compute-betaln

    0.0.0 • Public • Published

    betaln

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    Evaluates the natural logarithm of the Beta function.

    This function evaluates the natural logarithm of the Beta function which can be defined as follows:

    Equation for the natural logarithm of the beta function.

    Installation

    $ npm install compute-betaln

    For use in the browser, use browserify.

    Usage

    var betaln = require( 'compute-betaln' );

    betaln( x, y[, options] )

    Evaluates the natural logarithm of the Beta function (element-wise). . x may be either a number, an array, a typed array, or a matrix. y has to be either an array or matrix of equal dimensions as x or a single number. Correspondingly, the function returns either an array with the same length as the input array(s), a matrix with the same dimensions as the input matrix/matrices or a single number.

    var matrix = require( 'dstructs-matrix' ),
        data,
        mat,
        out,
        i;
     
    out = betaln( 0, 0 );
    // returns +Infinity
     
    out = betaln( 0.001, 0.001 );
    // returns ~7.601
     
    out = betaln( -1, 2 );
    // return NaN
     
    out = betaln( [1,2,3,4], 1 );
    // returns [ ~0, ~-0.693, ~-1.099, ~-1.386 ]
     
    out = betaln( 1, [1,2,3,4] );
    // returns [ ~0, ~-0.693, ~-1.099, ~-1.386 ]
     
    out = betaln( [ -10, -1, 0, 1, 10 ] );
    // returns [ -1, -0.8427, 0, 0.8427, 1 ]
     
    data = [ 0, 0.5, 1, 1.5, 2 ];
    out = betaln( data, 100 );
    // returns [ +Infintiy, ~-1.729, ~-4.605, ~-7.032, ~-9.22 ]
     
    data = new Int8Array( data );
    out = betaln( data, 100 );
    // returns Float64Array( [ +Infintiy, +Infinity, ~-4.605, ~-4.605, ~-9.22 ] )
     
    data = new Float64Array( 6 );
    for ( i = 0; i < 6; i++ ) {
        data[ i ] = i / 2;
    }
    mat = matrix( data, [3,2], 'float64' );
    /*
        [ 0  0.5
          1  1.5
          2  2.5 ]
    */
     
    out = betaln( mat, 0.5  );
    /*
        [ +Inf   ~1.145
          ~0.693 ~0.452
          ~0.288 ~0.164 ]
    */

    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 data = [
        ['beep', 0],
        ['boop', 0.5],
        ['bip', 1],
        ['bap', 1.5],
        ['baz', 2]
    ];
     
    function getValue( d, i ) {
        return d[ 1 ];
    }
     
    var out = betaln( data, 100, {
        'accessor': getValue
    });
    // returns [ +Infintiy, ~-1.729, ~-4.605, ~-7.032, ~-9.22 ]

    When evaluating the betaln function for values of two object arrays, provide an accessor function which accepts 3 arguments.

    var data = [
        ['beep', 2],
        ['boop', 3],
        ['bip', 4],
        ['bap', 5],
        ['baz', 6]
    ];
     
    var arr = [
        {'x': 2},
        {'x': 3},
        {'x': 4},
        {'x': 5},
        {'x': 6}
    ];
     
    function getValue( d, i, j ) {
        if ( j === 0 ) {
            return d[ 1 ];
        }
        return d.x;
    }
     
    var out = beta( data, arr, {
        'accessor': getValue
    });
    // returns [ ~-1.792, ~-3.402, ~-4.942, ~-6.446, ~-7.927 ]

    Note: j corresponds to the input array index, where j=0 is the index for the first input array and j=1 is the index for the second input array.

