Mean

Exponential distribution expected value.

The expected value for an exponential random variable is

where lambda > 0 is the rate parameter.

Installation

$ npm install distributions-exponential-mean

For use in the browser, use browserify.

Usage

var mean = require( 'distributions-exponential-mean' );

mean( lambda[, opts] )

Computes the expected value for an exponential distribution with parameter lambda. lambda may be either a number, an array, a typed array, or a matrix. All lambda values must be positive numbers. For non-positive inputs, NaN is returned.

var matrix = require( 'dstructs-matrix' ),
	lambda,
	mat,
	out,
	i;

out = mean( 1 );
// returns 1

lambda = [ 1, 5, 10, 20 ];
out = mean( lambda );
// returns [ 1, 0.2, 0.1, 0.05 ]

lambda = new Float32ArrayArray( lambda );
out = mean( lambda );
// returns Float64Array( [1,0.2,0.1,0.05] )

lambda =  matrix( [ 1, 5, 10, 20 ], [2,2] );
/*
	[ 1, 5,
	  10, 20 ]
*/

out = mean( lambda );
/*
	[ 1, 0.2,
	  0.1, 0.05 ]
*/

The function accepts the following options:

• accessor: accessor function for accessing array values.
• dtype: output typed array or matrix data type. Default: float64.
• copy: boolean indicating if the function 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],
	[1,5],
	[2,10],
	[3,20]
];

function getValue( d, i ) {
	return d[ 1 ];
}

var out = mean( lambda, {
	'accessor': getValue
});
// returns [ 1, 0.2, 0.1, 0.05 ]

To deepset an object array, provide a key path and, optionally, a key path separator.

var lambda = [
	{'x':[9,1]},
	{'x':[9,5]},
	{'x':[9,10]},
	{'x':[9,20]}
];

var out = mean( lambda, 'x|1', '|' );
/*
	[
		{'x':[9,1]},
		{'x':[9,0.2]},
		{'x':[9,0.1]},
		{'x':[9,0.05]},
	]
*/

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( [ 1,5,10,20 ] );

out = mean( lambda, {
	'dtype': 'int32'
});
// returns Int32Array( [ 1,0,0,0 ] )

// Works for plain arrays, as well...
out = mean( [1,5,10,20], {
	'dtype': 'int32'
});
// returns Int32Array( [ 1,0,0,0 ] )

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 lambda,
	bool,
	mat,
	out,
	i;

lambda = [ 1, 5, 10, 20 ];

out = mean( lambda, {
	'copy': false
});
// returns [ 1, 0.2, 0.1, 0.05 ]

bool = ( data === out );
// returns true

mat = matrix( [ 1, 5, 10, 20 ], [2,2] );
/*
	[ 1, 5,
	  10, 20 ]
*/

out = mean( mat, {
	'copy': false
});
/*
	[ 1, 0.2,
	  0.1, 0.05 ]
*/

bool = ( mat === out );
// returns true

Notes

• If the lambda parameter is not a positive number, the expected value is NaN.

  var lambda, out;
  
  out = mean( -1 );
  // returns NaN
  
  out = mean( 0 );
  // returns NaN
  
  out = mean( null );
  // returns NaN
  
  out = mean( true );
  // returns NaN
  
  out = mean( {'a':'b'} );
  // returns NaN
  
  out = mean( [ true, null, [] ] );
  // returns [ NaN, NaN, NaN ]
  
  function getValue( d, i ) {
  	return d.x;
  }
  lambda = [
  	{'x':true},
  	{'x':[]},
  	{'x':{}},
  	{'x':null}
  ];
  
  out = mean( lambda, {
  	'accessor': getValue
  });
  // returns [ NaN, NaN, NaN, NaN ]
  
  out = mean( 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, as NaN values are cast to 0.

  var out = mean( [ true, null, [] ], {
  	'dtype': 'int8'
  });
  // returns Int8Array( [0,0,0] );

Examples

var matrix = require( 'dstructs-matrix' ),
	mean = require( 'distributions-exponential-mean' );

var lambda,
	mat,
	out,
	tmp,
	i;

// Plain arrays...
lambda = new Array( 10 );
for ( i = 0; i < lambda.length; i++ ) {
	lambda[ i ] = i + 1;
}
out = mean( lambda );

// Object arrays (accessors)...
function getValue( d ) {
	return d.x;
}
for ( i = 0; i < lambda.length; i++ ) {
	lambda[ i ] = {
		'x': lambda[ i ]
	};
}
out = mean( lambda, {
	'accessor': getValue
});

// Deep set arrays...
for ( i = 0; i < lambda.length; i++ ) {
	lambda[ i ] = {
		'x': [ i, lambda[ i ].x ]
	};
}
out = mean( lambda, {
	'path': 'x/1',
	'sep': '/'
});

// Typed arrays...
lambda = new Int32Array( 10 );
for ( i = 0; i < lambda.length; i++ ) {
	lambda[ i ] = i + 1
}
out = mean( lambda );

// Matrices...
mat = matrix( lambda, [5,2], 'int32' );
out = mean( mat );

// Matrices (custom output data type)...
out = mean( 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

MIT license.

Copyright

Copyright © 2015. The Compute.io Authors.