time-to-botec/js/node_modules/@stdlib/stats/base/dsnanmeanors
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dsnanmeanors

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

The arithmetic mean is defined as

Equation for the arithmetic mean.

Usage

var dsnanmeanors = require( '@stdlib/stats/base/dsnanmeanors' );

dsnanmeanors( N, x, stride )

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

var Float32Array = require( '@stdlib/array/float32' );

var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
var N = x.length;

var v = dsnanmeanors( N, x, 1 );
// returns ~0.3333

The function has the following parameters:

  • N: number of indexed elements.
  • x: input Float32Array.
  • stride: index increment for x.

The N and stride parameters determine which elements in x are accessed at runtime. For example, to compute the arithmetic mean of every other element in x,

var Float32Array = require( '@stdlib/array/float32' );
var floor = require( '@stdlib/math/base/special/floor' );

var x = new Float32Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN ] );
var N = floor( x.length / 2 );

var v = dsnanmeanors( N, x, 2 );
// returns 1.25

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

var Float32Array = require( '@stdlib/array/float32' );
var floor = require( '@stdlib/math/base/special/floor' );

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

var N = floor( x0.length / 2 );

var v = dsnanmeanors( N, x1, 2 );
// returns 1.25

dsnanmeanors.ndarray( N, x, stride, offset )

Computes the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values and using ordinary recursive summation with extended accumulation and alternative indexing semantics.

var Float32Array = require( '@stdlib/array/float32' );

var x = new Float32Array( [ 1.0, -2.0, NaN, 2.0 ] );
var N = x.length;

var v = dsnanmeanors.ndarray( N, x, 1, 0 );
// returns ~0.33333

The function has the following additional parameters:

  • offset: starting index for x.

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 calculate the arithmetic mean for every other value in x starting from the second value

var Float32Array = require( '@stdlib/array/float32' );
var floor = require( '@stdlib/math/base/special/floor' );

var x = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
var N = floor( x.length / 2 );

var v = dsnanmeanors.ndarray( N, x, 2, 1 );
// returns 1.25

Notes

  • If N <= 0, both functions return NaN.
  • If every indexed element is NaN, both functions return NaN.
  • Accumulated intermediate values are stored as double-precision floating-point numbers.
  • 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 to compute an arithmetic mean is acceptable; in all other cases, exercise due caution.

Examples

var randu = require( '@stdlib/random/base/randu' );
var round = require( '@stdlib/math/base/special/round' );
var Float32Array = require( '@stdlib/array/float32' );
var dsnanmeanors = require( '@stdlib/stats/base/dsnanmeanors' );

var x;
var i;

x = new Float32Array( 10 );
for ( i = 0; i < x.length; i++ ) {
    if ( randu() < 0.2 ) {
        x[ i ] = NaN;
    } else {
        x[ i ] = round( (randu()*100.0) - 50.0 );
    }
}
console.log( x );

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