time-to-botec/squiggle/node_modules/@stdlib/blas/base/sdsdot/docs/repl.txt

{{alias}}( N, scalar, x, strideX, y, strideY )
    Computes the dot product of two single-precision floating-point vectors with
    extended accumulation.

    The `N`, `strideX`, and `strideY` parameters determine which elements in `x`
    and `y` are accessed at runtime.

    Indexing is relative to the first index. To introduce an offset, use a typed
    array view.

    If `N <= 0` the function returns `scalar`.

    Parameters
    ----------
    N: integer
        Number of indexed elements.

    scalar: number
        Scalar constant added to dot product.

    x: Float32Array
        First input array.

    strideX: integer
        Index increment for `x`.

    y: Float32Array
        Second input array.

    strideY: integer
        Index increment for `y`.

    Returns
    -------
    dot: number
        The dot product of `x` and `y`.

    Examples
    --------
    // Standard usage:
    > var x = new {{alias:@stdlib/array/float32}}( [ 4.0, 2.0, -3.0, 5.0, -1.0 ] );
    > var y = new {{alias:@stdlib/array/float32}}( [ 2.0, 6.0, -1.0, -4.0, 8.0 ] );
    > var dot = {{alias}}( x.length, 0.0, x, 1, y, 1 )
    -5.0

    // Strides:
    > x = new {{alias:@stdlib/array/float32}}( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
    > y = new {{alias:@stdlib/array/float32}}( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );
    > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
    > dot = {{alias}}( N, 0.0, x, 2, y, -1 )
    9.0

    // Using view offsets:
    > x = new {{alias:@stdlib/array/float32}}( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
    > y = new {{alias:@stdlib/array/float32}}( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
    > var x1 = new {{alias:@stdlib/array/float32}}( x.buffer, x.BYTES_PER_ELEMENT*1 );
    > var y1 = new {{alias:@stdlib/array/float32}}( y.buffer, y.BYTES_PER_ELEMENT*3 );
    > N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
    > dot = {{alias}}( N, 0.0, x1, -2, y1, 1 )
    128.0

{{alias}}.ndarray( N, scalar, x, strideX, offsetX, y, strideY, offsetY )
    Computes the dot product of two single-precision floating-point vectors
    using alternative indexing semantics and with extended accumulation.

    While typed array views mandate a view offset based on the underlying
    buffer, the `offsetX` and `offsetY` parameters support indexing based on a
    starting index.

    Parameters
    ----------
    N: integer
        Number of indexed elements.

    scalar: number
        Scalar constant added to dot product.

    x: Float32Array
        First input array.

    strideX: integer
        Index increment for `x`.

    offsetX: integer
        Starting index for `x`.

    y: Float32Array
        Second input array.

    strideY: integer
        Index increment for `y`.

    offsetY: integer
        Starting index for `y`.

    Returns
    -------
    dot: number
        The dot product of `x` and `y`.

    Examples
    --------
    // Standard usage:
    > var x = new {{alias:@stdlib/array/float32}}( [ 4.0, 2.0, -3.0, 5.0, -1.0 ] );
    > var y = new {{alias:@stdlib/array/float32}}( [ 2.0, 6.0, -1.0, -4.0, 8.0 ] );
    > var dot = {{alias}}.ndarray( x.length, 0.0, x, 1, 0, y, 1, 0 )
    -5.0

    // Strides:
    > x = new {{alias:@stdlib/array/float32}}( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
    > y = new {{alias:@stdlib/array/float32}}( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );
    > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
    > dot = {{alias}}.ndarray( N, 0.0, x, 2, 0, y, 2, 0 )
    9.0

    // Using offset indices:
    > x = new {{alias:@stdlib/array/float32}}( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
    > y = new {{alias:@stdlib/array/float32}}( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
    > N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
    > dot = {{alias}}.ndarray( N, 0.0, x, -2, x.length-1, y, 1, 3 )
    128.0

    References
    ----------
    - Lawson, Charles L., Richard J. Hanson, Fred T. Krogh, and David Ronald
    Kincaid. 1979. "Algorithm 539: Basic Linear Algebra Subprograms for Fortran
    Usage [F1]." *ACM Transactions on Mathematical Software* 5 (3). New York,
    NY, USA: Association for Computing Machinery: 324–25.
    doi:10.1145/355841.355848.

    See Also
    --------