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Intended to be used in method chaining style, e.g. `thread_rng().gen::<i32>()`, or cached locally, e.g. `let mut rng = thread_rng();`.",N,[[],["threadrng"]]],[5,"random","","Generates a random value using the thread-local random number generator.",N,[[],["t"]]],[0,"distributions","","Generating random samples from probability distributions.",N,N],[3,"Alphanumeric","rand::distributions","Sample a `char`, uniformly distributed over ASCII letters and numbers: a-z, A-Z and 0-9.",N,N],[3,"Uniform","","Sample values uniformly between two bounds.",N,N],[3,"OpenClosed01","","A distribution to sample floating point numbers uniformly in the half-open interval `(0, 1]`, i.e. including 1 but not 0.",N,N],[3,"Open01","","A distribution to sample floating point numbers uniformly in the open interval `(0, 1)`, i.e. not including either endpoint.",N,N],[3,"Bernoulli","","The Bernoulli distribution.",N,N],[3,"WeightedIndex","","A distribution using weighted sampling to pick a discretely selected item.",N,N],[3,"UnitSphereSurface","","Samples uniformly from the surface of the unit sphere in three dimensions.",N,N],[3,"UnitCircle","","Samples uniformly from the edge of the unit circle in two dimensions.",N,N],[3,"Gamma","","The Gamma distribution `Gamma(shape, scale)` distribution.",N,N],[3,"ChiSquared","","The chi-squared distribution `χ²(k)`, where `k` is the degrees of freedom.",N,N],[3,"FisherF","","The Fisher F distribution `F(m, n)`.",N,N],[3,"StudentT","","The Student t distribution, `t(nu)`, where `nu` is the degrees of freedom.",N,N],[3,"Beta","","The Beta distribution with shape parameters `alpha` and `beta`.",N,N],[3,"Normal","","The normal distribution `N(mean, std_dev**2)`.",N,N],[3,"LogNormal","","The log-normal distribution `ln N(mean, std_dev**2)`.",N,N],[3,"StandardNormal","","Samples floating-point numbers according to the normal distribution `N(0, 1)` (a.k.a. a standard normal, or Gaussian). This is equivalent to `Normal::new(0.0, 1.0)` but faster.",N,N],[3,"Exp","","The exponential distribution `Exp(lambda)`.",N,N],[3,"Exp1","","Samples floating-point numbers according to the exponential distribution, with rate parameter `λ = 1`. This is equivalent to `Exp::new(1.0)` or sampling with `-rng.gen::<f64>().ln()`, but faster.",N,N],[3,"Pareto","","Samples floating-point numbers according to the Pareto distribution",N,N],[3,"Poisson","","The Poisson distribution `Poisson(lambda)`.",N,N],[3,"Binomial","","The binomial distribution `Binomial(n, p)`.",N,N],[3,"Cauchy","","The Cauchy distribution `Cauchy(median, scale)`.",N,N],[3,"Dirichlet","","The dirichelet distribution `Dirichlet(alpha)`.",N,N],[3,"Triangular","","The triangular distribution.",N,N],[3,"Weibull","","Samples floating-point numbers according to the Weibull distribution",N,N],[3,"DistIter","","An iterator that generates random values of `T` with distribution `D`, using `R` as the source of randomness.",N,N],[3,"Standard","","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.",N,N],[3,"Weighted","","A value with a particular weight for use with `WeightedChoice`.",N,N],[12,"weight","","The numerical weight of this item",4,N],[12,"item","","The actual item which is being weighted",4,N],[3,"WeightedChoice","","A distribution that selects from a finite collection of weighted items.",N,N],[4,"WeightedError","","Error type returned from `WeightedIndex::new`.",N,N],[13,"NoItem","","The provided iterator contained no items.",5,N],[13,"NegativeWeight","","A weight lower than zero was used.",5,N],[13,"AllWeightsZero","","All items in the provided iterator had a weight of zero.",5,N],[0,"uniform","","A distribution uniformly sampling numbers within a given range.",N,N],[3,"Uniform","rand::distributions::uniform","Sample values uniformly between two bounds.",N,N],[3,"UniformInt","","The back-end implementing [`UniformSampler`] for integer types.",N,N],[3,"UniformFloat","","The back-end implementing [`UniformSampler`] for floating-point types.",N,N],[3,"UniformDuration","","The