time-to-botec

Benchmark sampling in different programming languages
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      1 
      2 {{alias}}( N, c, x, strideX, out, strideOut )
      3     Computes the mean and standard deviation of a double-precision floating-
      4     point strided array using a two-pass algorithm.
      5 
      6     The `N` and `stride` parameters determine which elements are accessed at
      7     runtime.
      8 
      9     Indexing is relative to the first index. To introduce an offset, use a typed
     10     array view.
     11 
     12     If `N <= 0`, the function returns a mean and standard deviation equal to
     13     `NaN`.
     14 
     15     Parameters
     16     ----------
     17     N: integer
     18         Number of indexed elements.
     19 
     20     c: number
     21         Degrees of freedom adjustment. Setting this parameter to a value other
     22         than `0` has the effect of adjusting the divisor during the calculation
     23         of the standard deviation according to `N - c` where `c` corresponds to
     24         the provided degrees of freedom adjustment. When computing the standard
     25         deviation of a population, setting this parameter to `0` is the standard
     26         choice (i.e., the provided array contains data constituting an entire
     27         population). When computing the corrected sample standard deviation,
     28         setting this parameter to `1` is the standard choice (i.e., the provided
     29         array contains data sampled from a larger population; this is commonly
     30         referred to as Bessel's correction).
     31 
     32     x: Float64Array
     33         Input array.
     34 
     35     strideX: integer
     36         Index increment for `x`.
     37 
     38     out: Float64Array
     39         Output array.
     40 
     41     strideOut: integer
     42         Index increment for `out`.
     43 
     44     Returns
     45     -------
     46     out: Float64Array
     47         Output array.
     48 
     49     Examples
     50     --------
     51     // Standard Usage:
     52     > var x = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 2.0 ] );
     53     > var out = new {{alias:@stdlib/array/float64}}( 2 );
     54     > {{alias}}( x.length, 1, x, 1, out, 1 )
     55     <Float64Array>[ ~0.3333, ~2.0817 ]
     56 
     57     // Using `N` and `stride` parameters:
     58     > x = new {{alias:@stdlib/array/float64}}( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );
     59     > out = new {{alias:@stdlib/array/float64}}( 2 );
     60     > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
     61     > {{alias}}( N, 1, x, 2, out, 1 )
     62     <Float64Array>[ ~0.3333, ~2.0817 ]
     63 
     64     // Using view offsets:
     65     > var x0 = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, 1.0 ] );
     66     > var x1 = new {{alias:@stdlib/array/float64}}( x0.buffer, x0.BYTES_PER_ELEMENT*1 );
     67     > N = {{alias:@stdlib/math/base/special/floor}}( x0.length / 2 );
     68     > out = new {{alias:@stdlib/array/float64}}( 2 );
     69     > {{alias}}( N, 1, x1, 2, out, 1 )
     70     <Float64Array>[ ~0.3333, ~2.0817 ]
     71 
     72 {{alias}}.ndarray( N, c, x, strideX, offsetX, out, strideOut, offsetOut )
     73     Computes the mean and standard deviation of a double-precision floating-
     74     point strided array using a two-pass algorithm and alternative indexing
     75     semantics.
     76 
     77     While typed array views mandate a view offset based on the underlying
     78     buffer, the `offset` parameter supports indexing semantics based on a
     79     starting index.
     80 
     81     Parameters
     82     ----------
     83     N: integer
     84         Number of indexed elements.
     85 
     86     c: number
     87         Degrees of freedom adjustment. Setting this parameter to a value other
     88         than `0` has the effect of adjusting the divisor during the calculation
     89         of the standard deviation according to `N - c` where `c` corresponds to
     90         the provided degrees of freedom adjustment. When computing the standard
     91         deviation of a population, setting this parameter to `0` is the standard
     92         choice (i.e., the provided array contains data constituting an entire
     93         population). When computing the corrected sample standard deviation,
     94         setting this parameter to `1` is the standard choice (i.e., the provided
     95         array contains data sampled from a larger population; this is commonly
     96         referred to as Bessel's correction).
     97 
     98     x: Float64Array
     99         Input array.
    100 
    101     strideX: integer
    102         Index increment for `x`.
    103 
    104     offsetX: integer
    105         Starting index for `x`.
    106 
    107     out: Float64Array
    108         Output array.
    109 
    110     strideOut: integer
    111         Index increment for `out`.
    112 
    113     offsetOut: integer
    114         Starting index for `out`.
    115 
    116     Returns
    117     -------
    118     out: Float64Array
    119         Output array.
    120 
    121     Examples
    122     --------
    123     // Standard Usage:
    124     > var x = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 2.0 ] );
    125     > var out = new {{alias:@stdlib/array/float64}}( 2 );
    126     > {{alias}}.ndarray( x.length, 1, x, 1, 0, out, 1, 0 )
    127     <Float64Array>[ ~0.3333, ~2.0817 ]
    128 
    129     // Using offset parameter:
    130     > var x = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, 1.0 ] );
    131     > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
    132     > out = new {{alias:@stdlib/array/float64}}( 2 );
    133     > {{alias}}.ndarray( N, 1, x, 2, 1, out, 1, 0 )
    134     <Float64Array>[ ~0.3333, ~2.0817 ]
    135 
    136     See Also
    137     --------
    138