| /** |
| * 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.hadoop.tools.rumen; |
| |
| import java.util.ArrayList; |
| |
| import org.junit.Test; |
| import static org.junit.Assert.*; |
| |
| public class TestPiecewiseLinearInterpolation { |
| |
| static private double maximumRelativeError = 0.002D; |
| |
| static private LoggedSingleRelativeRanking makeRR(double ranking, long datum) { |
| LoggedSingleRelativeRanking result = new LoggedSingleRelativeRanking(); |
| |
| result.setDatum(datum); |
| result.setRelativeRanking(ranking); |
| |
| return result; |
| } |
| |
| @Test |
| public void testOneRun() { |
| LoggedDiscreteCDF input = new LoggedDiscreteCDF(); |
| |
| input.setMinimum(100000L); |
| input.setMaximum(1100000L); |
| |
| ArrayList<LoggedSingleRelativeRanking> rankings = new ArrayList<LoggedSingleRelativeRanking>(); |
| |
| rankings.add(makeRR(0.1, 200000L)); |
| rankings.add(makeRR(0.5, 800000L)); |
| rankings.add(makeRR(0.9, 1000000L)); |
| |
| input.setRankings(rankings); |
| input.setNumberValues(3); |
| |
| CDFRandomGenerator gen = new CDFPiecewiseLinearRandomGenerator(input); |
| Histogram values = new Histogram(); |
| |
| for (int i = 0; i < 1000000; ++i) { |
| long value = gen.randomValue(); |
| values.enter(value); |
| } |
| |
| /* |
| * Now we build a percentiles CDF, and compute the sum of the squares of the |
| * actual percentiles vrs. the predicted percentiles |
| */ |
| int[] percentiles = new int[99]; |
| |
| for (int i = 0; i < 99; ++i) { |
| percentiles[i] = i + 1; |
| } |
| |
| long[] result = values.getCDF(100, percentiles); |
| long sumErrorSquares = 0L; |
| |
| for (int i = 0; i < 10; ++i) { |
| long error = result[i] - (10000L * i + 100000L); |
| System.out.println("element " + i + ", got " + result[i] + ", expected " |
| + (10000L * i + 100000L) + ", error = " + error); |
| sumErrorSquares += error * error; |
| } |
| |
| for (int i = 10; i < 50; ++i) { |
| long error = result[i] - (15000L * i + 50000L); |
| System.out.println("element " + i + ", got " + result[i] + ", expected " |
| + (15000L * i + 50000L) + ", error = " + error); |
| sumErrorSquares += error * error; |
| } |
| |
| for (int i = 50; i < 90; ++i) { |
| long error = result[i] - (5000L * i + 550000L); |
| System.out.println("element " + i + ", got " + result[i] + ", expected " |
| + (5000L * i + 550000L) + ", error = " + error); |
| sumErrorSquares += error * error; |
| } |
| |
| for (int i = 90; i <= 100; ++i) { |
| long error = result[i] - (10000L * i + 100000L); |
| System.out.println("element " + i + ", got " + result[i] + ", expected " |
| + (10000L * i + 100000L) + ", error = " + error); |
| sumErrorSquares += error * error; |
| } |
| |
| // normalize the error |
| double realSumErrorSquares = (double) sumErrorSquares; |
| |
| double normalizedError = realSumErrorSquares / 100 |
| / rankings.get(1).getDatum() / rankings.get(1).getDatum(); |
| double RMSNormalizedError = Math.sqrt(normalizedError); |
| |
| System.out.println("sumErrorSquares = " + sumErrorSquares); |
| |
| System.out.println("normalizedError: " + normalizedError |
| + ", RMSNormalizedError: " + RMSNormalizedError); |
| |
| System.out.println("Cumulative error is " + RMSNormalizedError); |
| |
| assertTrue("The RMS relative error per bucket, " + RMSNormalizedError |
| + ", exceeds our tolerance of " + maximumRelativeError, |
| RMSNormalizedError <= maximumRelativeError); |
| |
| } |
| } |