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About stdlib...

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dsnannsumors

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Calculate the sum of single-precision floating-point strided array elements, ignoring NaN values, using ordinary recursive summation with extended accumulation, and returning an extended precision result.

Usage

import dsnannsumors from 'https://cdn.jsdelivr.net/gh/stdlib-js/blas-ext-base-dsnannsumors@deno/mod.js';

dsnannsumors( N, x, strideX, out, strideOut )

Computes the sum of single-precision floating-point strided array elements, ignoring NaN values, using ordinary recursive summation with extended accumulation, and returning an extended precision result.

import Float32Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float32@deno/mod.js';
import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@deno/mod.js';

var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
var out = new Float64Array( 2 );

var v = dsnannsumors( x.length, x, 1, out, 1 );
// returns <Float64Array>[ 1.0, 3 ]

The function has the following parameters:

  • N: number of indexed elements.
  • x: input Float32Array.
  • strideX: stride length for x.
  • out: output Float64Array whose first element is the sum and whose second element is the number of non-NaN elements.
  • strideOut: stride length for out.

The N and stride parameters determine which elements are accessed at runtime. For example, to compute the sum of every other element:

import Float32Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float32@deno/mod.js';
import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@deno/mod.js';

var x = new Float32Array( [ 1.0, 2.0, NaN, -7.0, NaN, 3.0, 4.0, 2.0 ] );
var out = new Float64Array( 2 );

var v = dsnannsumors( 4, x, 2, out, 1 );
// returns <Float64Array>[ 5.0, 2 ]

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

import Float32Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float32@deno/mod.js';
import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@deno/mod.js';

var x0 = new Float32Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

var out0 = new Float64Array( 4 );
var out1 = new Float64Array( out0.buffer, out0.BYTES_PER_ELEMENT*2 ); // start at 3rd element

var v = dsnannsumors( 4, x1, 2, out1, 1 );
// returns <Float64Array>[ 5.0, 4 ]

dsnannsumors.ndarray( N, x, strideX, offsetX, out, strideOut, offsetOut )

Computes the sum of single-precision floating-point strided array elements, ignoring NaN values, using ordinary recursive summation with extended accumulation and alternative indexing semantics, and returning an extended precision result.

import Float32Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float32@deno/mod.js';
import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@deno/mod.js';

var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
var out = new Float64Array( 2 );

var v = dsnannsumors.ndarray( x.length, x, 1, 0, out, 1, 0 );
// returns <Float64Array>[ 1.0, 3 ]

The function has the following additional parameters:

  • offsetX: starting index for x.
  • offsetOut: starting index for out.

While typed array views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on starting indices. For example, to calculate the sum of every other element starting from the second element:

import Float32Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float32@deno/mod.js';
import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@deno/mod.js';

var x = new Float32Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var out = new Float64Array( 4 );

var v = dsnannsumors.ndarray( 4, x, 2, 1, out, 2, 1 );
// returns <Float64Array>[ 0.0, 5.0, 0.0, 4 ]

Notes

  • If N <= 0, both functions return a sum equal to 0.0.
  • Accumulated intermediate values are stored as double-precision floating-point numbers.

Examples

import discreteUniform from 'https://cdn.jsdelivr.net/gh/stdlib-js/random-base-discrete-uniform@deno/mod.js';
import bernoulli from 'https://cdn.jsdelivr.net/gh/stdlib-js/random-base-bernoulli@deno/mod.js';
import filledarrayBy from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-filled-by@deno/mod.js';
import Float32Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float32@deno/mod.js';
import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@deno/mod.js';
import dsnannsumors from 'https://cdn.jsdelivr.net/gh/stdlib-js/blas-ext-base-dsnannsumors@deno/mod.js';

function rand() {
    if ( bernoulli( 0.5 ) < 0.2 ) {
        return NaN;
    }
    return discreteUniform( 0, 100 );
}

var x = filledarrayBy( 10, 'float32', rand );
console.log( x );

var out = new Float64Array( 2 );
dsnannsumors( x.length, x, 1, out, 1 );
console.log( out );

See Also

  • @stdlib/blas-ext/base/dnannsumors: calculate the sum of double-precision floating-point strided array elements, ignoring NaN values and using ordinary recursive summation.
  • @stdlib/blas-ext/base/dsnansumors: calculate the sum of single-precision floating-point strided array elements, ignoring NaN values, using ordinary recursive summation with extended accumulation, and returning an extended precision result.
  • @stdlib/blas-ext/base/dssumors: calculate the sum of single-precision floating-point strided array elements using ordinary recursive summation with extended accumulation and returning an extended precision result.

Notice

This package is part of stdlib, a standard library with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.