Github user Lewuathe commented on a diff in the pull request:
https://github.com/apache/incubator-hivemall/pull/14#discussion_r97943877
--- Diff:
core/src/main/java/hivemall/model/NewSpaceEfficientDenseModel.java ---
@@ -0,0 +1,317 @@
+/*
+ * 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 hivemall.model.WeightValue.WeightValueWithCovar;
+import hivemall.utils.collections.IMapIterator;
+import hivemall.utils.hadoop.HiveUtils;
+import hivemall.utils.lang.Copyable;
+import hivemall.utils.lang.HalfFloat;
+import hivemall.utils.math.MathUtils;
+
+import java.util.Arrays;
+import javax.annotation.Nonnull;
+
+import org.apache.commons.logging.Log;
+import org.apache.commons.logging.LogFactory;
+
+public final class NewSpaceEfficientDenseModel extends
AbstractPredictionModel {
+ private static final Log logger =
LogFactory.getLog(NewSpaceEfficientDenseModel.class);
+
+ private int size;
+ private short[] weights;
+ private short[] covars;
+
+ // optional value for MIX
+ private short[] clocks;
+ private byte[] deltaUpdates;
+
+ public NewSpaceEfficientDenseModel(int ndims) {
+ this(ndims, false);
+ }
+
+ public NewSpaceEfficientDenseModel(int ndims, boolean withCovar) {
+ super();
+ int size = ndims + 1;
+ this.size = size;
+ this.weights = new short[size];
+ if (withCovar) {
+ short[] covars = new short[size];
+ Arrays.fill(covars, HalfFloat.ONE);
+ 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 float getWeight(final int i) {
+ final short w = weights[i];
+ return (w == HalfFloat.ZERO) ? HalfFloat.ZERO :
HalfFloat.halfFloatToFloat(w);
+ }
+
+ private float getCovar(final int i) {
+ return HalfFloat.halfFloatToFloat(covars[i]);
--- End diff --
I see.
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