machine = new MulticlassPA(num_classes, num_dimensions, aggressiveness);
-  } else if (StringCaseEqual(version, "warp")) {
-    machine = reinterpret_cast<MulticlassPA*>(
-      new MulticlassWarp(num_classes, num_dimensions, aggressiveness));
-  } else {
-    LOG(ERROR) << "Machine type (" << version << ") unknown";
-  }
-  return machine;
-}
-}  // namespace learning_multiclass_pa
diff --git a/bordeaux/learning/multiclass_pa/native/util.h b/bordeaux/learning/multiclass_pa/native/util.h
deleted file mode 100644
index a3aa4b571..000000000
--- a/bordeaux/learning/multiclass_pa/native/util.h
+++ /dev/null
@@ -1,33 +0,0 @@
-/*
- * 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.
- */
-
-// This file contains some utilities for MulticlassPA
-
-#ifndef LEARNING_MULTICLASS_PA_UTIL_H_
-#define LEARNING_MULTICLASS_PA_UTIL_H_
-
-#include <string>
-
-#include "learning/multiclass_pa/multiclass_pa.h"
-
-namespace learning_multiclass_pa {
-
-MulticlassPA* CreateMulticlassPA(const string& version,
-                                 int32 num_classes,
-                                 int32 num_dimensions,
-                                 float aggressiveness);
-}  // namespace learning_multiclass_pa
-#endif  // LEARNING_MULTICLASS_PA_UTIL_H_
diff --git a/bordeaux/learning/predictor_histogram/Android.mk b/bordeaux/learning/predictor_histogram/Android.mk
deleted file mode 100644
index 1c352f1cb..000000000
--- a/bordeaux/learning/predictor_histogram/Android.mk
+++ /dev/null
@@ -1,16 +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.
-#
-
-LOCAL_PATH := $(call my-dir)
diff --git a/bordeaux/learning/predictor_histogram/java/android/bordeaux/learning/HistogramPredictor.java b/bordeaux/learning/predictor_histogram/java/android/bordeaux/learning/HistogramPredictor.java
deleted file mode 100644
index e63f40dd9..000000000
--- a/bordeaux/learning/predictor_histogram/java/android/bordeaux/learning/HistogramPredictor.java
+++ /dev/null
@@ -1,384 +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.ByteArrayInputStream;
-import java.io.ByteArrayOutputStream;
-import java.io.IOException;
-import java.io.ObjectInputStream;
-import java.io.ObjectOutputStream;
-import java.io.Serializable;
-import java.util.ArrayList;
-import java.util.Collections;
-import java.util.Comparator;
-import java.util.HashMap;
-import java.util.HashSet;
-import java.util.Iterator;
-import java.util.List;
-import java.util.Map;
-import java.util.Map.Entry;
-import java.util.concurrent.ConcurrentHashMap;
-/**
- * A histogram based predictor which records co-occurrences of applations with a speficic
- * feature, for example, location, * time of day, etc. The histogram is kept in a two level
- * hash table. The first level key is the feature value and the second level key is the app
- * id.
- */
-// TODOS:
-// 1. Use forgetting factor to downweight istances propotional to the time
-// 2. Different features could have different weights on prediction scores.
-// 3. Add function to remove sampleid (i.e. remove apps that are uninstalled).
-
-
-public class HistogramPredictor {
-    final static String TAG = "HistogramPredictor";
-
-    private HashMap<String, HistogramCounter> mPredictor =
-            new HashMap<String, HistogramCounter>();
-
-    private HashMap<String, Integer> mClassCounts = new HashMap<String, Integer>();
-    private HashSet<String> mBlacklist = new HashSet<String>();
-
-    private static final int MINIMAL_FEATURE_VALUE_COUNTS = 5;
-    private static final int MINIMAL_APP_APPEARANCE_COUNTS = 5;
-
-    // This parameter ranges from 0 to 1 which determines the effect of app prior.
-    // When it is set to 0, app prior means completely neglected. When it is set to 1
-    // the predictor is a standard naive bayes model.
-    private static final int PRIOR_K_VALUE = 1;
-
-    private static final String[] APP_BLACKLIST = {
-        "com.android.contacts",
-        "com.android.chrome",
-        "com.android.providers.downloads.ui",
-        "com.android.settings",
-        "com.android.vending",
-        "com.android.mms",
-        "com.google.android.gm",
-        "com.google.android.gallery3d",
-        "com.google.android.apps.googlevoice",
-    };
-
-    public HistogramPredictor(String[] blackList) {
-        for (String appName : blackList) {
-            mBlacklist.add(appName);
-        }
-    }
-
-    /*
-     * This class keeps the histogram counts for each feature and provide the
-     * joint probabilities of <feature, class>.
-     */
-    private class HistogramCounter {
-        private HashMap<String, HashMap<String, Integer> > mCounter =
-                new HashMap<String, HashMap<String, Integer> >();
-
-        public HistogramCounter() {
-            mCounter.clear();
-        }
-
-        public void setCounter(HashMap<String, HashMap<String, Integer> > counter) {
-            resetCounter();
-            mCounter.putAll(counter);
-        }
-
-        public void resetCounter() {
-            mCounter.clear();
-        }
-
-        public void addSample(String className, String featureValue) {
-            HashMap<String, Integer> classCounts;
-
-            if (!mCounter.containsKey(featureValue)) {
-                classCounts = new HashMap<String, Integer>();
-                mCounter.put(featureValue, classCounts);
-            } else {
-                classCounts = mCounter.get(featureValue);
-            }
-            int count = (classCounts.containsKey(className)) ?
-                    classCounts.get(className) + 1 : 1;
-            classCounts.put(className, count);
-        }
-
-        public HashMap<String, Double> getClassScores(String featureValue) {
-            HashMap<String, Double> classScores = new HashMap<String, Double>();
-
-            if (mCounter.containsKey(featureValue)) {
-                int totalCount = 0;
-                for(Map.Entry<String, Integer> entry :
-                        mCounter.get(featureValue).entrySet()) {
-                    String app = entry.getKey();
-                    int count = entry.getValue();
-
-                    // For apps with counts less than or equal to one, we treated
-                    // those as having count one. Hence their score, i.e. log(count)
-                    // would be zero. classScroes stores only apps with non-zero scores.
-                    // Note that totalCount also neglect app with single occurrence.
-                    if (count > 1) {
-                        double score = Math.log((double) count);
-                        classScores.put(app, score);
-                        totalCount += count;
-                    }
-                }
-                if (totalCount < MINIMAL_FEATURE_VALUE_COUNTS) {
-                    classScores.clear();
-                }
-            }
-            return classScores;
-        }
-
-        public byte[] getModel() {
-            try {
-                ByteArrayOutputStream byteStream = new ByteArrayOutputStream();
-                ObjectOutputStream objStream = new ObjectOutputStream(byteStream);
-                synchronized(mCounter) {
-                    objStream.writeObject(mCounter);
-                }
-                byte[] bytes = byteStream.toByteArray();
-                return bytes;
-            } catch (IOException e) {
-                throw new RuntimeException("Can't get model");
-            }
-        }
-
-        public boolean setModel(final byte[] modelData) {
-            mCounter.clear();
-            HashMap<String, HashMap<String, Integer> > model;
-
-            try {
-                ByteArrayInputStream input = new ByteArrayInputStream(modelData);
-                ObjectInputStream objStream = new ObjectInputStream(input);
-                model = (HashMap<String, HashMap<String, Integer> >) objStream.readObject();
-            } catch (IOException e) {
-                throw new RuntimeException("Can't load model");
-            } catch (ClassNotFoundException e) {
-                throw new RuntimeException("Learning class not found");
-            }
-
-            synchronized(mCounter) {
-                mCounter.putAll(model);
-            }
-
-            return true;
-        }
-
-
-        public HashMap<String, HashMap<String, Integer> > getCounter() {
-            return mCounter;
-        }
-
-        public String toString() {
-            String result = "";
-            for (Map.Entry<String, HashMap<String, Integer> > entry :
-                     mCounter.entrySet()) {
-                result += "{ " + entry.getKey() + " : " +
-                    entry.getValue().toString() + " }";
-            }
-            return result;
-        }
-    }
-
-    /*
-     * Given a map of feature name -value pairs returns topK mostly likely apps to
-     * be launched with corresponding likelihoods. If topK is set zero, it will return
-     * the whole list.
-     */
-    public List<Map.Entry<String, Double> > findTopClasses(Map<String, String> features, int topK) {
-        // Most sophisticated function in this class
-        HashMap<String, Double> appScores = new HashMap<String, Double>();
-        int validFeatureCount = 0;
-
-        // compute all app scores
-        for (Map.Entry<String, HistogramCounter> entry : mPredictor.entrySet()) {
-            String featureName = entry.getKey();
-            HistogramCounter counter = entry.getValue();
-
-            if (features.containsKey(featureName)) {
-                String featureValue = features.get(featureName);
-                HashMap<String, Double> scoreMap = counter.getClassScores(featureValue);
-
-                if (scoreMap.isEmpty()) {
-                  continue;
-                }
-                validFeatureCount++;
-
-                for (Map.Entry<String, Double> item : scoreMap.entrySet()) {
-                    String appName = item.getKey();
-                    double appScore = item.getValue();
-                    if (appScores.containsKey(appName)) {
-                        appScore += appScores.get(appName);
-                    }
-                    appScores.put(appName, appScore);
-                }
-            }
-        }
-
-        HashMap<String, Double> appCandidates = new HashMap<String, Double>();
-        for (Map.Entry<String, Double> entry : appScores.entrySet()) {
-            String appName = entry.getKey();
-            if (mBlacklist.contains(appName)) {
-                Log.i(TAG, appName + " is in blacklist");
-                continue;
-            }
-            if (!mClassCounts.containsKey(appName)) {
-                throw new RuntimeException("class count error!");
-            }
-            int appCount = mClassCounts.get(appName);
-            if (appCount < MINIMAL_APP_APPEARANCE_COUNTS) {
-                Log.i(TAG, appName + " doesn't have enough counts");
-                continue;
-            }
-
-            double appScore = entry.getValue();
-            double appPrior = Math.log((double) appCount);
-            appCandidates.put(appName,
-                              appScore - appPrior * (validFeatureCount - PRIOR_K_VALUE));
-        }
-
-        // sort app scores
-        List<Map.Entry<String, Double> > appList =
-               new ArrayList<Map.Entry<String, Double> >(appCandidates.size());
-        appList.addAll(appCandidates.entrySet());
-        Collections.sort(appList, new  Comparator<Map.Entry<String, Double> >() {
-            public int compare(Map.Entry<String, Double> o1,
-                               Map.Entry<String, Double> o2) {
-                return o2.getValue().compareTo(o1.getValue());
-            }
-        });
-
-        if (topK == 0) {
-            topK = appList.size();
-        }
-        return appList.subList(0, Math.min(topK, appList.size()));
-    }
-
-    /*
-     * Add a new observation of given sample id and features to the histograms
-     */
-    public void addSample(String sampleId, Map<String, String> features) {
-        for (Map.Entry<String, String> entry : features.entrySet()) {
-            String featureName = entry.getKey();
-            String featureValue = entry.getValue();
-
-            useFeature(featureName);
-            HistogramCounter counter = mPredictor.get(featureName);
-            counter.addSample(sampleId, featureValue);
-        }
-
-        int sampleCount = (mClassCounts.containsKey(sampleId)) ?
-            mClassCounts.get(sampleId) + 1 : 1;
-        mClassCounts.put(sampleId, sampleCount);
-    }
-
-    /*
-     * reset predictor to a empty model
-     */
-    public void resetPredictor() {
-        // TODO: not sure this step would reduce memory waste
-        for (HistogramCounter counter : mPredictor.values()) {
-            counter.resetCounter();
-        }
-        mPredictor.clear();
-        mClassCounts.clear();
-    }
-
-    /*
-     * convert the prediction model into a byte array
-     */
-    public byte[] getModel() {
-        // TODO: convert model to a more memory efficient data structure.
-        HashMap<String, HashMap<String, HashMap<String, Integer > > > model =
-                new HashMap<String, HashMap<String, HashMap<String, Integer > > >();
-        for(Map.Entry<String, HistogramCounter> entry : mPredictor.entrySet()) {
-            model.put(entry.getKey(), entry.getValue().getCounter());
-        }
-
-        try {
-            ByteArrayOutputStream byteStream = new ByteArrayOutputStream();
-            ObjectOutputStream objStream = new ObjectOutputStream(byteStream);
-            objStream.writeObject(model);
-            byte[] bytes = byteStream.toByteArray();
-            return bytes;
-        } catch (IOException e) {
-            throw new RuntimeException("Can't get model");
-        }
-    }
-
-    /*
-     * set the prediction model from a model data in the format of byte array
-     */
-    public boolean setModel(final byte[] modelData) {
-        HashMap<String, HashMap<String, HashMap<String, Integer > > > model;
-
-        try {
-            ByteArrayInputStream input = new ByteArrayInputStream(modelData);
-            ObjectInputStream objStream = new ObjectInputStream(input);
-            model = (HashMap<String, HashMap<String, HashMap<String, Integer > > >)
-                    objStream.readObject();
-        } catch (IOException e) {
-            throw new RuntimeException("Can't load model");
-        } catch (ClassNotFoundException e) {
-            throw new RuntimeException("Learning class not found");
-        }
-
-        resetPredictor();
-        for (Map.Entry<String, HashMap<String, HashMap<String, Integer> > > entry :
-                model.entrySet()) {
-            useFeature(entry.getKey());
-            mPredictor.get(entry.getKey()).setCounter(entry.getValue());
-        }
-
-        // TODO: this is a temporary fix for now
-        loadClassCounter();
-
-        return true;
-    }
-
-    private void loadClassCounter() {
-        String TIME_OF_WEEK = "Time of Week";
-
-        if (!mPredictor.containsKey(TIME_OF_WEEK)) {
-            throw new RuntimeException("Precition model error: missing Time of Week!");
-        }
-
-        HashMap<String, HashMap<String, Integer> > counter =
-            mPredictor.get(TIME_OF_WEEK).getCounter();
-
-        mClassCounts.clear();
-        for (HashMap<String, Integer> map : counter.values()) {
-            for (Map.Entry<String, Integer> entry : map.entrySet()) {
-                int classCount = entry.getValue();
-                String className = entry.getKey();
-                // mTotalClassCount += classCount;
-
-                if (mClassCounts.containsKey(className)) {
-                    classCount += mClassCounts.get(className);
-                }
-                mClassCounts.put(className, classCount);
-            }
-        }
-        Log.i(TAG, "class counts: " + mClassCounts);
-    }
-
-    private void useFeature(String featureName) {
-        if (!mPredictor.containsKey(featureName)) {
-            mPredictor.put(featureName, new HistogramCounter());
-        }
-    }
-}
diff --git a/bordeaux/learning/stochastic_linear_ranker/Android.mk b/bordeaux/learning/stochastic_linear_ranker/Android.mk
deleted file mode 100644
index 62f0fd3a0..000000000
--- a/bordeaux/learning/stochastic_linear_ranker/Android.mk
+++ /dev/null
@@ -1,34 +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.
-#
-
-LOCAL_PATH := $(call my-dir)
-
-##
-# Build native code
-##
-include $(CLEAR_VARS)
-
-LOCAL_MODULE_TAGS := samples tests
-
-LOCAL_MODULE := libstochastic_linear
-
-LOCAL_SRC_FILES := native/stochastic_linear_ranker.cpp \
-                   native/sparse_weight_vector.cpp \
-                   jni/jni_stochastic_linear_ranker.cpp
-
-
-LOCAL_C_INCLUDES += $(LOCAL_PATH)/../native
-
-include $(BUILD_STATIC_LIBRARY)
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);
-}
diff --git a/bordeaux/learning/stochastic_linear_ranker/jni/jni_stochastic_linear_ranker.cpp b/bordeaux/learning/stochastic_linear_ranker/jni/jni_stochastic_linear_ranker.cpp
deleted file mode 100644
index 34709e457..000000000
--- a/bordeaux/learning/stochastic_linear_ranker/jni/jni_stochastic_linear_ranker.cpp
+++ /dev/null
@@ -1,461 +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.
- */
-
-#include "jni/jni_stochastic_linear_ranker.h"
-#include "native/common_defs.h"
-#include "native/sparse_weight_vector.h"
-#include "native/stochastic_linear_ranker.h"
-
-#include <vector>
-#include <string>
-using std::string;
-using std::vector;
-using std::unordered_map;
-using learning_stochastic_linear::StochasticLinearRanker;
-using learning_stochastic_linear::SparseWeightVector;
-
-void CreateSparseWeightVector(JNIEnv* env, const jobjectArray keys, const float* values,
-    const int length, SparseWeightVector<string> * sample) {
-
-  for (int i = 0; i < length; ++i) {
-    jboolean iscopy;
-    jstring s = (jstring) env->GetObjectArrayElement(keys, i);
-    const char *key = env->GetStringUTFChars(s, &iscopy);
-    sample->SetElement(key, static_cast<double>(values[i]));
-    env->ReleaseStringUTFChars(s,key);
-  }
-}
-
-void ConvertParameter2Object(JNIEnv* env, jobjectArray *keys, jobjectArray *values,
-    const char * name , const char * paramValue, int index) {
-
-    jstring jstrK = env->NewStringUTF(name);
-    jstring jstrV = env->NewStringUTF(paramValue);
-    env->SetObjectArrayElement(*keys, index, jstrK);
-    env->SetObjectArrayElement(*values, index, jstrV);
-}
-
-void DecomposeSparseWeightVector(JNIEnv* env, jobjectArray *keys, jfloatArray *values,
-    SparseWeightVector<string> *sample) {
-
-  SparseWeightVector<string>::Wmap w_ = sample->GetMap();
-  int i=0;
-  for ( SparseWeightVector<string>::Witer_const iter = w_.begin();
-    iter != w_.end(); ++iter) {
-    std::string key = iter->first;
-    float value = (float) iter->second;
-    jstring jstr = env->NewStringUTF(key.c_str());
-    env->SetObjectArrayElement(*keys, i, jstr);
-    jfloat s[1];
-    s[0] = value;
-    env->SetFloatArrayRegion(*values, i, 1, s);
-    i++;
-  }
-}
-
-jboolean Java_android_bordeaux_learning_StochasticLinearRanker_nativeSetWeightClassifier(
-    JNIEnv* env,
-    jobject /* thiz */,
-    jobjectArray key_array_model,
-    jfloatArray value_array_model,
-    jfloat normalizer_model,
-    jlong paPtr) {
-
-  StochasticLinearRanker<string>* classifier = (StochasticLinearRanker<string>*) paPtr;
-  if (classifier && key_array_model && value_array_model && normalizer_model) {
-    const int keys_m_len = env->GetArrayLength(key_array_model);
-    jfloat* values_m = env->GetFloatArrayElements(value_array_model, NULL);
-    const int values_m_len = env->GetArrayLength(value_array_model);
-
-    if (values_m && key_array_model && values_m_len == keys_m_len) {
-      SparseWeightVector<string> model;
-      CreateSparseWeightVector(env, key_array_model, values_m, values_m_len, &model);
-      model.SetNormalizer(normalizer_model);
-      classifier->LoadWeights(model);
-      env->ReleaseFloatArrayElements(value_array_model, values_m, JNI_ABORT);
-      return JNI_TRUE;
-    }
-  }
-  return JNI_FALSE;
-}
-
-jboolean Java_android_bordeaux_learning_StochasticLinearRanker_nativeSetParameterClassifier(
-    JNIEnv* env,
-    jobject /* thiz */,
-    jstring key,
-    jstring value,
-    jlong paPtr) {
-
-  StochasticLinearRanker<string>* classifier = (StochasticLinearRanker<string>*) paPtr;
-  jboolean iscopy;
-  const char *cKey = env->GetStringUTFChars(key, &iscopy);
-  const char *cValue = env->GetStringUTFChars(value, &iscopy);
-  float v;
-  if (strcmp(cKey, ITR_NUM) == 0){
-    sscanf(cValue, "%f", &v);
-    classifier->SetIterationNumber((uint64) v);
-    return JNI_TRUE;
-  }
-  else if (strcmp(cKey, NORM_CONSTRAINT) == 0){
-    sscanf(cValue, "%f", &v);
-    classifier->SetNormConstraint((double) v);
-    return JNI_TRUE;
-  }
-  else if (strcmp(cKey, REG_TYPE) == 0){
-    if (strcmp(cValue, REG_TYPE_L0 ) == 0)
-      classifier->SetRegularizationType(learning_stochastic_linear::L0);
-    else if (strcmp(cValue, REG_TYPE_L1 ) == 0)
-      classifier->SetRegularizationType(learning_stochastic_linear::L1);
-    else if (strcmp(cValue, REG_TYPE_L2 ) == 0)
-      classifier->SetRegularizationType(learning_stochastic_linear::L2);
-    else if (strcmp(cValue, REG_TYPE_L1L2 ) == 0)
-      classifier->SetRegularizationType(learning_stochastic_linear::L1L2);
-    else if (strcmp(cValue, REG_TYPE_L1LInf ) == 0)
-      classifier->SetRegularizationType(learning_stochastic_linear::L1LInf);
-    else {
-      ALOGE("Error: %s is not a Regularization Type", cValue);
-      return JNI_FALSE;
-    }
-    return JNI_TRUE;
-  }
-  else if (strcmp(cKey, LAMBDA) == 0){
-    sscanf(cValue, "%f", &v);
-    classifier->SetLambda((double) v);
-    return JNI_TRUE;
-  }
-  else if (strcmp(cKey, UPDATE_TYPE) == 0){
-    if (strcmp(cValue, UPDATE_TYPE_FULL_CS) == 0)
-      classifier->SetUpdateType(learning_stochastic_linear::FULL_CS);
-    else if (strcmp(cValue, UPDATE_TYPE_CLIP_CS) == 0)
-      classifier->SetUpdateType(learning_stochastic_linear::CLIP_CS);
-    else if (strcmp(cValue, UPDATE_TYPE_REG_CS ) == 0)
-      classifier->SetUpdateType(learning_stochastic_linear::REG_CS);
-    else if (strcmp(cValue, UPDATE_TYPE_SL) == 0)
-      classifier->SetUpdateType(learning_stochastic_linear::SL);
-    else if (strcmp(cValue, UPDATE_TYPE_ADAPTIVE_REG) == 0)
-      classifier->SetUpdateType(learning_stochastic_linear::ADAPTIVE_REG);
-    else {
-      ALOGE("Error: %s is not an Update Type", cValue);
-      return JNI_FALSE;
-    }
-    return JNI_TRUE;
-  }
-  else if (strcmp(cKey, ADAPT_MODE) == 0){
-    if (strcmp(cValue, ADAPT_MODE_CONST ) == 0)
-      classifier->SetAdaptationMode(learning_stochastic_linear::CONST);
-    else if (strcmp(cValue, ADAPT_MODE_INV_LINEAR ) == 0)
-      classifier->SetAdaptationMode(learning_stochastic_linear::INV_LINEAR);
-    else if (strcmp(cValue, ADAPT_MODE_INV_QUADRATIC ) == 0)
-      classifier->SetAdaptationMode(learning_stochastic_linear::INV_QUADRATIC);
-    else if (strcmp(cValue, ADAPT_MODE_INV_SQRT ) == 0)
-      classifier->SetAdaptationMode(learning_stochastic_linear::INV_SQRT);
-    else {
-      ALOGE("Error: %s is not an Adaptation Mode", cValue);
-      return JNI_FALSE;
-    }
-    return JNI_TRUE;
-  }
-  else if (strcmp(cKey, KERNEL_TYPE) == 0){
-    if (strcmp(cValue, KERNEL_TYPE_LINEAR ) == 0)
-      classifier->SetKernelType(learning_stochastic_linear::LINEAR);
-    else if (strcmp(cValue, KERNEL_TYPE_POLY ) == 0)
-      classifier->SetKernelType(learning_stochastic_linear::POLY);
-    else if (strcmp(cValue, KERNEL_TYPE_RBF ) == 0)
-      classifier->SetKernelType(learning_stochastic_linear::RBF);
-    else {
-      ALOGE("Error: %s is not a Kernel Type", cValue);
-      return JNI_FALSE;
-    }
-    return JNI_TRUE;
-  }
-  else if (strcmp(cKey, KERNEL_PARAM) == 0){
-    sscanf(cValue, "%f", &v);
-    classifier->SetKernelParam((double) v);
-    return JNI_TRUE;
-  }
-  else if (strcmp(cKey, KERNEL_GAIN) == 0){
-    sscanf(cValue, "%f", &v);
-    classifier->SetKernelGain((double) v);
-    return JNI_TRUE;
-  }
-  else if (strcmp(cKey, KERNEL_BIAS) == 0){
-    sscanf(cValue, "%f", &v);
-    classifier->SetKernelBias((double) v);
-    return JNI_TRUE;
-  }
-  else if (strcmp(cKey, LOSS_TYPE) == 0){
-    if (strcmp(cValue, LOSS_TYPE_PAIRWISE ) == 0)
-      classifier->SetRankLossType(learning_stochastic_linear::PAIRWISE);
-    else if (strcmp(cValue, LOSS_TYPE_RECIPROCAL_RANK ) == 0)
-      classifier->SetRankLossType(learning_stochastic_linear::RECIPROCAL_RANK);
-    else {
-      ALOGE("Error: %s is not a Kernel Type", cValue);
-      return JNI_FALSE;
-    }
-    return JNI_TRUE;
-  }
-  else if (strcmp(cKey, ACC_PROB) == 0){
-    sscanf(cValue, "%f", &v);
-    classifier->SetAcceptanceProbability((double) v);
-    return JNI_TRUE;
-  }
-  else if (strcmp(cKey, MIN_BATCH_SIZE) == 0){
-    sscanf(cValue, "%f", &v);
-    classifier->SetMiniBatchSize((uint64) v);
-    return JNI_TRUE;
-  }
-  else if (strcmp(cKey, GRAD_L0_NORM) == 0){
