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A Java's Collaborative Filtering library to carry out experiments in research of Collaborative Filtering based Recommender Systems. The library has been designed from researchers to researchers.
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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.commons.math3.random;
import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.util.FastMath;
/**
* Abstract class implementing the {@link RandomGenerator} interface.
* Default implementations for all methods other than {@link #nextDouble()} and
* {@link #setSeed(long)} are provided.
*
* All data generation methods are based on {@code code nextDouble()}.
* Concrete implementations must override
* this method and should provide better / more
* performant implementations of the other methods if the underlying PRNG
* supplies them.
*
* @since 1.1
*/
public abstract class AbstractRandomGenerator implements RandomGenerator {
/**
* Cached random normal value. The default implementation for
* {@link #nextGaussian} generates pairs of values and this field caches the
* second value so that the full algorithm is not executed for every
* activation. The value {@code Double.NaN} signals that there is
* no cached value. Use {@link #clear} to clear the cached value.
*/
private double cachedNormalDeviate = Double.NaN;
/**
* Construct a RandomGenerator.
*/
public AbstractRandomGenerator() {
super();
}
/**
* Clears the cache used by the default implementation of
* {@link #nextGaussian}. Implementations that do not override the
* default implementation of {@code nextGaussian} should call this
* method in the implementation of {@link #setSeed(long)}
*/
public void clear() {
cachedNormalDeviate = Double.NaN;
}
/** {@inheritDoc} */
public void setSeed(int seed) {
setSeed((long) seed);
}
/** {@inheritDoc} */
public void setSeed(int[] seed) {
// the following number is the largest prime that fits in 32 bits (it is 2^32 - 5)
final long prime = 4294967291l;
long combined = 0l;
for (int s : seed) {
combined = combined * prime + s;
}
setSeed(combined);
}
/**
* Sets the seed of the underlying random number generator using a
* {@code long} seed. Sequences of values generated starting with the
* same seeds should be identical.
*
* Implementations that do not override the default implementation of
* {@code nextGaussian} should include a call to {@link #clear} in the
* implementation of this method.
*
* @param seed the seed value
*/
public abstract void setSeed(long seed);
/**
* Generates random bytes and places them into a user-supplied
* byte array. The number of random bytes produced is equal to
* the length of the byte array.
*
* The default implementation fills the array with bytes extracted from
* random integers generated using {@link #nextInt}.
*
* @param bytes the non-null byte array in which to put the
* random bytes
*/
public void nextBytes(byte[] bytes) {
int bytesOut = 0;
while (bytesOut < bytes.length) {
int randInt = nextInt();
for (int i = 0; i < 3; i++) {
if ( i > 0) {
randInt >>= 8;
}
bytes[bytesOut++] = (byte) randInt;
if (bytesOut == bytes.length) {
return;
}
}
}
}
/**
* Returns the next pseudorandom, uniformly distributed {@code int}
* value from this random number generator's sequence.
* All 232 possible {@code int} values
* should be produced with (approximately) equal probability.
*
* The default implementation provided here returns
*
* (int) (nextDouble() * Integer.MAX_VALUE)
*
*
* @return the next pseudorandom, uniformly distributed {@code int}
* value from this random number generator's sequence
*/
public int nextInt() {
return (int) ((2d * nextDouble() - 1d) * Integer.MAX_VALUE);
}
/**
* Returns a pseudorandom, uniformly distributed {@code int} value
* between 0 (inclusive) and the specified value (exclusive), drawn from
* this random number generator's sequence.
*
* The default implementation returns
*
* (int) (nextDouble() * n
*
*
* @param n the bound on the random number to be returned. Must be
* positive.
* @return a pseudorandom, uniformly distributed {@code int}
* value between 0 (inclusive) and n (exclusive).
* @throws NotStrictlyPositiveException if {@code n <= 0}.
*/
public int nextInt(int n) {
if (n <= 0 ) {
throw new NotStrictlyPositiveException(n);
}
int result = (int) (nextDouble() * n);
return result < n ? result : n - 1;
}
/**
* Returns the next pseudorandom, uniformly distributed {@code long}
* value from this random number generator's sequence. All
* 264 possible {@code long} values
* should be produced with (approximately) equal probability.
*
* The default implementation returns
*
* (long) (nextDouble() * Long.MAX_VALUE)
*
*
* @return the next pseudorandom, uniformly distributed {@code long}
*value from this random number generator's sequence
*/
public long nextLong() {
return (long) ((2d * nextDouble() - 1d) * Long.MAX_VALUE);
}
/**
* Returns the next pseudorandom, uniformly distributed
* {@code boolean} value from this random number generator's
* sequence.
*
* The default implementation returns
*
* nextDouble() <= 0.5
*
*
* @return the next pseudorandom, uniformly distributed
* {@code boolean} value from this random number generator's
* sequence
*/
public boolean nextBoolean() {
return nextDouble() <= 0.5;
}
/**
* Returns the next pseudorandom, uniformly distributed {@code float}
* value between {@code 0.0} and {@code 1.0} from this random
* number generator's sequence.
*
* The default implementation returns
*
* (float) nextDouble()
*
*
* @return the next pseudorandom, uniformly distributed {@code float}
* value between {@code 0.0} and {@code 1.0} from this
* random number generator's sequence
*/
public float nextFloat() {
return (float) nextDouble();
}
/**
* Returns the next pseudorandom, uniformly distributed
* {@code double} value between {@code 0.0} and
* {@code 1.0} from this random number generator's sequence.
*
* This method provides the underlying source of random data used by the
* other methods.
*
* @return the next pseudorandom, uniformly distributed
* {@code double} value between {@code 0.0} and
* {@code 1.0} from this random number generator's sequence
*/
public abstract double nextDouble();
/**
* Returns the next pseudorandom, Gaussian ("normally") distributed
* {@code double} value with mean {@code 0.0} and standard
* deviation {@code 1.0} from this random number generator's sequence.
*
* The default implementation uses the Polar Method
* due to G.E.P. Box, M.E. Muller and G. Marsaglia, as described in
* D. Knuth, The Art of Computer Programming, 3.4.1C.
*
* The algorithm generates a pair of independent random values. One of
* these is cached for reuse, so the full algorithm is not executed on each
* activation. Implementations that do not override this method should
* make sure to call {@link #clear} to clear the cached value in the
* implementation of {@link #setSeed(long)}.
*
* @return the next pseudorandom, Gaussian ("normally") distributed
* {@code double} value with mean {@code 0.0} and
* standard deviation {@code 1.0} from this random number
* generator's sequence
*/
public double nextGaussian() {
if (!Double.isNaN(cachedNormalDeviate)) {
double dev = cachedNormalDeviate;
cachedNormalDeviate = Double.NaN;
return dev;
}
double v1 = 0;
double v2 = 0;
double s = 1;
while (s >=1 ) {
v1 = 2 * nextDouble() - 1;
v2 = 2 * nextDouble() - 1;
s = v1 * v1 + v2 * v2;
}
if (s != 0) {
s = FastMath.sqrt(-2 * FastMath.log(s) / s);
}
cachedNormalDeviate = v2 * s;
return v1 * s;
}
}
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