Github user myui commented on a diff in the pull request:
https://github.com/apache/incubator-hivemall/pull/66#discussion_r109558152
--- Diff: core/src/main/java/hivemall/lda/OnlineLDAModel.java ---
@@ -0,0 +1,497 @@
+/*
+ * 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.lda;
+
+import hivemall.utils.lang.Preconditions;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.Map;
+import java.util.HashMap;
+import java.util.SortedMap;
+import java.util.TreeMap;
+
+import org.apache.commons.math3.distribution.GammaDistribution;
+import org.apache.commons.math3.special.Gamma;
+
+public final class OnlineLDAModel {
+
+ private static final boolean printLambda = false;
+ private static final boolean printGamma = false;
+ private static final boolean printPhi = false;
+
+ // number of topics
+ private int K_;
+
+ // prior on weight vectors "theta ~ Dir(alpha_)"
+ private float alpha_ = 1 / 2.f;
+
+ // prior on topics "beta"
+ private float eta_ = 1 / 20.f;
+
+ // total number of documents
+ // in the truly online setting, this can be an estimate of the maximum
number of documents that could ever seen
+ private int D_ = 11102;
+
+ // defined by (tau0 + countEMStep)^(-kappa_)
+ private double rhot;
+
+ // positive value which downweights early iterations
+ private double tau0_ = 1020;
+
+ // exponential decay rate (i.e., learning rate) which must be in (0.5,
1] to guarantee convergence
+ private double kappa_ = 0.7;
+
+ // random number generator
+ GammaDistribution gd;
+ private double SHAPE = 100d;
+ private double SCALE = 1.d / SHAPE;
+
+ // parameters
+ private ArrayList<HashMap<String, float[]>> phi_;
+ private float[][] gamma_;
+ private HashMap<String, float[]> lambda_;
+
+ // check convergence in the expectation (E) step
+ private double DELTA = 1E-5;
+
+ // for update size of lambda_
+ private int dummySize = 100;
+ private float[][] dummyLambdas;
+
+ private ArrayList<HashMap<String, Float>> miniBatchMap;
+ private int miniBatchSize;
+
+ private int accumDocCount = 0;
+ private int accumWordCount = 0;
+
+ public OnlineLDAModel(int K, float alpha, float eta, int D, double
tau0, double kappa) {
+ Preconditions.checkArgument(0.d < tau0, "tau0 MUST be positive: "
+ tau0);
+ Preconditions.checkArgument(0.5 < kappa && kappa <= 1.d,
+ "kappa MUST be in (0.5, 1.0]: " + kappa);
+
+ K_ = K;
+ alpha_ = alpha;
+ eta_ = eta;
+ D_ = D;
+ tau0_ = tau0;
+ kappa_ = kappa;
+
+ // initialize a random number generator
+ gd = new GammaDistribution(SHAPE, SCALE);
+ gd.reseedRandomGenerator(1001);
+
+ // initialize the parameters
+ lambda_ = new HashMap<String, float[]>();
+
+ setDummyLambda();
+ }
+
+ private void setDummyLambda() {
+ dummyLambdas = new float[dummySize][];
+ double[] tmpDArray;
+ for (int b = 0; b < dummySize; b++) {
+ float[] tmpDummyLambda = new float[K_];
+ tmpDArray = gd.sample(K_);
+ for (int k = 0; k < K_; k++) {
+ tmpDummyLambda[k] = (float) tmpDArray[k];
+ }
+ dummyLambdas[b] = tmpDummyLambda;
+ }
+ }
+
+ public void train(String[][] miniBatch, int time) {
+ miniBatchSize = miniBatch.length;
+
+ rhot = Math.pow(tau0_ + time, -kappa_);
+
+ if (printLambda) {
--- End diff --
please remove all debug prints.
Use `Logger.debug()` for debug prints.
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