| /** |
| * 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); |
| } |
| } |