-    sscanf(cValue, "%f", &v);
-    classifier->SetGradientL0Norm((int32) v);
-    return JNI_TRUE;
-  }
-  ALOGE("Error: %s is not a ranker parameter", cKey);
-  return JNI_FALSE;
-}
-
-jint Java_android_bordeaux_learning_StochasticLinearRanker_nativeGetLengthClassifier(
-  JNIEnv* /* env */,
-  jobject /* thiz */,
-  jlong paPtr) {
-
-  StochasticLinearRanker<string>* classifier = (StochasticLinearRanker<string>*) paPtr;
-  SparseWeightVector<string> M_weights;
-  classifier->SaveWeights(&M_weights);
-
-  SparseWeightVector<string>::Wmap w_map = M_weights.GetMap();
-  int len = w_map.size();
-  return len;
-}
-
-std::string ConvertFloat2String(float v){
-    std::stringstream converter;
-    converter << v;
-    return converter.str();
-}
-
-void Java_android_bordeaux_learning_StochasticLinearRanker_nativeGetParameterClassifier(
-    JNIEnv* env,
-    jobject /* thiz */,
-    jobjectArray key_array_param,
-    jobjectArray value_array_param,
-    jlong paPtr){
-
-  std::string s;
-  StochasticLinearRanker<string>* classifier = (StochasticLinearRanker<string>*) paPtr;
-  s = ConvertFloat2String((float) classifier->GetIterationNumber());
-  ConvertParameter2Object(env, &key_array_param, &value_array_param, ITR_NUM, s.c_str(), 0 );
-
-  s = ConvertFloat2String((float) classifier->GetNormContraint());
-  ConvertParameter2Object(env, &key_array_param, &value_array_param, NORM_CONSTRAINT, s.c_str(), 1 );
-
-  float value = (float) classifier->GetRegularizationType();
-  switch ((int) value) {
-    case learning_stochastic_linear::L0 :
-      s = REG_TYPE_L0;
-      break;
-    case learning_stochastic_linear::L1 :
-      s = REG_TYPE_L1;
-      break;
-    case learning_stochastic_linear::L2 :
-      s = REG_TYPE_L2;
-      break;
-    case learning_stochastic_linear::L1L2 :
-      s = REG_TYPE_L1L2;
-      break;
-    case learning_stochastic_linear::L1LInf :
-      s = REG_TYPE_L1LInf;
-      break;
-  }
-  ConvertParameter2Object(env, &key_array_param, &value_array_param, REG_TYPE, s.c_str(), 2 );
-
-  s = ConvertFloat2String((float) classifier->GetLambda());
-  ConvertParameter2Object(env, &key_array_param, &value_array_param, LAMBDA, s.c_str(), 3 );
-
-  value = (float) classifier->GetUpdateType();
-  switch ((int) value) {
-    case learning_stochastic_linear::FULL_CS :
-      s = UPDATE_TYPE_FULL_CS;
-      break;
-    case learning_stochastic_linear::CLIP_CS :
-      s = UPDATE_TYPE_CLIP_CS;
-      break;
-    case learning_stochastic_linear::REG_CS :
-      s = UPDATE_TYPE_REG_CS;
-      break;
-    case learning_stochastic_linear::SL :
-      s = UPDATE_TYPE_SL;
-      break;
-    case learning_stochastic_linear::ADAPTIVE_REG :
-      s = UPDATE_TYPE_ADAPTIVE_REG;
-      break;
-  }
-  ConvertParameter2Object(env, &key_array_param, &value_array_param, UPDATE_TYPE, s.c_str(), 4 );
-
-  value = (float) classifier->GetAdaptationMode();
-  switch ((int) value) {
-    case learning_stochastic_linear::CONST :
-      s = ADAPT_MODE_CONST;
-      break;
-    case learning_stochastic_linear::INV_LINEAR :
-      s = ADAPT_MODE_INV_LINEAR;
-      break;
-    case learning_stochastic_linear::INV_QUADRATIC :
-      s = ADAPT_MODE_INV_QUADRATIC;
-      break;
-    case learning_stochastic_linear::INV_SQRT :
-      s = ADAPT_MODE_INV_SQRT;
-      break;
-  }
-  ConvertParameter2Object(env, &key_array_param, &value_array_param, ADAPT_MODE, s.c_str(), 5 );
-
-  value = (float) classifier->GetKernelType();
-  switch ((int) value) {
-    case learning_stochastic_linear::LINEAR :
-      s = KERNEL_TYPE_LINEAR;
-      break;
-    case learning_stochastic_linear::POLY :
-      s = KERNEL_TYPE_POLY;
-      break;
-    case learning_stochastic_linear::RBF :
-      s = KERNEL_TYPE_RBF;
-      break;
-  }
-  ConvertParameter2Object(env, &key_array_param, &value_array_param, KERNEL_TYPE, s.c_str(), 6 );
-
-  s = ConvertFloat2String((float) classifier->GetKernelParam());
-  ConvertParameter2Object(env, &key_array_param, &value_array_param, KERNEL_PARAM, s.c_str(), 7 );
-
-  s = ConvertFloat2String((float) classifier->GetKernelGain());
-  ConvertParameter2Object(env, &key_array_param, &value_array_param, KERNEL_GAIN, s.c_str(), 8 );
-
-  s = ConvertFloat2String((float)classifier->GetKernelBias());
-  ConvertParameter2Object(env, &key_array_param, &value_array_param, KERNEL_BIAS, s.c_str(), 9 );
-
-  value = (float) classifier->GetRankLossType();
-  switch ((int) value) {
-    case learning_stochastic_linear::PAIRWISE :
-      s = LOSS_TYPE_PAIRWISE;
-      break;
-    case learning_stochastic_linear::RECIPROCAL_RANK :
-      s = LOSS_TYPE_RECIPROCAL_RANK;
-      break;
-  }
-  ConvertParameter2Object(env, &key_array_param, &value_array_param, LOSS_TYPE, s.c_str(), 10 );
-
-  s = ConvertFloat2String((float) classifier->GetAcceptanceProbability());
-  ConvertParameter2Object(env, &key_array_param, &value_array_param, ACC_PROB, s.c_str(), 11 );
-
-  s = ConvertFloat2String((float) classifier->GetMiniBatchSize());
-  ConvertParameter2Object(env, &key_array_param, &value_array_param, MIN_BATCH_SIZE, s.c_str(), 12 );
-
-  s = ConvertFloat2String((float) classifier->GetGradientL0Norm());
-  ConvertParameter2Object(env, &key_array_param, &value_array_param, GRAD_L0_NORM, s.c_str(), 13 );
-}
-
-void Java_android_bordeaux_learning_StochasticLinearRanker_nativeGetWeightClassifier(
-  JNIEnv* env,
-  jobject /* thiz */,
-  jobjectArray key_array_model,
-  jfloatArray value_array_model,
-  jfloat /* normalizer */,
-  jlong paPtr) {
-
-  StochasticLinearRanker<string>* classifier = (StochasticLinearRanker<string>*) paPtr;
-  SparseWeightVector<string> M_weights;
-  classifier->SaveWeights(&M_weights);
-  SparseWeightVector<string>::Wmap w_map = M_weights.GetMap();
-  DecomposeSparseWeightVector(env, &key_array_model, &value_array_model, &M_weights);
-}
-
-jlong Java_android_bordeaux_learning_StochasticLinearRanker_initNativeClassifier(
-  JNIEnv* /* env */,
-  jobject /* thiz */) {
-  StochasticLinearRanker<string>* classifier = new StochasticLinearRanker<string>();
-  return ((jlong) classifier);
-}
-
-jboolean Java_android_bordeaux_learning_StochasticLinearRanker_deleteNativeClassifier(
-  JNIEnv* /* env */,
-  jobject /* thiz */,
-  jlong paPtr) {
-  StochasticLinearRanker<string>* classifier = (StochasticLinearRanker<string>*) paPtr;
-  delete classifier;
-  return JNI_TRUE;
-}
-
-jboolean 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) {
-  StochasticLinearRanker<string>* classifier = (StochasticLinearRanker<string>*) paPtr;
-
-  if (classifier && key_array_positive && value_array_positive &&
-      key_array_negative && value_array_negative) {
-
-    const int keys_p_len = env->GetArrayLength(key_array_positive);
-    jfloat* values_p = env->GetFloatArrayElements(value_array_positive, NULL);
-    const int values_p_len = env->GetArrayLength(value_array_positive);
-    jfloat* values_n = env->GetFloatArrayElements(value_array_negative, NULL);
-    const int values_n_len = env->GetArrayLength(value_array_negative);
-    const int keys_n_len = env->GetArrayLength(key_array_negative);
-
-    if (values_p && key_array_positive && values_p_len == keys_p_len &&
-      values_n && key_array_negative && values_n_len == keys_n_len) {
-
-      SparseWeightVector<string> sample_pos;
-      SparseWeightVector<string> sample_neg;
-      CreateSparseWeightVector(env, key_array_positive, values_p, values_p_len, &sample_pos);
-      CreateSparseWeightVector(env, key_array_negative, values_n, values_n_len, &sample_neg);
-      classifier->UpdateClassifier(sample_pos, sample_neg);
-      env->ReleaseFloatArrayElements(value_array_negative, values_n, JNI_ABORT);
-      env->ReleaseFloatArrayElements(value_array_positive, values_p, JNI_ABORT);
-
-      return JNI_TRUE;
-    }
-    env->ReleaseFloatArrayElements(value_array_negative, values_n, JNI_ABORT);
-    env->ReleaseFloatArrayElements(value_array_positive, values_p, JNI_ABORT);
-  }
-  return JNI_FALSE;
-}
-
-jfloat Java_android_bordeaux_learning_StochasticLinearRanker_nativeScoreSample(
-  JNIEnv* env,
-  jobject /* thiz */,
-  jobjectArray key_array,
-  jfloatArray value_array,
-  jlong paPtr) {
-
-  StochasticLinearRanker<string>* classifier = (StochasticLinearRanker<string>*) paPtr;
-
-  if (classifier && key_array && value_array) {
-
-    jfloat* values = env->GetFloatArrayElements(value_array, NULL);
-    const int values_len = env->GetArrayLength(value_array);
-    const int keys_len = env->GetArrayLength(key_array);
-
-    if (values && key_array && values_len == keys_len) {
-      SparseWeightVector<string> sample;
-      CreateSparseWeightVector(env, key_array, values, values_len, &sample);
-      env->ReleaseFloatArrayElements(value_array, values, JNI_ABORT);
-      return classifier->ScoreSample(sample);
-    }
-  }
-  return -1;
-}
diff --git a/bordeaux/learning/stochastic_linear_ranker/jni/jni_stochastic_linear_ranker.h b/bordeaux/learning/stochastic_linear_ranker/jni/jni_stochastic_linear_ranker.h
deleted file mode 100644
index fd187b009..000000000
--- a/bordeaux/learning/stochastic_linear_ranker/jni/jni_stochastic_linear_ranker.h
+++ /dev/null
@@ -1,182 +0,0 @@
-/*
- * 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 LEARNING_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_LEARNING_JNI_STOCHASTIC_LINEAR_RANKER_H */
diff --git a/bordeaux/learning/stochastic_linear_ranker/native/common_defs.h b/bordeaux/learning/stochastic_linear_ranker/native/common_defs.h
deleted file mode 100644
index 4df8686c5..000000000
--- a/bordeaux/learning/stochastic_linear_ranker/native/common_defs.h
+++ /dev/null
@@ -1,46 +0,0 @@
-/*
- * 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.
- */
-
-// Contains definitions commom to all classification and ranking algorithms
-// in this package.
-
-#ifndef LEARNING_STOCHASTIC_LINEAR_COMMON_DEFS_H_
-#define LEARNING_STOCHASTIC_LINEAR_COMMON_DEFS_H_
-
-namespace learning_stochastic_linear {
-// Defines standard types of regulariation methods.
-enum RegularizationType { L0, L1, L2, L1L2, L1LInf };
-
-// Defines standard adaptation modes for which Stochastic Sub-Gradient
-// methods are known to converge.
-enum AdaptationMode { CONST, INV_LINEAR, INV_QUADRATIC, INV_SQRT };
-enum UpdateType { FULL_CS, CLIP_CS, REG_CS, SL, ADAPTIVE_REG };
-enum RankLossType { PAIRWISE, RECIPROCAL_RANK };
-enum KernelType { LINEAR, POLY, RBF };
-enum MulticlassUpdateType { MAX, RANK };
-}  // namespace learning_stochastic_linear
-
-#ifdef ANDROID
-#define uint32 uint32_t
-#define int32 int32_t
-#define uint64 uint64_t
-#define int64 int64_t
-#include <cutils/log.h>
-#define CHECK_GT(x,y) if ((x)<(y)) ALOGE("CHECK_GT failed at file %s line %d", \
-                                   __FILE__, __LINE__);
-#endif
-
-#endif  // LEARNING_STOCHASTIC_LINEAR_COMMON_DEFS_H_
diff --git a/bordeaux/learning/stochastic_linear_ranker/native/learning_rate_controller-inl.h b/bordeaux/learning/stochastic_linear_ranker/native/learning_rate_controller-inl.h
deleted file mode 100644
index 8aa01f00b..000000000
--- a/bordeaux/learning/stochastic_linear_ranker/native/learning_rate_controller-inl.h
+++ /dev/null
@@ -1,127 +0,0 @@
-/*
- * 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.
- */
-
-// Implements learning rate adaptations common to most stochastic algorithms.
-
-#ifndef LEARNING_STOCHASTIC_LINEAR_LEARNING_RATE_CONTROLLER_INL_H_
-#define LEARNING_STOCHASTIC_LINEAR_LEARNING_RATE_CONTROLLER_INL_H_
-
-#include <cmath>
-#include "common_defs.h"
-
-namespace learning_stochastic_linear {
-
-class LearningRateController {
- public:
-  LearningRateController() {
-    iteration_num_ = 1;
-    lambda_ = 1.0;
-    mini_batch_size_ = 1;
-    mini_batch_counter_ = 1;
-    sample_num_ = 1;
-    mode_ = INV_LINEAR;
-    is_first_sample_ = true;
-  }
-  ~LearningRateController() {}
-  // Getters and Setters for learning rate parameter lambda_
-  double GetLambda() const {
-    return lambda_;
-  }
-  void SetLambda(double lambda) {
-    lambda_ = lambda;
-  }
-  // Operations on current iteration number
-  void SetIterationNumber(uint64 num) {
-    iteration_num_ = num;
-  }
-  void IncrementIteration() {
-    ++iteration_num_;
-  }
-  uint64 GetIterationNumber() const {
-    return iteration_num_;
-  }
-  // Mini batch operations
-  uint64 GetMiniBatchSize() const {
-    return mini_batch_size_;
-  }
-  void SetMiniBatchSize(uint64 size) {
-    //CHECK_GT(size, 0);
-    mini_batch_size_ = size;
-  }
-  void IncrementSample() {
-    // If this is the first sample we've already counted it to prevent NaNs
-    // in the learning rate computation
-    if (is_first_sample_) {
-      is_first_sample_ = false;
-      return;
-    }
-    ++sample_num_;
-    if (1 == mini_batch_size_) {
-      IncrementIteration();
-      mini_batch_counter_ = 0;
-    } else {
-      ++mini_batch_counter_;
-      if ((mini_batch_counter_ % mini_batch_size_ == 0)) {
-        IncrementIteration();
-        mini_batch_counter_ = 0;
-      }
-    }
-  }
-  uint64 GetMiniBatchCounter() const {
-    return mini_batch_counter_;
-  }
-  // Getters and setters for adaptation mode
-  AdaptationMode GetAdaptationMode() const {
-    return mode_;
-  }
-  void SetAdaptationMode(AdaptationMode m) {
-    mode_ = m;
-  }
-  double GetLearningRate() const {
-    if (mode_ == CONST) {
-      return (1.0 / (lambda_ * mini_batch_size_));
-    } else if (mode_ == INV_LINEAR) {
-      return (1.0 / (lambda_ * iteration_num_ * mini_batch_size_));
-    } else if (mode_ == INV_QUADRATIC) {
-      return (1.0 / (lambda_ *
-                     mini_batch_size_ *
-                     (static_cast<double>(iteration_num_) * iteration_num_)));
-    } else if (mode_ == INV_SQRT) {
-      return (1.0 / (lambda_ *
-                     mini_batch_size_ *
-                     sqrt((double)iteration_num_)));
-    }
-    return 0;
-  }
-  void CopyFrom(const LearningRateController &other) {
-    iteration_num_ = other.iteration_num_;
-    sample_num_ = other.sample_num_;
-    mini_batch_size_ = other.mini_batch_size_;
-    mini_batch_counter_ = other.mini_batch_counter_;
-    mode_ = other.mode_;
-    is_first_sample_ = other.is_first_sample_;
-  }
- private:
-  uint64 iteration_num_;
-  uint64 sample_num_;
-  uint64 mini_batch_size_;
-  uint64 mini_batch_counter_;
-  double lambda_;
-  AdaptationMode mode_;
-  bool is_first_sample_;
-};
-}  // namespace learning_stochastic_linear
-#endif  // LEARNING_STOCHASTIC_LINEAR_LEARNING_RATE_CONTROLLER_INL_H_
diff --git a/bordeaux/learning/stochastic_linear_ranker/native/sparse_weight_vector.cpp b/bordeaux/learning/stochastic_linear_ranker/native/sparse_weight_vector.cpp
deleted file mode 100644
index 52ce11853..000000000
--- a/bordeaux/learning/stochastic_linear_ranker/native/sparse_weight_vector.cpp
+++ /dev/null
@@ -1,398 +0,0 @@
-/*
- * 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.
- */
-
-#include "sparse_weight_vector.h"
-
-#include <algorithm>
-#include <list>
-#include <vector>
-#include <math.h>
-
-using std::vector;
-using std::list;
-using std::max;
-
-namespace learning_stochastic_linear {
-
-// Max/Min permitted values of normalizer_ for preventing under/overflows.
-static double kNormalizerMin = 1e-20;
-static double kNormalizerMax = 1e20;
-
-template<class Key, class Hash>
-bool SparseWeightVector<Key, Hash>::IsValid() const {
-  if (isnan(normalizer_) || __isinff(normalizer_))
-    return false;
-  for (Witer_const iter = w_.begin();
-       iter != w_.end();
-       ++iter) {
-    if (isnanf(iter->second) || __isinff(iter->second))
-      return false;
-  }
-  return true;
-}
-
-template<class Key, class Hash>
-void SparseWeightVector<Key, Hash>::AdditiveWeightUpdate(
-    const double multiplier,
-    const SparseWeightVector<Key, Hash> &w1,
-    const double additive_const) {
-  for (Witer_const iter = w1.w_.begin();
-      iter != w1.w_.end();
-      ++iter) {
-    w_[iter->first] += ((multiplier * iter->second) / w1.normalizer_
-                        + additive_const) * normalizer_;
-  }
-  return;
-}
-
-template<class Key, class Hash>
-void SparseWeightVector<Key, Hash>::AdditiveSquaredWeightUpdate(
-    const double multiplier,
-    const SparseWeightVector<Key, Hash> &w1,
-    const double additive_const) {
-  for (Witer_const iter = w1.w_.begin();
-      iter != w1.w_.end();
-      ++iter) {
-    w_[iter->first] += ((multiplier * iter->second * iter->second) /
-                          (w1.normalizer_ * w1.normalizer_)
-                        + additive_const) * normalizer_;
-  }
-  return;
-}
-
-template<class Key, class Hash>
-void SparseWeightVector<Key, Hash>::AdditiveInvSqrtWeightUpdate(
-    const double multiplier,
-    const SparseWeightVector<Key, Hash> &w1,
-    const double additive_const) {
-  for (Witer_const iter = w1.w_.begin();
-      iter != w1.w_.end();
-      ++iter) {
-    if(iter->second > 0.0) {
-      w_[iter->first] += ((multiplier * sqrt(w1.normalizer_)) /
-                          (sqrt(iter->second))
-                          + additive_const) * normalizer_;
-    }
-  }
-  return;
-}
-
-template<class Key, class Hash>
-void SparseWeightVector<Key, Hash>::AdditiveWeightUpdateBounded(
-    const double multiplier,
-    const SparseWeightVector<Key, Hash> &w1,
-    const double additive_const) {
-  double min_bound = 0;
-  double max_bound = 0;
-  for (Witer_const iter = w1.w_.begin();
-      iter != w1.w_.end();
-      ++iter) {
-    w_[iter->first] += ((multiplier * iter->second) / w1.normalizer_
-                        + additive_const) * normalizer_;
-    bool is_min_bounded = GetValue(wmin_, iter->first, &min_bound);
-    if (is_min_bounded) {
-      if ((w_[iter->first] / normalizer_) < min_bound) {
-        w_[iter->first] = min_bound*normalizer_;
-        continue;
-      }
-    }
-    bool is_max_bounded = GetValue(wmax_, iter->first, &max_bound);
-    if (is_max_bounded) {
-      if ((w_[iter->first] / normalizer_) > max_bound)
-        w_[iter->first] = max_bound*normalizer_;
-    }
-  }
-  return;
-}
-
-template<class Key, class Hash>
-void SparseWeightVector<Key, Hash>::MultWeightUpdate(
-    const SparseWeightVector<Key, Hash> &w1) {
-  for (Witer iter = w_.begin();
-      iter != w_.end();
-      ++iter) {
-    iter->second *= w1.GetElement(iter->first);
-  }
-  normalizer_ *= w1.normalizer_;
-  return;
-}
-
-template<class Key, class Hash>
-void SparseWeightVector<Key, Hash>::MultWeightUpdateBounded(
-    const SparseWeightVector<Key, Hash> &w1) {
-  double min_bound = 0;
-  double max_bound = 0;
-
-  normalizer_ *= w1.normalizer_;
-  for (Witer iter = w_.begin();
-      iter != w_.end();
-      ++iter) {
-    iter->second *= w1.GetElement(iter->first);
-    bool is_min_bounded = GetValue(wmin_, iter->first, &min_bound);
-    if (is_min_bounded) {
-      if ((iter->second / normalizer_) < min_bound) {
-        iter->second = min_bound*normalizer_;
-        continue;
-      }
-    }
-    bool is_max_bounded = GetValue(wmax_, iter->first, &max_bound);
-    if (is_max_bounded) {
-      if ((iter->second / normalizer_) > max_bound)
-        iter->second = max_bound*normalizer_;
-    }
-  }
-  return;
-}
-
-template<class Key, class Hash>
-void SparseWeightVector<Key, Hash>::ResetNormalizer() {
-  for (Witer iter = w_.begin();
-       iter != w_.end();
-       ++iter) {
-    iter->second /= normalizer_;
-  }
-  normalizer_ = 1.0;
-}
-
-template<class Key, class Hash>
-void SparseWeightVector<Key, Hash>::ReprojectToBounds() {
-  double min_bound = 0;
-  double max_bound = 0;
-
-  for (Witer iter = w_.begin();
-       iter != w_.end();
-       ++iter) {
-    bool is_min_bounded = GetValue(wmin_, iter->first, &min_bound);
-    if (is_min_bounded) {
-      if ((iter->second/normalizer_) < min_bound) {
-        iter->second = min_bound*normalizer_;
-        continue;
-      }
-    }
-    bool is_max_bounded = GetValue(wmax_, iter->first, &max_bound);
-    if (is_max_bounded) {
-      if ((iter->second/normalizer_) > max_bound)
-        iter->second = max_bound*normalizer_;
-    }
-  }
-}
-
-template<class Key, class Hash>
-double SparseWeightVector<Key, Hash>::DotProduct(
-    const SparseWeightVector<Key, Hash> &w1) const {
-  double result = 0;
-  if (w_.size() > w1.w_.size()) {
-    for (Witer_const iter = w1.w_.begin();
-        iter != w1.w_.end();
-        ++iter) {
-      result += iter->second * GetElement(iter->first);
-    }
-    result /= (this->normalizer_ * w1.normalizer_);
-  } else {
-    for (Witer_const iter = w_.begin();
-        iter != w_.end();
-        ++iter) {
-      result += iter->second * w1.GetElement(iter->first);
-    }
-    result /= (this->normalizer_ * w1.normalizer_);
-  }
-  return result;
-}
-
-template<class Key, class Hash>
-double SparseWeightVector<Key, Hash>::LxNorm(const double x) const {
-  double result = 0;
-  CHECK_GT(x, 0);
-  for (Witer_const iter = w_.begin();
-      iter != w_.end();
-      ++iter) {
-    result += pow(iter->second, x);
-  }
-  return (pow(result, 1.0 / x) / normalizer_);
-}
-
-template<class Key, class Hash>
-double SparseWeightVector<Key, Hash>::L2Norm() const {
-  double result = 0;
-  for (Witer_const iter = w_.begin();
-      iter != w_.end();
-      ++iter) {
-    result += iter->second * iter->second;
-  }
-  return sqrt(result)/normalizer_;
-}
-
-template<class Key, class Hash>
-double SparseWeightVector<Key, Hash>::L1Norm() const {
-  double result = 0;
-  for (Witer_const iter = w_.begin();
-      iter != w_.end();
-      ++iter) {
-    result += fabs(iter->second);
-  }
-  return result / normalizer_;
-}
-
-template<class Key, class Hash>
-double SparseWeightVector<Key, Hash>::L0Norm(
-    const double epsilon) const {
-  double result = 0;
-  for (Witer_const iter = w_.begin();
-      iter != w_.end();
-      ++iter) {
-    if (fabs(iter->second / normalizer_) > epsilon)
-      ++result;
-  }
-  return result;
-}
-
-// Algorithm for L0 projection which takes O(n log(n)), where n is
-// the number of non-zero elements in the vector.
-template<class Key, class Hash>
-void SparseWeightVector<Key, Hash>::ReprojectL0(const double l0_norm) {
-// First calculates the order-statistics of the sparse vector
-// and then reprojects to the L0 orthant with the requested norm.
-  CHECK_GT(l0_norm, 0);
-  uint64 req_l0_norm = static_cast<uint64>(l0_norm);
-  // Compute order statistics and the current L0 norm.
-  vector<double> abs_val_vec;
-  uint64 curr_l0_norm = 0;
-  const double epsilone = 1E-05;
-  for (Witer iter = w_.begin();
-      iter != w_.end();
-      ++iter) {
-    if (fabs(iter->second/normalizer_) > epsilone) {
-      abs_val_vec.push_back(fabs(iter->second/normalizer_));
-      ++curr_l0_norm;
-    }
-  }
-  // check if a projection is necessary
-  if (curr_l0_norm < req_l0_norm) {
-    return;
-  }
-  std::nth_element(&abs_val_vec[0],
-              &abs_val_vec[curr_l0_norm - req_l0_norm],
-              &abs_val_vec[curr_l0_norm]);
-  const double theta = abs_val_vec[curr_l0_norm - req_l0_norm];
-  // compute the final projection.
-  for (Witer iter = w_.begin();
-      iter != w_.end();
-      ++iter) {
-    if ((fabs(iter->second/normalizer_) - theta) < 0) {
-      iter->second = 0;
-    }
-  }
-}
-
-// Slow algorithm for accurate L1 projection which takes O(n log(n)), where n is
-// the number of non-zero elements in the vector.
-template<class Key, class Hash>
-void SparseWeightVector<Key, Hash>::ReprojectL1(const double l1_norm) {
-// First calculates the order-statistics of the sparse vector
-// applies a probability simplex projection to the abs(vector)
-// and reprojects back to the original with the appropriate sign.
-// For ref. see "Efficient Projections into the l1-ball for Learning
-// in High Dimensions"
-  CHECK_GT(l1_norm, 0);
-  // Compute order statistics and the current L1 norm.
-  list<double> abs_val_list;
-  double curr_l1_norm = 0;
-  for (Witer iter = w_.begin();
-      iter != w_.end();
-      ++iter) {
-    abs_val_list.push_back(fabs(iter->second/normalizer_));
-    curr_l1_norm += fabs(iter->second/normalizer_);
-  }
-  // check if a projection is necessary
-  if (curr_l1_norm < l1_norm) {
-    return;
-  }
-  abs_val_list.sort();
-  abs_val_list.reverse();
-  // Compute projection on the probability simplex.
-  double curr_index = 1;
-  double theta = 0;
-  double cum_sum = 0;
-  for (list<double>::iterator val_iter = abs_val_list.begin();
-       val_iter != abs_val_list.end();
-       ++val_iter) {
-    cum_sum += *val_iter;
-    theta = (cum_sum - l1_norm)/curr_index;
-    if (((*val_iter) - theta) <= 0) {
-      break;
-    }
-    ++curr_index;
-  }
-  // compute the final projection.
-  for (Witer iter = w_.begin();
-      iter != w_.end();
-      ++iter) {
-    int sign_mul = iter->second > 0;
-    iter->second = max(sign_mul * normalizer_ *
-                           (fabs(iter->second/normalizer_) - theta),
-                       0.0);
-  }
-}
-
-template<class Key, class Hash>
-void SparseWeightVector<Key, Hash>::ReprojectL2(const double l2_norm) {
-  CHECK_GT(l2_norm, 0);
-  double curr_l2_norm = L2Norm();
-  // Check if a projection is necessary.
