API reference

Random — continuous distributions (Generator)

Continuous distribution samplers on np.random.Generator. Obtain a generator with np.random.defaultRng(seed). All methods return a JS number when size is omitted and a float64 NDArray otherwise.

Name	Summary
`np.standardExponential`	Draw samples from the standard exponential distribution (rate=1).
`np.exponential`	Draw samples from an exponential distribution with given scale (inverse rate).
`np.standardGamma`	Draw samples from a standard Gamma distribution (scale=1).
`np.gamma`	Draw samples from a Gamma distribution.
`np.beta`	Draw samples from a Beta distribution with shape parameters `a` and `b`.
`np.chisquare`	Draw samples from a chi-square distribution with `df` degrees of freedom.
`np.f`	Draw samples from an F distribution.
`np.standardCauchy`	Draw samples from the standard Cauchy distribution (location=0, scale=1).
`np.pareto`	Draw samples from a Pareto II (Lomax) distribution with shape `a`.
`np.weibull`	Draw samples from a Weibull distribution with shape `a`.
`rng.power`	Draw samples from a power distribution with exponent `a` in (0, 1].
`np.laplace`	Draw samples from the Laplace (double exponential) distribution.
`np.gumbel`	Draw samples from a Gumbel distribution.
`np.logistic`	Draw samples from a logistic distribution.
`np.lognormal`	Draw samples from a log-normal distribution.
`np.rayleigh`	Draw samples from a Rayleigh distribution.
`np.standardT`	Draw samples from a standard Student's t-distribution.
`np.noncentralChisquare`	Draw samples from a noncentral chi-square distribution.
`np.noncentralF`	Draw samples from a noncentral F distribution.
`np.wald`	Draw samples from a Wald (inverse Gaussian) distribution.
`np.vonmises`	Draw samples from a von Mises distribution.
`np.triangular`	Draw samples from a triangular distribution over `[left, right]` with peak at `mode`.

`np.standardExponential`

rng.standardExponential(size?)

Draw samples from the standard exponential distribution (rate=1). Equivalent to numpy.random.Generator.standard_exponential.

NumPynumpy.random.Generator.standard_exponential

Parameters

[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.standardExponential(); // => 2.4042086039659947

TypeScript declaration

rng.standardExponential(size?: Size | null | undefined): number | NDArray

`np.exponential`

rng.exponential(scale?, size?)

Draw samples from an exponential distribution with given scale (inverse rate). Equivalent to numpy.random.Generator.exponential.

NumPynumpy.random.Generator.exponential

Parameters

[scale]number: Scale (1/rate). Default 1.0.
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.exponential(2); // => 4.808417207931989

TypeScript declaration

rng.exponential(scale?: number | undefined, size?: Size | null | undefined): number | NDArray

`np.standardGamma`

rng.standardGamma(shape, size?)

Draw samples from a standard Gamma distribution (scale=1). Equivalent to numpy.random.Generator.standard_gamma.

NumPynumpy.random.Generator.standard_gamma

Parameters

shapenumber: Shape parameter (> 0).
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.standardGamma(2); // => 2.0918172704999494

TypeScript declaration

rng.standardGamma(shape: number, size?: Size | null | undefined): number | NDArray

`np.gamma`

rng.gamma(shape, scale?, size?)

Draw samples from a Gamma distribution. Equivalent to numpy.random.Generator.gamma.

NumPynumpy.random.Generator.gamma

Parameters

shapenumber: Shape parameter (> 0).
[scale]number: Scale parameter (> 0). Default 1.0.
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.gamma(2, 3); // => 6.275451811499848

TypeScript declaration

rng.gamma(shape: number, scale?: number | undefined, size?: Size | null | undefined): number | NDArray

`np.beta`

rng.beta(a, b, size?)

Draw samples from a Beta distribution with shape parameters a and b. Equivalent to numpy.random.Generator.beta.

NumPynumpy.random.Generator.beta

Parameters

anumber: Alpha shape parameter (> 0).
bnumber: Beta shape parameter (> 0).
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.beta(2, 5); // => 0.24395464376443093

TypeScript declaration

rng.beta(a: number, b: number, size?: Size | null | undefined): number | NDArray

`np.chisquare`

rng.chisquare(df, size?)

