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/*
* SPDX-License-Identifier: Apache-2.0
*
* The OpenSearch Contributors require contributions made to
* this file be licensed under the Apache-2.0 license or a
* compatible open source license.
*/
/*
* 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.
*/
/*
* Modifications Copyright OpenSearch Contributors. See
* GitHub history for details.
*/
package org.opensearch.search.aggregations.pipeline;
import org.opensearch.common.Nullable;
import org.opensearch.core.common.io.stream.StreamInput;
import org.opensearch.core.common.io.stream.StreamOutput;
import org.opensearch.core.xcontent.XContentBuilder;
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
*
* @opensearch.internal
*/
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 double[] doPredict(Collection 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 double next(Collection values) {
return MovingFunctions.linearWeightedAvg(values.stream().mapToDouble(Double::doubleValue).toArray());
}
@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 settings, String pipelineName, int windowSize) throws ParseException {
checkUnrecognizedParams(settings);
return new LinearModel();
}
};
/**
* Builder for the linear model
*
* @opensearch.internal
*/
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;
}
}