Github user myui commented on a diff in the pull request:
https://github.com/apache/incubator-hivemall/pull/167#discussion_r226578559
--- Diff: core/src/main/java/hivemall/mf/CofactorModel.java ---
@@ -0,0 +1,715 @@
+/*
+ * 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.mf;
+
+import hivemall.annotations.VisibleForTesting;
+import hivemall.fm.Feature;
+import hivemall.utils.lang.Preconditions;
+import hivemall.utils.math.MathUtils;
+import it.unimi.dsi.fastutil.objects.Object2DoubleArrayMap;
+import it.unimi.dsi.fastutil.objects.Object2DoubleMap;
+import org.apache.commons.math3.linear.ArrayRealVector;
+import org.apache.commons.math3.linear.Array2DRowRealMatrix;
+import org.apache.commons.math3.linear.RealMatrix;
+import org.apache.commons.math3.linear.RealVector;
+import org.apache.commons.math3.linear.SingularValueDecomposition;
+import org.apache.hadoop.hive.ql.metadata.HiveException;
+
+import javax.annotation.Nonnegative;
+import javax.annotation.Nonnull;
+import javax.annotation.Nullable;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Random;
+
+
+public class CofactorModel {
+
+ public enum RankInitScheme {
+ random /* default */, gaussian;
+
+
+ @Nonnegative
+ private float maxInitValue;
+ @Nonnegative
+ private double initStdDev;
+ @Nonnull
+ public static CofactorModel.RankInitScheme resolve(@Nullable
String opt) {
+ if (opt == null) {
+ return random;
+ } else if ("gaussian".equalsIgnoreCase(opt)) {
+ return gaussian;
+ } else if ("random".equalsIgnoreCase(opt)) {
+ return random;
+ }
+ return random;
+ }
+
+ public void setMaxInitValue(float maxInitValue) {
+ this.maxInitValue = maxInitValue;
+ }
+
+ public void setInitStdDev(double initStdDev) {
+ this.initStdDev = initStdDev;
+ }
+
+
+ }
+
+ @Nonnegative
+ private final int factor;
+
+ // rank matrix initialization
+ private final RankInitScheme initScheme;
+
+ @Nonnull
+ private double globalBias;
+
+ // storing trainable latent factors and weights
+ private final Map<String, double[]> theta;
+ private final Map<String, double[]> beta;
+ private final Object2DoubleMap<String> betaBias;
+ private final Map<String, double[]> gamma;
+ private final Object2DoubleMap<String> gammaBias;
+
+ private final Random[] randU, randI;
+
+ // hyperparameters
+ private final float c0, c1;
+ private final float lambdaTheta, lambdaBeta, lambdaGamma;
+
+ // solve
+ private final RealMatrix B;
+ private final RealVector A;
+
+ // error message strings
+ private static final String ARRAY_NOT_SQUARE_ERR = "Array is not
square";
+ private static final String DIFFERENT_DIMS_ERR = "Matrix, vector or
array do not match in size";
+
+ public CofactorModel(@Nonnegative int factor, @Nonnull RankInitScheme
initScheme,
+ float c0, float c1, float lambdaTheta, float
lambdaBeta, float lambdaGamma) {
+
+ // rank init scheme is gaussian
+ //
https://github.com/dawenl/cofactor/blob/master/src/cofacto.py#L98
+ this.factor = factor;
+ this.initScheme = initScheme;
+ this.globalBias = 0.d;
+ this.lambdaTheta = lambdaTheta;
+ this.lambdaBeta = lambdaBeta;
+ this.lambdaGamma = lambdaGamma;
+
+ this.theta = new HashMap<>();
+ this.beta = new HashMap<>();
+ this.betaBias = new Object2DoubleArrayMap<>();
+ this.betaBias.defaultReturnValue(0.d);
+ this.gamma = new HashMap<>();
+ this.gammaBias = new Object2DoubleArrayMap<>();
+ this.gammaBias.defaultReturnValue(0.d);
+
+ this.B = new Array2DRowRealMatrix(this.factor, this.factor);
+ this.A = new ArrayRealVector(this.factor);
+
+ this.randU = newRandoms(factor, 31L);
+ this.randI = newRandoms(factor, 41L);
+
+ Preconditions.checkArgument(c0 >= 0.f && c0 <= 1.f);
+ Preconditions.checkArgument(c1 >= 0.f && c1 <= 1.f);
+ this.c0 = c0;
+ this.c1 = c1;
+
--- End diff --
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