-  if (curr_l2_norm > l2_norm) {
-    normalizer_ *= curr_l2_norm / l2_norm;
-  }
-}
-
-template<class Key, class Hash>
-int32 SparseWeightVector<Key, Hash>::Reproject(const double norm,
-                                               const RegularizationType r) {
-  CHECK_GT(norm, 0);
-  if (r == L0) {
-    ReprojectL0(norm);
-  } else if (r == L1) {
-    ReprojectL1(norm);
-  } else if (r == L2) {
-    ReprojectL2(norm);
-  } else {
-    // This else is just to ensure that if other RegularizationTypes are
-    // supported in the enum later which require manipulations not related
-    // to SparseWeightVector then we catch the accidental argument here.
-    ALOGE("Unsupported regularization type requested");
-    return -1;
-  }
-  // If the normalizer gets dangerously large or small, normalize the
-  // entire vector. This stops projections from sending the vector
-  // weights and the normalizer simultaneously all very small or
-  // large, causing under/over flows. But if you hit this too often
-  // it's a sign you've chosen a bad lambda.
-  if (normalizer_ < kNormalizerMin) {
-    ALOGE("Resetting normalizer to 1.0 to prevent underflow. "
-          "Is lambda too large?");
-    ResetNormalizer();
-  }
-  if (normalizer_ > kNormalizerMax) {
-    ALOGE("Resetting normalizer to 1.0 to prevent overflow. "
-          "Is lambda too small?");
-    ResetNormalizer();
-  }
-  return 0;
-}
-
-template class SparseWeightVector<std::string, std::unordered_map<std::string, double> >;
-template class SparseWeightVector<int, std::unordered_map<int, double> >;
-template class SparseWeightVector<uint64, std::unordered_map<uint64, double> >;
-}  // namespace learning_stochastic_linear
diff --git a/bordeaux/learning/stochastic_linear_ranker/native/sparse_weight_vector.h b/bordeaux/learning/stochastic_linear_ranker/native/sparse_weight_vector.h
deleted file mode 100644
index b5f1ff5af..000000000
--- a/bordeaux/learning/stochastic_linear_ranker/native/sparse_weight_vector.h
+++ /dev/null
@@ -1,204 +0,0 @@
-/*
- * 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.
- */
-
-// Purpose: A container for sparse weight vectors
-// Maintains the sparse vector as a list of (name, value) pairs alongwith
-// a normalizer_. All operations assume that (name, value/normalizer_) is the
-// true value in question.
-
-#ifndef LEARNING_STOCHASTIC_LINEAR_SPARSE_WEIGHT_VECTOR_H_
-#define LEARNING_STOCHASTIC_LINEAR_SPARSE_WEIGHT_VECTOR_H_
-
-#include <math.h>
-
-#include <iosfwd>
-#include <sstream>
-#include <string>
-#include <unordered_map>
-
-#include "common_defs.h"
-
-namespace learning_stochastic_linear {
-
-template<class Key = std::string, class Hash = std::unordered_map<Key, double> >
-class SparseWeightVector {
- public:
-  typedef Hash Wmap;
-  typedef typename Wmap::iterator Witer;
-  typedef typename Wmap::const_iterator Witer_const;
-  SparseWeightVector() {
-    normalizer_ = 1.0;
-  }
-  ~SparseWeightVector() {}
-  explicit SparseWeightVector(const SparseWeightVector<Key, Hash> &other) {
-    CopyFrom(other);
-  }
-  void operator=(const SparseWeightVector<Key, Hash> &other) {
-    CopyFrom(other);
-  }
-  void CopyFrom(const SparseWeightVector<Key, Hash> &other) {
-    w_ = other.w_;
-    wmin_ = other.wmin_;
-    wmax_ = other.wmax_;
-    normalizer_ = other.normalizer_;
-  }
-
-  // This function implements checks to prevent unbounded vectors. It returns
-  // true if the checks succeed and false otherwise. A vector is deemed invalid
-  // if any of these conditions are met:
-  // 1. it has no values.
-  // 2. its normalizer is nan or inf or close to zero.
-  // 3. any of its values are nan or inf.
-  // 4. its L0 norm is close to zero.
-  bool IsValid() const;
-
-  // Normalizer getters and setters.
-  double GetNormalizer() const {
-    return normalizer_;
-  }
-  void SetNormalizer(const double norm) {
-    normalizer_ = norm;
-  }
-  void NormalizerMultUpdate(const double mul) {
-    normalizer_ = normalizer_ * mul;
-  }
-  void NormalizerAddUpdate(const double add) {
-    normalizer_ += add;
-  }
-
-  // Divides all the values by the normalizer, then it resets it to 1.0
-  void ResetNormalizer();
-
-  // Bound getters and setters.
-  // True if there is a bound with val containing the bound. false otherwise.
-  bool GetElementMinBound(const Key &fname, double *val) const {
-    return GetValue(wmin_, fname, val);
-  }
-  bool GetElementMaxBound(const Key &fname, double *val) const {
-    return GetValue(wmax_, fname, val);
-  }
-  void SetElementMinBound(const Key &fname, const double bound) {
-    wmin_[fname] = bound;
-  }
-  void SetElementMaxBound(const Key &fname, const double bound) {
-    wmax_[fname] = bound;
-  }
-  // Element getters and setters.
-  double GetElement(const Key &fname) const {
-    double val = 0;
-    GetValue(w_, fname, &val);
-    return val;
-  }
-  void SetElement(const Key &fname, const double val) {
-    //DCHECK(!isnan(val));
-    w_[fname] = val;
-  }
-  void AddUpdateElement(const Key &fname, const double val) {
-    w_[fname] += val;
-  }
-  void MultUpdateElement(const Key &fname, const double val) {
-    w_[fname] *= val;
-  }
-  // Load another weight vectors. Will overwrite the current vector.
-  void LoadWeightVector(const SparseWeightVector<Key, Hash> &vec) {
-    w_.clear();
-    w_.insert(vec.w_.begin(), vec.w_.end());
-    wmax_.insert(vec.wmax_.begin(), vec.wmax_.end());
-    wmin_.insert(vec.wmin_.begin(), vec.wmin_.end());
-    normalizer_ = vec.normalizer_;
-  }
-  void Clear() {
-    w_.clear();
-    wmax_.clear();
-    wmin_.clear();
-  }
-  const Wmap& GetMap() const {
-    return w_;
-  }
-  // Vector Operations.
-  void AdditiveWeightUpdate(const double multiplier,
-                            const SparseWeightVector<Key, Hash> &w1,
-                            const double additive_const);
-  void AdditiveSquaredWeightUpdate(const double multiplier,
-                                   const SparseWeightVector<Key, Hash> &w1,
-                                   const double additive_const);
-  void AdditiveInvSqrtWeightUpdate(const double multiplier,
-                                   const SparseWeightVector<Key, Hash> &w1,
-                                   const double additive_const);
-  void MultWeightUpdate(const SparseWeightVector<Key, Hash> &w1);
-  double DotProduct(const SparseWeightVector<Key, Hash> &s) const;
-  // L-x norm. eg. L1, L2.
-  double LxNorm(const double x) const;
-  double L2Norm() const;
-  double L1Norm() const;
-  double L0Norm(const double epsilon) const;
-  // Bound preserving updates.
-  void AdditiveWeightUpdateBounded(const double multiplier,
-                                   const SparseWeightVector<Key, Hash> &w1,
-                                   const double additive_const);
-  void MultWeightUpdateBounded(const SparseWeightVector<Key, Hash> &w1);
-  void ReprojectToBounds();
-  void ReprojectL0(const double l0_norm);
-  void ReprojectL1(const double l1_norm);
-  void ReprojectL2(const double l2_norm);
-  // Reproject using the given norm.
-  // Will also rescale regularizer_ if it gets too small/large.
-  int32 Reproject(const double norm, const RegularizationType r);
-  // Convert this vector to a string, simply for debugging.
-  std::string DebugString() const {
-    std::stringstream stream;
-    stream << *this;
-    return stream.str();
-  }
- private:
-  // The weight map.
-  Wmap w_;
-  // Constraint bounds.
-  Wmap wmin_;
-  Wmap wmax_;
-  // Normalizing constant in magnitude measurement.
-  double normalizer_;
-  // This function is necessary since by default unordered_map inserts an
-  // element if it does not find the key through [] operator. It implements a
-  // lookup without the space overhead of an add.
-  bool GetValue(const Wmap &w1, const Key &fname, double *val) const {
-    Witer_const iter = w1.find(fname);
-    if (iter != w1.end()) {
-      (*val) = iter->second;
-      return true;
-    } else {
-      (*val) = 0;
-      return false;
-    }
-  }
-};
-
-// Outputs a SparseWeightVector, for debugging.
-template <class Key, class Hash>
-std::ostream& operator<<(std::ostream &stream,
-                    const SparseWeightVector<Key, Hash> &vector) {
-  typename SparseWeightVector<Key, Hash>::Wmap w_map = vector.GetMap();
-  stream << "[[ ";
-  for (typename SparseWeightVector<Key, Hash>::Witer_const iter = w_map.begin();
-       iter != w_map.end();
-       ++iter) {
-    stream << "<" << iter->first << ", " << iter->second << "> ";
-  }
-  return stream << " ]]";
-};
-
-}  // namespace learning_stochastic_linear
-#endif  // LEARNING_STOCHASTIC_LINEAR_SPARSE_WEIGHT_VECTOR_H_
diff --git a/bordeaux/learning/stochastic_linear_ranker/native/stochastic_linear_ranker.cpp b/bordeaux/learning/stochastic_linear_ranker/native/stochastic_linear_ranker.cpp
deleted file mode 100644
index db702ad57..000000000
--- a/bordeaux/learning/stochastic_linear_ranker/native/stochastic_linear_ranker.cpp
+++ /dev/null
@@ -1,139 +0,0 @@
-/*
- * 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.
- */
-
-#include <algorithm>
-#include <stdlib.h>
-
-#include "stochastic_linear_ranker.h"
-
-namespace learning_stochastic_linear {
-
-template<class Key, class Hash>
-void StochasticLinearRanker<Key, Hash>::UpdateSubGradient(
-    const SparseWeightVector<Key, Hash> &positive,
-    const SparseWeightVector<Key, Hash> &negative,
-    const double learning_rate,
-    const double positive_score,
-    const double negative_score,
-    const int32 gradient_l0_norm) {
-  SparseWeightVector<Key, Hash> gradient;
-  double final_learning_rate;
-  gradient.AdditiveWeightUpdate(1.0, positive, 0.0);
-  gradient.AdditiveWeightUpdate(-1.0, negative, 0.0);
-  if (update_type_ == FULL_CS || update_type_ == REG_CS) {
-    const double loss = std::max(0.0, (1 - positive_score + negative_score));
-    const double gradient_norm = gradient.L2Norm();
-    const double kMinGradientNorm = 1e-8;
-    const double kMaxGradientNorm = 1e8;
-    if (gradient_norm < kMinGradientNorm || gradient_norm > kMaxGradientNorm)
-      return;
-    if (update_type_ == FULL_CS)
-      final_learning_rate =
-          std::min(lambda_, loss / (gradient_norm * gradient_norm));
-    else
-      final_learning_rate =
-          loss / (gradient_norm * gradient_norm + 1 / (2 * lambda_));
-  } else {
-    gradient.AdditiveWeightUpdate(-lambda_, weight_, 0.0);
-    final_learning_rate = learning_rate;
-  }
-  if (gradient_l0_norm > 0) {
-    gradient.ReprojectL0(gradient_l0_norm);
-  }
-
-  if (gradient.IsValid())
-    weight_.AdditiveWeightUpdate(final_learning_rate, gradient, 0.0);
-}
-
-template<class Key, class Hash>
-int StochasticLinearRanker<Key, Hash>::UpdateClassifier(
-    const SparseWeightVector<Key, Hash> &positive,
-    const SparseWeightVector<Key, Hash> &negative) {
-  // Create a backup of the weight vector in case the iteration results in
-  // unbounded weights.
-  SparseWeightVector<Key, Hash> weight_backup;
-  weight_backup.CopyFrom(weight_);
-
-  const double positive_score = ScoreSample(positive);
-  const double negative_score = ScoreSample(negative);
-  if ((positive_score - negative_score) < 1) {
-    ++mini_batch_counter_;
-    if ((mini_batch_counter_ % mini_batch_size_ == 0) ||
-        (iteration_num_ == 0)) {
-      ++iteration_num_;
-      mini_batch_counter_ = 0;
-    }
-    learning_rate_controller_.IncrementSample();
-    double learning_rate = learning_rate_controller_.GetLearningRate();
-
-    if (rank_loss_type_ == PAIRWISE) {
-      UpdateSubGradient(positive, negative, learning_rate,
-                        positive_score, negative_score,
-                        gradient_l0_norm_);
-    } else if (rank_loss_type_ == RECIPROCAL_RANK) {
-      const double current_negative_score = ScoreSample(current_negative_);
-      if ((negative_score > current_negative_score) ||
-          ((rand()/RAND_MAX) < acceptence_probability_)) {
-        UpdateSubGradient(positive, negative, learning_rate,
-                          positive_score, negative_score,
-                          gradient_l0_norm_);
-        current_negative_.Clear();
-        current_negative_.LoadWeightVector(negative);
-      } else {
-        UpdateSubGradient(positive, current_negative_, learning_rate,
-                          positive_score, negative_score,
-                          gradient_l0_norm_);
-      }
-    } else {
-      ALOGE("Unknown rank loss type: %d", rank_loss_type_);
-    }
-
-    int return_code;
-    if ((mini_batch_counter_ == 0) && (update_type_ == SL)) {
-      return_code = 1;
-      switch (regularization_type_) {
-        case L1:
-          weight_.ReprojectL1(norm_constraint_);
-          break;
-        case L2:
-          weight_.ReprojectL2(norm_constraint_);
-          break;
-        case L0:
-          weight_.ReprojectL0(norm_constraint_);
-          break;
-        default:
-          ALOGE("Unsupported optimization type specified");
-          return_code = -1;
-      }
-    } else if (update_type_ == SL) {
-      return_code = 2;
-    } else {
-      return_code = 1;
-    }
-
-    if (!weight_.IsValid())
-      weight_.CopyFrom(weight_backup);
-    return return_code;
-  }
-
-  return 0;
-}
-
-template class StochasticLinearRanker<std::string, std::unordered_map<std::string, double> >;
-template class StochasticLinearRanker<int, std::unordered_map<int, double> >;
-template class StochasticLinearRanker<uint64, std::unordered_map<uint64, double> >;
-
-}  // namespace learning_stochastic_linear
diff --git a/bordeaux/learning/stochastic_linear_ranker/native/stochastic_linear_ranker.h b/bordeaux/learning/stochastic_linear_ranker/native/stochastic_linear_ranker.h
deleted file mode 100644
index 5419a5a30..000000000
--- a/bordeaux/learning/stochastic_linear_ranker/native/stochastic_linear_ranker.h
+++ /dev/null
@@ -1,244 +0,0 @@
-/*
- * 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.
- */
-
-// Stochastic Linear Ranking algorithms.
-// This class will implement a set of incremental algorithms for ranking tasks
-// They support both L1 and L2 regularizations.
-
-
-#ifndef LEARNING_STOCHASTIC_LINEAR_STOCHASTIC_LINEAR_RANKER_H_
-#define LEARNING_STOCHASTIC_LINEAR_STOCHASTIC_LINEAR_RANKER_H_
-
-#include <sys/types.h>
-
-#include <cmath>
-#include <string>
-#include <unordered_map>
-
-#include "cutils/log.h"
-#include "common_defs.h"
-#include "learning_rate_controller-inl.h"
-#include "sparse_weight_vector.h"
-
-namespace learning_stochastic_linear {
-
-// NOTE: This Stochastic Linear Ranker supports only the following update types:
-// SL: Stochastic Linear
-// CS: Constraint Satisfaction
-template<class Key = std::string, class Hash = std::unordered_map<std::string, double> >
-class StochasticLinearRanker {
- public:
-  // initialize lambda_ and constraint to a meaningful default. Will give
-  // equal weight to the error and regularizer.
-  StochasticLinearRanker() {
-    iteration_num_ = 0;
-    lambda_ = 1.0;
-    learning_rate_controller_.SetLambda(lambda_);
-    mini_batch_size_ = 1;
-    learning_rate_controller_.SetMiniBatchSize(mini_batch_size_);
-    adaptation_mode_ = INV_LINEAR;
-    learning_rate_controller_.SetAdaptationMode(adaptation_mode_);
-    update_type_ = SL;
-    regularization_type_ = L2;
-    kernel_type_ = LINEAR;
-    kernel_param_ = 1.0;
-    kernel_gain_ = 1.0;
-    kernel_bias_ = 0.0;
-    rank_loss_type_ = PAIRWISE;
-    acceptence_probability_ = 0.1;
-    mini_batch_counter_ = 0;
-    gradient_l0_norm_ = -1;
-    norm_constraint_ = 1.0;
-  }
-
-  ~StochasticLinearRanker() {}
-  // Getters and setters
-  double GetIterationNumber() const {
-    return iteration_num_;
-  }
-  double GetNormContraint() const {
-    return norm_constraint_;
-  }
-  RegularizationType GetRegularizationType() const {
-    return regularization_type_;
-  }
-  double GetLambda() const {
-    return lambda_;
-  }
-  uint64 GetMiniBatchSize() const {
-    return mini_batch_size_;
-  }
-  int32 GetGradientL0Norm() const {
-    return gradient_l0_norm_;
-  }
-  UpdateType GetUpdateType() const {
-    return update_type_;
-  }
-  AdaptationMode GetAdaptationMode() const {
-    return adaptation_mode_;
-  }
-  KernelType GetKernelType() const {
-    return kernel_type_;
-  }
-  // This function returns the basic kernel parameter. In case of
-  // polynomial kernel, it implies the degree of the polynomial.  In case of
-  // RBF kernel, it implies the sigma parameter. In case of linear kernel,
-  // it is not used.
-  double GetKernelParam() const {
-    return kernel_param_;
-  }
-  double GetKernelGain() const {
-    return kernel_gain_;;
-  }
-  double GetKernelBias() const {
-    return kernel_bias_;
-  }
-  RankLossType GetRankLossType() const {
-    return rank_loss_type_;
-  }
-  double GetAcceptanceProbability() const {
-    return acceptence_probability_;
-  }
-  void SetIterationNumber(uint64 num) {
-    iteration_num_=num;
-  }
-  void SetNormConstraint(const double norm) {
-    norm_constraint_ = norm;
-  }
-  void SetRegularizationType(const RegularizationType r) {
-    regularization_type_ = r;
-  }
-  void SetLambda(double l) {
-    lambda_ = l;
-    learning_rate_controller_.SetLambda(l);
-  }
-  void SetMiniBatchSize(const uint64 msize) {
-    mini_batch_size_ = msize;
-    learning_rate_controller_.SetMiniBatchSize(msize);
-  }
-  void SetAdaptationMode(AdaptationMode m) {
-    adaptation_mode_ = m;
-    learning_rate_controller_.SetAdaptationMode(m);
-  }
-  void SetKernelType(KernelType k ) {
-    kernel_type_ = k;
-  }
-  // This function sets the basic kernel parameter. In case of
-  // polynomial kernel, it implies the degree of the polynomial. In case of
-  // RBF kernel, it implies the sigma parameter. In case of linear kernel,
-  // it is not used.
-  void SetKernelParam(double param) {
-    kernel_param_ = param;
-  }
-  // This function sets the kernel gain. NOTE: in most use cases, gain should
-  // be set to 1.0.
-  void SetKernelGain(double gain) {
-    kernel_gain_ = gain;
-  }
-  // This function sets the kernel bias. NOTE: in most use cases, bias should
-  // be set to 0.0.
-  void SetKernelBias(double bias) {
-    kernel_bias_ = bias;
-  }
-  void SetUpdateType(UpdateType u) {
-    update_type_ = u;
-  }
-  void SetRankLossType(RankLossType r) {
-    rank_loss_type_ = r;
-  }
-  void SetAcceptanceProbability(double p) {
-    acceptence_probability_ = p;
-  }
-  void SetGradientL0Norm(const int32 gradient_l0_norm) {
-    gradient_l0_norm_ = gradient_l0_norm;
-  }
-  // Load an existing model
-  void LoadWeights(const SparseWeightVector<Key, Hash> &model) {
-    weight_.LoadWeightVector(model);
-  }
-  // Save current model
-  void SaveWeights(SparseWeightVector<Key, Hash> *model) {
-    model->LoadWeightVector(weight_);
-  }
-  // Scoring
-  double ScoreSample(const SparseWeightVector<Key, Hash> &sample) {
-    const double dot = weight_.DotProduct(sample);
-    double w_square;
-    double s_square;
-    switch (kernel_type_) {
-      case LINEAR:
-        return dot;
-      case POLY:
-        return pow(kernel_gain_ * dot + kernel_bias_, kernel_param_);
-      case RBF:
-        w_square = weight_.L2Norm();
-        s_square = sample.L2Norm();
-        return exp(-1 * kernel_param_ * (w_square + s_square - 2 * dot));
-      default:
-      ALOGE("unsupported kernel: %d", kernel_type_);
-    }
-    return -1;
-  }
-  // Learning Functions
-  // Return values:
-  // 1 :full update went through
-  // 2 :partial update went through (for SL only)
-  // 0 :no update necessary.
-  // -1:error.
-  int UpdateClassifier(const SparseWeightVector<Key, Hash> &positive,
-                       const SparseWeightVector<Key, Hash> &negative);
-
- private:
-  SparseWeightVector<Key, Hash> weight_;
-  double norm_constraint_;
-  double lambda_;
-  RegularizationType regularization_type_;
-  AdaptationMode adaptation_mode_;
-  UpdateType update_type_;
-  RankLossType rank_loss_type_;
-  KernelType kernel_type_;
-  // Kernel gain and bias are typically multiplicative and additive factors to
-  // the dot product while calculating the kernel function. Kernel param is
-  // kernel-specific. In case of polynomial kernel, it is the degree of the
-  // polynomial.
-  double kernel_param_;
-  double kernel_gain_;
-  double kernel_bias_;
-  double acceptence_probability_;
-  SparseWeightVector<Key, Hash> current_negative_;
-  LearningRateController learning_rate_controller_;
-  uint64 iteration_num_;
-  // We average out gradient updates for mini_batch_size_ samples
-  // before performing an iteration of the algorithm.
-  uint64 mini_batch_counter_;
-  uint64 mini_batch_size_;
-  // 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
-  int32 gradient_l0_norm_;
-  // Sub-Gradient Updates
-  // Pure Sub-Gradient update without any reprojection
-  // Note that a form of L2 regularization is built into this
-  void UpdateSubGradient(const SparseWeightVector<Key, Hash> &positive,
-                         const SparseWeightVector<Key, Hash> &negative,
-                         const double learning_rate,
-                         const double positive_score,
-                         const double negative_score,
-                         const int32 gradient_l0_norm);
-
-};
-}  // namespace learning_stochastic_linear
-#endif  // LEARNING_STOCHASTIC_LINEAR_STOCHASTIC_LINEAR_RANKER_H_
diff --git a/bordeaux/service/Android.mk b/bordeaux/service/Android.mk
deleted file mode 100644
index 1d4464b15..000000000
--- a/bordeaux/service/Android.mk
+++ /dev/null
@@ -1,79 +0,0 @@
-LOCAL_PATH:= $(call my-dir)
-include $(CLEAR_VARS)
-
-LOCAL_MODULE_TAGS := optional
-
-# Only compile source java files in this apk.
-LOCAL_SRC_FILES := $(call all-java-files-under, src)
-LOCAL_SRC_FILES += \
-        src/android/bordeaux/services/IBordeauxServiceCallback.aidl \
-        src/android/bordeaux/services/IAggregatorManager.aidl \
-        src/android/bordeaux/services/ILearning_MulticlassPA.aidl \
-        src/android/bordeaux/services/IPredictor.aidl \
-        src/android/bordeaux/services/ILearning_StochasticLinearRanker.aidl \
-        src/android/bordeaux/services/IBordeauxService.aidl \
-
-LOCAL_STATIC_JAVA_LIBRARIES := bordeaux_learners
-LOCAL_JNI_SHARED_LIBRARIES := libbordeaux
-
-LOCAL_PACKAGE_NAME := bordeaux
-
-
-include $(BUILD_PACKAGE)
-
-##
-# Build java lib
-##
-include $(CLEAR_VARS)
-
-LOCAL_MODULE_TAGS := optional
-
-LOCAL_SRC_FILES += \
-        src/android/bordeaux/services/IntFloat.java \
-        src/android/bordeaux/services/StringFloat.java \
-        src/android/bordeaux/services/StringString.java \
-        src/android/bordeaux/services/BordeauxClassifier.java \
-        src/android/bordeaux/services/BordeauxRanker.java \
-        src/android/bordeaux/services/BordeauxPredictor.java \
-        src/android/bordeaux/services/BordeauxAggregatorManager.java \
-        src/android/bordeaux/services/BordeauxManagerService.java \
-        src/android/bordeaux/services/IBordeauxServiceCallback.aidl \
-        src/android/bordeaux/services/IAggregatorManager.aidl \
-        src/android/bordeaux/services/IPredictor.aidl \
-        src/android/bordeaux/services/ILearning_MulticlassPA.aidl \
-        src/android/bordeaux/services/ILearning_StochasticLinearRanker.aidl \
-        src/android/bordeaux/services/IBordeauxService.aidl \
-
-LOCAL_MODULE :=  bordeaux_service
-LOCAL_STATIC_JAVA_LIBRARIES := bordeaux_learners
-
-LOCAL_PROGUARD_ENABLED := disabled
-
-include $(BUILD_STATIC_JAVA_LIBRARY)
-
-## Building the whole Bordeaux service
-include $(CLEAR_VARS)
-
-LOCAL_MODULE_TAGS := optional
-
-# Only compile source java files in this apk.
-LOCAL_SRC_FILES := $(call all-java-files-under, src)
-LOCAL_SRC_FILES += \
-        src/android/bordeaux/services/IBordeauxServiceCallback.aidl \
-        src/android/bordeaux/services/IAggregatorManager.aidl \
-        src/android/bordeaux/services/ILearning_MulticlassPA.aidl \
-        src/android/bordeaux/services/IPredictor.aidl \
-        src/android/bordeaux/services/ILearning_StochasticLinearRanker.aidl \
-        src/android/bordeaux/services/IBordeauxService.aidl \
-
-LOCAL_STATIC_JAVA_LIBRARIES := bordeaux_learners
-LOCAL_JNI_SHARED_LIBRARIES := libbordeaux
-
-LOCAL_JAVA_RESOURCE_DIRS := res
-LOCAL_MODULE := bordeaux_whole_service
-LOCAL_PROGUARD_ENABLED := disabled
-
-include $(BUILD_STATIC_JAVA_LIBRARY)
-
-# Use the folloing include to make our test apk.
