time-to-botec

Benchmark sampling in different programming languages
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      1 
      2 {{alias}}( N, sum, x, strideX, y, strideY )
      3     Computes the cumulative sum of strided array elements using ordinary
      4     recursive summation.
      5 
      6     The `N` and `stride` parameters determine which elements in `x` and `y` are
      7     accessed at 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 `y` unchanged.
     13 
     14     Parameters
     15     ----------
     16     N: integer
     17         Number of indexed elements.
     18 
     19     sum: number
     20         Initial sum.
     21 
     22     x: Array<number>|TypedArray
     23         Input array.
     24 
     25     strideX: integer
     26         Index increment for `x`.
     27 
     28     y: Array<number>|TypedArray
     29         Output array.
     30 
     31     strideY: integer
     32         Index increment for `y`.
     33 
     34     Returns
     35     -------
     36     out: Array<number>|TypedArray
     37         Output array.
     38 
     39     Examples
     40     --------
     41     // Standard Usage:
     42     > var x = [ 1.0, -2.0, 2.0 ];
     43     > var y = [ 0.0, 0.0, 0.0 ];
     44     > {{alias}}( x.length, 0.0, x, 1, y, 1 )
     45     [ 1.0, -1.0, 1.0 ]
     46 
     47     // Using `N` and `stride` parameters:
     48     > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];
     49     > y = [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ];
     50     > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
     51     > {{alias}}( N, 0.0, x, 2, y, 2 )
     52     [ -2.0, 0.0, -1.0, 0.0, 1.0, 0.0 ]
     53 
     54     // Using view offsets:
     55     > var x0 = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );
     56     > var y0 = new {{alias:@stdlib/array/float64}}( x0.length );
     57     > var x1 = new {{alias:@stdlib/array/float64}}( x0.buffer, x0.BYTES_PER_ELEMENT*1 );
     58     > var y1 = new {{alias:@stdlib/array/float64}}( y0.buffer, y0.BYTES_PER_ELEMENT*3 );
     59     > N = {{alias:@stdlib/math/base/special/floor}}( x0.length / 2 );
     60     > {{alias}}( N, 0.0, x1, 2, y1, 1 )
     61     <Float64Array>[ -2.0, 0.0, -1.0 ]
     62     > y0
     63     <Float64Array>[ 0.0, 0.0, 0.0, -2.0, 0.0, -1.0 ]
     64 
     65 {{alias}}.ndarray( N, sum, x, strideX, offsetX, y, strideY, offsetY )
     66     Computes the cumulative sum of strided array elements using ordinary
     67     recursive summation and alternative indexing semantics.
     68 
     69     While typed array views mandate a view offset based on the underlying
     70     buffer, the `offset` parameter supports indexing semantics based on a
     71     starting index.
     72 
     73     Parameters
     74     ----------
     75     N: integer
     76         Number of indexed elements.
     77 
     78     sum: number
     79         Initial sum.
     80 
     81     x: Array<number>|TypedArray
     82         Input array.
     83 
     84     strideX: integer
     85         Index increment for `x`.
     86 
     87     offsetX: integer
     88         Starting index for `x`.
     89 
     90     y: Array<number>|TypedArray
     91         Output array.
     92 
     93     strideY: integer
     94         Index increment for `y`.
     95 
     96     offsetY: integer
     97         Starting index for `y`.
     98 
     99     Returns
    100     -------
    101     out: Array<number>|TypedArray
    102         Output array.
    103 
    104     Examples
    105     --------
    106     // Standard Usage:
    107     > var x = [ 1.0, -2.0, 2.0 ];
    108     > var y = [ 0.0, 0.0, 0.0 ];
    109     > {{alias}}.ndarray( x.length, 0.0, x, 1, 0, y, 1, 0 )
    110     <Float64Array>[ 1.0, -1.0, 1.0 ]
    111 
    112     // Advanced indexing:
    113     > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ];
    114     > y = [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ];
    115     > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
    116     > {{alias}}.ndarray( N, 0.0, x, 2, 1, y, -1, y.length-1 )
    117     <Float64Array>[ 0.0, 0.0, 0.0, -1.0, 0.0, -2.0 ]
    118 
    119     See Also
    120     --------
    121