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Java example source code file (MathBenchmarking.java)
The MathBenchmarking.java Java example source code/* * Copyright (C) 2011 The Guava Authors * * Licensed 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 com.google.common.math; import java.math.BigInteger; import java.util.Random; /** * Utilities for benchmarks. * * In many cases, we wish to vary the order of magnitude of the input as much as we * want to vary the input itself, so most methods which generate values use * an exponential distribution varying the order of magnitude of the generated values * uniformly at random. * * @author Louis Wasserman */ final class MathBenchmarking { static final int ARRAY_SIZE = 0x10000; static final int ARRAY_MASK = 0x0ffff; static final Random RANDOM_SOURCE = new Random(314159265358979L); static final int MAX_EXPONENT = 100; /* * Duplicated from LongMath. * binomial(biggestBinomials[k], k) fits in a long, but not * binomial(biggestBinomials[k] + 1, k). */ static final int[] biggestBinomials = {Integer.MAX_VALUE, Integer.MAX_VALUE, Integer.MAX_VALUE, 3810779, 121977, 16175, 4337, 1733, 887, 534, 361, 265, 206, 169, 143, 125, 111, 101, 94, 88, 83, 79, 76, 74, 72, 70, 69, 68, 67, 67, 66, 66, 66, 66}; /** * Generates values in a distribution equivalent to randomNonNegativeBigInteger * but omitting zero. */ static BigInteger randomPositiveBigInteger(int numBits) { BigInteger result; do { result = randomNonNegativeBigInteger(numBits); } while (result.signum() == 0); return result; } /** * Generates a number in [0, 2^numBits) with an exponential distribution. * The floor of the log2 of the result is chosen uniformly at random in * [0, numBits), and then the result is chosen in that range uniformly at random. * Zero is treated as having log2 == 0. */ static BigInteger randomNonNegativeBigInteger(int numBits) { int digits = RANDOM_SOURCE.nextInt(numBits); if (digits == 0) { return new BigInteger(1, RANDOM_SOURCE); } else { return new BigInteger(digits, RANDOM_SOURCE) .setBit(digits); } } /** * Equivalent to calling randomPositiveBigInteger(numBits) and then flipping * the sign with 50% probability. */ static BigInteger randomNonZeroBigInteger(int numBits) { BigInteger result = randomPositiveBigInteger(numBits); return RANDOM_SOURCE.nextBoolean() ? result : result.negate(); } /** * Chooses a number in (-2^numBits, 2^numBits) at random, with density * concentrated in numbers of lower magnitude. */ static BigInteger randomBigInteger(int numBits) { while (true) { if (RANDOM_SOURCE.nextBoolean()) { return randomNonNegativeBigInteger(numBits); } BigInteger neg = randomNonNegativeBigInteger(numBits).negate(); if (neg.signum() != 0) { return neg; } } } /** * Generates a number in [0, 2^numBits) with an exponential distribution. * The floor of the log2 of the absolute value of the result is chosen uniformly * at random in [0, numBits), and then the result is chosen from those possibilities * uniformly at random. * * Zero is treated as having log2 == 0. */ static double randomDouble(int maxExponent) { double result = RANDOM_SOURCE.nextDouble(); result = Math.scalb(result, RANDOM_SOURCE.nextInt(maxExponent + 1)); return RANDOM_SOURCE.nextBoolean() ? result : -result; } /** * Returns a random integer between zero and {@code MAX_EXPONENT}. */ static int randomExponent() { return RANDOM_SOURCE.nextInt(MAX_EXPONENT + 1); } static double randomPositiveDouble() { return Math.exp(randomDouble(6)); } } Other Java examples (source code examples)Here is a short list of links related to this Java MathBenchmarking.java source code file: |
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