-include $(call all-makefiles-under,$(LOCAL_PATH))
diff --git a/bordeaux/service/AndroidManifest.xml b/bordeaux/service/AndroidManifest.xml
deleted file mode 100644
index 5e5ec3f2c..000000000
--- a/bordeaux/service/AndroidManifest.xml
+++ /dev/null
@@ -1,23 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<manifest xmlns:android="http://schemas.android.com/apk/res/android"
-      android:versionCode="1"
-      android:versionName="1.0" package="android.bordeaux">
-    <uses-sdk android:minSdkVersion="4" android:targetSdkVersion="14" />
-    <uses-permission android:name="android.permission.INTERNET" />
-    <uses-permission android:name="android.permission.ACCESS_FINE_LOCATION" />
-    <application android:label="@string/bordeaux_app"
-                 android:debuggable="true"
-                 android:hardwareAccelerated="true">
-<!-- BEGIN_INCLUDE(remote_service_declaration) -->
-        <service android:name=".services.BordeauxService"
-                android:icon="@drawable/ic_bordeaux"
-                android:process=":remote" >
-            <intent-filter>
-                <action android:name="android.bordeaux.services.IBordeauxService" />
-                <!-- This is an action code you can use to select the service
-                     without explicitly supplying the implementation class. -->
-                <action android:name="android.bordeaux.services.BORDEAUX_SERVICE" />
-            </intent-filter>
-        </service>
-    </application>
-</manifest>
diff --git a/bordeaux/service/res/drawable-hdpi/ic_bordeaux.png b/bordeaux/service/res/drawable-hdpi/ic_bordeaux.png
deleted file mode 100644
index b191d46d2..000000000
--- a/bordeaux/service/res/drawable-hdpi/ic_bordeaux.png
+++ /dev/null
diff --git a/bordeaux/service/res/drawable-mdpi/ic_bordeaux.png b/bordeaux/service/res/drawable-mdpi/ic_bordeaux.png
deleted file mode 100644
index 8975f9bf4..000000000
--- a/bordeaux/service/res/drawable-mdpi/ic_bordeaux.png
+++ /dev/null
diff --git a/bordeaux/service/res/drawable-xhdpi/ic_bordeaux.png b/bordeaux/service/res/drawable-xhdpi/ic_bordeaux.png
deleted file mode 100644
index ac7513d3e..000000000
--- a/bordeaux/service/res/drawable-xhdpi/ic_bordeaux.png
+++ /dev/null
diff --git a/bordeaux/service/res/layout/lava_messenger_service_binding.xml b/bordeaux/service/res/layout/lava_messenger_service_binding.xml
deleted file mode 100644
index 4fa314c05..000000000
--- a/bordeaux/service/res/layout/lava_messenger_service_binding.xml
+++ /dev/null
@@ -1,69 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<!-- Copyright (C) 2010 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.
--->
-
-<!-- Demonstrates starting and stopping a local service.
-     See corresponding Java code com.android.sdk.app.LocalSerice.java. -->
-
-<LinearLayout xmlns:android="http://schemas.android.com/apk/res/android" android:orientation="vertical" android:padding="4dip"
-    android:gravity="center_horizontal"
-    android:layout_width="match_parent" android:layout_height="match_parent">
-
-    <TextView
-        android:layout_width="match_parent" android:layout_height="wrap_content"
-        android:layout_weight="0"
-        android:paddingBottom="4dip"
-        android:text="@string/lava_service_binding"/>
-
-    <Button android:id="@+id/startService"
-        android:layout_width="wrap_content" android:layout_height="wrap_content" 
-        android:text="@string/start_service">
-        <requestFocus />
-    </Button>
-
-    <Button android:id="@+id/stopService"
-        android:layout_width="wrap_content" android:layout_height="wrap_content" 
-        android:text="@string/stop_service">
-        <requestFocus />
-    </Button>
-
-    <Button android:id="@+id/bind"
-        android:layout_width="wrap_content" android:layout_height="wrap_content" 
-        android:text="@string/bind_service">
-        <requestFocus />
-    </Button>
-
-    <Button android:id="@+id/unbind"
-        android:layout_width="wrap_content" android:layout_height="wrap_content" 
-        android:text="@string/unbind_service">
-    </Button>
-
-    <Button android:id="@+id/sendData"
-        android:layout_width="wrap_content" android:layout_height="wrap_content" 
-        android:text="@string/send_data">
-    </Button>
-
-    <Button android:id="@+id/kill"
-        android:layout_width="wrap_content" android:layout_height="wrap_content" 
-        android:text="@string/kill_process">
-    </Button>
-
-    <TextView android:id="@+id/callback"
-        android:layout_width="match_parent" android:layout_height="wrap_content"
-        android:layout_weight="0"
-        android:gravity="center_horizontal" android:paddingTop="4dip"/>
-
-</LinearLayout>
-
diff --git a/bordeaux/service/res/layout/lava_service_binding.xml b/bordeaux/service/res/layout/lava_service_binding.xml
deleted file mode 100644
index c41980c4e..000000000
--- a/bordeaux/service/res/layout/lava_service_binding.xml
+++ /dev/null
@@ -1,59 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<!-- Copyright (C) 2010 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.
--->
-
-<!-- Demonstrates starting and stopping a local service.
-     See corresponding Java code com.android.sdk.app.LocalSerice.java. -->
-
-<LinearLayout xmlns:android="http://schemas.android.com/apk/res/android" android:orientation="vertical" android:padding="4dip"
-    android:gravity="center_horizontal"
-    android:layout_width="match_parent" android:layout_height="match_parent">
-
-    <TextView
-        android:layout_width="match_parent" android:layout_height="wrap_content"
-        android:layout_weight="0"
-        android:paddingBottom="4dip"
-        android:text="@string/lava_service_binding"/>
-
-    <Button android:id="@+id/startService"
-        android:layout_width="wrap_content" android:layout_height="wrap_content" 
-        android:text="@string/start_service">
-        <requestFocus />
-    </Button>
-
-    <Button android:id="@+id/stopService"
-        android:layout_width="wrap_content" android:layout_height="wrap_content" 
-        android:text="@string/stop_service">
-        <requestFocus />
-    </Button>
-
-    <Button android:id="@+id/bind"
-        android:layout_width="wrap_content" android:layout_height="wrap_content" 
-        android:text="@string/bind_service">
-        <requestFocus />
-    </Button>
-
-    <Button android:id="@+id/unbind"
-        android:layout_width="wrap_content" android:layout_height="wrap_content" 
-        android:text="@string/unbind_service">
-    </Button>
-
-    <TextView android:id="@+id/callback"
-        android:layout_width="match_parent" android:layout_height="wrap_content"
-        android:layout_weight="0"
-        android:gravity="center_horizontal" android:paddingTop="4dip"/>
-
-</LinearLayout>
-
diff --git a/bordeaux/service/res/layout/lava_service_controller.xml b/bordeaux/service/res/layout/lava_service_controller.xml
deleted file mode 100644
index 804589aba..000000000
--- a/bordeaux/service/res/layout/lava_service_controller.xml
+++ /dev/null
@@ -1,42 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<!-- Copyright (C) 2007 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.
--->
-
-<!-- Demonstrates starting and stopping a local service.
-     See corresponding Java code com.android.sdk.app.LocalSerice.java. -->
-
-<LinearLayout xmlns:android="http://schemas.android.com/apk/res/android" android:orientation="vertical" android:padding="4dip"
-    android:gravity="center_horizontal"
-    android:layout_width="match_parent" android:layout_height="match_parent">
-
-    <TextView
-        android:layout_width="match_parent" android:layout_height="wrap_content"
-        android:layout_weight="0"
-        android:paddingBottom="4dip"
-        android:text="@string/lava_service_controller"/>
-
-    <Button android:id="@+id/start"
-        android:layout_width="wrap_content" android:layout_height="wrap_content" 
-        android:text="@string/start_service">
-        <requestFocus />
-    </Button>
-
-    <Button android:id="@+id/stop"
-        android:layout_width="wrap_content" android:layout_height="wrap_content" 
-        android:text="@string/stop_service">
-    </Button>
-
-</LinearLayout>
-
diff --git a/bordeaux/service/res/values/strings.xml b/bordeaux/service/res/values/strings.xml
deleted file mode 100644
index f49d1a090..000000000
--- a/bordeaux/service/res/values/strings.xml
+++ /dev/null
@@ -1,59 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<!-- Copyright (C) 2007 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.
--->
-
-<resources xmlns:xliff="urn:oasis:names:tc:xliff:document:1.2">
-    <string name="bordeaux_app">Bordeaux Apps</string>
-    <string name="send_data">Send Learning Data</string>
-
-    <string name="local_service_started">Local service has started</string>
-    <string name="local_service_stopped">Local service has stopped</string>
-    <string name="local_service_label">Sample Local Service</string>
-
-    <string name="activity_local_service_controller">App/Service/Local Service Controller</string>
-    <string name="local_service_controller">This demonstrates how you can implement persistent services that
-        may be started and stopped as desired.</string>
-    <string name="start_service">Start Service</string>
-    <string name="stop_service">Stop Service</string>
-
-    <string name="activity_local_service_binding">App/Service/Local Service Binding</string>
-    <string name="local_service_binding">This demonstrates how you can connect with a persistent
-        service.  Notice how it automatically starts for you, and play around with the
-        interaction between this and Local Service Controller.</string>
-    <string name="bind_service">Bind Service</string>
-    <string name="unbind_service">Unbind Service</string>
-    <string name="local_service_connected">Connected to local service</string>
-    <string name="local_service_disconnected">Disconnected from local service</string>
-
-    <string name="remote_service_started">Remote service has started</string>
-    <string name="remote_service_stopped">Remote service has stopped</string>
-    <string name="remote_service_label">Sample Remote Service</string>
-
-    <string name="activity_lava_service_controller">Bordeaux Service Controller</string>
-    <string name="lava_service_controller">This demonstrates how you can implement persistent services
-        running in a separate process that may be started and stopped as desired.</string>
-
-    <string name="activity_lava_service_binding">Bordeaux Service</string>
-    <string name="lava_service_binding">This demonstrates how you can connect with a persistent
-        service running in another process.  Use the kill button to see what happens when
-        the process crashes.</string>
-    <string name="kill_process">Kill Process</string>
-    <string name="remote_service_connected">Connected to remote service</string>
-    <string name="remote_service_disconnected">Disconnected from remote service</string>
-    <string name="remote_service_unbind_disconn">Unbinding due to disconnect</string>
-    <string name="remote_call_failed">Failure calling remote service</string>
-
-
-  </resources>
diff --git a/bordeaux/service/src/android/bordeaux/services/Aggregator.java b/bordeaux/service/src/android/bordeaux/services/Aggregator.java
deleted file mode 100644
index a5aced04a..000000000
--- a/bordeaux/service/src/android/bordeaux/services/Aggregator.java
+++ /dev/null
@@ -1,28 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import java.util.Map;
-
-abstract class Aggregator {
-    protected AggregatorManager mAggregatorManager;
-    public void setManager(AggregatorManager m) {
-        mAggregatorManager = m;
-    }
-    abstract public String[] getListOfFeatures();
-    abstract public Map<String,String> getFeatureValue(String featureName);
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/AggregatorManager.java b/bordeaux/service/src/android/bordeaux/services/AggregatorManager.java
deleted file mode 100644
index 0fea228a7..000000000
--- a/bordeaux/service/src/android/bordeaux/services/AggregatorManager.java
+++ /dev/null
@@ -1,160 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-
-import android.bordeaux.services.StringString;
-import android.content.Context;
-import android.util.Log;
-import java.util.ArrayList;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-
-class AggregatorManager extends IAggregatorManager.Stub  {
-    private final String TAG = "AggregatorMnager";
-    // this maps from the aggregator name to the registered aggregator instance
-    private static HashMap<String, Aggregator> mAggregators = new HashMap<String, Aggregator>();
-    // this maps from the feature names to the aggregator that generates the feature
-    private static HashMap<String, Aggregator> sFeatureMap = new HashMap<String, Aggregator>();
-    private static AggregatorManager mManager = null;
-
-    private String mFakeLocation = null;
-    private String mFakeTimeOfDay = null;
-    private String mFakeDayOfWeek = null;
-
-    private AggregatorManager() {
-        sFeatureMap = new HashMap<String, Aggregator>();
-    }
-
-    public static AggregatorManager getInstance() {
-        if (mManager == null )
-            mManager = new AggregatorManager();
-        return mManager;
-    }
-
-    public String[] getListOfFeatures() {
-        String[] s = new String[sFeatureMap.size()];
-        int i = 0;
-        for (Map.Entry<String, Aggregator> x : sFeatureMap.entrySet()) {
-           s[i] = x.getKey();
-           i++;
-        }
-        return s;
-    }
-
-    public void registerAggregator(Aggregator agg, AggregatorManager m) {
-        if (mAggregators.get(agg.getClass().getName()) != null) {
-            // only one instance
-            // throw new RuntimeException("Can't register more than one instance");
-        }
-        mAggregators.put(agg.getClass().getName(), agg);
-        agg.setManager(m);
-        String[] fl = agg.getListOfFeatures();
-        for ( int i  = 0; i< fl.length; i ++)
-            sFeatureMap.put(fl[i], agg);
-    }
-
-    // Start of IAggregatorManager interface
-    public ArrayList<StringString> getData(String dataName) {
-        return getList(getDataMap(dataName));
-    }
-
-    public List<String> getLocationClusters() {
-        LocationStatsAggregator agg = (LocationStatsAggregator)
-                mAggregators.get(LocationStatsAggregator.class.getName());
-        if (agg == null) return new ArrayList<String> ();
-        return agg.getClusterNames();
-    }
-
-    public List<String> getTimeOfDayValues() {
-        TimeStatsAggregator agg = (TimeStatsAggregator)
-                mAggregators.get(TimeStatsAggregator.class.getName());
-        if (agg == null) return new ArrayList<String>();
-        return agg.getTimeOfDayValues();
-    }
-
-    public List<String> getDayOfWeekValues() {
-        TimeStatsAggregator agg = (TimeStatsAggregator)
-                mAggregators.get(TimeStatsAggregator.class.getName());
-        if (agg == null) return new ArrayList<String>();
-        return agg.getDayOfWeekValues();
-    }
-
-    // Set an empty string "" to disable the fake location
-    public boolean setFakeLocation(String location) {
-        LocationStatsAggregator agg = (LocationStatsAggregator)
-                mAggregators.get(LocationStatsAggregator.class.getName());
-        if (agg == null) return false;
-        agg.setFakeLocation(location);
-        mFakeLocation = location;
-        return true;
-    }
-
-    // Set an empty string "" to disable the fake time of day
-    public boolean setFakeTimeOfDay(String time_of_day) {
-        TimeStatsAggregator agg = (TimeStatsAggregator)
-                mAggregators.get(TimeStatsAggregator.class.getName());
-        if (agg == null) return false;
-        agg.setFakeTimeOfDay(time_of_day);
-        mFakeTimeOfDay = time_of_day;
-        return true;
-    }
-
-    // Set an empty string "" to disable the fake day of week
-    public boolean setFakeDayOfWeek(String day_of_week) {
-        TimeStatsAggregator agg = (TimeStatsAggregator)
-                mAggregators.get(TimeStatsAggregator.class.getName());
-        if (agg == null) return false;
-        agg.setFakeDayOfWeek(day_of_week);
-        mFakeDayOfWeek = day_of_week;
-        return true;
-    }
-
-    // Get the current mode, if fake mode return true
-    public boolean getFakeMode() {
-        boolean fakeMode = false;
-        // checking any features that are in the fake mode
-        if (mFakeLocation != null && mFakeLocation.length() != 0)
-            fakeMode = true;
-        if (mFakeTimeOfDay != null && mFakeTimeOfDay.length() != 0)
-            fakeMode = true;
-        if (mFakeDayOfWeek != null && mFakeDayOfWeek.length() != 0)
-            fakeMode = true;
-        return fakeMode;
-    }
-    // End of IAggregatorManger interface
-
-    public Map<String, String> getDataMap(String dataName) {
-        if (sFeatureMap.get(dataName) != null)
-            return sFeatureMap.get(dataName).getFeatureValue(dataName);
-        else
-            Log.e(TAG, "There is no feature called " + dataName);
-        return null;
-    }
-
-    private ArrayList<StringString> getList(final Map<String, String> sample) {
-        ArrayList<StringString> StringString_sample = new ArrayList<StringString>();
-        for (Map.Entry<String, String> x : sample.entrySet()) {
-           StringString v = new StringString();
-           v.key = x.getKey();
-           v.value = x.getValue();
-           StringString_sample.add(v);
-        }
-        return StringString_sample;
-    }
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/AggregatorRecordStorage.java b/bordeaux/service/src/android/bordeaux/services/AggregatorRecordStorage.java
deleted file mode 100644
index 4bf677f59..000000000
--- a/bordeaux/service/src/android/bordeaux/services/AggregatorRecordStorage.java
+++ /dev/null
@@ -1,120 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import android.content.ContentValues;
-import android.content.Context;
-import android.database.Cursor;
-import android.database.SQLException;
-import android.database.sqlite.SQLiteDatabase;
-import android.database.sqlite.SQLiteOpenHelper;
-import android.util.Log;
-
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-
-/* This class implements record like data storage for aggregator.
- * The data is stored in the sqlite database row by row without primary key, all
- * columns are assume having string value.
- * Sample usage:
- *       AggregatorRecordStorage db = new AggregatorRecordStorage(this, "TestTable",
- *               new String[]{"clusterid", "long", "lat"});
- *       db.removeAllData();
- *       HashMap<String, String> row = new HashMap<String, String>();
- *       row.put("clusterid", "home");
- *       row.put("long", "110.203");
- *       row.put("lat", "-13.787");
- *       db.addData(row);
- *       row.put("clusterid", "office");
- *       row.put("long", "1.203");
- *       row.put("lat", "33.787");
- *       db.addData(row);
- *       List<Map<String,String> > allData = db.getAllData();
- *       Log.i(TAG,"Total data in database: " + allData.size());
- */
-class AggregatorRecordStorage extends AggregatorStorage {
-    private static final String TAG = "AggregatorRecordStorage";
-    private String mTableName;
-    private List<String> mColumnNames;
-
-    public AggregatorRecordStorage(Context context, String tableName, String [] columnNames) {
-        if (columnNames.length < 1) {
-            throw new RuntimeException("No column keys");
-        }
-        mColumnNames = Arrays.asList(columnNames);
-        mTableName = tableName;
-
-        String tableCmd = "create table " + tableName + "( " + columnNames[0] +
-          " TEXT";
-        for (int i = 1; i < columnNames.length; ++i)
-            tableCmd = tableCmd + ", " + columnNames[i] + " TEXT";
-        tableCmd = tableCmd + ");";
-        Log.i(TAG, tableCmd);
-        try {
-            mDbHelper = new DBHelper(context, tableName, tableCmd);
-            mDatabase = mDbHelper.getWritableDatabase();
-        } catch (SQLException e) {
-            throw new RuntimeException("Can't open table: " + tableName);
-        }
-    }
-
-    // Adding one more row to the table.
-    // the data is a map of <column_name, value> pair.
-    public boolean addData(Map<String, String> data) {
-        ContentValues content = new ContentValues();
-        for (Map.Entry<String, String> item : data.entrySet()) {
-            content.put(item.getKey(), item.getValue());
-        }
-        long rowID =
-                mDatabase.insert(mTableName, null, content);
-        return rowID >= 0;
-    }
-
-    // Return all data as a list of Map.
-    // Notice that the column names are repeated for each row.
-    public List<Map<String, String>> getAllData() {
-        ArrayList<Map<String, String> > allData = new ArrayList<Map<String, String> >();
-
-        Cursor cursor = mDatabase.rawQuery("select * from " + mTableName + ";", null);
-        if (cursor.getCount() == 0) {
-            return allData;
-        }
-        cursor.moveToFirst();
-        do {
-            HashMap<String, String> oneRow = new HashMap<String, String>();
-            for (String column : mColumnNames) {
-                int columnIndex = cursor.getColumnIndex(column);
-                if (!cursor.isNull(columnIndex)) {
-                    String value = cursor.getString(columnIndex);
-                    oneRow.put(column, value);
-                }
-            }
-            allData.add(oneRow);
-        } while (cursor.moveToNext());
-        return allData;
-    }
-
-    // Empty the storage.
-    public int removeAllData() {
-        int nDeleteRows = mDatabase.delete(mTableName, "1", null);
-        Log.i(TAG, "Number of rows in table deleted: " + nDeleteRows);
-        return nDeleteRows;
-    }
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/AggregatorStorage.java b/bordeaux/service/src/android/bordeaux/services/AggregatorStorage.java
deleted file mode 100644
index 1d8886e66..000000000
--- a/bordeaux/service/src/android/bordeaux/services/AggregatorStorage.java
+++ /dev/null
@@ -1,57 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import android.content.Context;
-import android.database.SQLException;
-import android.database.sqlite.SQLiteDatabase;
-import android.database.sqlite.SQLiteOpenHelper;
-import android.util.Log;
-
-// Base Helper class for aggregator storage database
-class AggregatorStorage {
-    private static final String TAG = "AggregatorStorage";
-    private static final String DATABASE_NAME = "aggregator";
-    private static final int DATABASE_VERSION = 1;
-
-    protected DBHelper mDbHelper;
-    protected SQLiteDatabase mDatabase;
-
-    class DBHelper extends SQLiteOpenHelper {
-        private String mTableCmd;
-        private String mTableName;
-        DBHelper(Context context, String tableName, String tableCmd) {
-            super(context, DATABASE_NAME, null, DATABASE_VERSION);
-            mTableName = tableName;
-            mTableCmd = tableCmd;
-        }
-
-        @Override
-        public void onCreate(SQLiteDatabase db) {
-            db.execSQL(mTableCmd);
-        }
-
-        @Override
-        public void onUpgrade(SQLiteDatabase db, int oldVersion, int newVersion) {
-            Log.w(TAG, "Upgrading database from version " + oldVersion + " to "
-                  + newVersion + ", which will destroy all old data");
-
-            db.execSQL("DROP TABLE IF EXISTS " + mTableName);
-            onCreate(db);
-        }
-    }
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/BaseCluster.java b/bordeaux/service/src/android/bordeaux/services/BaseCluster.java
deleted file mode 100644
index 03574a9fa..000000000
--- a/bordeaux/service/src/android/bordeaux/services/BaseCluster.java
+++ /dev/null
@@ -1,213 +0,0 @@
-/*
- * 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.
- */
-package android.bordeaux.services;
-import android.location.Location;
-import android.text.format.Time;
-import android.util.Log;
-
-import java.lang.Math;
-import java.util.ArrayList;
-import java.util.HashMap;
-import java.util.Map;
-
-public class BaseCluster {
-
-    public static String TAG = "BaseCluster";
-
-    public double[] mCenter;
-  // protected double[] mCenter;
-
-    protected static final int VECTOR_LENGTH = 3;
-
-    private static final long FORGETTING_ENUMERATOR = 95;
-    private static final long FORGETTING_DENOMINATOR = 100;
-
-    // Histogram illustrates the pattern of visit during time of day,
-    protected HashMap<String, Long> mHistogram = new HashMap<String, Long>();
-
-    protected long mDuration;
-
-    protected String mSemanticId;
-
-    protected static final double EARTH_RADIUS = 6378100f;
-
-    public BaseCluster(Location location) {
-        mCenter = getLocationVector(location);
-        mDuration = 0;
-    }
-
-    public BaseCluster(String semanticId, double longitude, double latitude,
-                     long duration) {
-        mSemanticId = semanticId;
-        mCenter = getLocationVector(longitude, latitude);
-        mDuration = duration;
-    }
-
-    public String getSemanticId() {
-        return mSemanticId;
-    }
-
-    public void generateSemanticId(long index) {
-        mSemanticId = "cluser: " + String.valueOf(index);
-    }
-
-    public long getDuration() {
-        return mDuration;
-    }
-
-    public boolean hasSemanticId() {
-        return mSemanticId != null;
-    }
-
-    protected double[] getLocationVector(Location location) {
-        return getLocationVector(location.getLongitude(), location.getLatitude());
-    }
-
-    protected double[] getLocationVector(double longitude, double latitude) {
-        double vector[] = new double[VECTOR_LENGTH];
-        double lambda = Math.toRadians(longitude);
-        double phi = Math.toRadians(latitude);
-
-        vector[0] = Math.cos(lambda) * Math.cos(phi);
-        vector[1] = Math.sin(lambda) * Math.cos(phi);
-        vector[2] = Math.sin(phi);
-        return vector;
-    }
-
-    protected double getCenterLongitude() {
-        // Because latitude ranges from -90 to 90 degrees, cosPhi >= 0.
-        double cosPhi = Math.cos(Math.asin(mCenter[2]));
-        double longitude = Math.toDegrees(Math.asin(mCenter[1] / cosPhi));
-        if (mCenter[0] < 0) {
-            longitude = (longitude > 0) ? 180f - longitude : -180 - longitude;
-        }
-        return longitude;
-    }
-
-    protected double getCenterLatitude() {
-        return Math.toDegrees(Math.asin(mCenter[2]));
-    }
-
-    private double computeDistance(double[] vector1, double[] vector2) {
-        double product = 0f;
-        for (int i = 0; i < VECTOR_LENGTH; ++i) {
-            product += vector1[i] * vector2[i];
-        }
-        double radian = Math.acos(Math.min(product, 1f));
-        return radian * EARTH_RADIUS;
-    }
-
-    /*
-     * This computes the distance from loation to the cluster center in meter.
-     */
-    public float distanceToCenter(Location location) {
-        return (float) computeDistance(mCenter, getLocationVector(location));
-    }
-
-    public float distanceToCluster(BaseCluster cluster) {
-        return (float) computeDistance(mCenter, cluster.mCenter);
-    }
-
-    public void absorbCluster(BaseCluster cluster) {
-        averageCenter(cluster.mCenter, cluster.mDuration);
-        absorbHistogram(cluster);
-    }
-
-    public void setCluster(BaseCluster cluster) {
-        for (int i = 0; i < VECTOR_LENGTH; ++i) {
-            mCenter[i] = cluster.mCenter[i];
-        }
-        mHistogram.clear();
-        mHistogram.putAll(cluster.mHistogram);
-        mDuration = cluster.mDuration;
-    }
-
-    private void absorbHistogram(BaseCluster cluster) {
-        for (Map.Entry<String, Long> entry : cluster.mHistogram.entrySet()) {
-            String timeLabel = entry.getKey();
-            long duration = entry.getValue();
-
-            if (mHistogram.containsKey(timeLabel)) {
-                duration += mHistogram.get(timeLabel);
-            }
-            mHistogram.put(timeLabel, duration);
-        }
-        mDuration += cluster.mDuration;
-    }
-
-    public boolean passThreshold(long durationThreshold) {
-        // TODO: might want to keep semantic cluster
-        return mDuration > durationThreshold;
-    }
-
-    public final HashMap<String, Long> getHistogram() {
-        return mHistogram;
-    }
-
-    public void setHistogram(Map<String, Long> histogram) {
-        mHistogram.clear();
-        mHistogram.putAll(histogram);
-
-        mDuration = 0;
-        if (mHistogram.containsKey(TimeStatsAggregator.WEEKEND)) {
-            mDuration += mHistogram.get(TimeStatsAggregator.WEEKEND);
-        }
-        if (mHistogram.containsKey(TimeStatsAggregator.WEEKDAY)) {
-            mDuration += mHistogram.get(TimeStatsAggregator.WEEKDAY);
-        }
-    }
-
-    public void forgetPastHistory() {
-        mDuration *= FORGETTING_ENUMERATOR;
-        mDuration /= FORGETTING_DENOMINATOR;
-
-        for (Map.Entry<String, Long> entry : mHistogram.entrySet()) {
-            String key = entry.getKey();
-            long value = entry.getValue();
-
-            value *= FORGETTING_ENUMERATOR;
-            value /= FORGETTING_DENOMINATOR;
-
-            mHistogram.put(key, value);
-        }
-    }
-
-    protected void normalizeCenter() {
-        double norm = 0;
-        for (int i = 0; i < VECTOR_LENGTH; ++i) {
-            norm += mCenter[i] * mCenter[i];
-        }
-        norm = Math.sqrt(norm);
-        for (int i = 0; i < VECTOR_LENGTH; ++i) {
-            mCenter[i] /= norm;
-        }
-    }
-
-    protected void averageCenter(double[] newCenter, long newDuration) {
-        double weight = ((double) mDuration) / (mDuration + newDuration);
-        double newWeight = 1f - weight;
-
-        double norm = 0;
-        for (int i = 0; i < VECTOR_LENGTH; ++i) {
-            mCenter[i] = weight * mCenter[i] + newWeight * newCenter[i];
-            norm += mCenter[i] * mCenter[i];
-        }
-        norm = Math.sqrt(norm);
-        for (int i = 0; i < VECTOR_LENGTH; ++i) {
-            mCenter[i] /= norm;
-        }
-    }
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/BordeauxAggregatorManager.java b/bordeaux/service/src/android/bordeaux/services/BordeauxAggregatorManager.java
deleted file mode 100644
index 7a01233ab..000000000
--- a/bordeaux/service/src/android/bordeaux/services/BordeauxAggregatorManager.java
+++ /dev/null
@@ -1,148 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import android.bordeaux.services.IAggregatorManager;
-import android.bordeaux.services.StringString;
-import android.content.Context;
-import android.os.RemoteException;
-import android.util.Log;
-import java.util.List;
-import java.util.ArrayList;
-import java.util.HashMap;
-import java.util.Map;
-
-/** AggregatorManger for Learning framework.
