Module rand::source ·
Generating random samples from probability distributions
This module is the home of the
Distribution trait and several of its
implementations. It is the workhorse behind some of the convenient
functionality of the
Rng trait, e.g.
Rng::gen and of course
Abstractly, a probability distribution describes the probability of occurrence of each value in its sample space.
More concretely, an implementation of
Distribution<T> for type
X is an
algorithm for choosing values from the sample space (a subset of
according to the distribution
X represents, using an external source of
randomness (an RNG supplied to the
X may implement
Distribution<T> for multiple types
Any type implementing
Distribution is stateless (i.e. immutable),
but it may have internal parameters set at construction time (for example,
Uniform allows specification of its sample space as a range within
Standard distribution is important to mention. This is the
distribution used by
Rng::gen and represents the “default” way to
produce a random value for many different types, including most primitive
types, tuples, arrays, and a few derived types. See the documentation of
Standard for more details.
Standard for user types
T makes it
possible to generate type
Rng::gen, and by extension also
Alphanumeric is a simple distribution to sample random letters and
numbers of the
char type; in contrast
Standard may sample any valid
Uniform numeric ranges
Uniform distribution is more flexible than
Standard, but also
more specialised: it supports fewer target types, but allows the sample
space to be specified as an arbitrary range within its target type
Uniform are in some sense uniform distributions.
Values may be sampled from this distribution using [
by creating a distribution object with
From<Range>. When the range limits are not
known at compile time it is typically faster to reuse an existing
Uniform object than to call [
T may also implement
although this is less straightforward than for
Standard (see the
documentation in the
uniform module). Doing so enables generation of
values of type
T with [
Open and half-open ranges
There are surprisingly many ways to uniformly generate random floats. A
range between 0 and 1 is standard, but the exact bounds (open vs closed)
and accuracy differ. In addition to the
Standard distribution Rand offers
OpenClosed01. See “Floating point implementation” section of
Standard documentation for more details.
Sampling a simple true/false outcome with a given probability has a name:
Bernoulli distribution (this is used by
For weighted sampling from a sequence of discrete values, use the
This crate no longer includes other non-uniform distributions; instead
it is recommended that you use either
- A distribution uniformly sampling numbers within a given range.
allocWeighted index sampling
- Sample a
u8, uniformly distributed over ASCII letters and numbers: a-z, A-Z and 0-9.
- The Bernoulli distribution.
- An iterator that generates random values of
Ras the source of randomness.
- A distribution of values of type
Sderived from the distribution
Dby mapping its output of type
Tthrough the closure
- A distribution to sample floating point numbers uniformly in the open interval
(0, 1), i.e. not including either endpoint.
- A distribution to sample floating point numbers uniformly in the half-open interval
(0, 1], i.e. including 1 but not 0.
- A distribution to sample items uniformly from a slice.
- A generic random value distribution, implemented for many primitive types. Usually generates values with a numerically uniform distribution, and with a range appropriate to the type.
- Sample values uniformly between two bounds.
allocA distribution using weighted sampling of discrete items
- Error type returned from
allocError type returned from
- Types (distributions) that can be used to create a random instance of