/* * Licensed to Elasticsearch under one or more contributor * license agreements. See the NOTICE file distributed with * this work for additional information regarding copyright * ownership. Elasticsearch 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 org.elasticsearch.search.aggregations.pipeline.movavg.models; import org.elasticsearch.common.Nullable; import org.elasticsearch.common.io.stream.StreamInput; import org.elasticsearch.common.io.stream.StreamOutput; import org.elasticsearch.common.xcontent.XContentBuilder; import org.elasticsearch.search.aggregations.pipeline.movavg.MovAvgPipelineAggregationBuilder; import java.io.IOException; import java.text.ParseException; import java.util.Arrays; import java.util.Collection; import java.util.Map; /** * Calculate a linearly weighted moving average, such that older values are * linearly less important. "Time" is determined by position in collection */ public class LinearModel extends MovAvgModel { public static final String NAME = "linear"; public LinearModel() { } /** * Read from a stream. */ public LinearModel(StreamInput in) { } @Override public void writeTo(StreamOutput out) throws IOException { // No state to write } @Override public String getWriteableName() { return NAME; } @Override public boolean canBeMinimized() { return false; } @Override public MovAvgModel neighboringModel() { return new LinearModel(); } @Override public MovAvgModel clone() { return new LinearModel(); } @Override protected <T extends Number> double[] doPredict(Collection<T> values, int numPredictions) { double[] predictions = new double[numPredictions]; // EWMA just emits the same final prediction repeatedly. Arrays.fill(predictions, next(values)); return predictions; } @Override public <T extends Number> double next(Collection<T> values) { double avg = 0; long totalWeight = 1; long current = 1; for (T v : values) { avg += v.doubleValue() * current; totalWeight += current; current += 1; } return avg / totalWeight; } @Override public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException { builder.field(MovAvgPipelineAggregationBuilder.MODEL.getPreferredName(), NAME); return builder; } public static final AbstractModelParser PARSER = new AbstractModelParser() { @Override public MovAvgModel parse(@Nullable Map<String, Object> settings, String pipelineName, int windowSize) throws ParseException { checkUnrecognizedParams(settings); return new LinearModel(); } }; public static class LinearModelBuilder implements MovAvgModelBuilder { @Override public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException { builder.field(MovAvgPipelineAggregationBuilder.MODEL.getPreferredName(), NAME); return builder; } @Override public MovAvgModel build() { return new LinearModel(); } } @Override public int hashCode() { return 0; } @Override public boolean equals(Object obj) { if (obj == null) { return false; } if (getClass() != obj.getClass()) { return false; } return true; } }