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/*
 * 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.ensemble;

import org.apache.hadoop.hive.ql.exec.Description;
import org.apache.hadoop.hive.ql.exec.UDAF;
import org.apache.hadoop.hive.ql.exec.UDAFEvaluator;
import org.apache.hadoop.io.FloatWritable;

@SuppressWarnings("deprecation")
@Description(name = "argmin_kld",
        value = "_FUNC_(float mean, float covar) - Returns mean or covar that minimize a KL-distance among distributions",
        extended = "The returned value is (1.0 / (sum(1.0 / covar))) * (sum(mean / covar)")
public final class ArgminKLDistanceUDAF extends UDAF {

    public static class ArgminMeanUDAFEvaluator implements UDAFEvaluator {

        private PartialResult partial;

        public static class PartialResult {
            float sum_mean_div_covar;
            float sum_inv_covar;

            PartialResult() {
                this.sum_mean_div_covar = 0.f;
                this.sum_inv_covar = 0.f;
            }
        }

        public void init() {
            this.partial = null;
        }

        public boolean iterate(FloatWritable mean, FloatWritable covar) {
            if (mean == null || covar == null) {
                return true;
            }
            if (partial == null) {
                this.partial = new PartialResult();
            }
            final float covar_f = covar.get();
            if (covar_f == 0.f) {// avoid null division
                return true;
            }
            partial.sum_mean_div_covar += (mean.get() / covar_f);
            partial.sum_inv_covar += (1.f / covar_f);
            return true;
        }

        public PartialResult terminatePartial() {
            return partial;
        }

        public boolean merge(PartialResult o) {
            if (o == null) {
                return true;
            }
            if (partial == null) {
                this.partial = new PartialResult();
            }
            partial.sum_mean_div_covar += o.sum_mean_div_covar;
            partial.sum_inv_covar += o.sum_inv_covar;
            return true;
        }

        public FloatWritable terminate() {
            if (partial == null) {
                return null;
            }
            if (partial.sum_inv_covar == 0.f) {// avoid null division
                return new FloatWritable(0.f);
            }
            float mean = (1.f / partial.sum_inv_covar) * partial.sum_mean_div_covar;
            return new FloatWritable(mean);
        }
    }

}