back-end implementing [`UniformSampler`] for `Duration`.",N,N],[8,"SampleUniform","","Helper trait for creating objects using the correct implementation of [`UniformSampler`] for the sampling type.",N,N],[16,"Sampler","","The `UniformSampler` implementation supporting type `X`.",6,N],[8,"UniformSampler","","Helper trait handling actual uniform sampling.",N,N],[16,"X","","The type sampled by this implementation.",7,N],[10,"new","","Construct self, with inclusive lower bound and exclusive upper bound `[low, high)`.",7,[[["b1"],["b2"]],["self"]]],[10,"new_inclusive","","Construct self, with inclusive bounds `[low, high]`.",7,[[["b1"],["b2"]],["self"]]],[10,"sample","","Sample a value.",7,N],[11,"sample_single","","Sample a single value uniformly from a range with inclusive lower bound and exclusive upper bound `[low, high)`.",7,N],[8,"SampleBorrow","","Helper trait similar to [`Borrow`] but implemented only for SampleUniform and references to SampleUniform in order to resolve ambiguity issues.",N,N],[10,"borrow","","Immutably borrows from an owned value. 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Panics if `n <= 0`.",17,[[["f64"]],["studentt"]]],[11,"new","","Construct an object representing the `Beta(alpha, beta)` distribution.",18,[[["f64"],["f64"]],["beta"]]],[11,"new","","Construct a new `Normal` distribution with the given mean and standard deviation.",19,[[["f64"],["f64"]],["normal"]]],[11,"new","","Construct a new `LogNormal` distribution with the given mean and standard deviation.",20,[[["f64"],["f64"]],["lognormal"]]],[11,"new","","Construct a new `Exp` with the given shape parameter `lambda`. Panics if `lambda <= 0`.",21,[[["f64"]],["exp"]]],[11,"new","","Construct a new Pareto distribution with given `scale` and `shape`.",22,[[["f64"],["f64"]],["pareto"]]],[11,"new","","Construct a new `Poisson` with the given shape parameter `lambda`. Panics if `lambda <= 0`.",23,[[["f64"]],["poisson"]]],[11,"new","","Construct a new `Binomial` with the given shape parameters `n` (number of trials) and `p` (probability of success).",24,[[["u64"],["f64"]],["binomial"]]],[11,"new","","Construct a new `Cauchy` with the given shape parameters `median` the peak location and `scale` the scale factor. Panics if `scale <= 0`.",25,[[["f64"],["f64"]],["cauchy"]]],[11,"new","","Construct a new `Dirichlet` with the given alpha parameter `alpha`.",26,[[["v"]],["dirichlet"]]],[11,"new_with_param","","Construct a new `Dirichlet` with the given shape parameter `alpha` and `size`.",26,[[["f64"],["usize"]],["dirichlet"]]],[11,"new","","Construct a new `Triangular` with minimum `min`, maximum `max` and mode `mode`.",27,[[["f64"],["f64"],["f64"]],["triangular"]]],[11,"new","","Construct a new `Weibull` distribution with given `scale` and `shape`.",28,[[["f64"],["f64"]],["weibull"]]],[8,"Distribution","","Types (distributions) that can be used to create a random instance of `T`.",N,N],[10,"sample","","Generate a random value of `T`, using `rng` as the source of randomness.",29,[[["self"],["r"]],["t"]]],[11,"sample_iter","","Create an iterator that generates random values of `T`, using `rng` as the source of randomness.",29,[[["self"],["r"]],["distiter"]]],[11,"new","","Create a new `WeightedChoice`.",30,N],[0,"prelude","rand","Convenience re-export of common members",N,N],[0,"prng","","Pseudo-random number generators.",N,N],[0,"rngs","","Random number generators and adapters for common usage:",N,N],[3,"JitterRng","rand::rngs","A true random number generator based on jitter in the CPU execution time, and jitter in memory access time.",N,N],[3,"EntropyRng","","An interface returning random data from external source(s), provided specifically for securely seeding algorithmic generators (PRNGs).",N,N],[3,"SmallRng","","An RNG recommended when small state, cheap initialization and good performance are required. The PRNG algorithm in `SmallRng` is chosen to be efficient on the current platform, without consideration for cryptography or security. The size of its state is much smaller than for [`StdRng`].",N,N],[3,"StdRng","","The standard RNG. The PRNG algorithm in `StdRng` is chosen to be efficient on the current platform, to be statistically strong and unpredictable (meaning a cryptographically secure PRNG).",N,N],[3,"ThreadRng","","The type returned by [`thread_rng`], essentially just a reference to the PRNG in thread-local memory.",N,N],[3,"OsRng","","A random number generator that retrieves randomness straight from the operating system.",N,N],[4,"TimerError","","An error that can occur when [`JitterRng::test_timer`] fails.",N,N],[13,"NoTimer","","No timer available.",31,N],[13,"CoarseTimer","","Timer too coarse to use as an entropy source.",31,N],[13,"NotMonotonic","","Timer is not monotonically increasing.",31,N],[13,"TinyVariantions","","Variations of deltas of time too small.",31,N],[13,"TooManyStuck","","Too many stuck results (indicating no added entropy).",31,N],[0,"adapter","","Wrappers / adapters forming RNGs",N,N],[3,"ReadRng","rand::rngs::adapter","An RNG that reads random bytes straight from any type supporting `std::io::Read`, for example files.",N,N],[3,"ReseedingRng","","A wrapper around any PRNG that implements [`BlockRngCore`], that adds the ability to reseed it.",N,N],[11,"new","","Create a new `ReadRng` from a `Read`.",32,[[["r"]],["readrng"]]],[11,"new","","Create a new `ReseedingRng` from an existing PRNG, combined with a RNG to use as reseeder.",33,[[["r"],["u64"],["rsdr"]],["self"]]],[11,"reseed","","Reseed the internal PRNG.",33,[[["self"]],["result",["error"]]]],[11,"new","rand::rngs","Create a new `EntropyRng`.",34,[[],["self"]]],[11,"new","","Create a new `JitterRng`. Makes use of `std::time` for a timer, or a platform-specific function with higher accuracy if necessary and available.",35,[[],["result",["jitterrng","timererror"]]]],[11,"new_with_timer","","Create a new `JitterRng`. A custom timer can be supplied, making it possible to use `JitterRng` in `no_std` environments.",35,N],[11,"set_rounds","","Configures how many rounds are used to generate each 64-bit value. This must be greater than zero, and has a big impact on performance and output quality.",35,[[["self"],["u8"]]]],[11,"test_timer","","Basic quality tests on the timer, by measuring CPU timing jitter a few hundred times.",35,[[["self"]],["result",["u8","timererror"]]]],[11,"timer_stats","","Statistical test: return the timer delta of one normal run of the `JitterRng` entropy collector.",35,[[["self"],["bool"]],["i64"]]],[0,"mock","","Mock random number generator",N,N],[3,"StepRng","rand::rngs::mock","A simple implementation of `RngCore` for testing purposes.",N,N],[11,"new","","Create a `StepRng`, yielding an arithmetic sequence starting with `initial` and incremented by `increment` each time.",36,[[["u64"],["u64"]],["self"]]],[11,"new","rand::rngs","Create a new `OsRng`.",37,[[],["result",["osrng","error"]]]],[0,"seq","rand","Functions for randomly accessing and sampling sequences.",N,N],[3,"SliceChooseIter","rand::seq","Iterator over multiple choices, as returned by `SliceRandom::choose_multiple.",N,N],[5,"sample_iter","","Randomly sample `amount` elements from a finite iterator.",N,[[["r"],["i"],["usize"]],["result",["vec","vec"]]]],[5,"sample_slice","","Randomly sample exactly `amount` values from `slice`.",N,N],[5,"sample_slice_ref","","Randomly sample exactly `amount` references from `slice`.",N,N],[0,"index","","Index sampling",N,N],[4,"IndexVec","rand::seq::index","A vector of indices.",N,N],[4,"IndexVecIter","","Return type of `IndexVec::iter`.",N,N],[4,"IndexVecIntoIter","","Return type of `IndexVec::into_iter`.",N,N],[5,"sample","","Randomly sample exactly `amount` distinct indices from `0..length`, and return them in random order (fully shuffled).",N,[[["r"],["usize"],["usize"]],["indexvec"]]],[11,"len","","Returns the number of indices",38,[[["self"]],["usize"]]],[11,"index","","Return the value at the given `index`.",38,[[["self"],["usize"]],["usize"]]],[11,"into_vec","","Return result as a `Vec<usize>`. 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