API reference

Random — discrete distributions (Generator)

Discrete 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 an int64 NDArray otherwise.

Name	Summary
`np.binomial`	Draw samples from a binomial distribution: number of successes in `n` Bernoulli trials with success probability `p`.
`np.negativeBinomial`	Draw samples from a negative binomial distribution: number of failures before `n` successes, each with probability `p`.
`np.poisson`	Draw samples from a Poisson distribution with expected rate `lam`.
`np.zipf`	Draw samples from a Zipf distribution with parameter `a` (> 1).
`np.hypergeometric`	Draw samples from a hypergeometric distribution: number of successes in `nsample` draws without replacement from a population of `ngood` good and `nbad` bad items.
`np.logseries`	Draw samples from a logarithmic series distribution with parameter `p` in [0, 1).

`np.binomial`

rng.binomial(n, p, size?)

Draw samples from a binomial distribution: number of successes in n Bernoulli trials with success probability p. Equivalent to numpy.random.Generator.binomial.

NumPynumpy.random.Generator.binomial

Parameters

nnumber: Number of trials (integer >= 0).
pnumber: Probability of success in [0, 1].
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<int64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.binomial(10, 0.5);          // => 6
rng.binomial(10, 0);            // => 0
rng.binomial(10, 1);            // => 10

TypeScript declaration

rng.binomial(n: number, p: number, size?: Size | null | undefined): number | NDArray

`np.negativeBinomial`

rng.negativeBinomial(n, p, size?)

Draw samples from a negative binomial distribution: number of failures before n successes, each with probability p. Equivalent to numpy.random.Generator.negative_binomial.

NumPynumpy.random.Generator.negative_binomial

Parameters

nnumber: Number of successes (> 0).
pnumber: Probability of success in (0, 1].
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<int64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.negativeBinomial(5, 0.5);  // => 6

TypeScript declaration

rng.negativeBinomial(n: number, p: number, size?: Size | null | undefined): number | NDArray

`np.poisson`

rng.poisson(lam?, size?)

Draw samples from a Poisson distribution with expected rate lam. Equivalent to numpy.random.Generator.poisson.

NumPynumpy.random.Generator.poisson

Parameters

[lam]number: Expected number of events (>= 0). Default 1.0.
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<int64>

Example

TypeScript

const rng = np.random.defaultRng(42);
typeof rng.poisson(5);          // => "number"
rng.poisson(0);                 // => 0

TypeScript declaration

rng.poisson(lam?: number | undefined, size?: Size | null | undefined): number | NDArray

`np.zipf`

rng.zipf(a, size?)

Draw samples from a Zipf distribution with parameter a (> 1). Equivalent to numpy.random.Generator.zipf.

NumPynumpy.random.Generator.zipf

Parameters

anumber: Distribution parameter (> 1).
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<int64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.zipf(2) >= 1;  // => true

TypeScript declaration

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

`np.hypergeometric`

rng.hypergeometric(ngood, nbad, nsample, size?)

Draw samples from a hypergeometric distribution: number of successes in nsample draws without replacement from a population of ngood good and nbad bad items. Equivalent to numpy.random.Generator.hypergeometric.

NumPynumpy.random.Generator.hypergeometric

Parameters

ngoodnumber: Number of good items in the population (>= 0).
nbadnumber: Number of bad items in the population (>= 0).
nsamplenumber: Number of draws (>= 0, <= ngood + nbad).
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<int64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.hypergeometric(0, 0, 0);  // => 0

TypeScript declaration

rng.hypergeometric(ngood: number, nbad: number, nsample: number, size?: Size | null | undefined): number | NDArray

`np.logseries`

rng.logseries(p, size?)

Draw samples from a logarithmic series distribution with parameter p in [0, 1). Equivalent to numpy.random.Generator.logseries.

NumPynumpy.random.Generator.logseries

Parameters

pnumber: Distribution parameter in [0, 1).
[size]number | number[]: Output shape; omit for a scalar.

Returns

number | NDArray<int64>

Example

TypeScript

const rng = np.random.defaultRng(42);
rng.logseries(0.9) >= 1;  // => true

TypeScript declaration

rng.logseries(p: number, size?: Size | null | undefined): number | NDArray