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* 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.ParseField;
import org.elasticsearch.common.ParseFieldMatcher;
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.MovAvgParser;
import java.io.IOException;
import java.text.ParseException;
import java.util.Arrays;
import java.util.Collection;
import java.util.Map;
/**
* Calculate a exponentially weighted moving average
*/
public class EwmaModel extends MovAvgModel {
protected static final ParseField NAME_FIELD = new ParseField("ewma");
/**
* Controls smoothing of data. Also known as "level" value.
* Alpha = 1 retains no memory of past values
* (e.g. random walk), while alpha = 0 retains infinite memory of past values (e.g.
* mean of the series).
*/
private final double alpha;
public EwmaModel(double alpha) {
this.alpha = alpha;
}
@Override
public boolean canBeMinimized() {
return true;
}
@Override
public MovAvgModel neighboringModel() {
double alpha = Math.random();
return new EwmaModel(alpha);
}
@Override
public MovAvgModel clone() {
return new EwmaModel(this.alpha);
}
@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;
boolean first = true;
for (T v : values) {
if (first) {
avg = v.doubleValue();
first = false;
} else {
avg = (v.doubleValue() * alpha) + (avg * (1 - alpha));
}
}
return avg;
}
public static final MovAvgModelStreams.Stream STREAM = new MovAvgModelStreams.Stream() {
@Override
public MovAvgModel readResult(StreamInput in) throws IOException {
return new EwmaModel(in.readDouble());
}
@Override
public String getName() {
return NAME_FIELD.getPreferredName();
}
};
@Override
public void writeTo(StreamOutput out) throws IOException {
out.writeString(STREAM.getName());
out.writeDouble(alpha);
}
public static class SingleExpModelParser extends AbstractModelParser {
@Override
public String getName() {
return NAME_FIELD.getPreferredName();
}
@Override
public MovAvgModel parse(@Nullable Map<String, Object> settings, String pipelineName, int windowSize,
ParseFieldMatcher parseFieldMatcher) throws ParseException {
double alpha = parseDoubleParam(settings, "alpha", 0.3);
checkUnrecognizedParams(settings);
return new EwmaModel(alpha);
}
}
public static class EWMAModelBuilder implements MovAvgModelBuilder {
private Double alpha;
/**
* Alpha controls the smoothing of the data. Alpha = 1 retains no memory of past values
* (e.g. a random walk), while alpha = 0 retains infinite memory of past values (e.g.
* the series mean). Useful values are somewhere in between. Defaults to 0.5.
*
* @param alpha A double between 0-1 inclusive, controls data smoothing
*
* @return The builder to continue chaining
*/
public EWMAModelBuilder alpha(double alpha) {
this.alpha = alpha;
return this;
}
@Override
public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
builder.field(MovAvgParser.MODEL.getPreferredName(), NAME_FIELD.getPreferredName());
builder.startObject(MovAvgParser.SETTINGS.getPreferredName());
if (alpha != null) {
builder.field("alpha", alpha);
}
builder.endObject();
return builder;
}
}
}