[Numbers](@id lib-numbers)

# [Numbers](@id lib-numbers) ## Standard Numeric Types * `Bool` * `Int8` * `UInt8` * `Int16` * `UInt16` * `Int32` * `UInt32` * `Int64` * `UInt64` * `Int128` * `UInt128` * `Float16` * `Float32` * `Float64` * `Complex64` * `Complex128` ## Data Formats ```@docs Base.bin Base.hex Base.dec Base.oct Base.base Base.digits Base.digits! Base.bits Base.parse(::Type, ::Any, ::Any) Base.tryparse Base.big Base.signed Base.unsigned Base.float(::Any) Base.Math.significand Base.Math.exponent Base.complex(::Complex) Base.bswap Base.num2hex Base.hex2num Base.hex2bytes Base.bytes2hex ``` ## General Number Functions and Constants ```@docs Base.one Base.oneunit Base.zero Base.pi Base.im Base.eu Base.catalan Base.eulergamma Base.golden Base.Inf Base.Inf32 Base.Inf16 Base.NaN Base.NaN32 Base.NaN16 Base.issubnormal Base.isfinite Base.isinf Base.isnan Base.iszero Base.nextfloat Base.prevfloat Base.isinteger Base.isreal Core.Float32 Core.Float64 Base.GMP.BigInt Base.MPFR.BigFloat Base.Rounding.rounding Base.Rounding.setrounding(::Type, ::Any) Base.Rounding.setrounding(::Function, ::Type, ::RoundingMode) Base.Rounding.get_zero_subnormals Base.Rounding.set_zero_subnormals ``` ### Integers ```@docs Base.count_ones Base.count_zeros Base.leading_zeros Base.leading_ones Base.trailing_zeros Base.trailing_ones Base.isodd Base.iseven ``` ## BigFloats The `BigFloat` type implements arbitrary-precision floating-point arithmetic using the [GNU MPFR library](http://www.mpfr.org/). ```@docs Base.precision Base.MPFR.precision(::Type{BigFloat}) Base.MPFR.setprecision Base.MPFR.BigFloat(x, prec::Int) BigFloat(x::Union{Integer, AbstractFloat, String}, rounding::RoundingMode) Base.MPFR.BigFloat(x, prec::Int, rounding::RoundingMode) Base.MPFR.BigFloat(x::String) ``` ## Random Numbers Random number generation in Julia uses the [Mersenne Twister library](http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/SFMT/#dSFMT) via `MersenneTwister` objects. Julia has a global RNG, which is used by default. Other RNG types can be plugged in by inheriting the `AbstractRNG` type; they can then be used to have multiple streams of random numbers. Besides `MersenneTwister`, Julia also provides the `RandomDevice` RNG type, which is a wrapper over the OS provided entropy. Most functions related to random generation accept an optional `AbstractRNG` as the first argument, `rng` , which defaults to the global one if not provided. Morever, some of them accept optionally dimension specifications `dims...` (which can be given as a tuple) to generate arrays of random values. A `MersenneTwister` or `RandomDevice` RNG can generate random numbers of the following types: `Float16`, `Float32`, `Float64`, `Bool`, `Int8`, `UInt8`, `Int16`, `UInt16`, `Int32`, `UInt32`, `Int64`, `UInt64`, `Int128`, `UInt128`, `BigInt` (or complex numbers of those types). Random floating point numbers are generated uniformly in ``[0, 1)``. As `BigInt` represents unbounded integers, the interval must be specified (e.g. `rand(big(1:6))`). ```@docs Base.Random.srand Base.Random.MersenneTwister Base.Random.RandomDevice Base.Random.rand Base.Random.rand! Base.Random.bitrand Base.Random.randn Base.Random.randn! Base.Random.randexp Base.Random.randexp! Base.Random.randjump ```

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