The variance for a Poisson random variable is
where lambda > 0
is the mean parameter.
$ npm install distributions-poisson-variance
For use in the browser, use browserify.
var variance = require( 'distributions-poisson-variance' );
Computes the variance for a Poisson 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 = variance( 2 );
// returns ~2.000
lambda = [ 2, 4, 8, 16 ];
out = variance( lambda );
// returns [ ~2.000, ~4.000, ~8.000, ~16.000 ]
lambda = new Float32Array( lambda );
out = variance( lambda );
// returns Float64Array( [~2.000,~4.000,~8.000,~16.000] )
lambda = matrix( [ 2, 4, 8, 16 ], [2,2] );
/*
[ 2 4,
8 16 ]
*/
out = variance( lambda );
/*
[ ~2.000 ~4.000,
~8.000 ~16.000 ]
*/
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 = [
[0,2],
[1,4],
[2,8],
[3,16]
];
function getValue( d, i ) {
return d[ 1 ];
}
var out = variance( lambda, {
'accessor': getValue
});
// returns [ ~2.000, ~4.000, ~8.000, ~16.000 ]
To deepset an object array
, provide a key path and, optionally, a key path separator.
var lambda = [
{'x':[9,2]},
{'x':[9,4]},
{'x':[9,8]},
{'x':[9,16]}
];
var out = variance( lambda, {
'path': 'x|1',
'sep': '|'
});
/*
[
{'x':[9,~2.000]},
{'x':[9,~4.000]},
{'x':[9,~8.000]},
{'x':[9,~16.000]},
]
*/
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( [ 2,4,8,16 ] );
out = variance( lambda, {
'dtype': 'int32'
});
// returns Int32Array( [ 2,4,8,16 ] )
// Works for plain arrays, as well...
out = variance( [2,4,8,16], {
'dtype': 'int32'
});
// returns Int32Array( [ 2,4,8,16 ] )
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 = [ 2, 4, 8, 16 ];
out = variance( lambda, {
'copy': false
});
// returns [ ~2.000, ~4.000, ~8.000, ~16.000 ]
bool = ( data === out );
// returns true
mat = matrix( [ 2, 4, 8, 16 ], [2,2] );
/*
[ 2 4,
8 16 ]
*/
out = variance( mat, {
'copy': false
});
/*
[ ~2.000 ~4.000,
~8.000 ~16.000 ]
*/
bool = ( mat === out );
// returns true
-
If an element is not a positive number, the variance is
NaN
.var lambda, out; out = variance( -1 ); // returns NaN out = variance( 0 ); // returns NaN out = variance( null ); // returns NaN out = variance( true ); // returns NaN out = variance( {'a':'b'} ); // returns NaN out = variance( [ true, null, [] ] ); // returns [ NaN, NaN, NaN ] function getValue( d, i ) { return d.x; } lambda = [ {'x':true}, {'x':[]}, {'x':{}}, {'x':null} ]; out = variance( lambda, { 'accessor': getValue }); // returns [ NaN, NaN, NaN, NaN ] out = variance( 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 = variance( [ true, null, [] ], { 'dtype': 'int8' }); // returns Int8Array( [0,0,0] );
var matrix = require( 'dstructs-matrix' ),
variance = require( 'distributions-poisson-variance' );
var lambda,
mat,
out,
tmp,
i;
// Plain arrays...
lambda = new Array( 10 );
for ( i = 0; i < lambda.length; i++ ) {
lambda[ i ] = i + 1;
}
out = variance( lambda );
// Object arrays (accessors)...
function getValue( d ) {
return d.x;
}
for ( i = 0; i < lambda.length; i++ ) {
lambda[ i ] = {
'x': lambda[ i ]
};
}
out = variance( lambda, {
'accessor': getValue
});
// Deep set arrays...
for ( i = 0; i < lambda.length; i++ ) {
lambda[ i ] = {
'x': [ i, lambda[ i ].x ]
};
}
out = variance( lambda, {
'path': 'x/1',
'sep': '/'
});
// Typed arrays...
lambda = new Float64Array( 10 );
for ( i = 0; i < lambda.length; i++ ) {
lambda[ i ] = i + 1;
}
out = variance( lambda );
// Matrices...
mat = matrix( lambda, [5,2], 'float64' );
out = variance( mat );
// Matrices (custom output data type)...
out = variance( mat, {
'dtype': 'uint8'
});
To run the example code from the top-level application directory,
$ node ./examples/index.js
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.
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
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