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Calculate the arithmetic mean of a double-precision floating-point strided array, ignoring
NaN
values and using a two-pass error correction algorithm.
The arithmetic mean is defined as
npm install @stdlib/stats-base-dnanmeanpn
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var dnanmeanpn = require( '@stdlib/stats-base-dnanmeanpn' );
Computes the arithmetic mean of a double-precision floating-point strided array x
, ignoring NaN
values and using a two-pass error correction algorithm.
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
var N = x.length;
var v = dnanmeanpn( N, x, 1 );
// returns ~0.3333
The function has the following parameters:
- N: number of indexed elements.
- x: input
Float64Array
. - 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 Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );
var x = new Float64Array( [ 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 = dnanmeanpn( N, x, 2 );
// returns 1.25
Note that indexing is relative to the first index. To introduce an offset, use typed array
views.
var Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );
var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] );
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var N = floor( x0.length / 2 );
var v = dnanmeanpn( N, x1, 2 );
// returns 1.25
Computes the arithmetic mean of a double-precision floating-point strided array, ignoring NaN
values and using a two-pass error correction algorithm and alternative indexing semantics.
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );
var N = x.length;
var v = dnanmeanpn.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 Float64Array = require( '@stdlib/array-float64' );
var floor = require( '@stdlib/math-base-special-floor' );
var x = new Float64Array( [ 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 = dnanmeanpn.ndarray( N, x, 2, 1 );
// returns 1.25
- If
N <= 0
, both functions returnNaN
. - If every indexed element is
NaN
, both functions returnNaN
.
var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var Float64Array = require( '@stdlib/array-float64' );
var dnanmeanpn = require( '@stdlib/stats-base-dnanmeanpn' );
var x;
var i;
x = new Float64Array( 10 );
for ( i = 0; i < x.length; i++ ) {
if ( randu() < 0.2 ) {
x[ i ] = NaN;
} else {
x[ i ] = round( randu() * 10.0 );
}
}
console.log( x );
var v = dnanmeanpn( x.length, x, 1 );
console.log( v );
- Neely, Peter M. 1966. "Comparison of Several Algorithms for Computation of Means, Standard Deviations and Correlation Coefficients." Communications of the ACM 9 (7). Association for Computing Machinery: 496โ99. doi:10.1145/365719.365958.
- Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co-)Variance." In Proceedings of the 30th International Conference on Scientific and Statistical Database Management. New York, NY, USA: Association for Computing Machinery. doi:10.1145/3221269.3223036.
@stdlib/stats-base/dmeanpn
: calculate the arithmetic mean of a double-precision floating-point strided array using a two-pass error correction algorithm.@stdlib/stats-base/dnanmean
: calculate the arithmetic mean of a double-precision floating-point strided array, ignoring NaN values.@stdlib/stats-base/nanmeanpn
: calculate the arithmetic mean of a strided array, ignoring NaN values and using a two-pass error correction algorithm.@stdlib/stats-base/snanmeanpn
: calculate the arithmetic mean of a single-precision floating-point strided array, ignoring NaN values and using a two-pass error correction algorithm.
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See LICENSE.
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