The OpenD Programming Language

categoricalPMF

Computes the Categorical probability mass function (PMF).

elementType!(Slice!(Iterator, 1, kind)) categoricalPMF(size_t x, Slice!(Iterator, 1, kind) p)
T categoricalPMF(size_t x, T[] p)
@safe pure nothrow @nogc
T
categoricalPMF
(
T
)
(
const size_t x
,
scope const T[] p...
)
if (
isFloatingPoint!T
)

Parameters

x size_t: value to evaluate PMF
p T[]: slice containing the probability associated with the Categorical Distribution

Examples

import mir.ndslice.slice: sliced;
import mir.test: shouldApprox;

static immutable x = [0.1, 0.5, 0.4];
auto p = x.sliced;

0.categoricalPMF(p).shouldApprox == 0.1;
1.categoricalPMF(p).shouldApprox == 0.5;
2.categoricalPMF(p).shouldApprox == 0.4;

Can also use dynamic array

import mir.test: shouldApprox;

double[] p = [0.1, 0.5, 0.4];

0.categoricalPMF(p).shouldApprox == 0.1;
1.categoricalPMF(p).shouldApprox == 0.5;
2.categoricalPMF(p).shouldApprox == 0.4;

import mir.math.sum: sum;
import mir.ndslice.slice: sliced;
import mir.test: shouldApprox;

static immutable x = [1.0, 5, 4];
auto p = x.sliced;
auto q = p / sum(p);

0.categoricalPMF(q).shouldApprox == 0.1;
1.categoricalPMF(q).shouldApprox == 0.5;
2.categoricalPMF(q).shouldApprox == 0.4;

See Also

Categorical Distribution

Meta

Source

See Implementation

mir stat distribution categorical functions