distributions-t-pdf v0.0.1
Probability Density Function
Student t distribution probability density function (PDF).
The probability density function (PDF) for a t distribution random variable is
where v > 0 is the degrees of freedom.
Installation
$ npm install distributions-t-pdfFor use in the browser, use browserify.
Usage
var pdf = require( 'distributions-t-pdf' );pdf( x, options )
Evaluates the probability density function (PDF) for the t distribution. x may be either a number, an array, a typed array, or a matrix.
var matrix = require( 'dstructs-matrix' ),
	mat,
	out,
	x,
	i;
out = pdf( 1 );
// returns ~0.159
out = pdf( -1 );
// returns ~0.159
x = [ 0, 0.5, 1, 1.5, 2, 2.5 ];
out = pdf( x );
// returns [ ~0.318, ~0.255, ~0.159, ~0.0979, ~0.0637, ~0.0439 ]
x = new Int8Array( x );
out = pdf( x );
// returns Float64Array( [~0.318,0.318,~0.159,~0.159,~0.0637,~0.0637] )
x = new Float32Array( 6 );
for ( i = 0; i < 6; i++ ) {
	x[ i ] = i * 0.5;
}
mat = matrix( x, [3,2], 'float32' );
/*
	[ 0  0.5
	  1  1.5
	  2  2.5 ]
*/
out = pdf( mat );
/*
	[ ~0.318  ~0.255
	  ~0.159  ~0.0979
	  ~0.0637 ~0.0439 ]
*/The function accepts the following options:
- v: degrees of freedom. Default: 1.
- __accessor__: accessor `function` for accessing `array` values.
- __dtype__: output [`typed array`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Typed_arrays) or [`matrix`](https://github.com/dstructs/matrix) data type. Default: `float64`.
- copy: booleanindicating if thefunctionshould return a new data structure. Default:true.
- path: deepget/deepset key path.
- sep: deepget/deepset key path separator. Default: '.'.
A t distribution is a function of one parameter: v(degrees of freedom). By default, v is equal to 1. To adjust it, set the corresponding option.
var x = [ 0, 0.5, 1, 1.5, 2, 2.5 ];
var out = pdf( x, {
	'v': 6,
});
// returns [ ~0.383, ~0.332, ~0.223, ~0.126, ~0.064, ~0.0315 ]For non-numeric arrays, provide an accessor function for accessing array values.
var data = [
	[0,0],
	[1,0.5],
	[2,1],
	[3,1.5],
	[4,2],
	[5,2.5]
];
function getValue( d, i ) {
	return d[ 1 ];
}
var out = pdf( data, {
	'accessor': getValue
});
// returns [ ~0.318, ~0.255, ~0.159, ~0.0979, ~0.0637, ~0.0439 ]To deepset an object array, provide a key path and, optionally, a key path separator.
var data = [
	{'x':[0,0]},
	{'x':[1,0.5]},
	{'x':[2,1]},
	{'x':[3,1.5]},
	{'x':[4,2]},
	{'x':[5,2.5]}
];
var out = pdf( data, {
	'path': 'x/1',
	'sep': '/'
});
/*
	[
		{'x':[0,~0.318]},
		{'x':[1,~0.255]},
		{'x':[2,~0.159]},
		{'x':[3,~0.0979]},
		{'x':[4,~0.0637]},
		{'x':[5,~0.0439]}
	]
*/
var bool = ( data === out );
// returns trueBy default, when provided a typed array or matrix, the output data structure is float64 in order to preserve precision. To specify a different data type, set the dtype option (see matrix for a list of acceptable data types).
var x, out;
x = new Int8Array( [0,1,2,3,4] );
out = pdf( x, {
	'dtype': 'int32'
});
// returns Int32Array( [0,0,0,0,0] )
// Works for plain arrays, as well...
out = pdf( [0,0.5,1,1.5,2], {
	'dtype': 'uint8'
});
// returns Uint8Array( [0,0,0,0,0] )By default, the function returns a new data structure. To mutate the input data structure (e.g., when input values can be discarded or when optimizing memory usage), set the copy option to false.
var bool,
	mat,
	out,
	x,
	i;
x = [ 0, 0.5, 1, 1.5, 2 ];
out = pdf( x, {
	'copy': false
});
// returns [ ~0.318, ~0.255, ~0.159, ~0.0979, ~0.0637, ~0.0439 ]
bool = ( x === out );
// returns true
x = new Float32Array( 6 );
for ( i = 0; i < 6; i++ ) {
	x[ i ] = i * 0.5;
}
mat = matrix( x, [3,2], 'float32' );
/*
	[ 0  0.5
	  1  1.5
	  2  2.5 ]
*/
out = pdf( mat, {
	'copy': false
});
/*
	[ ~0.318  ~0.255
	  ~0.159  ~0.0979
	  ~0.0637 ~0.0439 ]
*/
bool = ( mat === out );
// returns trueNotes
- If an element is not a numeric value, the evaluated PDF(https://en.wikipedia.org/wiki/Student t_distribution) is - NaN.- var data, out; out = pdf( null ); // returns NaN out = pdf( true ); // returns NaN out = pdf( {'a':'b'} ); // returns NaN out = pdf( [ true, null, [] ] ); // returns [ NaN, NaN, NaN ] function getValue( d, i ) { return d.x; } data = [ {'x':true}, {'x':[]}, {'x':{}}, {'x':null} ]; out = pdf( data, { 'accessor': getValue }); // returns [ NaN, NaN, NaN, NaN ] out = pdf( data, { 'path': 'x' }); /* [ {'x':NaN}, {'x':NaN}, {'x':NaN, {'x':NaN} ] */
- Be careful when providing a data structure which contains non-numeric elements and specifying an - integeroutput data type, as- NaNvalues are cast to- 0.- var out = pdf( [ true, null, [] ], { 'dtype': 'int8' }); // returns Int8Array( [0,0,0] );
Examples
var pdf = require( 'distributions-t-pdf' ),
	matrix = require( 'dstructs-matrix' );
var data,
	mat,
	out,
	tmp,
	i;
// Plain arrays...
data = new Array( 10 );
for ( i = 0; i < data.length; i++ ) {
	data[ i ] = i * 0.5;
}
out = pdf( data );
// Object arrays (accessors)...
function getValue( d ) {
	return d.x;
}
for ( i = 0; i < data.length; i++ ) {
	data[ i ] = {
		'x': data[ i ]
	};
}
out = pdf( data, {
	'accessor': getValue
});
// Deep set arrays...
for ( i = 0; i < data.length; i++ ) {
	data[ i ] = {
		'x': [ i, data[ i ].x ]
	};
}
out = pdf( data, {
	'path': 'x/1',
	'sep': '/'
});
// Typed arrays...
data = new Float32Array( 10 );
for ( i = 0; i < data.length; i++ ) {
	data[ i ] = i * 0.5;
}
out = pdf( data );
// Matrices...
mat = matrix( data, [5,2], 'float32' );
out = pdf( mat );
// Matrices (custom output data type)...
out = pdf( mat, {
	'dtype': 'uint8'
});To run the example code from the top-level application directory,
$ node ./examples/index.jsTests
Unit
Unit tests use the Mocha test framework with Chai assertions. To run the tests, execute the following command in the top-level application directory:
$ make testAll new feature development should have corresponding unit tests to validate correct functionality.
Test Coverage
This repository uses Istanbul as its code coverage tool. To generate a test coverage report, execute the following command in the top-level application directory:
$ make test-covIstanbul creates a ./reports/coverage directory. To access an HTML version of the report,
$ make view-covLicense
Copyright
Copyright © 2015. The Compute.io Authors.