1 files changed, 0 insertions, 186 deletions
diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/RecognizeCommands.java b/tensorflow/examples/android/src/org/tensorflow/demo/RecognizeCommands.java
deleted file mode 100644
index 9e91aea7ef..0000000000
--- a/tensorflow/examples/android/src/org/tensorflow/demo/RecognizeCommands.java
+++ /dev/null
@@ -1,186 +0,0 @@
-/*
- * Copyright 2017 The TensorFlow Authors. All Rights Reserved.
- *
- * 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 org.tensorflow.demo;
-
-import android.util.Log;
-import android.util.Pair;
-import java.util.ArrayDeque;
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.Deque;
-import java.util.List;
-
-/** Reads in results from an instantaneous audio recognition model and smoothes them over time. */
-public class RecognizeCommands {
-  // Configuration settings.
-  private List<String> labels = new ArrayList<String>();
-  private long averageWindowDurationMs;
-  private float detectionThreshold;
-  private int suppressionMs;
-  private int minimumCount;
-  private long minimumTimeBetweenSamplesMs;
-
-  // Working variables.
-  private Deque<Pair<Long, float[]>> previousResults = new ArrayDeque<Pair<Long, float[]>>();
-  private String previousTopLabel;
-  private int labelsCount;
-  private long previousTopLabelTime;
-  private float previousTopLabelScore;
-
-  private static final String SILENCE_LABEL = "_silence_";
-  private static final long MINIMUM_TIME_FRACTION = 4;
-
-  public RecognizeCommands(
-      List<String> inLabels,
-      long inAverageWindowDurationMs,
-      float inDetectionThreshold,
-      int inSuppressionMS,
-      int inMinimumCount,
-      long inMinimumTimeBetweenSamplesMS) {
-    labels = inLabels;
-    averageWindowDurationMs = inAverageWindowDurationMs;
-    detectionThreshold = inDetectionThreshold;
-    suppressionMs = inSuppressionMS;
-    minimumCount = inMinimumCount;
-    labelsCount = inLabels.size();
-    previousTopLabel = SILENCE_LABEL;
-    previousTopLabelTime = Long.MIN_VALUE;
-    previousTopLabelScore = 0.0f;
-    minimumTimeBetweenSamplesMs = inMinimumTimeBetweenSamplesMS;
-  }
-
-  /** Holds information about what's been recognized. */
-  public static class RecognitionResult {
-    public final String foundCommand;
-    public final float score;
-    public final boolean isNewCommand;
-
-    public RecognitionResult(String inFoundCommand, float inScore, boolean inIsNewCommand) {
-      foundCommand = inFoundCommand;
-      score = inScore;
-      isNewCommand = inIsNewCommand;
-    }
-  }
-
-  private static class ScoreForSorting implements Comparable<ScoreForSorting> {
-    public final float score;
-    public final int index;
-
-    public ScoreForSorting(float inScore, int inIndex) {
-      score = inScore;
-      index = inIndex;
-    }
-
-    @Override
-    public int compareTo(ScoreForSorting other) {
-      if (this.score > other.score) {
-        return -1;
-      } else if (this.score < other.score) {
-        return 1;
-      } else {
-        return 0;
-      }
-    }
-  }
-
-  public RecognitionResult processLatestResults(float[] currentResults, long currentTimeMS) {
-    if (currentResults.length != labelsCount) {
-      throw new RuntimeException(
-          "The results for recognition should contain "
-              + labelsCount
-              + " elements, but there are "
-              + currentResults.length);
-    }
-
-    if ((!previousResults.isEmpty()) && (currentTimeMS < previousResults.getFirst().first)) {
-      throw new RuntimeException(
-          "You must feed results in increasing time order, but received a timestamp of "
-              + currentTimeMS
-              + " that was earlier than the previous one of "
-              + previousResults.getFirst().first);
-    }
-
-    final int howManyResults = previousResults.size();
-    // Ignore any results that are coming in too frequently.
-    if (howManyResults > 1) {
-      final long timeSinceMostRecent = currentTimeMS - previousResults.getLast().first;
-      if (timeSinceMostRecent < minimumTimeBetweenSamplesMs) {
-        return new RecognitionResult(previousTopLabel, previousTopLabelScore, false);
-      }
-    }
-
-    // Add the latest results to the head of the queue.
-    previousResults.addLast(new Pair<Long, float[]>(currentTimeMS, currentResults));
-
-    // Prune any earlier results that are too old for the averaging window.
-    final long timeLimit = currentTimeMS - averageWindowDurationMs;
-    while (previousResults.getFirst().first < timeLimit) {
-      previousResults.removeFirst();
-    }
-
-    // If there are too few results, assume the result will be unreliable and
-    // bail.
-    final long earliestTime = previousResults.getFirst().first;
-    final long samplesDuration = currentTimeMS - earliestTime;
-    if ((howManyResults < minimumCount)
-        || (samplesDuration < (averageWindowDurationMs / MINIMUM_TIME_FRACTION))) {
-      Log.v("RecognizeResult", "Too few results");
-      return new RecognitionResult(previousTopLabel, 0.0f, false);
-    }
-
-    // Calculate the average score across all the results in the window.
-    float[] averageScores = new float[labelsCount];
-    for (Pair<Long, float[]> previousResult : previousResults) {
-      final float[] scoresTensor = previousResult.second;
-      int i = 0;
-      while (i < scoresTensor.length) {
-        averageScores[i] += scoresTensor[i] / howManyResults;
-        ++i;
-      }
-    }
-
-    // Sort the averaged results in descending score order.
-    ScoreForSorting[] sortedAverageScores = new ScoreForSorting[labelsCount];
-    for (int i = 0; i < labelsCount; ++i) {
-      sortedAverageScores[i] = new ScoreForSorting(averageScores[i], i);
-    }
-    Arrays.sort(sortedAverageScores);
-
-    // See if the latest top score is enough to trigger a detection.
-    final int currentTopIndex = sortedAverageScores[0].index;
-    final String currentTopLabel = labels.get(currentTopIndex);
-    final float currentTopScore = sortedAverageScores[0].score;
-    // If we've recently had another label trigger, assume one that occurs too
-    // soon afterwards is a bad result.
-    long timeSinceLastTop;
-    if (previousTopLabel.equals(SILENCE_LABEL) || (previousTopLabelTime == Long.MIN_VALUE)) {
-      timeSinceLastTop = Long.MAX_VALUE;
-    } else {
-      timeSinceLastTop = currentTimeMS - previousTopLabelTime;
-    }
-    boolean isNewCommand;
-    if ((currentTopScore > detectionThreshold) && (timeSinceLastTop > suppressionMs)) {
-      previousTopLabel = currentTopLabel;
-      previousTopLabelTime = currentTimeMS;
-      previousTopLabelScore = currentTopScore;
-      isNewCommand = true;
-    } else {
-      isNewCommand = false;
-    }
-    return new RecognitionResult(currentTopLabel, currentTopScore, isNewCommand);
-  }
-}