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

    var data = [
        {'x':[0,10]},
        {'x':[1,100]},
        {'x':[2,1000]},
        {'x':[3,10000]},
        {'x':[4,100000]}
    ];
     
    var out = betaln( data, 0.1, 'x|1', '|' );
    /*
        [
            {'x':[0,~2.0.27]},
            {'x':[1,~1.793]},
            {'x':[2,~1.562]},
            {'x':[3,~1.332]},
            {'x':[4,~1.101]}
        ]
    */
     
    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 data, out;
     
    data = new Int8Array( [1,2,3,4] );
     
    out = betaln( data, 8, {
        'dtype': 'int32'
    });
    // returns Int32Array( [-2,-4,-5,-7] )
     
    // Works for plain arrays, as well...
    out = betaln( [ 1, 2, 3, 4 ], 8, {
        'dtype': 'int8'
    });
    // returns Int8Array( [-2,-4,-5,-7] )

    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 data,
        bool,
        mat,
        out,
        i;
     
    var data = [ 1, 2, 3, 4 ];
     
    var out = betaln( data, 100, {
        'copy': false
    });
    // returns [ ~0, ~-0.693, ~-1.099, ~-1.386 ]
     
    bool = ( data === out );
    // returns true
     
    data = new Float64Array( 6 );
    for ( i = 0; i < 6; i++ ) {
        data[ i ] = i / 2;
    }
    mat = matrix( data, [3,2], 'float64' );
    /*
        [ 0  0.5
          1  1.5
          2  2.5 ]
    */
     
    out = betaln( mat, 0.5, {
        'copy': false
    });
    /*
        [ +Inf   ~1.145
          ~0.693 ~0.452
          ~0.288 ~0.164 ]
    */
     
    bool = ( mat === out );
    // returns true

    Notes

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

      var data, out;
       
      out = betaln( null, 1 );
      // returns NaN
       
      out = betaln( true, 1 );
      // returns NaN
       
      out = betaln( {'a':'b'}, 1 );
      // returns NaN
       
      out = betaln( [ true, null, [] ], 1 );
      // returns [ NaN, NaN, NaN ]
       
      function getValue( d, i ) {
          return d.x;
      }
      data = [
          {'x':true},
          {'x':[]},
          {'x':{}},
          {'x':null}
      ];
       
      out = betaln( data, 1, {
          'accessor': getValue
      });
      // returns [ NaN, NaN, NaN, NaN ]
       
      out = betaln( data, 1, {
          '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 = betaln( [ true, null, [] ], 1, {
          'dtype': 'int8'
      });
      // returns Int8Array( [0,0,0] );

    Examples

    var matrix = require( 'dstructs-matrix' ),
        betaln = require( 'compute-betaln' );
     
    var data,
        mat,
        out,
        tmp,
        i;
     
    // Plain arrays...
    data = new Array( 10 );
    for ( i = 0; i < data.length; i++ ) {
        data[ i ] = Math.random();
    }
    out = betaln( data, 0.5 );
     
    // Object arrays (accessors)...
    function getValue( d ) {
        return d.x;
    }
    for ( i = 0; i < data.length; i++ ) {
        data[ i ] = {
            'x': data[ i ]
        };
    }
    out = betaln( data, 0.5, {
        'accessor': getValue
    });
     
    // Deep set arrays...
    for ( i = 0; i < data.length; i++ ) {
        data[ i ] = {
            'x': [ i, data[ i ].x ]
        };
    }
    out = betaln( data, 0.5, {
        'path': 'x/1',
        'sep': '/'
    });
     
    // Typed arrays...
    data = new Float32Array( 10 );
    for ( i = 0; i < data.length; i++ ) {
        data[ i ] = Math.random();
    }
    tmp = betaln( data, 0.5 );
    out = '';
    for ( i = 0; i < data.length; i++ ) {
        out += tmp[ i ];
        if ( i < data.length-1 ) {
            out += ',';
        }
    }
     
    // Matrices...
    mat = matrix( data, [5,2], 'float32' );
    out = betaln( mat, 0.5 );
     
    // Matrices (custom output data type)...
    out = betaln( mat, 0.5, {
        '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 compute-betaln

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    Version

    0.0.0

    License

    MIT

    Last publish

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