Exponential distribution entropy.
The entropy for an exponential random variable is
where lambda > 0
is the rate parameter.
$ npm install distributions-exponential-entropy
For use in the browser, use browserify.
var entropy = require( 'distributions-exponential-entropy' );
Computes the entropy 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 = entropy( 0.5 );
// returns ~1.693
lambda = [ 0.5, 1, 2, 4 ];
out = entropy( lambda );
// returns [ ~1.693, 1.000, ~0.307, ~-0.386 ]
lambda = new Float32Array( lambda );
out = entropy( lambda );
// returns Float64Array( [~1.693,1.000,~0.307,~-0.386] )
lambda = matrix( [ 0.5, 1, 2, 4 ], [2,2] );
/*
[ 0.5 1
2 4 ]
*/
out = entropy( lambda );
/*
[ ~1.693 1.000
~0.307 ~-0.386 ]
*/
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 = [
[0,0.5],
[1,1],
[2,2],
[3,4]
];
function getValue( d, i ) {
return d[ 1 ];
}
var out = entropy( lambda, {
'accessor': getValue
});
// returns [ ~1.693, 1.000, ~0.307, ~-0.386 ]
To deepset an object array
, provide a key path and, optionally, a key path separator.
var lambda = [
{'x':[9,0.5]},
{'x':[9,1]},
{'x':[9,2]},
{'x':[9,4]}
];
var out = entropy( lambda, 'x|1', '|' );
/*
[
{'x':[9,~1.693]},
{'x':[9,1.000]},
{'x':[9,~0.307]},
{'x':[9,~-0.386]},
]
*/
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( [ 0.5,1,2,4 ] );
out = entropy( lambda, {
'dtype': 'int32'
});
// returns Int32Array( [ 1,1,0,-1 ] )
// Works for plain arrays, as well...
out = entropy( [0.5,1,2,4], {
'dtype': 'int32'
});
// returns Int32Array( [ 1,1,0,-1 ] )
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 = [ 0.5, 1, 2, 4 ];
out = entropy( lambda, {
'copy': false
});
// returns [ ~1.693, 1.000, ~0.307, ~-0.386 ]
bool = ( data === out );
// returns true
mat = matrix( [ 0.5, 1, 2, 4 ], [2,2] );
/*
[ 0.5 1
2 4 ]
*/
out = entropy( mat, {
'copy': false
});
/*
[ ~1.693 1.000
~0.307 ~-0.386 ]
*/
bool = ( mat === out );
// returns true
-
If an element is not a positive number, the entropy is
NaN
.var lambda, out; out = entropy( -1 ); // returns NaN out = entropy( 0 ); // returns NaN out = entropy( null ); // returns NaN out = entropy( true ); // returns NaN out = entropy( {'a':'b'} ); // returns NaN out = entropy( [ true, null, [] ] ); // returns [ NaN, NaN, NaN ] function getValue( d, i ) { return d.x; } lambda = [ {'x':true}, {'x':[]}, {'x':{}}, {'x':null} ]; out = entropy( lambda, { 'accessor': getValue }); // returns [ NaN, NaN, NaN, NaN ] out = entropy( 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 = entropy( [ true, null, [] ], { 'dtype': 'int8' }); // returns Int8Array( [0,0,0] );
var matrix = require( 'dstructs-matrix' ),
entropy = require( 'distributions-exponential-entropy' );
var lambda,
mat,
out,
tmp,
i;
// Plain arrays...
lambda = new Array( 10 );
for ( i = 0; i < lambda.length; i++ ) {
lambda[ i ] = i;
}
out = entropy( lambda );
// Object arrays (accessors)...
function getValue( d ) {
return d.x;
}
for ( i = 0; i < lambda.length; i++ ) {
lambda[ i ] = {
'x': lambda[ i ]
};
}
out = entropy( lambda, {
'accessor': getValue
});
// Deep set arrays...
for ( i = 0; i < lambda.length; i++ ) {
lambda[ i ] = {
'x': [ i, lambda[ i ].x ]
};
}
out = entropy( lambda, {
'path': 'x/1',
'sep': '/'
});
// Typed arrays...
lambda = new Float64Array( 10 );
for ( i = 0; i < lambda.length; i++ ) {
lambda[ i ] = i;
}
out = entropy( lambda );
// Matrices...
mat = matrix( lambda, [5,2], 'float64' );
out = entropy( mat );
// Matrices (custom output data type)...
out = entropy( 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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