| /** |
| * 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 org.apache.ambari.metrics.adservice.seriesgenerator; |
| |
| import java.util.Random; |
| |
| public class MonotonicMetricSeries implements AbstractMetricSeries { |
| |
| double startValue = 0.0; |
| double slope = 0.5; |
| double deviationPercentage = 0.0; |
| double outlierProbability = 0.0; |
| double outlierDeviationLowerPercentage = 0.0; |
| double outlierDeviationHigherPercentage = 0.0; |
| boolean outliersAboveValue = true; |
| |
| Random random = new Random(); |
| double nonOutlierProbability; |
| |
| // y = mx + c |
| double y; |
| double m; |
| double x; |
| double c; |
| |
| public MonotonicMetricSeries(double startValue, |
| double slope, |
| double deviationPercentage, |
| double outlierProbability, |
| double outlierDeviationLowerPercentage, |
| double outlierDeviationHigherPercentage, |
| boolean outliersAboveValue) { |
| this.startValue = startValue; |
| this.slope = slope; |
| this.deviationPercentage = deviationPercentage; |
| this.outlierProbability = outlierProbability; |
| this.outlierDeviationLowerPercentage = outlierDeviationLowerPercentage; |
| this.outlierDeviationHigherPercentage = outlierDeviationHigherPercentage; |
| this.outliersAboveValue = outliersAboveValue; |
| init(); |
| } |
| |
| private void init() { |
| y = startValue; |
| m = slope; |
| x = 1; |
| c = y - (m * x); |
| nonOutlierProbability = 1.0 - outlierProbability; |
| } |
| |
| @Override |
| public double nextValue() { |
| |
| double value; |
| double probability = random.nextDouble(); |
| |
| y = m * x + c; |
| if (probability <= nonOutlierProbability) { |
| double valueDeviationLowerLimit = y - deviationPercentage * y; |
| double valueDeviationHigherLimit = y + deviationPercentage * y; |
| value = valueDeviationLowerLimit + (valueDeviationHigherLimit - valueDeviationLowerLimit) * random.nextDouble(); |
| } else { |
| if (outliersAboveValue) { |
| double outlierLowerLimit = y + outlierDeviationLowerPercentage * y; |
| double outlierUpperLimit = y + outlierDeviationHigherPercentage * y; |
| value = outlierLowerLimit + (outlierUpperLimit - outlierLowerLimit) * random.nextDouble(); |
| } else { |
| double outlierLowerLimit = y - outlierDeviationLowerPercentage * y; |
| double outlierUpperLimit = y - outlierDeviationHigherPercentage * y; |
| value = outlierUpperLimit + (outlierLowerLimit - outlierUpperLimit) * random.nextDouble(); |
| } |
| } |
| x++; |
| return value; |
| } |
| |
| @Override |
| public double[] getSeries(int n) { |
| double[] series = new double[n]; |
| for (int i = 0; i < n; i++) { |
| series[i] = nextValue(); |
| } |
| return series; |
| } |
| |
| } |