Github user myui commented on a diff in the pull request:
https://github.com/apache/incubator-hivemall/pull/116#discussion_r141543757
--- Diff: core/src/main/java/hivemall/embedding/CBoWModel.java ---
@@ -0,0 +1,131 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you 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 hivemall.embedding;
+
+import hivemall.math.random.PRNG;
+import hivemall.utils.collections.maps.Int2FloatOpenHashTable;
+
+import javax.annotation.Nonnull;
+import java.util.List;
+
+public final class CBoWModel extends AbstractWord2VecModel {
+ protected CBoWModel(final int dim, final int win, final int neg, final
int iter,
+ final float startingLR, final long numTrainWords, final
Int2FloatOpenHashTable S,
+ final int[] aliasWordId) {
+ super(dim, win, neg, iter, startingLR, numTrainWords, S,
aliasWordId);
+ }
+
+ protected void trainOnDoc(@Nonnull final int[] doc) {
+ final int vecDim = dim;
+ final int numNegative = neg;
+ final PRNG _rnd = rnd;
+ final Int2FloatOpenHashTable _S = S;
+ final int[] _aliasWordId = aliasWordId;
+ float label, gradient;
+
+ // reuse instance
+ int windowSize, k, numContext, targetWord, inWord, positiveWord;
+
+ updateLearningRate();
+
+ int docLength = doc.length;
+ for (int t = 0; t < iter; t++) {
+ for (int positiveWordPosition = 0; positiveWordPosition <
docLength; positiveWordPosition++) {
+ windowSize = _rnd.nextInt(win) + 1;
+
+ numContext = windowSize * 2 + Math.min(0,
positiveWordPosition - windowSize)
+ + Math.min(0, docLength - positiveWordPosition -
windowSize - 1);
+
+ float[] gradVec = new float[vecDim];
+ float[] averageVec = new float[vecDim];
+
+ // collect context words
+ for (int contextPosition = positiveWordPosition -
windowSize; contextPosition < positiveWordPosition
+ + windowSize + 1; contextPosition++) {
+ if (contextPosition == positiveWordPosition ||
contextPosition < 0
+ || contextPosition >= docLength) {
+ continue;
+ }
+
+ inWord = doc[contextPosition];
+
+ // average vector of input word vectors
+ if (!inputWeights.containsKey(inWord * vecDim)) {
+ initWordWeights(inWord);
+ }
+
+ for (int i = 0; i < vecDim; i++) {
+ averageVec[i] += inputWeights.get(inWord * vecDim
+ i) / numContext;
+ }
+ }
+ positiveWord = doc[positiveWordPosition];
+ // negative sampling
+ for (int d = 0; d < numNegative + 1; d++) {
+ if (d == 0) {
+ targetWord = positiveWord;
+ label = 1.f;
+ } else {
+ do {
+ k = _rnd.nextInt(_S.size());
+ if (_S.get(k) > _rnd.nextDouble()) {
+ targetWord = k;
+ } else {
+ targetWord = _aliasWordId[k];
+ }
+ } while (targetWord == positiveWord);
+ label = 0.f;
+ }
+
+ gradient = grad(label, averageVec, targetWord) * lr;
+ for (int i = 0; i < vecDim; i++) {
+ gradVec[i] += gradient *
contextWeights.get(targetWord * vecDim + i);
+ contextWeights.put(targetWord * vecDim + i,
+ contextWeights.get(targetWord * vecDim + i) +
gradient * averageVec[i]);
+ }
+ }
+
+ // update inWord vector
+ for (int contextPosition = positiveWordPosition -
windowSize; contextPosition < positiveWordPosition
+ + windowSize + 1; contextPosition++) {
+ if (contextPosition == positiveWordPosition ||
contextPosition < 0
+ || contextPosition >= docLength) {
+ continue;
+ }
+
+ inWord = doc[contextPosition];
+ for (int i = 0; i < vecDim; i++) {
+ inputWeights.put(inWord * vecDim + i,
inputWeights.get(inWord * vecDim + i)
+ + gradVec[i]);
+ }
+ }
+ }
+ }
+
+ wordCount += docLength * iter;
+ }
+
+ private float grad(final float label, @Nonnull final float[] w, final
int c) {
+ float dotValue = 0.f;
+ for (int i = 0; i < dim; i++) {
+ dotValue += w[i] * contextWeights.get(c * dim + i);
+ }
+
+ return (label - sigmoid(dotValue, sigmoidTable));
--- End diff --
remove redundant outermost `(`.
---
