Github user myui commented on a diff in the pull request:
https://github.com/apache/incubator-hivemall/pull/14#discussion_r97715714
--- Diff: core/src/main/java/hivemall/model/NewDenseModel.java ---
@@ -0,0 +1,293 @@
+/*
+ * Hivemall: Hive scalable Machine Learning Library
+ *
+ * Copyright (C) 2015 Makoto YUI
+ * Copyright (C) 2013-2015 National Institute of Advanced Industrial
Science and Technology (AIST)
+ *
+ * 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 hivemall.model;
+
+import java.util.Arrays;
+import javax.annotation.Nonnull;
+
+import org.apache.commons.logging.Log;
+import org.apache.commons.logging.LogFactory;
+
+import hivemall.model.WeightValue.WeightValueWithCovar;
+import hivemall.utils.collections.IMapIterator;
+import hivemall.utils.hadoop.HiveUtils;
+import hivemall.utils.lang.Copyable;
+import hivemall.utils.math.MathUtils;
+
+public final class NewDenseModel extends AbstractPredictionModel {
+ private static final Log logger =
LogFactory.getLog(NewDenseModel.class);
+
+ private int size;
+ private float[] weights;
+ private float[] covars;
+
+ // optional value for MIX
+ private short[] clocks;
+ private byte[] deltaUpdates;
+
+ public NewDenseModel(int ndims) {
+ this(ndims, false);
+ }
+
+ public NewDenseModel(int ndims, boolean withCovar) {
+ super();
+ int size = ndims + 1;
+ this.size = size;
+ this.weights = new float[size];
+ if (withCovar) {
+ float[] covars = new float[size];
+ Arrays.fill(covars, 1f);
+ this.covars = covars;
+ } else {
+ this.covars = null;
+ }
+ this.clocks = null;
+ this.deltaUpdates = null;
+ }
+
+ @Override
+ protected boolean isDenseModel() {
+ return true;
+ }
+
+ @Override
+ public boolean hasCovariance() {
+ return covars != null;
+ }
+
+ @Override
+ public void configureParams(boolean sum_of_squared_gradients, boolean
sum_of_squared_delta_x,
+ boolean sum_of_gradients) {}
+
+ @Override
+ public void configureClock() {
+ if (clocks == null) {
+ this.clocks = new short[size];
+ this.deltaUpdates = new byte[size];
+ }
+ }
+
+ @Override
+ public boolean hasClock() {
+ return clocks != null;
+ }
+
+ @Override
+ public void resetDeltaUpdates(int feature) {
+ deltaUpdates[feature] = 0;
+ }
+
+ private void ensureCapacity(final int index) {
+ if (index >= size) {
+ int bits = MathUtils.bitsRequired(index);
+ int newSize = (1 << bits) + 1;
+ int oldSize = size;
+ logger.info("Expands internal array size from " + oldSize + "
to " + newSize + " ("
+ + bits + " bits)");
+ this.size = newSize;
+ this.weights = Arrays.copyOf(weights, newSize);
+ if (covars != null) {
+ this.covars = Arrays.copyOf(covars, newSize);
+ Arrays.fill(covars, oldSize, newSize, 1.f);
+ }
+ if(clocks != null) {
+ this.clocks = Arrays.copyOf(clocks, newSize);
+ this.deltaUpdates = Arrays.copyOf(deltaUpdates, newSize);
+ }
+ }
+ }
+
+ @SuppressWarnings("unchecked")
+ @Override
+ public <T extends IWeightValue> T get(Object feature) {
+ final int i = HiveUtils.parseInt(feature);
+ if (i >= size) {
+ return null;
+ }
+ if(covars != null) {
+ return (T) new WeightValueWithCovar(weights[i], covars[i]);
+ } else {
+ return (T) new WeightValue(weights[i]);
+ }
+ }
+
+ @Override
+ public <T extends IWeightValue> void set(Object feature, T value) {
--- End diff --
Fixed
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