- */
-public class BordeauxAggregatorManager {
-    static final String TAG = "BordeauxAggregatorManager";
-    static final String AggregatorManager_NOTAVAILABLE = "AggregatorManager not Available";
-    private Context mContext;
-    private IAggregatorManager mAggregatorManager;
-
-    public boolean retrieveAggregatorManager() {
-        if (mAggregatorManager == null) {
-            mAggregatorManager = BordeauxManagerService.getAggregatorManager(mContext);
-            if (mAggregatorManager == null) {
-                Log.e(TAG, AggregatorManager_NOTAVAILABLE);
-                return false;
-            }
-        }
-        return true;
-    }
-
-    public BordeauxAggregatorManager (Context context) {
-        mContext = context;
-        mAggregatorManager = BordeauxManagerService.getAggregatorManager(mContext);
-    }
-
-    public Map<String, String> GetData(final String dataName) {
-        if (!retrieveAggregatorManager())
-            throw new RuntimeException(AggregatorManager_NOTAVAILABLE);
-        try {
-            return getMap(mAggregatorManager.getData(dataName));
-        } catch (RemoteException e) {
-            Log.e(TAG,"Exception in Getting " + dataName);
-            throw new RuntimeException(AggregatorManager_NOTAVAILABLE);
-        }
-    }
-
-    public List<String> getLocationClusters() {
-        if (!retrieveAggregatorManager())
-            throw new RuntimeException(AggregatorManager_NOTAVAILABLE);
-        try {
-            return mAggregatorManager.getLocationClusters();
-        } catch (RemoteException e) {
-            Log.e(TAG,"Error getting location clusters");
-            throw new RuntimeException(AggregatorManager_NOTAVAILABLE);
-        }
-    }
-
-    public List<String> getTimeOfDayValues() {
-        if (!retrieveAggregatorManager())
-            throw new RuntimeException(AggregatorManager_NOTAVAILABLE);
-        try {
-            return mAggregatorManager.getTimeOfDayValues();
-        } catch (RemoteException e) {
-            Log.e(TAG,"Error getting time of day values");
-            throw new RuntimeException(AggregatorManager_NOTAVAILABLE);
-        }
-    }
-
-    public List<String> getDayOfWeekValues() {
-        if (!retrieveAggregatorManager())
-            throw new RuntimeException(AggregatorManager_NOTAVAILABLE);
-        try {
-            return mAggregatorManager.getDayOfWeekValues();
-        } catch (RemoteException e) {
-            Log.e(TAG,"Error getting day of week values");
-            throw new RuntimeException(AggregatorManager_NOTAVAILABLE);
-        }
-    }
-
-    public boolean setFakeLocation(final String name) {
-        if (!retrieveAggregatorManager())
-            throw new RuntimeException(AggregatorManager_NOTAVAILABLE);
-        try {
-            return mAggregatorManager.setFakeLocation(name);
-        } catch (RemoteException e) {
-            Log.e(TAG,"Error setting fake location:" + name);
-            throw new RuntimeException(AggregatorManager_NOTAVAILABLE);
-        }
-    }
-
-    public boolean setFakeTimeOfDay(final String time_of_day) {
-        if (!retrieveAggregatorManager())
-            throw new RuntimeException(AggregatorManager_NOTAVAILABLE);
-        try {
-            return mAggregatorManager.setFakeTimeOfDay(time_of_day);
-        } catch (RemoteException e) {
-            Log.e(TAG,"Error setting fake time of day:" + time_of_day);
-            throw new RuntimeException(AggregatorManager_NOTAVAILABLE);
-        }
-    }
-
-    public boolean setFakeDayOfWeek(final String day_of_week) {
-        if (!retrieveAggregatorManager())
-            throw new RuntimeException(AggregatorManager_NOTAVAILABLE);
-        try {
-            return mAggregatorManager.setFakeDayOfWeek(day_of_week);
-        } catch (RemoteException e) {
-            Log.e(TAG,"Error setting fake day of week:" + day_of_week);
-            throw new RuntimeException(AggregatorManager_NOTAVAILABLE);
-        }
-    }
-
-    public boolean getFakeMode() {
-        if (!retrieveAggregatorManager())
-            throw new RuntimeException(AggregatorManager_NOTAVAILABLE);
-        try {
-            return mAggregatorManager.getFakeMode();
-        } catch (RemoteException e) {
-            Log.e(TAG,"Error getting fake mode");
-            throw new RuntimeException(AggregatorManager_NOTAVAILABLE);
-        }
-    }
-
-    private Map<String, String> getMap(final List<StringString> sample) {
-        HashMap<String, String> map = new HashMap<String, String>();
-        for (int i =0; i < sample.size(); i++) {
-            map.put(sample.get(i).key, sample.get(i).value);
-        }
-        return (Map) map;
-    }
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/BordeauxClassifier.java b/bordeaux/service/src/android/bordeaux/services/BordeauxClassifier.java
deleted file mode 100644
index 348cd0cb8..000000000
--- a/bordeaux/service/src/android/bordeaux/services/BordeauxClassifier.java
+++ /dev/null
@@ -1,103 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import android.bordeaux.services.ILearning_MulticlassPA;
-import android.bordeaux.services.IntFloat;
-import android.content.Context;
-import android.os.RemoteException;
-import android.util.Log;
-
-import java.util.ArrayList;
-import java.util.List;
-import java.util.HashMap;
-import java.util.Map;
-
-/** Classifier for the Learning framework.
- *  For training: call trainOneSample
- *  For classifying: call classify
- *  Data is represented as sparse key, value pair. And key is an integer, value
- *  is a float. Class label(target) for the training data is an integer.
- *  Note: since the actual classifier is running in a remote the service.
- *  Sometimes the connection may be lost or not established.
- *
- */
-public class BordeauxClassifier {
-    static final String TAG = "BordeauxClassifier";
-    private Context mContext;
-    private String mName;
-    private ILearning_MulticlassPA mClassifier;
-    private ArrayList<IntFloat> getArrayList(final HashMap<Integer, Float> sample) {
-        ArrayList<IntFloat> intfloat_sample = new ArrayList<IntFloat>();
-        for (Map.Entry<Integer, Float> x : sample.entrySet()) {
-           IntFloat v = new IntFloat();
-           v.index = x.getKey();
-           v.value = x.getValue();
-           intfloat_sample.add(v);
-        }
-        return intfloat_sample;
-    }
-
-    private boolean retrieveClassifier() {
-        if (mClassifier == null)
-            mClassifier = BordeauxManagerService.getClassifier(mContext, mName);
-        // if classifier is not available, return false
-        if (mClassifier == null) {
-            Log.i(TAG,"Classifier not available.");
-            return false;
-        }
-        return true;
-    }
-
-    public BordeauxClassifier(Context context) {
-        mContext = context;
-        mName = "defaultClassifier";
-        mClassifier = BordeauxManagerService.getClassifier(context, mName);
-    }
-
-    public BordeauxClassifier(Context context, String name) {
-        mContext = context;
-        mName = name;
-        mClassifier = BordeauxManagerService.getClassifier(context, mName);
-    }
-
-    public boolean update(final HashMap<Integer, Float> sample, int target) {
-        if (!retrieveClassifier())
-            return false;
-        try {
-            mClassifier.TrainOneSample(getArrayList(sample), target);
-        } catch (RemoteException e) {
-            Log.e(TAG,"Exception: training one sample.");
-            return false;
-        }
-        return true;
-    }
-
-    public int classify(final HashMap<Integer, Float> sample) {
-        // if classifier is not available return -1 as an indication of fail.
-        if (!retrieveClassifier())
-            return -1;
-        try {
-            return mClassifier.Classify(getArrayList(sample));
-        } catch (RemoteException e) {
-            Log.e(TAG,"Exception: classify the sample.");
-            // return an invalid number.
-            // TODO: throw exception.
-            return -1;
-        }
-    }
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/BordeauxManagerService.java b/bordeaux/service/src/android/bordeaux/services/BordeauxManagerService.java
deleted file mode 100644
index 00fbb30f8..000000000
--- a/bordeaux/service/src/android/bordeaux/services/BordeauxManagerService.java
+++ /dev/null
@@ -1,152 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import android.content.ComponentName;
-import android.content.Context;
-import android.content.Intent;
-import android.content.ServiceConnection;
-import android.graphics.Canvas;
-import android.graphics.Paint;
-import android.graphics.PointF;
-import android.os.Bundle;
-import android.os.Handler;
-import android.os.IBinder;
-import android.os.Message;
-import android.os.Process;
-import android.os.RemoteException;
-import android.util.Log;
-
-/**
- * {@hide}
- * This is used to provide a convenience to access the actual remote running
- * service.
- * TODO: Eventally the remote service will be running in the system server, and
- * this will need to be served as a stub for the remote running service. And
- * extends from IBordeauxManager.stub
- */
-public class BordeauxManagerService {
-
-    static private final String TAG = "BordeauxMangerService";
-    static private IBordeauxService mService = null;
-    static private ILearning_StochasticLinearRanker mRanker = null;
-    static private IAggregatorManager mAggregatorManager = null;
-    static private IPredictor mPredictor = null;
-    static private ILearning_MulticlassPA mClassifier = null;
-    static private boolean mStarted = false;
-
-    public BordeauxManagerService() {
-    }
-
-    static private synchronized void bindServices(Context context) {
-        if (mStarted) return;
-        context.bindService(new Intent(IBordeauxService.class.getName()),
-               mConnection, Context.BIND_AUTO_CREATE);
-        mStarted = true;
-    }
-
-    // Call the release, before the Context gets destroyed.
-    static public synchronized void release(Context context) {
-        if (mStarted && mConnection != null) {
-            context.unbindService(mConnection);
-            mService = null;
-            mStarted = false;
-        }
-    }
-
-    static public synchronized IBordeauxService getService(Context context) {
-        if (mService == null) bindServices(context);
-        return mService;
-    }
-
-    static public synchronized IAggregatorManager getAggregatorManager(Context context) {
-        if (mService == null) {
-            bindServices(context);
-            return null;
-        }
-        try {
-            mAggregatorManager = IAggregatorManager.Stub.asInterface(
-                    mService.getAggregatorManager());
-        } catch (RemoteException e) {
-            mAggregatorManager = null;
-        }
-        return mAggregatorManager;
-    }
-
-    static public synchronized IPredictor getPredictor(Context context, String name) {
-        if (mService == null) {
-            bindServices(context);
-            return null;
-        }
-        try {
-            mPredictor = IPredictor.Stub.asInterface(mService.getPredictor(name));
-        } catch (RemoteException e) {
-            mPredictor = null;
-        }
-        return mPredictor;
-    }
-
-    static public synchronized ILearning_StochasticLinearRanker
-            getRanker(Context context, String name) {
-        if (mService == null) {
-            bindServices(context);
-            return null;
-        }
-        try {
-            mRanker =
-                    ILearning_StochasticLinearRanker.Stub.asInterface(
-                            mService.getRanker(name));
-        } catch (RemoteException e) {
-            mRanker = null;
-        }
-        return mRanker;
-    }
-
-    static public synchronized ILearning_MulticlassPA
-            getClassifier(Context context, String name) {
-        if (mService == null) {
-            bindServices(context);
-            return null;
-        }
-        try {
-            mClassifier =
-                    ILearning_MulticlassPA.Stub.asInterface(mService.getClassifier(name));
-        } catch (RemoteException e) {
-            mClassifier = null;
-        }
-        return mClassifier;
-    }
-
-    /**
-     * Class for interacting with the main interface of the service.
-     */
-    static private ServiceConnection mConnection = new ServiceConnection() {
-        public void onServiceConnected(ComponentName className,
-                IBinder service) {
-            // This is called when the connection with the service has been
-            // established.
-            mService = IBordeauxService.Stub.asInterface(service);
-        }
-
-        public void onServiceDisconnected(ComponentName className) {
-            // This is called when the connection with the service has been
-            // unexpectedly disconnected -- that is, its process crashed.
-            mService = null;
-            mStarted = false; // needs to bind again
-        }
-    };
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/BordeauxPredictor.java b/bordeaux/service/src/android/bordeaux/services/BordeauxPredictor.java
deleted file mode 100644
index ac46af11f..000000000
--- a/bordeaux/service/src/android/bordeaux/services/BordeauxPredictor.java
+++ /dev/null
@@ -1,117 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import android.bordeaux.services.IPredictor;
-import android.content.Context;
-import android.os.RemoteException;
-import android.util.Log;
-import android.util.Pair;
-
-import java.util.ArrayList;
-import java.util.List;
-import java.util.HashMap;
-import java.util.Map;
-
-/** Predictor for the Learning framework.
- */
-public class BordeauxPredictor {
-    static final String TAG = "BordeauxPredictor";
-    static final String PREDICTOR_NOTAVAILABLE = "Predictor is not available.";
-    private Context mContext;
-    private String mName;
-    private IPredictor mPredictor;
-
-    public BordeauxPredictor(Context context) {
-        mContext = context;
-        mName = "defaultPredictor";
-        mPredictor = BordeauxManagerService.getPredictor(context, mName);
-    }
-
-    public BordeauxPredictor(Context context, String name) {
-        mContext = context;
-        mName = name;
-        mPredictor = BordeauxManagerService.getPredictor(context, mName);
-    }
-
-    public boolean reset() {
-        if (!retrievePredictor()){
-            Log.e(TAG, "reset: " + PREDICTOR_NOTAVAILABLE);
-            return false;
-        }
-        try {
-            mPredictor.resetPredictor();
-            return true;
-        } catch (RemoteException e) {
-        }
-        return false;
-    }
-
-    public boolean retrievePredictor() {
-        if (mPredictor == null) {
-            mPredictor = BordeauxManagerService.getPredictor(mContext, mName);
-        }
-        if (mPredictor == null) {
-            Log.e(TAG, "retrievePredictor: " + PREDICTOR_NOTAVAILABLE);
-            return false;
-        }
-        return true;
-    }
-
-    public void addSample(String sampleName) {
-        if (!retrievePredictor())
-            throw new RuntimeException(PREDICTOR_NOTAVAILABLE);
-        try {
-            mPredictor.pushNewSample(sampleName);
-        } catch (RemoteException e) {
-            Log.e(TAG,"Exception: pushing a new example");
-            throw new RuntimeException(PREDICTOR_NOTAVAILABLE);
-        }
-    }
-
-    public ArrayList<Pair<String, Float> > getTopSamples() {
-        return getTopSamples(0);
-    }
-
-    public ArrayList<Pair<String, Float> > getTopSamples(int topK) {
-        try {
-            ArrayList<StringFloat> topList =
-                    (ArrayList<StringFloat>) mPredictor.getTopCandidates(topK);
-
-            ArrayList<Pair<String, Float> > topSamples =
-                    new ArrayList<Pair<String, Float> >(topList.size());
-            for (int i = 0; i < topList.size(); ++i) {
-                topSamples.add(new Pair<String, Float>(topList.get(i).key, topList.get(i).value));
-            }
-            return topSamples;
-        } catch(RemoteException e) {
-            Log.e(TAG,"Exception: getTopSamples");
-            throw new RuntimeException(PREDICTOR_NOTAVAILABLE);
-        }
-    }
-
-    public boolean setParameter(String key, String value) {
-        if (!retrievePredictor())
-            throw new RuntimeException(PREDICTOR_NOTAVAILABLE);
-        try {
-            return mPredictor.setPredictorParameter(key, value);
-        } catch (RemoteException e) {
-            Log.e(TAG,"Exception: setting predictor parameter");
-            throw new RuntimeException(PREDICTOR_NOTAVAILABLE);
-        }
-    }
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/BordeauxRanker.java b/bordeaux/service/src/android/bordeaux/services/BordeauxRanker.java
deleted file mode 100644
index 1ae5fcb62..000000000
--- a/bordeaux/service/src/android/bordeaux/services/BordeauxRanker.java
+++ /dev/null
@@ -1,139 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import android.bordeaux.services.ILearning_StochasticLinearRanker;
-import android.bordeaux.services.StringFloat;
-import android.content.Context;
-import android.os.RemoteException;
-import android.util.Log;
-
-import java.util.ArrayList;
-import java.util.List;
-import java.util.HashMap;
-import java.util.Map;
-
-/** Ranker for the Learning framework.
- *  For training: call updateClassifier with a pair of samples.
- *  For ranking: call scoreSample to the score of the rank
- *  Data is represented as sparse key, value pair. And key is a String, value
- *  is a float.
- *  Note: since the actual ranker is running in a remote the service.
- *  Sometimes the connection may be lost or not established.
- *
- */
-public class BordeauxRanker {
-    static final String TAG = "BordeauxRanker";
-    static final String RANKER_NOTAVAILABLE = "Ranker not Available";
-    private Context mContext;
-    private String mName;
-    private ILearning_StochasticLinearRanker mRanker;
-    private ArrayList<StringFloat> getArrayList(final HashMap<String, Float> sample) {
-        ArrayList<StringFloat> stringfloat_sample = new ArrayList<StringFloat>();
-        for (Map.Entry<String, Float> x : sample.entrySet()) {
-           StringFloat v = new StringFloat();
-           v.key = x.getKey();
-           v.value = x.getValue();
-           stringfloat_sample.add(v);
-        }
-        return stringfloat_sample;
-    }
-
-    public boolean retrieveRanker() {
-        if (mRanker == null)
-            mRanker = BordeauxManagerService.getRanker(mContext, mName);
-        // if classifier is not available, return false
-        if (mRanker == null) {
-            Log.e(TAG,"Ranker not available.");
-            return false;
-        }
-        return true;
-    }
-
-    public BordeauxRanker(Context context) {
-        mContext = context;
-        mName = "defaultRanker";
-        mRanker = BordeauxManagerService.getRanker(context, mName);
-    }
-
-    public BordeauxRanker(Context context, String name) {
-        mContext = context;
-        mName = name;
-        mRanker = BordeauxManagerService.getRanker(context, mName);
-    }
-
-    // Update the ranker with two samples, sample1 has higher rank than
-    // sample2.
-    public boolean update(final HashMap<String, Float> sample1,
-                          final HashMap<String, Float> sample2) {
-        if (!retrieveRanker())
-            return false;
-        try {
-            mRanker.UpdateClassifier(getArrayList(sample1), getArrayList(sample2));
-        } catch (RemoteException e) {
-            Log.e(TAG,"Exception: updateClassifier.");
-            return false;
-        }
-        return true;
-    }
-
-    public boolean reset() {
-        if (!retrieveRanker()){
-            Log.e(TAG,"Exception: Ranker is not availible");
-            return false;
-        }
-        try {
-            mRanker.ResetRanker();
-            return true;
-        } catch (RemoteException e) {
-        }
-        return false;
-    }
-
-    public float scoreSample(final HashMap<String, Float> sample) {
-        if (!retrieveRanker())
-            throw new RuntimeException(RANKER_NOTAVAILABLE);
-        try {
-            return mRanker.ScoreSample(getArrayList(sample));
-        } catch (RemoteException e) {
-            Log.e(TAG,"Exception: scoring the sample.");
-            throw new RuntimeException(RANKER_NOTAVAILABLE);
-        }
-    }
-
-    public boolean setPriorWeight(final HashMap<String, Float> sample) {
-        if (!retrieveRanker())
-            throw new RuntimeException(RANKER_NOTAVAILABLE);
-        try {
-            return mRanker.SetModelPriorWeight(getArrayList(sample));
-        } catch (RemoteException e) {
-            Log.e(TAG,"Exception: set prior Weights");
-            throw new RuntimeException(RANKER_NOTAVAILABLE);
-        }
-    }
-
-    public boolean setParameter(String key, String value) {
-        if (!retrieveRanker())
-            throw new RuntimeException(RANKER_NOTAVAILABLE);
-        try {
-            return mRanker.SetModelParameter(key, value);
-        } catch (RemoteException e) {
-            Log.e(TAG,"Exception: Setting Parameter");
-            throw new RuntimeException(RANKER_NOTAVAILABLE);
-        }
-    }
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/BordeauxService.java b/bordeaux/service/src/android/bordeaux/services/BordeauxService.java
deleted file mode 100644
index 48d41d942..000000000
--- a/bordeaux/service/src/android/bordeaux/services/BordeauxService.java
+++ /dev/null
@@ -1,197 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import android.app.Activity;
-import android.app.Notification;
-import android.app.NotificationManager;
-import android.app.PendingIntent;
-import android.app.Service;
-import android.content.ComponentName;
-import android.content.Context;
-import android.content.Intent;
-import android.content.pm.PackageManager;
-import android.content.ServiceConnection;
-import android.os.Bundle;
-import android.os.Handler;
-import android.os.IBinder;
-import android.os.Message;
-import android.os.Process;
-import android.os.RemoteCallbackList;
-import android.os.RemoteException;
-import android.view.View;
-import android.view.View.OnClickListener;
-import android.widget.Button;
-import android.widget.TextView;
-import android.widget.Toast;
-
-//import android.bordeaux.R;
-import android.util.Log;
-
-import java.io.*;
-
-/**
- * Machine Learning service that runs in a remote process.
- * The application doesn't use this class directly.
- *
- */
-public class BordeauxService extends Service {
-    private final String TAG = "BordeauxService";
-    /**
-     * This is a list of callbacks that have been registered with the
-     * service.
-     * It's a place holder for future communications with all registered
-     * clients.
-     */
-    final RemoteCallbackList<IBordeauxServiceCallback> mCallbacks =
-            new RemoteCallbackList<IBordeauxServiceCallback>();
-
-    int mValue = 0;
-    NotificationManager mNotificationManager;
-
-    BordeauxSessionManager mSessionManager;
-    AggregatorManager mAggregatorManager;
-    TimeStatsAggregator mTimeStatsAggregator;
-    LocationStatsAggregator mLocationStatsAggregator;
-    MotionStatsAggregator mMotionStatsAggregator;
-
-    @Override
-    public void onCreate() {
-        Log.i(TAG, "Bordeaux service created.");
-        //mNotificationManager = (NotificationManager)getSystemService(NOTIFICATION_SERVICE);
-        mSessionManager = new BordeauxSessionManager(this);
-        mMotionStatsAggregator = new MotionStatsAggregator();
-        mLocationStatsAggregator = new LocationStatsAggregator(this);
-        mTimeStatsAggregator = new TimeStatsAggregator();
-        mAggregatorManager = AggregatorManager.getInstance();
-        mAggregatorManager.registerAggregator(mMotionStatsAggregator, mAggregatorManager);
-        mAggregatorManager.registerAggregator(mLocationStatsAggregator, mAggregatorManager);
-        mAggregatorManager.registerAggregator(mTimeStatsAggregator, mAggregatorManager);
-
-        // Display a notification about us starting.
-        // TODO: don't display the notification after the service is
-        // automatically started by the system, currently it's useful for
-        // debugging.
-        showNotification();
-    }
-
-    @Override
-    public void onDestroy() {
-        // Save the sessions
-        mSessionManager.saveSessions();
-
-        // Cancel the persistent notification.
-        //mNotificationManager.cancel(R.string.remote_service_started);
-
-        // Tell the user we stopped.
-        //Toast.makeText(this, R.string.remote_service_stopped, Toast.LENGTH_SHORT).show();
-
-        // Unregister all callbacks.
-        mCallbacks.kill();
-
-        mLocationStatsAggregator.release();
-
-        Log.i(TAG, "Bordeaux service stopped.");
-    }
-
-    @Override
-    public IBinder onBind(Intent intent) {
-        // Return the requested interface.
-        if (IBordeauxService.class.getName().equals(intent.getAction())) {
-            return mBinder;
-        }
-        return null;
-    }
-
-
-    // The main interface implemented by the service.
-    private final IBordeauxService.Stub mBinder = new IBordeauxService.Stub() {
-        private IBinder getLearningSession(Class learnerClass, String name) {
-            PackageManager pm = getPackageManager();
-            String uidname = pm.getNameForUid(getCallingUid());
-            Log.i(TAG,"Name for uid: " + uidname);
-            BordeauxSessionManager.SessionKey key =
-                    mSessionManager.getSessionKey(uidname, learnerClass, name);
-            Log.i(TAG, "request learning session: " + key.value);
-            try {
-                IBinder iLearner = mSessionManager.getSessionBinder(learnerClass, key);
-                return iLearner;
-            } catch (RuntimeException e) {
-                Log.e(TAG, "Error getting learning interface" + e);
-                return null;
-            }
-        }
-
-        public IBinder getClassifier(String name) {
-            return getLearningSession(Learning_MulticlassPA.class, name);
-        }
-
-        public IBinder getRanker(String name) {
-            return getLearningSession(Learning_StochasticLinearRanker.class, name);
-        }
-
-        public IBinder getPredictor(String name) {
-            return getLearningSession(Predictor.class, name);
-        }
-
-        public IBinder getAggregatorManager() {
-            return (IBinder) mAggregatorManager;
-        }
-
-        public void registerCallback(IBordeauxServiceCallback cb) {
-            if (cb != null) mCallbacks.register(cb);
-        }
-
-        public void unregisterCallback(IBordeauxServiceCallback cb) {
-            if (cb != null) mCallbacks.unregister(cb);
-        }
-    };
-
-    @Override
-    public void onTaskRemoved(Intent rootIntent) {
-        Toast.makeText(this, "Task removed: " + rootIntent, Toast.LENGTH_LONG).show();
-    }
-
-    /**
-     * Show a notification while this service is running.
-     * TODO: remove the code after production (when service is loaded
-     * automatically by the system).
-     */
-    private void showNotification() {
-        /*// In this sample, we'll use the same text for the ticker and the expanded notification
-        CharSequence text = getText(R.string.remote_service_started);
-
-        // The PendingIntent to launch our activity if the user selects this notification
-        PendingIntent contentIntent =
-                PendingIntent.getActivity(this, 0,
-                                          new Intent("android.bordeaux.DEBUG_CONTROLLER"), 0);
-
-       // // Set the info for the views that show in the notification panel.
-
-        Notification.Builder builder = new Notification.Builder(this);
-        builder.setSmallIcon(R.drawable.ic_bordeaux);
-        builder.setWhen(System.currentTimeMillis());
-        builder.setTicker(text);
-        builder.setContentTitle(text);
-        builder.setContentIntent(contentIntent);
-        Notification notification = builder.getNotification();
-        // Send the notification.
-        // We use a string id because it is a unique number.  We use it later to cancel.
