hadoop CDFRandomGenerator 源码
haddop CDFRandomGenerator 代码
文件路径：/hadoop-tools/hadoop-rumen/src/main/java/org/apache/hadoop/tools/rumen/CDFRandomGenerator.java
/**
* 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.Arrays;
import java.util.List;
import java.util.Random;
/**
* An instance of this class generates random values that confirm to the
* embedded {@link LoggedDiscreteCDF} . The discrete CDF is a pointwise
* approximation of the "real" CDF. We therefore have a choice of interpolation
* rules.
*
* A concrete subclass of this abstract class will implement valueAt(double)
* using a class-dependent interpolation rule.
*
*/
public abstract class CDFRandomGenerator {
final double[] rankings;
final long[] values;
final Random random;
CDFRandomGenerator(LoggedDiscreteCDF cdf) {
this(cdf, new Random());
}
CDFRandomGenerator(LoggedDiscreteCDF cdf, long seed) {
this(cdf, new Random(seed));
}
private CDFRandomGenerator(LoggedDiscreteCDF cdf, Random random) {
this.random = random;
rankings = new double[cdf.getRankings().size() + 2];
values = new long[cdf.getRankings().size() + 2];
initializeTables(cdf);
}
protected final void initializeTables(LoggedDiscreteCDF cdf) {
rankings[0] = 0.0;
values[0] = cdf.getMinimum();
rankings[rankings.length - 1] = 1.0;
values[rankings.length - 1] = cdf.getMaximum();
List<LoggedSingleRelativeRanking> subjects = cdf.getRankings();
for (int i = 0; i < subjects.size(); ++i) {
rankings[i + 1] = subjects.get(i).getRelativeRanking();
values[i + 1] = subjects.get(i).getDatum();
}
}
protected int floorIndex(double probe) {
int result = Arrays.binarySearch(rankings, probe);
return Math.abs(result + 1) - 1;
}
protected double getRankingAt(int index) {
return rankings[index];
}
protected long getDatumAt(int index) {
return values[index];
}
public long randomValue() {
return valueAt(random.nextDouble());
}
public abstract long valueAt(double probability);
}
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