Draw samples from a chi-square distribution with df degrees of freedom. Equivalent to numpy.random.Generator.chisquare.

NumPynumpy.random.Generator.chisquare

Parameters

dfnumber: Degrees of freedom (> 0).
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.chisquare(2); // => 4.808417207931989

TypeScript declaration

rng.chisquare(df: number, size?: Size | null | undefined): number | NDArray

`np.f`

rng.f(dfnum, dfden, size?)

Draw samples from an F distribution. Equivalent to numpy.random.Generator.f.

NumPynumpy.random.Generator.f

Parameters

dfnumnumber: Numerator degrees of freedom (> 0).
dfdennumber: Denominator degrees of freedom (> 0).
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.f(2, 5); // => 6.208757071292293

TypeScript declaration

rng.f(dfnum: number, dfden: number, size?: Size | null | undefined): number | NDArray

`np.standardCauchy`

rng.standardCauchy(size?)

Draw samples from the standard Cauchy distribution (location=0, scale=1). Equivalent to numpy.random.Generator.standard_cauchy.

NumPynumpy.random.Generator.standard_cauchy

Parameters

[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
typeof rng.standardCauchy(); // => "number"

TypeScript declaration

rng.standardCauchy(size?: Size | null | undefined): number | NDArray

`np.pareto`

rng.pareto(a, size?)

Draw samples from a Pareto II (Lomax) distribution with shape a. Equivalent to numpy.random.Generator.pareto.

NumPynumpy.random.Generator.pareto

Parameters

anumber: Shape parameter (> 0).
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.pareto(3) >= 0; // => true

TypeScript declaration

rng.pareto(a: number, size?: Size | null | undefined): number | NDArray

`np.weibull`

rng.weibull(a, size?)

Draw samples from a Weibull distribution with shape a. Equivalent to numpy.random.Generator.weibull.

NumPynumpy.random.Generator.weibull

Parameters

anumber: Shape parameter (>= 0).
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.weibull(1); // => 2.4042086039659947

TypeScript declaration

rng.weibull(a: number, size?: Size | null | undefined): number | NDArray

`rng.power`

rng.power(a, size?)

Draw samples from a power distribution with exponent a in (0, 1]. Equivalent to numpy.random.Generator.power.

NumPynumpy.random.Generator.power

Parameters

anumber: Exponent in (0, 1].
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.power(0.5); // => 0.8274868469979773

TypeScript declaration

rng.power(a: number, size?: Size | null | undefined): number | NDArray

`np.laplace`

rng.laplace(loc?, scale?, size?)

Draw samples from the Laplace (double exponential) distribution. Equivalent to numpy.random.Generator.laplace.

NumPynumpy.random.Generator.laplace

Parameters

[loc]number: Location (mean). Default 0.0.
[scale]number: Scale (>= 0). Default 1.0.
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
typeof rng.laplace(); // => "number"

TypeScript declaration

rng.laplace(loc?: number | undefined, scale?: number | undefined, size?: Size | null | undefined): number | NDArray

`np.gumbel`

rng.gumbel(loc?, scale?, size?)

Draw samples from a Gumbel distribution. Equivalent to numpy.random.Generator.gumbel.

NumPynumpy.random.Generator.gumbel

Parameters

[loc]number: Location. Default 0.0.
[scale]number: Scale (>= 0). Default 1.0.
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
typeof rng.gumbel(); // => "number"

TypeScript declaration

rng.gumbel(loc?: number | undefined, scale?: number | undefined, size?: Size | null | undefined): number | NDArray

`np.logistic`

rng.logistic(loc?, scale?, size?)

Draw samples from a logistic distribution. Equivalent to numpy.random.Generator.logistic.

NumPynumpy.random.Generator.logistic

Parameters

[loc]number: Location. Default 0.0.
[scale]number: Scale (>= 0). Default 1.0.
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
typeof rng.logistic(); // => "number"

TypeScript declaration

rng.logistic(loc?: number | undefined, scale?: number | undefined, size?: Size | null | undefined): number | NDArray

`np.lognormal`

rng.lognormal(mean?, sigma?, size?)