-        mNotificationManager.notify(R.string.remote_service_started, notification); */
-    }
-
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/BordeauxSessionManager.java b/bordeaux/service/src/android/bordeaux/services/BordeauxSessionManager.java
deleted file mode 100644
index 89fcac29e..000000000
--- a/bordeaux/service/src/android/bordeaux/services/BordeauxSessionManager.java
+++ /dev/null
@@ -1,206 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import android.bordeaux.services.IBordeauxLearner.ModelChangeCallback;
-import android.content.Context;
-import android.os.IBinder;
-import android.util.Log;
-
-import java.lang.NoSuchMethodException;
-import java.lang.InstantiationException;
-import java.util.concurrent.ConcurrentHashMap;
-import java.util.HashMap;
-import java.util.ArrayList;
-import java.util.List;
-import java.util.Map;
-import java.util.Set;
-
-// This class manages the learning sessions from multiple applications.
-// The learning sessions are automatically backed up to the storage.
-//
-class BordeauxSessionManager {
-
-    static private final String TAG = "BordeauxSessionManager";
-    private BordeauxSessionStorage mSessionStorage;
-
-    static class Session {
-        Class learnerClass;
-        IBordeauxLearner learner;
-        boolean modified = false;
-    };
-
-    static class SessionKey {
-        String value;
-    };
-
-    // Thread to periodically save the sessions to storage
-    class PeriodicSave extends Thread implements Runnable {
-        long mSavingInterval = 60000; // 60 seconds
-        boolean mQuit = false;
-        PeriodicSave() {}
-        public void run() {
-            while (!mQuit) {
-                try {
-                    sleep(mSavingInterval);
-                } catch (InterruptedException e) {
-                    // thread waked up.
-                    // ignore
-                }
-                saveSessions();
-            }
-        }
-    }
-
-    PeriodicSave mSavingThread = new PeriodicSave();
-
-    private ConcurrentHashMap<String, Session> mSessions =
-            new ConcurrentHashMap<String, Session>();
-
-    public BordeauxSessionManager(final Context context) {
-        mSessionStorage = new BordeauxSessionStorage(context);
-        mSavingThread.start();
-    }
-
-    class LearningUpdateCallback implements ModelChangeCallback {
-        private String mKey;
-
-        public LearningUpdateCallback(String key) {
-            mKey = key;
-        }
-
-        public void modelChanged(IBordeauxLearner learner) {
-            // Save the session
-            Session session = mSessions.get(mKey);
-            if (session != null) {
-                synchronized(session) {
-                    if (session.learner != learner) {
-                        throw new RuntimeException("Session data corrupted!");
-                    }
-                    session.modified = true;
-                }
-            }
-        }
-    }
-
-    // internal unique key that identifies the learning instance.
-    // Composed by the package id of the calling process, learning class name
-    // and user specified name.
-    public SessionKey getSessionKey(String callingUid, Class learnerClass, String name) {
-        SessionKey key = new SessionKey();
-        key.value = callingUid + "#" + "_" + name + "_" + learnerClass.getName();
-        return key;
-    }
-
-    public IBinder getSessionBinder(Class learnerClass, SessionKey key) {
-        if (mSessions.containsKey(key.value)) {
-            return mSessions.get(key.value).learner.getBinder();
-        }
-        // not in memory cache
-        try {
-            // try to find it in the database
-            Session stored = mSessionStorage.getSession(key.value);
-            if (stored != null) {
-                // set the callback, so that we can save the state
-                stored.learner.setModelChangeCallback(new LearningUpdateCallback(key.value));
-                // found session in the storage, put in the cache
-                mSessions.put(key.value, stored);
-                return stored.learner.getBinder();
-            }
-
-            // if session is not already stored, create a new one.
-            Log.i(TAG, "create a new learning session: " + key.value);
-            IBordeauxLearner learner =
-                    (IBordeauxLearner) learnerClass.getConstructor().newInstance();
-            // set the callback, so that we can save the state
-            learner.setModelChangeCallback(new LearningUpdateCallback(key.value));
-            Session session = new Session();
-            session.learnerClass = learnerClass;
-            session.learner = learner;
-            mSessions.put(key.value, session);
-            return learner.getBinder();
-        } catch (Exception e) {
-            throw new RuntimeException("Can't instantiate class: " +
-                                       learnerClass.getName());
-        }
-    }
-
-    public void saveSessions() {
-        for (Map.Entry<String, Session> session : mSessions.entrySet()) {
-            synchronized(session) {
-                // Save the session if it's modified.
-                if (session.getValue().modified) {
-                    SessionKey skey = new SessionKey();
-                    skey.value = session.getKey();
-                    saveSession(skey);
-                }
-            }
-        }
-    }
-
-    public boolean saveSession(SessionKey key) {
-        Session session = mSessions.get(key.value);
-        if (session != null) {
-            synchronized(session) {
-                byte[] model = session.learner.getModel();
-
-                // write to database
-                boolean res = mSessionStorage.saveSession(key.value, session.learnerClass, model);
-                if (res)
-                    session.modified = false;
-                else {
-                    Log.e(TAG, "Can't save session: " + key.value);
-                }
-                return res;
-            }
-        }
-        Log.e(TAG, "Session not found: " + key.value);
-        return false;
-    }
-
-    // Load all session data into memory.
-    // The session data will be loaded into the memory from the database, even
-    // if this method is not called.
-    public void loadSessions() {
-        synchronized(mSessions) {
-            mSessionStorage.getAllSessions(mSessions);
-            for (Map.Entry<String, Session> session : mSessions.entrySet()) {
-                // set the callback, so that we can save the state
-                session.getValue().learner.setModelChangeCallback(
-                        new LearningUpdateCallback(session.getKey()));
-            }
-        }
-    }
-
-    public void removeAllSessionsFromCaller(String callingUid) {
-        // remove in the hash table
-        ArrayList<String> remove_keys = new ArrayList<String>();
-        for (Map.Entry<String, Session> session : mSessions.entrySet()) {
-            if (session.getKey().startsWith(callingUid + "#")) {
-                remove_keys.add(session.getKey());
-            }
-        }
-        for (String key : remove_keys) {
-            mSessions.remove(key);
-        }
-        // remove all session data from the callingUid in database
-        // % is used as wild match for the rest of the string in sql
-        int nDeleted = mSessionStorage.removeSessions(callingUid + "#%");
-        if (nDeleted > 0)
-            Log.i(TAG, "Successfully deleted " + nDeleted + "sessions");
-    }
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/BordeauxSessionStorage.java b/bordeaux/service/src/android/bordeaux/services/BordeauxSessionStorage.java
deleted file mode 100644
index 89c347204..000000000
--- a/bordeaux/service/src/android/bordeaux/services/BordeauxSessionStorage.java
+++ /dev/null
@@ -1,152 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import android.content.ContentValues;
-import android.content.Context;
-import android.database.Cursor;
-import android.database.SQLException;
-import android.database.sqlite.SQLiteDatabase;
-import android.database.sqlite.SQLiteOpenHelper;
-import android.util.Log;
-
-import java.lang.System;
-import java.util.concurrent.ConcurrentHashMap;
-
-// This class manages the database for storing the session data.
-//
-class BordeauxSessionStorage {
-
-    private static final String TAG = "BordeauxSessionStorage";
-    // unique key for the session
-    public static final String COLUMN_KEY = "key";
-    // name of the learning class
-    public static final String COLUMN_CLASS = "class";
-    // data of the learning model
-    public static final String COLUMN_MODEL = "model";
-    // last update time
-    public static final String COLUMN_TIME = "time";
-
-    private static final String DATABASE_NAME = "bordeaux";
-    private static final String SESSION_TABLE = "sessions";
-    private static final int DATABASE_VERSION = 1;
-    private static final String DATABASE_CREATE =
-        "create table " + SESSION_TABLE + "( " + COLUMN_KEY +
-        " TEXT primary key, " + COLUMN_CLASS + " TEXT, " +
-        COLUMN_MODEL + " BLOB, " + COLUMN_TIME + " INTEGER);";
-
-    private SessionDBHelper mDbHelper;
-    private SQLiteDatabase mDbSessions;
-
-    BordeauxSessionStorage(final Context context) {
-        try {
-            mDbHelper = new SessionDBHelper(context);
-            mDbSessions = mDbHelper.getWritableDatabase();
-        } catch (SQLException e) {
-            throw new RuntimeException("Can't open session database");
-        }
-    }
-
-    private class SessionDBHelper extends SQLiteOpenHelper {
-        SessionDBHelper(Context context) {
-            super(context, DATABASE_NAME, null, DATABASE_VERSION);
-        }
-
-        @Override
-        public void onCreate(SQLiteDatabase db) {
-            db.execSQL(DATABASE_CREATE);
-        }
-
-        @Override
-        public void onUpgrade(SQLiteDatabase db, int oldVersion, int newVersion) {
-            Log.w(TAG, "Upgrading database from version " + oldVersion + " to "
-                  + newVersion + ", which will destroy all old data");
-
-            db.execSQL("DROP TABLE IF EXISTS " + SESSION_TABLE);
-            onCreate(db);
-        }
-    }
-
-    private ContentValues createSessionEntry(String key, Class learner, byte[] model) {
-        ContentValues entry = new ContentValues();
-        entry.put(COLUMN_KEY, key);
-        entry.put(COLUMN_TIME, System.currentTimeMillis());
-        entry.put(COLUMN_MODEL, model);
-        entry.put(COLUMN_CLASS, learner.getName());
-        return entry;
-    }
-
-    boolean saveSession(String key, Class learner, byte[] model) {
-        ContentValues content = createSessionEntry(key, learner, model);
-        long rowID =
-                mDbSessions.insertWithOnConflict(SESSION_TABLE, null, content,
-                                                 SQLiteDatabase.CONFLICT_REPLACE);
-        return rowID >= 0;
-    }
-
-    private BordeauxSessionManager.Session getSessionFromCursor(Cursor cursor) {
-        BordeauxSessionManager.Session session = new BordeauxSessionManager.Session();
-        String className = cursor.getString(cursor.getColumnIndex(COLUMN_CLASS));
-        try {
-            session.learnerClass = Class.forName(className);
-            session.learner = (IBordeauxLearner) session.learnerClass.getConstructor().newInstance();
-        } catch (Exception e) {
-            throw new RuntimeException("Can't instantiate class: " + className);
-        }
-        byte[] model = cursor.getBlob(cursor.getColumnIndex(COLUMN_MODEL));
-        session.learner.setModel(model);
-        return session;
-    }
-
-    BordeauxSessionManager.Session getSession(String key) {
-        Cursor cursor = mDbSessions.query(true, SESSION_TABLE,
-                new String[]{COLUMN_KEY, COLUMN_CLASS, COLUMN_MODEL, COLUMN_TIME},
-                COLUMN_KEY + "=\"" + key + "\"", null, null, null, null, null);
-        if ((cursor == null) | (cursor.getCount() == 0)) {
-            cursor.close();
-            return null;
-        }
-        if (cursor.getCount() > 1) {
-            cursor.close();
-            throw new RuntimeException("Unexpected duplication in session table for key:" + key);
-        }
-        cursor.moveToFirst();
-        BordeauxSessionManager.Session s = getSessionFromCursor(cursor);
-        cursor.close();
-        return s;
-    }
-
-    void getAllSessions(ConcurrentHashMap<String, BordeauxSessionManager.Session> sessions) {
-        Cursor cursor = mDbSessions.rawQuery("select * from ?;", new String[]{SESSION_TABLE});
-        if (cursor == null) return;
-        cursor.moveToFirst();
-        do {
-            String key = cursor.getString(cursor.getColumnIndex(COLUMN_KEY));
-            BordeauxSessionManager.Session session = getSessionFromCursor(cursor);
-            sessions.put(key, session);
-        } while (cursor.moveToNext());
-    }
-
-    // remove all sessions that have the key that matches the given sql regular
-    // expression.
-    int removeSessions(String reKey) {
-        int nDeleteRows = mDbSessions.delete(SESSION_TABLE, "? like \"?\"",
-                                             new String[]{COLUMN_KEY, reKey});
-        Log.i(TAG, "Number of rows in session table deleted: " + nDeleteRows);
-        return nDeleteRows;
-    }
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/ClusterManager.java b/bordeaux/service/src/android/bordeaux/services/ClusterManager.java
deleted file mode 100644
index 71e77a34e..000000000
--- a/bordeaux/service/src/android/bordeaux/services/ClusterManager.java
+++ /dev/null
@@ -1,338 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import android.content.Context;
-import android.location.Location;
-import android.text.format.Time;
-import android.util.Log;
-
-import java.util.ArrayList;
-import java.util.HashMap;
-import java.util.HashSet;
-import java.util.List;
-import java.util.Map;
-
-/**
- * ClusterManager incrementally indentify representitve clusters from the input location
- * stream. Clusters are updated online using leader based clustering algorithm. The input
- * locations initially are kept by the clusters. Periodially, a cluster consolidating
- * procedure is carried out to refine the cluster centers. After consolidation, the
- * location data are released.
- */
-public class ClusterManager {
-
-    private static String TAG = "ClusterManager";
-
-    private static float LOCATION_CLUSTER_RADIUS = 25; // meter
-
-    private static float SEMANTIC_CLUSTER_RADIUS = 75; // meter
-
-    // Consoliate location clusters (and check for new semantic clusters)
-    // every 10 minutes (600 seconds).
-    private static final long CONSOLIDATE_INTERVAL = 600;
-
-    // A location cluster can be labeled as a semantic cluster if it has been
-    // stayed for at least 10 minutes (600 seconds) within a day.
-    private static final long SEMANTIC_CLUSTER_THRESHOLD = 600; // seconds
-
-    // Reset location cluters every 24 hours (86400 seconds).
-    private static final long LOCATION_REFRESH_PERIOD = 86400; // seconds
-
-    private static String UNKNOWN_LOCATION = "Unknown Location";
-
-    private Location mLastLocation = null;
-
-    private long mClusterDuration;
-
-    private long mConsolidateRef = 0;
-
-    private long mRefreshRef = 0;
-
-    private long mSemanticClusterCount = 0;
-
-    private ArrayList<LocationCluster> mLocationClusters = new ArrayList<LocationCluster>();
-
-    private ArrayList<BaseCluster> mSemanticClusters = new ArrayList<BaseCluster>();
-
-    private AggregatorRecordStorage mStorage;
-
-    private static String SEMANTIC_TABLE = "SemanticTable";
-
-    private static String SEMANTIC_ID = "ID";
-
-    private static final String SEMANTIC_LONGITUDE = "Longitude";
-
-    private static final String SEMANTIC_LATITUDE = "Latitude";
-
-    private static final String SEMANTIC_DURATION = "Duration";
-
-    private static final String[] SEMANTIC_COLUMNS =
-        new String[]{ SEMANTIC_ID,
-                      SEMANTIC_LONGITUDE,
-                      SEMANTIC_LATITUDE,
-                      SEMANTIC_DURATION,
-                      TimeStatsAggregator.WEEKEND,
-                      TimeStatsAggregator.WEEKDAY,
-                      TimeStatsAggregator.MORNING,
-                      TimeStatsAggregator.NOON,
-                      TimeStatsAggregator.AFTERNOON,
-                      TimeStatsAggregator.EVENING,
-                      TimeStatsAggregator.NIGHT,
-                      TimeStatsAggregator.LATENIGHT };
-
-    private static final int mFeatureValueStart = 4;
-    private static final int mFeatureValueEnd = 11;
-
-    public ClusterManager(Context context) {
-        mStorage = new AggregatorRecordStorage(context, SEMANTIC_TABLE, SEMANTIC_COLUMNS);
-
-        loadSemanticClusters();
-    }
-
-    public void addSample(Location location) {
-        float bestClusterDistance = Float.MAX_VALUE;
-        int bestClusterIndex = -1;
-        long lastDuration;
-        long currentTime = location.getTime() / 1000; // measure time in seconds
-
-        if (mLastLocation != null) {
-            if (location.getTime() == mLastLocation.getTime()) {
-                return;
-            }
-            // get the duration spent in the last location
-            long duration = (location.getTime() - mLastLocation.getTime()) / 1000;
-            mClusterDuration += duration;
-
-            Log.v(TAG, "sample duration: " + duration +
-                  ", number of clusters: " + mLocationClusters.size());
-
-            synchronized (mLocationClusters) {
-                // add the last location to cluster.
-                // first find the cluster it belongs to.
-                for (int i = 0; i < mLocationClusters.size(); ++i) {
-                    float distance = mLocationClusters.get(i).distanceToCenter(mLastLocation);
-                    Log.v(TAG, "clulster " + i + " is within " + distance + " meters");
-                    if (distance < bestClusterDistance) {
-                        bestClusterDistance = distance;
-                        bestClusterIndex = i;
-                    }
-                }
-
-                // add the location to the selected cluster
-                if (bestClusterDistance < LOCATION_CLUSTER_RADIUS) {
-                    mLocationClusters.get(bestClusterIndex).addSample(mLastLocation, duration);
-                } else {
-                    // if it is far away from all existing clusters, create a new cluster.
-                  LocationCluster cluster = new LocationCluster(mLastLocation, duration);
-                  mLocationClusters.add(cluster);
-                }
-            }
-        } else {
-            mConsolidateRef = currentTime;
-            mRefreshRef = currentTime;
-
-            if (mLocationClusters.isEmpty()) {
-                mClusterDuration = 0;
-            }
-        }
-
-        long collectDuration = currentTime - mConsolidateRef;
-        Log.v(TAG, "collect duration: " + collectDuration);
-        if (collectDuration > CONSOLIDATE_INTERVAL) {
-            // TODO : conslidation takes time. move this to a separate thread later.
-            consolidateClusters();
-            mConsolidateRef = currentTime;
-
-            long refreshDuration = currentTime - mRefreshRef;
-            Log.v(TAG, "refresh duration: " + refreshDuration);
-            if (refreshDuration >  LOCATION_REFRESH_PERIOD) {
-                updateSemanticClusters();
-                mRefreshRef = currentTime;
-            }
-            saveSemanticClusters();
-        }
-
-        mLastLocation = location;
-    }
-
-    private void consolidateClusters() {
-        synchronized (mSemanticClusters) {
-            LocationCluster locationCluster;
-            for (int i = mLocationClusters.size() - 1; i >= 0; --i) {
-                locationCluster = mLocationClusters.get(i);
-                locationCluster.consolidate();
-            }
-
-            // merge clusters whose regions are overlapped. note that after merge
-            // cluster center changes but cluster size remains unchanged.
-            for (int i = mLocationClusters.size() - 1; i >= 0; --i) {
-                locationCluster = mLocationClusters.get(i);
-                for (int j = i - 1; j >= 0; --j) {
-                    float distance =
-                        mLocationClusters.get(j).distanceToCluster(locationCluster);
-                    if (distance < LOCATION_CLUSTER_RADIUS) {
-                        mLocationClusters.get(j).absorbCluster(locationCluster);
-                        mLocationClusters.remove(locationCluster);
-                    }
-                }
-            }
-            Log.v(TAG, mLocationClusters.size() + " location clusters after consolidate");
-
-            // assign each candidate to a semantic cluster and check if new semantic
-            // clusters are found
-            for (LocationCluster candidate : mLocationClusters) {
-                if (candidate.hasSemanticId() ||
-                    candidate.hasSemanticClusterId() ||
-                    !candidate.passThreshold(SEMANTIC_CLUSTER_THRESHOLD)) {
-                    continue;
-                }
-
-                // find the closest semantic cluster
-                float bestClusterDistance = Float.MAX_VALUE;
-                String bestClusterId = "Unused Id";
-                for (BaseCluster cluster : mSemanticClusters) {
-                    float distance = cluster.distanceToCluster(candidate);
-                    Log.v(TAG, distance + "distance to semantic cluster: " +
-                          cluster.getSemanticId());
-
-                    if (distance < bestClusterDistance) {
-                        bestClusterDistance = distance;
-                        bestClusterId = cluster.getSemanticId();
-                    }
-                }
-
-                // if candidate doesn't belong to any semantic cluster, create a new
-                // semantic cluster
-                if (bestClusterDistance > SEMANTIC_CLUSTER_RADIUS) {
-                    candidate.generateSemanticId(mSemanticClusterCount++);
-                    mSemanticClusters.add(candidate);
-                } else {
-                    candidate.setSemanticClusterId(bestClusterId);
-                }
-            }
-            Log.v(TAG, mSemanticClusters.size() + " semantic clusters after consolidate");
-        }
-    }
-
-    private void updateSemanticClusters() {
-        synchronized (mSemanticClusters) {
-            // create index to cluster map
-            HashMap<String, BaseCluster> semanticIdMap =
-                new HashMap<String, BaseCluster>();
-            for (BaseCluster cluster : mSemanticClusters) {
-                // TODO: apply forgetting factor on existing semantic cluster stats,
-                // duration, histogram, etc.
-                cluster.forgetPastHistory();
-                semanticIdMap.put(cluster.getSemanticId(), cluster);
-            }
-
-            // assign each candidate to a semantic cluster
-            for (LocationCluster cluster : mLocationClusters) {
-                if (cluster.hasSemanticClusterId()) {
-                    BaseCluster semanticCluster =
-                        semanticIdMap.get(cluster.getSemanticClusterId());
-                    semanticCluster.absorbCluster(cluster);
-                }
-            }
-            // reset location clusters.
-            mLocationClusters.clear();
-        }
-    }
-
-    private void loadSemanticClusters() {
-        List<Map<String, String> > allData = mStorage.getAllData();
-        HashMap<String, Long> histogram = new HashMap<String, Long>();
-
-        synchronized (mSemanticClusters) {
-            mSemanticClusters.clear();
-            for (Map<String, String> map : allData) {
-                String semanticId = map.get(SEMANTIC_ID);
-                double longitude = Double.parseDouble(map.get(SEMANTIC_LONGITUDE));
-                double latitude = Double.parseDouble(map.get(SEMANTIC_LATITUDE));
-                long duration = Long.parseLong(map.get(SEMANTIC_DURATION));
-                BaseCluster cluster =
-                    new BaseCluster(semanticId, longitude, latitude, duration);
-
-                histogram.clear();
-                for (int i = mFeatureValueStart; i <= mFeatureValueEnd; i++) {
-                    String featureValue = SEMANTIC_COLUMNS[i];
-                    if (map.containsKey(featureValue)) {
-                      histogram.put(featureValue, Long.valueOf(map.get(featureValue)));
-                    }
-                }
-                cluster.setHistogram(histogram);
-                mSemanticClusters.add(cluster);
-            }
-            mSemanticClusterCount = mSemanticClusters.size();
-            Log.e(TAG, "load " + mSemanticClusterCount + " semantic clusters.");
-        }
-    }
-
-    private void saveSemanticClusters() {
-        HashMap<String, String> rowFeatures = new HashMap<String, String>();
-
-        mStorage.removeAllData();
-        synchronized (mSemanticClusters) {
-            for (BaseCluster cluster : mSemanticClusters) {
-                rowFeatures.clear();
-                rowFeatures.put(SEMANTIC_ID, cluster.getSemanticId());
-
-                rowFeatures.put(SEMANTIC_LONGITUDE,
-                            String.valueOf(cluster.getCenterLongitude()));
-                rowFeatures.put(SEMANTIC_LATITUDE,
-                            String.valueOf(cluster.getCenterLatitude()));
-                rowFeatures.put(SEMANTIC_DURATION,
-                            String.valueOf(cluster.getDuration()));
-
-                HashMap<String, Long> histogram = cluster.getHistogram();
-                for (Map.Entry<String, Long> entry : histogram.entrySet()) {
-                    rowFeatures.put(entry.getKey(), String.valueOf(entry.getValue()));
-                }
-                mStorage.addData(rowFeatures);
-                Log.e(TAG, "saving semantic cluster: " + rowFeatures);
-            }
-        }
-    }
-
-    public String getSemanticLocation() {
-        String label = LocationStatsAggregator.UNKNOWN_LOCATION;
-
-        // instead of using the last location, try acquiring the latest location.
-        if (mLastLocation != null) {
-            // TODO: use fast neatest neighbor search speed up location search
-            synchronized (mSemanticClusters) {
-                for (BaseCluster cluster: mSemanticClusters) {
-                    if (cluster.distanceToCenter(mLastLocation) < SEMANTIC_CLUSTER_RADIUS) {
-                        return cluster.getSemanticId();
-                    }
-                }
-            }
-        }
-        return label;
-    }
-
-    public List<String> getClusterNames() {
-        ArrayList<String> clusters = new ArrayList<String>();
-        synchronized (mSemanticClusters) {
-            for (BaseCluster cluster: mSemanticClusters) {
-                clusters.add(cluster.getSemanticId());
-            }
-        }
-        return clusters;
-    }
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/FeatureAssembly.java b/bordeaux/service/src/android/bordeaux/services/FeatureAssembly.java
deleted file mode 100644
index 75a4a9f28..000000000
--- a/bordeaux/service/src/android/bordeaux/services/FeatureAssembly.java
+++ /dev/null
@@ -1,112 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import android.os.IBinder;
-import android.util.Log;
-import android.util.Pair;
-import java.io.Serializable;
-import java.util.HashMap;
-import java.util.Map;
-import java.util.List;
-import java.util.Arrays;
-import java.util.HashSet;
-import java.util.Set;
-import java.util.Iterator;
-
-class FeatureAssembly {
-    private static final String TAG = "FeatureAssembly";
-    private List<String> mPossibleFeatures;
-    private HashSet<String> mUseFeatures;
-    private HashSet<Pair<String, String> > mUsePairedFeatures;
-    private AggregatorManager mAggregatorManager;
-
-    public FeatureAssembly() {
-        mAggregatorManager = AggregatorManager.getInstance();
-        mPossibleFeatures = Arrays.asList(mAggregatorManager.getListOfFeatures());
-        mUseFeatures = new HashSet<String>();
-        mUsePairedFeatures = new HashSet<Pair<String, String> >();
-    }
-
-    public boolean registerFeature(String s) {
-        if (mPossibleFeatures.contains(s)) {
-            mUseFeatures.add(s);
-            return true;
-        } else {
-            return false;
-        }
-    }
-
-    public boolean registerFeaturePair(String[] features) {
-        if (features.length != 2 ||
-            !mPossibleFeatures.contains(features[0]) ||
-            !mPossibleFeatures.contains(features[1])) {
-            return false;
-        } else {
-            mUseFeatures.add(features[0]);
-            mUseFeatures.add(features[1]);
-            mUsePairedFeatures.add(Pair.create(features[0], features[1]));
-            return true;
-        }
-    }
-
-    public Set<String> getUsedFeatures() {
-        return (Set) mUseFeatures;
-    }
-
-    public Map<String, String> getFeatureMap() {
-        HashMap<String, String> featureMap = new HashMap<String, String>();
-
-        Iterator itr = mUseFeatures.iterator();
-        while(itr.hasNext()) {
-            String f = (String) itr.next();
-            Map<String, String> features = mAggregatorManager.getDataMap(f);
-
-            // TODO: sanity check for now.
