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    distributions-lognormal-cdf

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    Cumulative Distribution Function

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    Lognormal distribution cumulative distribution function.

    The cumulative distribution function for a lognormal random variable is

    Cumulative distribution function for a lognormal distribution.

    where mu is the location parameter and sigma is the scale parameter.

    Installation

    $ npm install distributions-lognormal-cdf

    For use in the browser, use browserify.

    Usage

    var cdf = require( 'distributions-lognormal-cdf' );

    cdf( x[, options] )

    Evaluates the cumulative distribution function for the lognormal 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 = cdf( 1 );
    // returns 0.5
     
    = [ -1, 0, 1, 2, 3 ];
    out = cdf( x );
    // returns [ 0, 0, ~0.5, ~0.756, ~0.864 ]
     
    = new Float32Array( x );
    out = cdf( x );
    // returns Float64Array( [0,0,~0.5,~0.756,~0.864] )
     
    = new Float32Array( 6 );
    for ( i = 0; i < 6; i++ ) {
        x[ i ] = i;
    }
    mat = matrix( x, [3,2], 'float32' );
    /*
        [ 0 1
          2 3
          4 5 ]
    */
     
    out = cdf( mat );
    /*
        [  0     ~0.5
          ~0.756 ~0.864
          ~0.917 ~0.946 ]
    */

    The function accepts the following options:

    • mu: location parameter. Default: 0.
    • sigma: scale parameter. Default: 1.
    • 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: '.'.

    A Lognormal distribution is a function of two parameters: mu(location parameter) and sigma(scale parameter). By default, mu is equal to 0 and sigma is equal to 1. To adjust either parameter, set the corresponding option.

    var x = [ -1, 0, 1, 2, 3 ];
     
    var out = cdf( x, {
        'mu': 3,
        'sigma': 2
    });
    // returns [ 0, ~0.0668, ~0.124, ~0.171, ~0.21, ~0.243 ]

    For non-numeric arrays, provide an accessor function for accessing array values.

    var data = [
        [0,-1],
        [1,0],
        [2,1],
        [3,2],
        [4,3],
    ];
     
    function getValue( d, i ) {
        return d[ 1 ];
    }
     
    var out = cdf( data, {
        'accessor': getValue
    });
    // returns [ 0, 0, ~0.5, ~0.756, ~0.864 ]

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

    var data = [
        {'x':[0,-4]},
        {'x':[1,-2]},
        {'x':[2,0]},
        {'x':[3,2]},
        {'x':[4,4]},
    ];
     
    var out = cdf( data, {
        'path': 'x/1',
        'sep': '/'
    });
    /*
        [
            {'x':[0,0]},
            {'x':[1,0]},
            {'x':[2,~0.5]},
            {'x':[3,~0.756]},
            {'x':[4,~0.864]},
        ]
    */
     
    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 x, out;
     
    = new Float64Array( [-1,0,1,2,3] );
     
    out = cdf( x, {
        'dtype': 'float32'
    });
    // returns Float32Array( [0,0,~0.5,~0.756,~0.864] )
     
    // Works for plain arrays, as well...
    out = cdf( [-1,0,1,2,3], {
        'dtype': 'float32'
    });
    // returns Float32Array( [0,0,~0.5,~0.756,~0.864] )

    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;
     
    = [ -1, 0, 1, 2, 3 ];
     
    out = cdf( x, {
        'copy': false
    });
    // returns [ 0, 0, ~0.5, ~0.756, ~0.864 ]
     
    bool = ( x === out );
    // returns true
     
    = new Float32Array( 6 );
    for ( i = 0; i < 6; i++ ) {
        x[ i ] = i - 3 ;
    }
    mat = matrix( x, [3,2], 'float32' );
    /*
        [ 0 1
          2 3
          4 5 ]
    */
     
    out = cdf( mat, {
        'copy': false
    });
    /*
        [  0     ~0.5
          ~0.756 ~0.864
          ~0.917 ~0.946 ]
    */
     
    bool = ( mat === out );
    // returns true

    Notes

    • If an element is not a numeric value, the evaluated cumulative distribution function is NaN.

      var data, out;
       
      out = cdf( null );
      // returns NaN
       
      out = cdf( true );
      // returns NaN
       
      out = cdf( {'a':'b'} );
      // returns NaN
       
      out = cdf( [ true, null, [] ] );
      // returns [ NaN, NaN, NaN ]
       
      function getValue( d, i ) {
          return d.x;
      }
      data = [
          {'x':true},
          {'x':[]},
          {'x':{}},
          {'x':null}
      ];
       
      out = cdf( data, {
          'accessor': getValue
      });
      // returns [ NaN, NaN, NaN, NaN ]
       
      out = cdf( data, {
          'path': 'x'
      });
      /*
          [
              {'x':NaN},
              {'x':NaN},
              {'x':NaN,
              {'x':NaN}
          ]
      */

    Examples

    var cdf = require( 'distributions-lognormal-cdf' ),
        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 - 5;
    }
    out = cdf( data );
     
    // Object arrays (accessors)...
    function getValue( d ) {
        return d.x;
    }
    for ( i = 0; i < data.length; i++ ) {
        data[ i ] = {
            'x': data[ i ]
        };
    }
    out = cdf( data, {
        'accessor': getValue
    });
     
    // Deep set arrays...
    for ( i = 0; i < data.length; i++ ) {
        data[ i ] = {
            'x': [ i, data[ i ].x ]
        };
    }
    out = cdf( data, {
        'path': 'x/1',
        'sep': '/'
    });
     
    // Typed arrays...
    data = new Float32Array( 10 );
    for ( i = 0; i < data.length; i++ ) {
        data[ i ] = i - 5;
    }
    out = cdf( data );
     
    // Matrices...
    mat = matrix( data, [5,2], 'float32' );
    out = cdf( mat );
     
    // Matrices (custom output data type)...
    out = cdf( 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.

    Install

    npm i distributions-lognormal-cdf

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    Version

    0.0.0

    License

    MIT

    Last publish

    Collaborators

    • kgryte
    • planeshifter