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/**
* 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.drill.synth;
import com.google.common.base.Preconditions;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.math.list.DoubleArrayList;
import org.apache.mahout.math.random.Sampler;
import java.util.Random;
/**
*
* Generates samples from a generalized Chinese restaurant process (or Pittman-Yor process).
*
* The number of values drawn exactly once will asymptotically be equal to the discount parameter
* as the total number of draws T increases without bound. The number of unique values sampled will
* increase as O(alpha * log T) if discount = 0 or O(alpha * T^discount) for discount > 0.
*/
public final class ChineseRestaurant implements Sampler<Integer> {
private final double alpha;
private double weight = 0;
private double discount = 0;
private final DoubleArrayList weights = new DoubleArrayList();
private final Random rand = RandomUtils.getRandom();
/**
* Constructs a Dirichlet process sampler. This is done by setting discount = 0.
* @param alpha The strength parameter for the Dirichlet process.
*/
public ChineseRestaurant(double alpha) {
this(alpha, 0);
}
/**
* Constructs a Pitman-Yor sampler.
*
* @param alpha The strength parameter that drives the number of unique values as a function of draws.
* @param discount The discount parameter that drives the percentage of values that occur once in a large sample.
*/
public ChineseRestaurant(double alpha, double discount) {
Preconditions.checkArgument(alpha > 0);
Preconditions.checkArgument(discount >= 0 && discount <= 1);
this.alpha = alpha;
this.discount = discount;
}
public Integer sample() {
double u = rand.nextDouble() * (alpha + weight);
for (int j = 0; j < weights.size(); j++) {
// select existing options with probability (w_j - d) / (alpha + w)
if (u < weights.get(j) - discount) {
weights.set(j, weights.get(j) + 1);
weight++;
return j;
} else {
u -= weights.get(j) - discount;
}
}
// if no existing item selected, pick new item with probability (alpha - d*t) / (alpha + w)
// where t is number of pre-existing cases
weights.add(1);
weight++;
return weights.size() - 1;
}
/**
* @return the number of unique values that have been returned.
*/
public int size() {
return weights.size();
}
/**
* @return the number draws so far.
*/
public int count() {
return (int) weight;
}
/**
* @param j Which value to test.
* @return The number of times that j has been returned so far.
*/
public int count(int j) {
Preconditions.checkArgument(j >= 0);
if (j < weights.size()) {
return (int) weights.get(j);
} else {
return 0;
}
}
public void setCount(int term, double count) {
while (weights.size() <= term) {
weights.add(0);
}
weight += (count - weights.get(term));
weights.set(term, count);
}
}