-            if (features.size() > 1) {
-              throw new RuntimeException("Incorrect feature format extracted from aggregator.");
-            }
-            featureMap.putAll(features);
-        }
-
-        if (!mUsePairedFeatures.isEmpty()) {
-            itr = mUsePairedFeatures.iterator();
-            while(itr.hasNext()) {
-                Pair<String, String> pair = (Pair<String, String>) itr.next();
-                if (featureMap.containsKey(pair.first) &&
-                    featureMap.containsKey(pair.second)) {
-                    String key = pair.first + Predictor.FEATURE_SEPARATOR + pair.second;
-                    String value = featureMap.get(pair.first) + Predictor.FEATURE_SEPARATOR +
-                            featureMap.get(pair.second);
-                    featureMap.put(key, value);
-                }
-            }
-        }
-        return (Map)featureMap;
-    }
-
-
-    public String augmentFeatureInputString(String s) {
-        String fs = s;
-        Iterator itr = mUseFeatures.iterator();
-        while(itr.hasNext()) {
-            String f = (String) itr.next();
-            fs = fs + "+" + mAggregatorManager.getDataMap(f).get(f);
-        }
-        return fs;
-    }
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/IAggregatorManager.aidl b/bordeaux/service/src/android/bordeaux/services/IAggregatorManager.aidl
deleted file mode 100644
index 06afb401f..000000000
--- a/bordeaux/service/src/android/bordeaux/services/IAggregatorManager.aidl
+++ /dev/null
@@ -1,38 +0,0 @@
-/*
- * Copyright (C) 2007 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.services;
-
-import android.bordeaux.services.StringString;
-
-interface IAggregatorManager {
-    List<StringString> getData(in String dataName);
-
-    // TODO: remove the following interfaces in production
-    // they are only used for demo purpose
-    List<String> getLocationClusters();
-    List<String> getTimeOfDayValues();
-    List<String> getDayOfWeekValues();
-    // use "" to disable the fake location
-    boolean setFakeLocation(in String cluster);
-    // use "" to disable the fake time
-    boolean setFakeTimeOfDay(in String time_of_day);
-    // use "" to disable the fake day
-    boolean setFakeDayOfWeek(in String day_of_week);
-    // return whether the service is in fake mode
-    // it's in fake mode, if any of the location, time and day is fake
-    boolean getFakeMode();
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/IBordeauxLearner.java b/bordeaux/service/src/android/bordeaux/services/IBordeauxLearner.java
deleted file mode 100644
index 114d29410..000000000
--- a/bordeaux/service/src/android/bordeaux/services/IBordeauxLearner.java
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import android.os.IBinder;
-
-interface IBordeauxLearner {
-
-    interface ModelChangeCallback {
-
-        public void modelChanged(IBordeauxLearner learner);
-
-    }
-
-    public byte [] getModel();
-
-    public boolean setModel(final byte [] modelData);
-
-    public IBinder getBinder();
-
-    // call back for the learner model change
-    public void setModelChangeCallback(ModelChangeCallback callback);
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/IBordeauxService.aidl b/bordeaux/service/src/android/bordeaux/services/IBordeauxService.aidl
deleted file mode 100644
index 4aa4f08de..000000000
--- a/bordeaux/service/src/android/bordeaux/services/IBordeauxService.aidl
+++ /dev/null
@@ -1,51 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import android.bordeaux.services.IBordeauxServiceCallback;
-
-/**
- * Main learning interface of bordeaux service
- * (running in another process).
- */
-interface IBordeauxService {
-    /* Request a Ranker
-    */
-    IBinder getRanker(String name);
-
-    /* Request a MulticlassPA
-    */
-    IBinder getClassifier(String name);
-
-    /* Request to access AggregatorManager
-    */
-    IBinder getAggregatorManager();
-    /* Request a Predictor
-    */
-    IBinder getPredictor(String name);
-    /**
-     * Often you want to allow a service to call back to its clients.
-     * This shows how to do so, by registering a callback interface with
-     * the service.
-     */
-    void registerCallback(IBordeauxServiceCallback cb);
-
-    /**
-     * Remove a previously registered callback interface.
-     */
-    void unregisterCallback(IBordeauxServiceCallback cb);
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/IBordeauxServiceCallback.aidl b/bordeaux/service/src/android/bordeaux/services/IBordeauxServiceCallback.aidl
deleted file mode 100644
index ad87ceeda..000000000
--- a/bordeaux/service/src/android/bordeaux/services/IBordeauxServiceCallback.aidl
+++ /dev/null
@@ -1,29 +0,0 @@
-/*
- * Copyright (C) 2007 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.services;
-
-/**
- * Example of a callback interface used by IRemoteService to send
- * synchronous notifications back to its clients.  Note that this is a
- * one-way interface so the server does not block waiting for the client.
- */
-oneway interface IBordeauxServiceCallback {
-    /**
-     * Called when the service has a new value for you.
-     */
-    void valueChanged(int value);
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/ILearning_MulticlassPA.aidl b/bordeaux/service/src/android/bordeaux/services/ILearning_MulticlassPA.aidl
deleted file mode 100644
index a62eb788b..000000000
--- a/bordeaux/service/src/android/bordeaux/services/ILearning_MulticlassPA.aidl
+++ /dev/null
@@ -1,29 +0,0 @@
-/*
- * Copyright (C) 2007 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.services;
-
-/**
- * Example of a secondary interface associated with a service.  (Note that
- * the interface itself doesn't impact, it is just a matter of how you
- * retrieve it from the service.)
- */
-import android.bordeaux.services.IntFloat;
-
-interface ILearning_MulticlassPA {
-    void TrainOneSample(in List<IntFloat> sample, int target);
-    int Classify(in List<IntFloat> sample);
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/ILearning_StochasticLinearRanker.aidl b/bordeaux/service/src/android/bordeaux/services/ILearning_StochasticLinearRanker.aidl
deleted file mode 100644
index f89ce0aef..000000000
--- a/bordeaux/service/src/android/bordeaux/services/ILearning_StochasticLinearRanker.aidl
+++ /dev/null
@@ -1,34 +0,0 @@
-/*
- * Copyright (C) 2007 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.services;
-
-/**
- * Example of a secondary interface associated with a service.  (Note that
- * the interface itself doesn't impact, it is just a matter of how you
- * retrieve it from the service.)
- */
-import android.bordeaux.services.StringFloat;
-
-
-interface ILearning_StochasticLinearRanker {
-
-    boolean UpdateClassifier(in List<StringFloat> sample_1, in List<StringFloat> sample_2);
-    float ScoreSample(in List<StringFloat> sample);
-    void ResetRanker();
-    boolean SetModelPriorWeight(in List<StringFloat> weight);
-    boolean SetModelParameter(in String key, in String value);
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/IPredictor.aidl b/bordeaux/service/src/android/bordeaux/services/IPredictor.aidl
deleted file mode 100644
index 0986cd0a7..000000000
--- a/bordeaux/service/src/android/bordeaux/services/IPredictor.aidl
+++ /dev/null
@@ -1,26 +0,0 @@
-/*
- * Copyright (C) 2007 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.services;
-
-import android.bordeaux.services.StringFloat;
-
-interface IPredictor {
-    boolean setPredictorParameter(in String key, in String value);
-    void pushNewSample(in String sampleName);
-    void resetPredictor();
-    List<StringFloat> getTopCandidates(in int topK);
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/IntFloat.aidl b/bordeaux/service/src/android/bordeaux/services/IntFloat.aidl
deleted file mode 100644
index 3b561aecb..000000000
--- a/bordeaux/service/src/android/bordeaux/services/IntFloat.aidl
+++ /dev/null
@@ -1,3 +0,0 @@
-package android.bordeaux.services;
-
-parcelable IntFloat;
diff --git a/bordeaux/service/src/android/bordeaux/services/IntFloat.java b/bordeaux/service/src/android/bordeaux/services/IntFloat.java
deleted file mode 100644
index b9803264c..000000000
--- a/bordeaux/service/src/android/bordeaux/services/IntFloat.java
+++ /dev/null
@@ -1,40 +0,0 @@
-package android.bordeaux.services;
-
-import android.os.Parcel;
-import android.os.Parcelable;
-
-public final class IntFloat implements Parcelable {
-    public int index;
-    public float value;
-
-    public static final Parcelable.Creator<IntFloat> CREATOR = new Parcelable.Creator<IntFloat>() {
-        public IntFloat createFromParcel(Parcel in) {
-            return new IntFloat(in);
-        }
-
-        public IntFloat[] newArray(int size) {
-            return new IntFloat[size];
-        }
-    };
-
-    public IntFloat() {
-    }
-
-    private IntFloat(Parcel in) {
-        readFromParcel(in);
-    }
-
-    public int describeContents() {
-        return 0;
-    }
-
-    public void writeToParcel(Parcel out, int flags) {
-        out.writeInt(index);
-        out.writeFloat(value);
-    }
-
-    public void readFromParcel(Parcel in) {
-        index = in.readInt();
-        value = in.readFloat();
-    }
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/Learning_MulticlassPA.java b/bordeaux/service/src/android/bordeaux/services/Learning_MulticlassPA.java
deleted file mode 100644
index 438398d0f..000000000
--- a/bordeaux/service/src/android/bordeaux/services/Learning_MulticlassPA.java
+++ /dev/null
@@ -1,89 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import android.bordeaux.learning.MulticlassPA;
-import android.os.IBinder;
-
-import java.util.List;
-import java.util.ArrayList;
-
-public class Learning_MulticlassPA extends ILearning_MulticlassPA.Stub
-        implements IBordeauxLearner {
-    private MulticlassPA mMulticlassPA_learner;
-    private ModelChangeCallback modelChangeCallback = null;
-
-    class IntFloatArray {
-        int[] indexArray;
-        float[] floatArray;
-    };
-
-    private IntFloatArray splitIntFloatArray(List<IntFloat> sample) {
-        IntFloatArray splited = new IntFloatArray();
-        ArrayList<IntFloat> s = (ArrayList<IntFloat>)sample;
-        splited.indexArray = new int[s.size()];
-        splited.floatArray = new float[s.size()];
-        for (int i = 0; i < s.size(); i++) {
-            splited.indexArray[i] = s.get(i).index;
-            splited.floatArray[i] = s.get(i).value;
-        }
-        return splited;
-    }
-
-    public Learning_MulticlassPA() {
-        mMulticlassPA_learner = new MulticlassPA(2, 2, 0.001f);
-    }
-
-    // Beginning of the IBordeauxLearner Interface implementation
-    public byte [] getModel() {
-        return null;
-    }
-
-    public boolean setModel(final byte [] modelData) {
-        return false;
-    }
-
-    public IBinder getBinder() {
-        return this;
-    }
-
-    public void setModelChangeCallback(ModelChangeCallback callback) {
-        modelChangeCallback = callback;
-    }
-    // End of IBordeauxLearner Interface implemenation
-
-    // This implementation, combines training and prediction in one step.
-    // The return value is the prediction value for the supplied sample. It
-    // also update the model with the current sample.
-    public void TrainOneSample(List<IntFloat> sample, int target) {
-        IntFloatArray splited = splitIntFloatArray(sample);
-        mMulticlassPA_learner.sparseTrainOneExample(splited.indexArray,
-                                                    splited.floatArray,
-                                                    target);
-        if (modelChangeCallback != null) {
-            modelChangeCallback.modelChanged(this);
-        }
-    }
-
-    public int Classify(List<IntFloat> sample) {
-        IntFloatArray splited = splitIntFloatArray(sample);
-        int prediction = mMulticlassPA_learner.sparseGetClass(splited.indexArray,
-                                                              splited.floatArray);
-        return prediction;
-    }
-
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/Learning_StochasticLinearRanker.java b/bordeaux/service/src/android/bordeaux/services/Learning_StochasticLinearRanker.java
deleted file mode 100644
index c648bd2d1..000000000
--- a/bordeaux/service/src/android/bordeaux/services/Learning_StochasticLinearRanker.java
+++ /dev/null
@@ -1,146 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import android.bordeaux.learning.StochasticLinearRanker;
-import android.bordeaux.services.IBordeauxLearner.ModelChangeCallback;
-import android.os.IBinder;
-import android.util.Log;
-import java.util.List;
-import java.util.ArrayList;
-import java.io.*;
-import java.lang.ClassNotFoundException;
-import java.util.Arrays;
-import java.util.ArrayList;
-import java.util.List;
-import java.util.Scanner;
-import java.io.ByteArrayOutputStream;
-import java.util.HashMap;
-import java.util.Map;
-
-public class Learning_StochasticLinearRanker extends ILearning_StochasticLinearRanker.Stub
-        implements IBordeauxLearner {
-
-    private final String TAG = "ILearning_StochasticLinearRanker";
-    private StochasticLinearRankerWithPrior mLearningSlRanker = null;
-    private ModelChangeCallback modelChangeCallback = null;
-
-    public Learning_StochasticLinearRanker(){
-    }
-
-    public void ResetRanker(){
-        if (mLearningSlRanker == null)
-            mLearningSlRanker = new StochasticLinearRankerWithPrior();
-        mLearningSlRanker.resetRanker();
-    }
-
-    public boolean UpdateClassifier(List<StringFloat> sample_1, List<StringFloat> sample_2){
-        ArrayList<StringFloat> temp_1 = (ArrayList<StringFloat>)sample_1;
-        String[] keys_1 = new String[temp_1.size()];
-        float[] values_1 = new float[temp_1.size()];
-        for (int i = 0; i < temp_1.size(); i++){
-            keys_1[i] = temp_1.get(i).key;
-            values_1[i] = temp_1.get(i).value;
-        }
-        ArrayList<StringFloat> temp_2 = (ArrayList<StringFloat>)sample_2;
-        String[] keys_2 = new String[temp_2.size()];
-        float[] values_2 = new float[temp_2.size()];
-        for (int i = 0; i < temp_2.size(); i++){
-            keys_2[i] = temp_2.get(i).key;
-            values_2[i] = temp_2.get(i).value;
-        }
-        if (mLearningSlRanker == null)
-            mLearningSlRanker = new StochasticLinearRankerWithPrior();
-        boolean res = mLearningSlRanker.updateClassifier(keys_1,values_1,keys_2,values_2);
-        if (res && modelChangeCallback != null) {
-            modelChangeCallback.modelChanged(this);
-        }
-        return res;
-    }
-
-    public float ScoreSample(List<StringFloat> sample) {
-        ArrayList<StringFloat> temp = (ArrayList<StringFloat>)sample;
-        String[] keys = new String[temp.size()];
-        float[] values = new float[temp.size()];
-        for (int i = 0; i < temp.size(); i++){
-            keys[i] = temp.get(i).key;
-            values[i] = temp.get(i).value;
-        }
-        if (mLearningSlRanker == null)
-            mLearningSlRanker = new StochasticLinearRankerWithPrior();
-        return mLearningSlRanker.scoreSample(keys,values);
-    }
-
-    public boolean SetModelPriorWeight(List<StringFloat> sample) {
-        ArrayList<StringFloat> temp = (ArrayList<StringFloat>)sample;
-        HashMap<String, Float> weights = new HashMap<String, Float>();
-        for (int i = 0; i < temp.size(); i++)
-            weights.put(temp.get(i).key, temp.get(i).value);
-        if (mLearningSlRanker == null)
-            mLearningSlRanker = new StochasticLinearRankerWithPrior();
-        return mLearningSlRanker.setModelPriorWeights(weights);
-    }
-
-    public boolean SetModelParameter(String key, String value) {
-        if (mLearningSlRanker == null)
-            mLearningSlRanker = new StochasticLinearRankerWithPrior();
-        return mLearningSlRanker.setModelParameter(key,value);
-    }
-
-    // Beginning of the IBordeauxLearner Interface implementation
-    public byte [] getModel() {
-        if (mLearningSlRanker == null)
-            mLearningSlRanker = new StochasticLinearRankerWithPrior();
-        StochasticLinearRankerWithPrior.Model model = mLearningSlRanker.getModel();
-        try {
-            ByteArrayOutputStream byteStream = new ByteArrayOutputStream();
-            ObjectOutputStream objStream = new ObjectOutputStream(byteStream);
-            objStream.writeObject(model);
-            //return byteStream.toByteArray();
-            byte[] bytes = byteStream.toByteArray();
-            return bytes;
-        } catch (IOException e) {
-            throw new RuntimeException("Can't get model");
-        }
-    }
-
-    public boolean setModel(final byte [] modelData) {
-        try {
-            ByteArrayInputStream input = new ByteArrayInputStream(modelData);
-            ObjectInputStream objStream = new ObjectInputStream(input);
-            StochasticLinearRankerWithPrior.Model model =
-                    (StochasticLinearRankerWithPrior.Model) objStream.readObject();
-            if (mLearningSlRanker == null)
-                mLearningSlRanker = new StochasticLinearRankerWithPrior();
-            boolean res = mLearningSlRanker.loadModel(model);
-            return res;
-        } catch (IOException e) {
-            throw new RuntimeException("Can't load model");
-        } catch (ClassNotFoundException e) {
-            throw new RuntimeException("Learning class not found");
-        }
-    }
-
-    public IBinder getBinder() {
-        return this;
-    }
-
-    public void setModelChangeCallback(ModelChangeCallback callback) {
-        modelChangeCallback = callback;
-    }
-    // End of IBordeauxLearner Interface implemenation
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/LocationCluster.java b/bordeaux/service/src/android/bordeaux/services/LocationCluster.java
deleted file mode 100644
index c9f753f76..000000000
--- a/bordeaux/service/src/android/bordeaux/services/LocationCluster.java
+++ /dev/null
@@ -1,149 +0,0 @@
-/*
- * 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.
- */
-package android.bordeaux.services;
-
-import android.location.Location;
-import android.text.format.Time;
-import android.util.Log;
-
-import java.lang.Math;
-import java.util.ArrayList;
-import java.util.HashMap;
-import java.util.Map;
-
-public class LocationCluster extends BaseCluster {
-    public static String TAG = "LocationCluster";
-
-    private ArrayList<Location> mLocations = new ArrayList<Location>();
-    private HashMap<String, Long> mNewHistogram = new HashMap<String, Long>();
-
-    private String mSemanticClusterId = null;
-
-    public void setSemanticClusterId(String semanticClusterId) {
-        mSemanticClusterId = semanticClusterId;
-    }
-
-    public String getSemanticClusterId() {
-        return mSemanticClusterId;
-    }
-
-    public boolean hasSemanticClusterId() {
-        return mSemanticClusterId != null;
-    }
-
-    // TODO: make it a singleton class
-    public LocationCluster(Location location, long duration) {
-        super(location);
-        addSample(location, duration);
-    }
-
-    public void addSample(Location location, long duration) {
-        updateTemporalHistogram(location.getTime(), duration);
-
-        // use time field to store duation of this location
-        // TODO: extend Location class with additional field for this.
-        location.setTime(duration);
-        mLocations.add(location);
-    }
-
-    public void consolidate() {
-        // If there is no new location added during this period, do nothing.
-        if (mLocations.size() == 0) {
-            return;
-        }
-
-        double[] newCenter = {0f, 0f, 0f};
-        long newDuration = 0l;
-        // update cluster center
-        for (Location location : mLocations) {
-            double[] vector = getLocationVector(location);
-            long duration = location.getTime(); // in seconds
-
-            if (duration == 0) {
-                throw new RuntimeException("location duration is zero");
-            }
-
-            newDuration += duration;
-            for (int i = 0; i < VECTOR_LENGTH; ++i) {
-                newCenter[i] += vector[i] * duration;
-            }
-        }
-        if (newDuration == 0l) {
-            throw new RuntimeException("new duration is zero!");
-        }
-        for (int i = 0; i < VECTOR_LENGTH; ++i) {
-            newCenter[i] /= newDuration;
-        }
-        // remove location data
-        mLocations.clear();
-
-        // The updated center is the weighted average of the existing and the new
-        // centers. Note that if the cluster is consolidated for the first time,
-        // the weight for the existing cluster would be zero.
-        averageCenter(newCenter, newDuration);
-
-        // update histogram
-        for (Map.Entry<String, Long> entry : mNewHistogram.entrySet()) {
-            String timeLabel = entry.getKey();
-            long duration = entry.getValue();
-            if (mHistogram.containsKey(timeLabel)) {
-                duration += mHistogram.get(timeLabel);
-            }
-            mHistogram.put(timeLabel, duration);
-        }
-        mDuration += newDuration;
-        mNewHistogram.clear();
-    }
-
-    /*
-     * if the new created cluster whose covered area overlaps with any existing
-     * cluster move the center away from that cluster till there is no overlap.
-     */
-    public void moveAwayCluster(LocationCluster cluster, float distance) {
-        double[] vector = new double[VECTOR_LENGTH];
-
-        double dot = 0f;
-        for (int i = 0; i < VECTOR_LENGTH; ++i) {
-            dot += mCenter[i] * cluster.mCenter[i];
-        }
-        double norm = 0f;
-        for (int i = 0; i < VECTOR_LENGTH; ++i) {
-            vector[i] = mCenter[i] - dot * cluster.mCenter[i];
-            norm += vector[i] * vector[i];
-        }
-        norm = Math.sqrt(norm);
-
-        double radian = distance / EARTH_RADIUS;
-        for (int i = 0; i < VECTOR_LENGTH; ++i) {
-            mCenter[i] = cluster.mCenter[i] * Math.cos(radian) +
-                    (vector[i] / norm) * Math.sin(radian);
-        }
-    }
-
-    private void updateTemporalHistogram(long time, long duration) {
-        HashMap<String, String> timeFeatures = TimeStatsAggregator.getAllTimeFeatures(time);
-
-        String timeOfWeek = timeFeatures.get(TimeStatsAggregator.TIME_OF_WEEK);
-        long totalDuration = (mNewHistogram.containsKey(timeOfWeek)) ?
-            mNewHistogram.get(timeOfWeek) + duration : duration;
-        mNewHistogram.put(timeOfWeek, totalDuration);
-
-        String timeOfDay = timeFeatures.get(TimeStatsAggregator.TIME_OF_DAY);
-        totalDuration = (mNewHistogram.containsKey(timeOfDay)) ?
-            mNewHistogram.get(timeOfDay) + duration : duration;
-        mNewHistogram.put(timeOfDay, totalDuration);
-    }
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/LocationStatsAggregator.java b/bordeaux/service/src/android/bordeaux/services/LocationStatsAggregator.java
deleted file mode 100644
index 6f792be77..000000000
--- a/bordeaux/service/src/android/bordeaux/services/LocationStatsAggregator.java
+++ /dev/null
@@ -1,239 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import android.app.AlarmManager;
-import android.app.PendingIntent;
-import android.content.BroadcastReceiver;
-import android.content.Context;
-import android.content.Intent;
-import android.content.IntentFilter;
-import android.location.Criteria;
-import android.location.Location;
-import android.location.LocationListener;
-import android.location.LocationManager;
-import android.location.LocationProvider;
-import android.os.Bundle;
-import android.os.Handler;
-import android.os.HandlerThread;
-import android.os.Message;
-import android.os.Process;
-import android.os.SystemClock;
-import android.text.format.Time;
-import android.util.Log;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-
-// TODO: add functionality to detect speed (use GPS) when needed
-// withouth draining the battery quickly
-public class LocationStatsAggregator extends Aggregator {
-    final String TAG = "LocationStatsAggregator";
-    public static final String CURRENT_LOCATION = "Current Location";
-    public static final String CURRENT_SPEED = "Current Speed";
-    public static final String UNKNOWN_LOCATION = "Unknown Location";
-
-    private static final long REPEAT_INTERVAL = 120000;
-
-    private static final long FRESH_THRESHOLD = 90000;
-
-    private static final int LOCATION_CHANGE = 1;
-
-    // record time when the location provider is set
-    private long mProviderSetTime;
-
-    private Handler mHandler;
-    private HandlerThread mHandlerThread;
-    private AlarmManager mAlarmManager;
-    private LocationManager mLocationManager;
-
-    private ClusterManager mClusterManager;
-
-    private Criteria mCriteria = new Criteria();
-
-    private LocationUpdater mLocationUpdater;
-
-    private Context mContext;
-    private PendingIntent mPendingIntent;
-
-    // Fake location, used for testing.
-    private String mFakeLocation = null;
-
-    public LocationStatsAggregator(final Context context) {
-        mLocationManager =
-            (LocationManager) context.getSystemService(Context.LOCATION_SERVICE);
-        mAlarmManager =
-            (AlarmManager) context.getSystemService(Context.ALARM_SERVICE);
-
-        setClusteringThread(context);
-
-        mCriteria.setAccuracy(Criteria.ACCURACY_COARSE);
-        mCriteria.setPowerRequirement(Criteria.POWER_LOW);
-        /*
-        mCriteria.setAltitudeRequired(false);
-        mCriteria.setBearingRequired(false);
-        mCriteria.setSpeedRequired(true);
-        */
-        mCriteria.setCostAllowed(true);
-
-
-        IntentFilter filter = new IntentFilter(LocationUpdater.LOCATION_UPDATE);
-        mLocationUpdater = new LocationUpdater();
-        context.registerReceiver(mLocationUpdater, filter);
-
-        Intent intent = new Intent(LocationUpdater.LOCATION_UPDATE);
-
-        mContext = context;
-        mPendingIntent = PendingIntent.getBroadcast(mContext, 0, intent, 0);
-
-        mAlarmManager.setInexactRepeating(AlarmManager.ELAPSED_REALTIME_WAKEUP,
-                                          SystemClock.elapsedRealtime() + 30000, //
-                                          REPEAT_INTERVAL,
-                                          mPendingIntent);
-    }
-
-    public void release() {
-        mContext.unregisterReceiver(mLocationUpdater);
-        mAlarmManager.cancel(mPendingIntent);
-    }
-
-    public String[] getListOfFeatures(){
-        String[] list = { CURRENT_LOCATION } ;
-        return list;
-    }
-
-    public Map<String,String> getFeatureValue(String featureName) {
-        HashMap<String,String> feature = new HashMap<String,String>();
-
-        if (featureName.equals(CURRENT_LOCATION)) {
-            // TODO: check last known location first before sending out location request.
-            /*
-              Location location =
-              mLocationManager.getLastKnownLocation(LocationManager.GPS_PROVIDER);
-            */
-            String location = mClusterManager.getSemanticLocation();
-            if (!location.equals(UNKNOWN_LOCATION)) {
-                if (mFakeLocation != null) {
-                    feature.put(CURRENT_LOCATION, mFakeLocation);
-                } else {
-                    feature.put(CURRENT_LOCATION, location);
-                }
-            }
-        }
-        return (Map) feature;
-    }
-
-    public List<String> getClusterNames() {
-        return mClusterManager.getClusterNames();
-    }
-
-    // set a fake location using cluster name.
