distributions-exponential-skewness
v0.0.0
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Exponential distribution skewness.
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Skewness
Exponential distribution skewness.
The skewness for an exponential random variable is
where lambda > 0
is the rate parameter.
Installation
$ npm install distributions-exponential-skewness
For use in the browser, use browserify.
Usage
var skewness = require( 'distributions-exponential-skewness' );
skewness( lambda[, opts] )
Computes the skewness for an exponential distribution with parameter lambda
. lambda
may be either a number
, an array
, a typed array
, or a matrix
.
var matrix = require( 'dstructs-matrix' ),
data,
mat,
out,
i;
out = skewness( -1 );
// returns NaN
lambda = [ -1, 0, 0.5, 1 ];
out = skewness( lambda );
// returns [ NaN, NaN, 2.000, 2.000 ]
lambda = new Float32Array( lambda );
out = skewness( lambda );
// returns Float64Array( [NaN,NaN,2.000,2.000] )
lambda = matrix( [ -1, 0, 0.5, 1 ], [2,2] );
/*
[ -1 0
0.5 1 ]
*/
out = skewness( lambda );
/*
[ NaN NaN
2.000 2.000 ]
*/
The function accepts the following options
:
- accessor: accessor
function
for accessingarray
values. - dtype: output
typed array
ormatrix
data type. Default:float64
. - copy:
boolean
indicating if thefunction
should return a new data structure. Default:true
. - path: deepget/deepset key path.
- sep: deepget/deepset key path separator. Default:
'.'
.
For non-numeric arrays
, provide an accessor function
for accessing array
values.
var lambda = [
[0,-1],
[1,0],
[2,0.5],
[3,1]
];
function getValue( d, i ) {
return d[ 1 ];
}
var out = skewness( lambda, {
'accessor': getValue
});
// returns [ NaN, NaN, 2.000, 2.000 ]
To deepset an object array
, provide a key path and, optionally, a key path separator.
var lambda = [
{'x':[9,-1]},
{'x':[9,0]},
{'x':[9,0.5]},
{'x':[9,1]}
];
var out = skewness( lambda, 'x|1', '|' );
/*
[
{'x':[9,NaN]},
{'x':[9,NaN]},
{'x':[9,2.000]},
{'x':[9,2.000]},
]
*/
var bool = ( data === out );
// returns true
By 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 lambda, out;
lambda = new Float64Array( [ -1,0,0.5,1 ] );
// Beware: `NaN` is cast to `0` for integer-typed arrays!
out = skewness( lambda, {
'dtype': 'int32'
});
// returns Int32Array( [ 0,0,2,2 ] )
// Works for plain arrays, as well...
out = skewness( [-1,0,0.5,1], {
'dtype': 'int32'
});
// returns Int32Array( [ 0,0,2,2 ] )
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 lambda,
bool,
mat,
out,
i;
lambda = [ -1, 0, 0.5, 1 ];
out = skewness( lambda, {
'copy': false
});
// returns [ NaN, NaN, 2.000, 2.000 ]
bool = ( data === out );
// returns true
mat = matrix( [ -1, 0, 0.5, 1 ], [2,2] );
/*
[ -1 0,
0.5 1 ]
*/
out = skewness( mat, {
'copy': false
});
/*
[ NaN NaN
2.000 2.000 ]
*/
bool = ( mat === out );
// returns true
Notes
If an element is not a positive number, the skewness is
NaN
.var lambda, out; out = skewness( -1 ); // returns NaN out = skewness( 0 ); // returns NaN out = skewness( null ); // returns NaN out = skewness( true ); // returns NaN out = skewness( {'a':'b'} ); // returns NaN out = skewness( [ true, null, [] ] ); // returns [ NaN, NaN, NaN ] function getValue( d, i ) { return d.x; } lambda = [ {'x':true}, {'x':[]}, {'x':{}}, {'x':null} ]; out = skewness( lambda, { 'accessor': getValue }); // returns [ NaN, NaN, NaN, NaN ] out = skewness( lambda, { '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
integer
output data type, asNaN
values are cast to0
.var out = skewness( [ true, null, [] ], { 'dtype': 'int8' }); // returns Int8Array( [0,0,0] );
Examples
var matrix = require( 'dstructs-matrix' ),
skewness = require( 'distributions-exponential-skewness' );
var lambda,
mat,
out,
tmp,
i;
// Plain arrays...
lambda = new Array( 10 );
for ( i = 0; i < lambda.length; i++ ) {
lambda[ i ] = i + 1;
}
out = skewness( lambda );
// Object arrays (accessors)...
function getValue( d ) {
return d.x;
}
for ( i = 0; i < lambda.length; i++ ) {
lambda[ i ] = {
'x': lambda[ i ]
};
}
out = skewness( lambda, {
'accessor': getValue
});
// Deep set arrays...
for ( i = 0; i < lambda.length; i++ ) {
lambda[ i ] = {
'x': [ i, lambda[ i ].x ]
};
}
out = skewness( lambda, {
'path': 'x/1',
'sep': '/'
});
// Typed arrays...
lambda = new Float64Array( 10 );
for ( i = 0; i < lambda.length; i++ ) {
lambda[ i ] = i + 1;
}
out = skewness( lambda );
// Matrices...
mat = matrix( lambda, [5,2], 'float64' );
out = skewness( mat );
// Matrices (custom output data type)...
out = skewness( mat, {
'dtype': 'uint8'
});
To run the example code from the top-level application directory,
$ node ./examples/index.js
Tests
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 test
All 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-cov
Istanbul creates a ./reports/coverage
directory. To access an HTML version of the report,
$ make view-cov
License
Copyright
Copyright © 2015. The Compute.io Authors.