Draw samples from a log-normal distribution. Equivalent to numpy.random.Generator.lognormal.

NumPynumpy.random.Generator.lognormal

Parameters

[mean]number: Mean of the underlying normal. Default 0.0.
[sigma]number: Std dev of underlying normal (>= 0). Default 1.0.
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.lognormal() > 0; // => true

TypeScript declaration

rng.lognormal(mean?: number | undefined, sigma?: number | undefined, size?: Size | null | undefined): number | NDArray

`np.rayleigh`

rng.rayleigh(scale?, size?)

Draw samples from a Rayleigh distribution. Equivalent to numpy.random.Generator.rayleigh.

NumPynumpy.random.Generator.rayleigh

Parameters

[scale]number: Scale parameter (>= 0). Default 1.0.
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.rayleigh() > 0; // => true

TypeScript declaration

rng.rayleigh(scale?: number | undefined, size?: Size | null | undefined): number | NDArray

`np.standardT`

rng.standardT(df, size?)

Draw samples from a standard Student's t-distribution. Equivalent to numpy.random.Generator.standard_t.

NumPynumpy.random.Generator.standard_t

Parameters

dfnumber: Degrees of freedom (> 0).
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
typeof rng.standardT(2); // => "number"

TypeScript declaration

rng.standardT(df: number, size?: Size | null | undefined): number | NDArray

`np.noncentralChisquare`

rng.noncentralChisquare(df, nonc, size?)

Draw samples from a noncentral chi-square distribution. Equivalent to numpy.random.Generator.noncentral_chisquare.

NumPynumpy.random.Generator.noncentral_chisquare

Parameters

dfnumber: Degrees of freedom (> 0).
noncnumber: Noncentrality parameter (>= 0).
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.noncentralChisquare(3, 1) > 0; // => true

TypeScript declaration

rng.noncentralChisquare(df: number, nonc: number, size?: Size | null | undefined): number | NDArray

`np.noncentralF`

rng.noncentralF(dfnum, dfden, nonc, size?)

Draw samples from a noncentral F distribution. Equivalent to numpy.random.Generator.noncentral_f.

NumPynumpy.random.Generator.noncentral_f

Parameters

dfnumnumber: Numerator degrees of freedom (> 0).
dfdennumber: Denominator degrees of freedom (> 0).
noncnumber: Noncentrality parameter (>= 0).
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.noncentralF(3, 5, 1) > 0; // => true

TypeScript declaration

rng.noncentralF(dfnum: number, dfden: number, nonc: number, size?: Size | null | undefined): number | NDArray

`np.wald`

rng.wald(mean, scale, size?)

Draw samples from a Wald (inverse Gaussian) distribution. Equivalent to numpy.random.Generator.wald.

NumPynumpy.random.Generator.wald

Parameters

meannumber: Distribution mean (> 0).
scalenumber: Scale parameter (> 0).
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.wald(1, 1) > 0; // => true

TypeScript declaration

rng.wald(mean: number, scale: number, size?: Size | null | undefined): number | NDArray

`np.vonmises`

rng.vonmises(mu, kappa, size?)

Draw samples from a von Mises distribution. Equivalent to numpy.random.Generator.vonmises.

NumPynumpy.random.Generator.vonmises

Parameters

munumber: Mode angle in radians.
kappanumber: Concentration parameter (>= 0).
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
typeof rng.vonmises(0, 1); // => "number"

TypeScript declaration

rng.vonmises(mu: number, kappa: number, size?: Size | null | undefined): number | NDArray

`np.triangular`

rng.triangular(left, mode, right, size?)

Draw samples from a triangular distribution over [left, right] with peak at mode. Equivalent to numpy.random.Generator.triangular.

NumPynumpy.random.Generator.triangular

Parameters

leftnumber: Lower bound.
modenumber: Peak value in [left, right].
rightnumber: Upper bound (> left).
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<float64>

Example

TypeScript

const rng = np.random.defaultRng(42);
const v = rng.triangular(0, 0.5, 1);
v >= 0 && v <= 1; // => true

TypeScript declaration

rng.triangular(left: number, mode: number, right: number, size?: Size | null | undefined): number | NDArray