-    // Set an empty string "" to disable the fake location
-    public void setFakeLocation(String name) {
-        if (name != null && name.length() != 0)
-            mFakeLocation = name;
-        else mFakeLocation = null;
-    }
-
-    private Location getLastKnownLocation() {
-        List<String> providers = mLocationManager.getAllProviders();
-        Location bestResult = null;
-        float bestAccuracy = Float.MAX_VALUE;
-        long bestTime;
-
-        // get the latest location data
-        long currTime =  System.currentTimeMillis();
-        for (String provider : providers) {
-            Location location = mLocationManager.getLastKnownLocation(provider);
-
-            if (location != null) {
-                float accuracy = location.getAccuracy();
-                long time = location.getTime();
-
-                if (currTime - time < FRESH_THRESHOLD && accuracy < bestAccuracy) {
-                    bestResult = location;
-                    bestAccuracy = accuracy;
-                    bestTime = time;
-                }
-            }
-        }
-        if (bestResult != null) {
-            Log.i(TAG, "found location for free: " + bestResult);
-        }
-        return bestResult;
-    }
-
-    private class LocationUpdater extends BroadcastReceiver {
-        String TAG = "LocationUpdater";
-
-        public static final String LOCATION_UPDATE = "android.bordeaux.services.LOCATION_UPDATE";
-
-        @Override
-        public void onReceive(Context context, Intent intent) {
-            Location location = getLastKnownLocation();
-
-            if (location == null) {
-                String provider = mLocationManager.getBestProvider(mCriteria, true);
-                Log.i(TAG, "Best Available Location Provider: " + provider);
-                mLocationManager.requestSingleUpdate(provider, mLocationListener,
-                                                     mHandlerThread.getLooper());
-            } else {
-                mHandler.sendMessage(mHandler.obtainMessage(LOCATION_CHANGE, location));
-            }
-        }
-    }
-
-    private void setClusteringThread(Context context) {
-        mClusterManager = new ClusterManager(context);
-
-        mHandlerThread = new HandlerThread("Location Handler",
-                Process.THREAD_PRIORITY_BACKGROUND);
-        mHandlerThread.start();
-        mHandler = new Handler(mHandlerThread.getLooper()) {
-
-            @Override
-            public void handleMessage(Message msg) {
-                if (!(msg.obj instanceof Location)) {
-                    return;
-                }
-                Location location = (Location) msg.obj;
-                switch(msg.what) {
-                    case LOCATION_CHANGE:
-                        mClusterManager.addSample(location);
-                        break;
-                    default:
-                        super.handleMessage(msg);
-                }
-            }
-        };
-    }
-
-    private final LocationListener mLocationListener = new LocationListener() {
-        private static final String TAG = "LocationListener";
-
-        public void onLocationChanged(Location location) {
-            mHandler.sendMessage(mHandler.obtainMessage(LOCATION_CHANGE, location));
-            mLocationManager.removeUpdates(this);
-        }
-
-        public void onStatusChanged(String provider, int status, Bundle extras) { }
-
-        public void onProviderEnabled(String provider) { }
-
-        public void onProviderDisabled(String provider) { }
-    };
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/MotionStatsAggregator.java b/bordeaux/service/src/android/bordeaux/services/MotionStatsAggregator.java
deleted file mode 100644
index c34779e5e..000000000
--- a/bordeaux/service/src/android/bordeaux/services/MotionStatsAggregator.java
+++ /dev/null
@@ -1,39 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import android.util.Log;
-import java.util.HashMap;
-import java.util.Map;
-
-public class MotionStatsAggregator extends Aggregator {
-    final String TAG = "MotionStatsAggregator";
-    public static final String CURRENT_MOTION = "Current Motion";
-    public String[] getListOfFeatures(){
-        String [] list = new String[1];
-        list[0] = CURRENT_MOTION;
-        return list;
-    }
-    public Map<String,String> getFeatureValue(String featureName) {
-        HashMap<String,String> m = new HashMap<String,String>();
-        if (featureName.equals(CURRENT_MOTION))
-            m.put(CURRENT_MOTION,"Running"); //TODO maybe use clustering for user motion
-        else
-            Log.e(TAG, "There is no motion feature called " + featureName);
-        return (Map) m;
-    }
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/Predictor.java b/bordeaux/service/src/android/bordeaux/services/Predictor.java
deleted file mode 100644
index 9d9047a86..000000000
--- a/bordeaux/service/src/android/bordeaux/services/Predictor.java
+++ /dev/null
@@ -1,174 +0,0 @@
-/*
- * Copyright (C) 2012 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you my 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.services;
-
-import android.os.IBinder;
-import android.util.Log;
-import java.util.HashMap;
-import java.util.ArrayList;
-import java.util.List;
-import java.util.Map;
-import java.util.HashSet;
-import java.util.Iterator;
-import java.io.Serializable;
-import java.io.*;
-import java.lang.Boolean;
-import android.bordeaux.services.FeatureAssembly;
-import android.bordeaux.learning.HistogramPredictor;
-
-/**
- * This is interface to implement Prediction based on histogram that
- * uses predictor_histogram from learnerning section
- */
-public class Predictor extends IPredictor.Stub
-        implements IBordeauxLearner {
-    private final String TAG = "Predictor";
-    private ModelChangeCallback modelChangeCallback = null;
-
-    private FeatureAssembly mFeatureAssembly = new FeatureAssembly();
-
-    public static final String SET_FEATURE = "SetFeature";
-    public static final String SET_PAIRED_FEATURES = "SetPairedFeatures";
-    public static final String FEATURE_SEPARATOR = ":";
-    public static final String USE_HISTORY = "UseHistory";
-    public static final String PREVIOUS_SAMPLE = "PreviousSample";
-
-    private boolean mUseHistory = false;
-    private long mHistorySpan = 0;
-    private String mPrevSample;
-    private long mPrevSampleTime;
-
-    // TODO: blacklist should be set outside Bordeaux service!
-    private static final String[] APP_BLACKLIST = {
-        "com.android.contacts",
-        "com.android.chrome",
-        "com.android.providers.downloads.ui",
-        "com.android.settings",
-        "com.android.vending",
-        "com.android.mms",
-        "com.google.android.gm",
-        "com.google.android.gallery3d",
-        "com.google.android.apps.googlevoice",
-    };
-
-    private HistogramPredictor mPredictor = new HistogramPredictor(APP_BLACKLIST);
-
-
-    /**
-     * Reset the Predictor
-     */
-    public void resetPredictor(){
-        mPredictor.resetPredictor();
-
-        if (modelChangeCallback != null) {
-            modelChangeCallback.modelChanged(this);
-        }
-    }
-
-    /**
-     * Input is a sampleName e.g.action name. This input is then augmented with requested build-in
-     * features such as time and location to create sampleFeatures. The sampleFeatures is then
-     * pushed to the histogram
-     */
-    public void pushNewSample(String sampleName) {
-        Map<String, String> sampleFeatures = getSampleFeatures();
-        Log.i(TAG, "pushNewSample " + sampleName + ": " + sampleFeatures);
-
-        // TODO: move to the end of the function?
-        mPrevSample = sampleName;
-        mPrevSampleTime = System.currentTimeMillis();
-
-        mPredictor.addSample(sampleName, sampleFeatures);
-        if (modelChangeCallback != null) {
-            modelChangeCallback.modelChanged(this);
-        }
-    }
-
-    private Map<String, String> getSampleFeatures() {
-        Map<String, String> sampleFeatures = mFeatureAssembly.getFeatureMap();
-        long currTime = System.currentTimeMillis();
-
-        if (mUseHistory && mPrevSample != null &&
-            ((currTime - mPrevSampleTime) < mHistorySpan)) {
-            sampleFeatures.put(PREVIOUS_SAMPLE, mPrevSample);
-        }
-
-        return sampleFeatures;
-    }
-
-    // TODO: getTopK samples instead get scord for debugging only
-    /**
-     * return probabilty of an exmple using the histogram
-     */
-    public List<StringFloat> getTopCandidates(int topK) {
-        Map<String, String> features = getSampleFeatures();
-        List<Map.Entry<String, Double> > topApps = mPredictor.findTopClasses(features, topK);
-
-        int listSize =  topApps.size();
-        ArrayList<StringFloat> result = new ArrayList<StringFloat>(listSize);
-        for (int i = 0; i < listSize; ++i) {
-            Map.Entry<String, Double> entry = topApps.get(i);
-            result.add(new StringFloat(entry.getKey(), entry.getValue().floatValue()));
-        }
-        return result;
-    }
-
-    /**
-     * Set parameters for 1) using History in probability estimations e.g. consider the last event
-     * and 2) featureAssembly e.g. time and location.
-     */
-    public boolean setPredictorParameter(String key, String value) {
-        Log.i(TAG, "setParameter : " + key + ", " + value);
-        boolean result = true;
-        if (key.equals(SET_FEATURE)) {
-            result = mFeatureAssembly.registerFeature(value);
-            if (!result) {
-               Log.e(TAG,"Setting on feauture: " + value + " which is not available");
-            }
-        } else if (key.equals(SET_PAIRED_FEATURES)) {
-            String[] features = value.split(FEATURE_SEPARATOR);
-            result = mFeatureAssembly.registerFeaturePair(features);
-            if (!result) {
-                Log.e(TAG,"Setting feauture pair: " + value + " is not valid");
-            }
-        } else if (key.equals(USE_HISTORY)) {
-            mUseHistory = true;
-            mHistorySpan = Long.parseLong(value);
-        } else {
-            Log.e(TAG,"Setting parameter " + key + " with " + value + " is not valid");
-        }
-        return result;
-    }
-
-    // Beginning of the IBordeauxLearner Interface implementation
-    public byte [] getModel() {
-      return mPredictor.getModel();
-    }
-
-    public boolean setModel(final byte [] modelData) {
-      return mPredictor.setModel(modelData);
-    }
-
-    public IBinder getBinder() {
-        return this;
-    }
-
-    public void setModelChangeCallback(ModelChangeCallback callback) {
-        modelChangeCallback = callback;
-    }
-    // End of IBordeauxLearner Interface implemenation
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/StochasticLinearRankerWithPrior.java b/bordeaux/service/src/android/bordeaux/services/StochasticLinearRankerWithPrior.java
deleted file mode 100644
index fd56a2fd6..000000000
--- a/bordeaux/service/src/android/bordeaux/services/StochasticLinearRankerWithPrior.java
+++ /dev/null
@@ -1,211 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-import android.util.Log;
-
-import android.bordeaux.learning.StochasticLinearRanker;
-import java.util.HashMap;
-import java.util.Map;
-import java.io.Serializable;
-
-public class StochasticLinearRankerWithPrior extends StochasticLinearRanker {
-    private final String TAG = "StochasticLinearRankerWithPrior";
-    private final float EPSILON = 0.0001f;
-
-    /* If the is parameter is true, the final score would be a
-    linear combination of user model and prior model */
-    private final String USE_PRIOR = "usePriorInformation";
-
-    /* When prior model is used, this parmaeter will set the mixing factor, alpha. */
-    private final String SET_ALPHA = "setAlpha";
-
-    /* When prior model is used, If this parameter is true then algorithm will use
-    the automatic cross validated alpha for mixing user model and prior model */
-    private final String USE_AUTO_ALPHA = "useAutoAlpha";
-
-    /* When automatic cross validation is active, this parameter will
-    set the forget rate in cross validation. */
-    private final String SET_FORGET_RATE = "setForgetRate";
-
-    /* When automatic cross validation is active, this parameter will
-    set the minium number of required training pairs before using the user model */
-    private final String SET_MIN_TRAIN_PAIR = "setMinTrainingPair";
-
-    private final String SET_USER_PERF = "setUserPerformance";
-    private final String SET_PRIOR_PERF = "setPriorPerformance";
-    private final String SET_NUM_TRAIN_PAIR = "setNumberTrainingPairs";
-    private final String SET_AUTO_ALPHA = "setAutoAlpha";
-
-
-
-    private HashMap<String, Float> mPriorWeights = new HashMap<String, Float>();
-    private float mAlpha = 0;
-    private float mAutoAlpha = 0;
-    private float mForgetRate = 0;
-    private float mUserRankerPerf = 0;
-    private float mPriorRankerPerf = 0;
-    private int mMinReqTrainingPair = 0;
-    private int mNumTrainPair = 0;
-    private boolean mUsePrior = false;
-    private boolean mUseAutoAlpha = false;
-
-    static public class Model implements Serializable {
-        public StochasticLinearRanker.Model uModel = new StochasticLinearRanker.Model();
-        public HashMap<String, Float> priorWeights = new HashMap<String, Float>();
-        public HashMap<String, String> priorParameters = new HashMap<String, String>();
-    }
-
-    @Override
-    public void resetRanker(){
-        super.resetRanker();
-        mPriorWeights.clear();
-        mAlpha = 0;
-        mAutoAlpha = 0;
-        mForgetRate = 0;
-        mMinReqTrainingPair = 0;
-        mUserRankerPerf = 0;
-        mPriorRankerPerf = 0;
-        mNumTrainPair = 0;
-        mUsePrior = false;
-        mUseAutoAlpha = false;
-    }
-
-    @Override
-    public float scoreSample(String[] keys, float[] values) {
-        if (!mUsePrior){
-            return super.scoreSample(keys, values);
-        } else {
-            if (mUseAutoAlpha) {
-                if (mNumTrainPair > mMinReqTrainingPair)
-                    return (1 - mAutoAlpha) * super.scoreSample(keys,values) +
-                            mAutoAlpha * priorScoreSample(keys,values);
-                else
-                    return priorScoreSample(keys,values);
-            } else
-                return (1 - mAlpha) * super.scoreSample(keys,values) +
-                        mAlpha * priorScoreSample(keys,values);
-        }
-    }
-
-    public float priorScoreSample(String[] keys, float[] values) {
-        float score = 0;
-        for (int i=0; i< keys.length; i++){
-            if (mPriorWeights.get(keys[i]) != null )
-                score = score + mPriorWeights.get(keys[i]) * values[i];
-        }
-        return score;
-    }
-
-    @Override
-    public boolean updateClassifier(String[] keys_positive,
-                                    float[] values_positive,
-                                    String[] keys_negative,
-                                    float[] values_negative){
-        if (mUsePrior && mUseAutoAlpha && (mNumTrainPair > mMinReqTrainingPair))
-            updateAutoAlpha(keys_positive, values_positive, keys_negative, values_negative);
-        mNumTrainPair ++;
-        return super.updateClassifier(keys_positive, values_positive,
-                                      keys_negative, values_negative);
-    }
-
-    void updateAutoAlpha(String[] keys_positive,
-                     float[] values_positive,
-                     String[] keys_negative,
-                     float[] values_negative) {
-        float positiveUserScore = super.scoreSample(keys_positive, values_positive);
-        float negativeUserScore = super.scoreSample(keys_negative, values_negative);
-        float positivePriorScore = priorScoreSample(keys_positive, values_positive);
-        float negativePriorScore = priorScoreSample(keys_negative, values_negative);
-        float userDecision = 0;
-        float priorDecision = 0;
-        if (positiveUserScore > negativeUserScore)
-            userDecision = 1;
-        if (positivePriorScore > negativePriorScore)
-            priorDecision = 1;
-        mUserRankerPerf = (1 - mForgetRate) * mUserRankerPerf + userDecision;
-        mPriorRankerPerf = (1 - mForgetRate) * mPriorRankerPerf + priorDecision;
-        mAutoAlpha = (mPriorRankerPerf + EPSILON) / (mUserRankerPerf + mPriorRankerPerf + EPSILON);
-    }
-
-    public Model getModel(){
-        Model m = new Model();
-        m.uModel = super.getUModel();
-        m.priorWeights.putAll(mPriorWeights);
-        m.priorParameters.put(SET_ALPHA, String.valueOf(mAlpha));
-        m.priorParameters.put(SET_AUTO_ALPHA, String.valueOf(mAutoAlpha));
-        m.priorParameters.put(SET_FORGET_RATE, String.valueOf(mForgetRate));
-        m.priorParameters.put(SET_MIN_TRAIN_PAIR, String.valueOf(mMinReqTrainingPair));
-        m.priorParameters.put(SET_USER_PERF, String.valueOf(mUserRankerPerf));
-        m.priorParameters.put(SET_PRIOR_PERF, String.valueOf(mPriorRankerPerf));
-        m.priorParameters.put(SET_NUM_TRAIN_PAIR, String.valueOf(mNumTrainPair));
-        m.priorParameters.put(USE_AUTO_ALPHA, String.valueOf(mUseAutoAlpha));
-        m.priorParameters.put(USE_PRIOR, String.valueOf(mUsePrior));
-        return m;
-    }
-
-    public boolean loadModel(Model m) {
-        mPriorWeights.clear();
-        mPriorWeights.putAll(m.priorWeights);
-        for (Map.Entry<String, String> e : m.priorParameters.entrySet()) {
-            boolean res = setModelParameter(e.getKey(), e.getValue());
-            if (!res) return false;
-        }
-        return super.loadModel(m.uModel);
-    }
-
-    public boolean setModelPriorWeights(HashMap<String, Float> pw){
-        mPriorWeights.clear();
-        mPriorWeights.putAll(pw);
-        return true;
-    }
-
-    public boolean setModelParameter(String key, String value){
-        if (key.equals(USE_AUTO_ALPHA)){
-            mUseAutoAlpha = Boolean.parseBoolean(value);
-        } else if (key.equals(USE_PRIOR)){
-            mUsePrior = Boolean.parseBoolean(value);
-        } else if (key.equals(SET_ALPHA)){
-            mAlpha = Float.valueOf(value.trim()).floatValue();
-        }else if (key.equals(SET_AUTO_ALPHA)){
-            mAutoAlpha = Float.valueOf(value.trim()).floatValue();
-        }else if (key.equals(SET_FORGET_RATE)){
-            mForgetRate = Float.valueOf(value.trim()).floatValue();
-        }else if (key.equals(SET_MIN_TRAIN_PAIR)){
-            mMinReqTrainingPair = (int) Float.valueOf(value.trim()).floatValue();
-        }else if (key.equals(SET_USER_PERF)){
-            mUserRankerPerf = Float.valueOf(value.trim()).floatValue();
-        }else if (key.equals(SET_PRIOR_PERF)){
-            mPriorRankerPerf = Float.valueOf(value.trim()).floatValue();
-        }else if (key.equals(SET_NUM_TRAIN_PAIR)){
-            mNumTrainPair = (int) Float.valueOf(value.trim()).floatValue();
-        }else
-            return super.setModelParameter(key, value);
-        return true;
-    }
-
-    public void print(Model m){
-        super.print(m.uModel);
-        String Spw = "";
-        for (Map.Entry<String, Float> e : m.priorWeights.entrySet())
-            Spw = Spw + "<" + e.getKey() + "," + e.getValue() + "> ";
-        Log.i(TAG, "Prior model is " + Spw);
-        String Spp = "";
-        for (Map.Entry<String, String> e : m.priorParameters.entrySet())
-            Spp = Spp + "<" + e.getKey() + "," + e.getValue() + "> ";
-        Log.i(TAG, "Prior parameters are " + Spp);
-    }
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/StringFloat.aidl b/bordeaux/service/src/android/bordeaux/services/StringFloat.aidl
deleted file mode 100644
index f01f166eb..000000000
--- a/bordeaux/service/src/android/bordeaux/services/StringFloat.aidl
+++ /dev/null
@@ -1,3 +0,0 @@
-package android.bordeaux.services;
-
-parcelable StringFloat;
diff --git a/bordeaux/service/src/android/bordeaux/services/StringFloat.java b/bordeaux/service/src/android/bordeaux/services/StringFloat.java
deleted file mode 100644
index c95ccd29c..000000000
--- a/bordeaux/service/src/android/bordeaux/services/StringFloat.java
+++ /dev/null
@@ -1,46 +0,0 @@
-package android.bordeaux.services;
-
-import android.os.Parcel;
-import android.os.Parcelable;
-
-public final class StringFloat implements Parcelable {
-    public String key;
-    public float value;
-
-    public static final Parcelable.Creator<StringFloat>
-                        CREATOR = new Parcelable.Creator<StringFloat>() {
-        public StringFloat createFromParcel(Parcel in) {
-            return new StringFloat(in);
-        }
-
-        public StringFloat[] newArray(int size) {
-            return new StringFloat[size];
-        }
-    };
-
-    public StringFloat() {
-    }
-
-    public StringFloat(String newKey, float newValue) {
-        key = newKey;
-        value = newValue;
-    }
-
-    private StringFloat(Parcel in) {
-        readFromParcel(in);
-    }
-
-    public int describeContents() {
-        return 0;
-    }
-
-    public void writeToParcel(Parcel out, int flags) {
-        out.writeString(key);
-        out.writeFloat(value);
-    }
-
-    public void readFromParcel(Parcel in) {
-        key  = in.readString();
-        value = in.readFloat();
-    }
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/StringString.aidl b/bordeaux/service/src/android/bordeaux/services/StringString.aidl
deleted file mode 100644
index 3cb89b9cf..000000000
--- a/bordeaux/service/src/android/bordeaux/services/StringString.aidl
+++ /dev/null
@@ -1,3 +0,0 @@
-package android.bordeaux.services;
-
-parcelable StringString;
diff --git a/bordeaux/service/src/android/bordeaux/services/StringString.java b/bordeaux/service/src/android/bordeaux/services/StringString.java
deleted file mode 100644
index 109462e9f..000000000
--- a/bordeaux/service/src/android/bordeaux/services/StringString.java
+++ /dev/null
@@ -1,41 +0,0 @@
-package android.bordeaux.services;
-
-import android.os.Parcel;
-import android.os.Parcelable;
-
-public final class StringString implements Parcelable {
-    public String key;
-    public String value;
-
-    public static final Parcelable.Creator<StringString>
-                        CREATOR = new Parcelable.Creator<StringString>() {
-        public StringString createFromParcel(Parcel in) {
-            return new StringString(in);
-        }
-
-        public StringString[] newArray(int size) {
-            return new StringString[size];
-        }
-    };
-
-    public StringString() {
-    }
-
-    private StringString(Parcel in) {
-        readFromParcel(in);
-    }
-
-    public int describeContents() {
-        return 0;
-    }
-
-    public void writeToParcel(Parcel out, int flags) {
-        out.writeString(key);
-        out.writeString(value);
-    }
-
-    public void readFromParcel(Parcel in) {
-        key  = in.readString();
-        value = in.readString();
-    }
-}
diff --git a/bordeaux/service/src/android/bordeaux/services/TimeStatsAggregator.java b/bordeaux/service/src/android/bordeaux/services/TimeStatsAggregator.java
deleted file mode 100644
index a3e123c74..000000000
--- a/bordeaux/service/src/android/bordeaux/services/TimeStatsAggregator.java
+++ /dev/null
@@ -1,199 +0,0 @@
-/*
- * 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.
- */
-
-package android.bordeaux.services;
-
-import android.text.format.Time;
-import android.util.Log;
-
-import java.util.Arrays;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-
-// import java.util.Date;
-
-// TODO: use build in functions in
-// import android.text.format.Time;
-public class TimeStatsAggregator extends Aggregator {
-    final String TAG = "TimeStatsAggregator";
-
-    public static final String TIME_OF_WEEK = "Time of Week";
-    public static final String DAY_OF_WEEK = "Day of Week";
-    public static final String TIME_OF_DAY = "Time of Day";
-    public static final String PERIOD_OF_DAY = "Period of Day";
-
-    static final String WEEKEND = "Weekend";
-    static final String WEEKDAY = "Weekday";
-    static final String MONDAY = "Monday";
-    static final String TUESDAY = "Tuesday";
-    static final String WEDNESDAY = "Wednesday";
-    static final String THURSDAY = "Thursday";
-    static final String FRIDAY = "Friday";
-    static final String SATURDAY = "Saturday";
-    static final String SUNDAY = "Sunday";
-    static final String MORNING = "Morning";
-    static final String NOON = "Noon";
-    static final String AFTERNOON = "AfterNoon";
-    static final String EVENING = "Evening";
-    static final String NIGHT = "Night";
-    static final String LATENIGHT = "LateNight";
-    static final String DAYTIME = "Daytime";
-    static final String NIGHTTIME = "Nighttime";
-
-    static String mFakeTimeOfDay = null;
-    static String mFakeDayOfWeek = null;
-
-    static final String[] TIME_OF_DAY_VALUES =
-       {MORNING, NOON, AFTERNOON, EVENING, NIGHT, LATENIGHT};
-
-    static final String[] DAY_OF_WEEK_VALUES =
-       {SUNDAY, MONDAY, TUESDAY, WEDNESDAY, THURSDAY, FRIDAY, SATURDAY};
-
-    static final String[] DAYTIME_VALUES = {MORNING, NOON, AFTERNOON, EVENING};
-
-    public String[] getListOfFeatures(){
-        String [] list = new String[4];
-        list[0] = TIME_OF_WEEK;
-        list[1] = DAY_OF_WEEK;
-        list[2] = TIME_OF_DAY;
-        list[3] = PERIOD_OF_DAY;
-        return list;
-    }
-
-    public Map<String,String> getFeatureValue(String featureName) {
-        HashMap<String,String> feature = new HashMap<String,String>();
-
-        HashMap<String, String> features =
-            getAllTimeFeatures(System.currentTimeMillis());
-        if (features.containsKey(featureName)) {
-            feature.put(featureName, features.get(featureName));
-        } else {
-            Log.e(TAG, "There is no Time feature called " + featureName);
-        }
-        return (Map)feature;
-    }
-
-    private static String getTimeOfDay(int hour) {
-        if (hour >= 5 && hour < 11) {
-            return MORNING;
-        } else if (hour >= 11 && hour < 14) {
-            return NOON;
-        } else if (hour >= 14 && hour < 18) {
-            return AFTERNOON;
-        } else if (hour >= 18 && hour < 21) {
-            return EVENING;
-        } else if ((hour >= 21 && hour < 24) ||
-                   (hour >= 0 && hour < 1))  {
-            return NIGHT;
-        } else {
-            return LATENIGHT;
-        }
-    }
-
-    private static String getDayOfWeek(int day) {
-        switch (day) {
-            case Time.SATURDAY:
-                return SATURDAY;
-            case Time.SUNDAY:
-                return SUNDAY;
-            case Time.MONDAY:
-                return MONDAY;
-            case Time.TUESDAY:
-                return TUESDAY;
-            case Time.WEDNESDAY:
-                return WEDNESDAY;
-            case Time.THURSDAY:
-                return THURSDAY;
-            default:
-                return FRIDAY;
-        }
-    }
-
-    private static String getPeriodOfDay(int hour) {
-        if (hour > 6 && hour < 19) {
-            return DAYTIME;
-        } else {
-            return NIGHTTIME;
-        }
-    }
-
-    static HashMap<String, String> getAllTimeFeatures(long utcTime) {
-        HashMap<String, String> features = new HashMap<String, String>();
-        Time time = new Time();
-        time.set(utcTime);
-
-        if (mFakeTimeOfDay != null && mFakeTimeOfDay.length() != 0) {
-            List<String> day_list = Arrays.asList(DAYTIME_VALUES);
-
-            if (day_list.contains(mFakeTimeOfDay)) {
-                features.put(PERIOD_OF_DAY, DAYTIME);
-            } else {
-                features.put(PERIOD_OF_DAY, NIGHTTIME);
-            }
-            features.put(TIME_OF_DAY, mFakeTimeOfDay);
-        } else {
-            features.put(PERIOD_OF_DAY, getPeriodOfDay(time.hour));
-            features.put(TIME_OF_DAY, getTimeOfDay(time.hour));
-        }
-
-        if (mFakeDayOfWeek != null && mFakeDayOfWeek.length() != 0) {
-            features.put(DAY_OF_WEEK, mFakeDayOfWeek);
-            if (mFakeDayOfWeek.equals(SUNDAY) ||
-                mFakeDayOfWeek.equals(SATURDAY) ||
-                mFakeDayOfWeek.equals(FRIDAY) &&
-                    features.get(PERIOD_OF_DAY).equals(NIGHTTIME)) {
-                features.put(TIME_OF_WEEK, WEEKEND);
-            } else {
-                features.put(TIME_OF_WEEK, WEEKDAY);
-            }
-        }
-        else {
-            features.put(DAY_OF_WEEK, getDayOfWeek(time.weekDay));
-            if (time.weekDay == Time.SUNDAY || time.weekDay == Time.SATURDAY ||
-                    (time.weekDay == Time.FRIDAY &&
-                    features.get(PERIOD_OF_DAY).equals(NIGHTTIME))) {
-                features.put(TIME_OF_WEEK, WEEKEND);
-            } else {
-                features.put(TIME_OF_WEEK, WEEKDAY);
-            }
-        }
-
-        return features;
-    }
-
-    // get all possible time_of_day values
-    public static List<String> getTimeOfDayValues() {
-        return Arrays.asList(TIME_OF_DAY_VALUES);
-    }
-
-    // get all possible day values
-    public static List<String> getDayOfWeekValues() {
-        return Arrays.asList(DAY_OF_WEEK_VALUES);
-    }
-
-    // set the fake time of day
-    // set to "" to disable the fake time
-    public static void setFakeTimeOfDay(String time_of_day) {
-        mFakeTimeOfDay = time_of_day;
-    }
-
-    // set the fake day of week
-    // set to "" to disable the fake day
-    public static void setFakeDayOfWeek(String day_of_week) {
-        mFakeDayOfWeek = day_of_week;
-    }
-}