Calculate the arithmetic mean of a double-precision floating-point strided array using Welford's algorithm.
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Calculate the [arithmetic mean][arithmetic-mean] of a double-precision floating-point strided array using Welford’s algorithm.
math
\mu = \frac{1}{n} \sum_{i=0}^{n-1} x_ibash
npm install @stdlib/stats-base-dmeanwdscript tag without installation and bundlers, use the [ES Module][es-module] available on the [esm][esm-url] branch (see [README][esm-readme]).deno][deno-url] branch (see [README][deno-readme] for usage intructions).umd][umd-url] branch (see [README][umd-readme]).javascript
var dmeanwd = require( '@stdlib/stats-base-dmeanwd' );x using Welford’s algorithm.javascript
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );
var v = dmeanwd( x.length, x, 1 );
// returns ~0.3333Float64Array][@stdlib/array/float64].x.N and stride parameters determine which elements in the strided array are accessed at runtime. For example, to compute the [arithmetic mean][arithmetic-mean] of every other element in x,javascript
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );
var v = dmeanwd( 4, x, 2 );
// returns 1.25typed array][mdn-typed-array] views.javascript
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 = dmeanwd( 4, x1, 2 );
// returns 1.25javascript
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 1.0, -2.0, 2.0 ] );
var v = dmeanwd.ndarray( x.length, x, 1, 0 );
// returns ~0.33333x.typed array][mdn-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][arithmetic-mean] for every other element in x starting from the second elementjavascript
var Float64Array = require( '@stdlib/array-float64' );
var x = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var v = dmeanwd.ndarray( 4, x, 2, 1 );
// returns 1.25N <= 0, both functions return NaN.javascript
var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var dmeanwd = require( '@stdlib/stats-base-dmeanwd' );
var x = discreteUniform( 10, -50, 50, {
'dtype': 'float64'
});
console.log( x );
var v = dmeanwd( x.length, x, 1 );
console.log( v );c
#include "stdlib/stats/base/dmeanwd.h"c
const double x[] = { 1.0, -2.0, 2.0 };
double v = stdlib_strided_dmeanwd( 3, x, 1 );
// returns ~0.333[in] CBLAS_INT number of indexed elements.[in] double* input array.[in] CBLAS_INT stride length for X.c
double stdlib_strided_dmeanwd( const CBLAS_INT N, const double *X, const CBLAS_INT strideX );c
const double x[] = { 1.0, -2.0, 2.0 };
double v = stdlib_strided_dmeanwd_ndarray( 3, x, 1, 0 );
// returns ~0.333[in] CBLAS_INT number of indexed elements.[in] double* input array.[in] CBLAS_INT stride length for X.[in] CBLAS_INT starting index for X.c
double stdlib_strided_dmeanwd_ndarray( const CBLAS_INT N, const double *X, const CBLAS_INT strideX, const CBLAS_INT offsetX );c
#include "stdlib/stats/base/dmeanwd.h"
#include <stdint.h>
#include <stdio.h>
int main( void ) {
// Create a strided array:
const double x[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 };
// Specify the number of elements:
const int N = 4;
// Specify the stride length:
const int strideX = 2;
// Compute the minimum value:
double v = stdlib_strided_dmeanwd( N, x, strideX );
// Print the result:
printf( "mean: %lf\n", v );
}
