gapxsumors

Add a scalar constant to each strided array element and compute the sum using ordinary recursive summation.

Usage

var gapxsumors = require( '@stdlib/blas/ext/base/gapxsumors' );

gapxsumors( N, alpha, x, strideX )

Adds a scalar constant to each strided array element and computes the sum using ordinary recursive summation.

var x = [ 1.0, -2.0, 2.0 ];

var v = gapxsumors( x.length, 5.0, x, 1 );
// returns 16.0

The function has the following parameters:

• N: number of indexed elements.
• x: input Array or typed array.
• strideX: stride length.

The N and stride parameters determine which elements in the strided array are accessed at runtime. For example, to access every other element:

var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ];

var v = gapxsumors( 4, 5.0, x, 2 );
// returns 25.0

Note that indexing is relative to the first index. To introduce an offset, use typed array views.

var Float64Array = require( '@stdlib/array/float64' );

var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

var v = gapxsumors( 4, 5.0, x1, 2 );
// returns 25.0

gapxsumors.ndarray( N, alpha, x, strideX, offsetX )

Adds a scalar constant to each strided array element and computes the sum using ordinary recursive summation and alternative indexing semantics.

var x = [ 1.0, -2.0, 2.0 ];

var v = gapxsumors.ndarray( x.length, 5.0, x, 1, 0 );
// returns 16.0

The function has the following additional parameters:

• offsetX: starting index.

While typed array views mandate a view offset based on the underlying buffer, the offset parameter supports indexing semantics based on a starting index. For example, to access every other element starting from the second element:

var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ];

var v = gapxsumors.ndarray( 4, 5.0, x, 2, 1 );
// returns 25.0

Notes

• If N <= 0, both functions return 0.0.
• Ordinary recursive summation (i.e., a "simple" sum) is performant, but can incur significant numerical error. If performance is paramount and error tolerated, using ordinary recursive summation is acceptable; in all other cases, exercise due caution.
• Both functions support array-like objects having getter and setter accessors for array element access (e.g., @stdlib/array/base/accessor)
• Depending on the environment, the typed versions (dapxsumors, sapxsumors, etc.) are likely to be significantly more performant.

Examples

var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
var gapxsumors = require( '@stdlib/blas/ext/base/gapxsumors' );

var x = discreteUniform( 10, -100, 100, {
    'dtype': 'float64'
});
console.log( x );

var v = gapxsumors( x.length, 5.0, x, 1 );
console.log( v );

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See Also

• @stdlib/blas/ext/base/dapxsumors: add a scalar constant to each double-precision floating-point strided array element and compute the sum using ordinary recursive summation.
• @stdlib/blas/ext/base/gapxsum: add a scalar constant to each strided array element and compute the sum.
• @stdlib/blas/ext/base/gsumors: calculate the sum of strided array elements using ordinary recursive summation.
• @stdlib/blas/ext/base/sapxsumors: add a scalar constant to each single-precision floating-point strided array element and compute the sum using ordinary recursive summation.