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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.
*/
/**
* Randomness providers
*
*
* This package provides
* {@link org.apache.commons.rng.simple.RandomSource factory methods}
* by which low-level classes implemented in module "commons-rng-core"
* are instantiated.
*
* Classes in package org.apache.commons.rng.simple.internal
* should not be used directly.
*
* The generators are not thread-safe: Parallel applications must
* use different generator instances in different threads.
*
*
*
* In the case of pseudo-random generators, the source of randomness is
* usually a set of numbers whose bits representation are scrambled in such
* a way as to produce a random-looking sequence.
*
* The main property of the sequence is that the numbers must be uniformly
* distributed within their allowed range.
*
* Classes in this package do not provide any further processing of the
* number generation such as to match other types of distribution.
*
*
*
* Which source of randomness to choose may depend on which properties
* are more important.
* Considerations can include speed of generation, memory usage, period
* size, equidistribution, correlation, etc.
*
* For some of the generators, interesting properties (of the reference
* implementations) are proven in scientific papers.
* Some generators can also suffer from potential weaknesses.
*
*
*
* For simple sampling, any of the generators implemented in this library
* may be sufficient.
*
* For Monte-Carlo simulations that require generating high-dimensional
* vectors), equidistribution and non-correlation are crucial.
* The Mersenne Twister and Well generators have
* equidistribution properties proven according to their bits pool size
* which is directly related to their period (all of them have maximal
* period, i.e. a generator with size {@code n} pool has a period
* 2n-1).
* They also have equidistribution properties for 32 bits blocks up to
* {@code s/32} dimension where {@code s} is their pool size.
*
* For example, {@code Well19937c} is equidistributed up to dimension 623
* (i.e. 19937 divided by 32).
* It means that a Monte-Carlo simulation generating vectors of {@code n}
* (32-bits integer) variables at each iteration has some guarantee on the
* properties of its components as long as {@code n < 623}.
* Note that if the variables are of type {@code double}, the limit is
* divided by two (since 64 bits are needed to create a {@code double}).
*
* Reference to the relevant publications are listed in the specific
* documentation of each class.
*
*
*
* Memory usage can vary a lot between providers.
* The state of {@code MersenneTwister} is composed of 624 integers,
* using about 2.5 kB.
* The Well generators use 6 integer arrays, the length of each
* being equal to the pool size; thus, for example, {@code Well44497b}
* uses about 33 kB.
*
*/
package org.apache.commons.rng.simple;