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Compute a moving arithmetic mean and unbiased sample variance incrementally.
For a window of size W, the arithmetic mean is defined as
and the unbiased sample variance is defined as
import incrmmeanvar from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-incr-mmeanvar@esm/index.mjs';Returns an accumulator function which incrementally computes a moving arithmetic mean and unbiased sample variance. The window parameter defines the number of values over which to compute the moving arithmetic mean and unbiased sample variance.
var accumulator = incrmmeanvar( 3 );By default, the returned accumulator function returns the accumulated values as a two-element array. To avoid unnecessary memory allocation, the function supports providing an output (destination) object.
import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';
var accumulator = incrmmeanvar( new Float64Array( 2 ), 3 );If provided an input value x, the accumulator function returns updated accumulated values. If not provided an input value x, the accumulator function returns the current accumulated values.
var accumulator = incrmmeanvar( 3 );
var out = accumulator();
// returns null
// Fill the window...
out = accumulator( 2.0 ); // [2.0]
// returns [ 2.0, 0.0 ]
out = accumulator( 1.0 ); // [2.0, 1.0]
// returns [ 1.5, 0.5 ]
out = accumulator( 3.0 ); // [2.0, 1.0, 3.0]
// returns [ 2.0, 1.0 ]
// Window begins sliding...
out = accumulator( -7.0 ); // [1.0, 3.0, -7.0]
// returns [ -1.0, 28.0 ]
out = accumulator( -5.0 ); // [3.0, -7.0, -5.0]
// returns [ -3.0, 28.0 ]
out = accumulator();
// returns [ -3.0, 28.0 ]- Input values are not type checked. If provided
NaN, the accumulated values areNaNfor at leastW-1future invocations. If non-numeric inputs are possible, you are advised to type check and handle accordingly before passing the value to the accumulator function. - As
Wvalues are needed to fill the window buffer, the firstW-1returned values are calculated from smaller sample sizes. Until the window is full, each returned value is calculated from all provided values.
<!DOCTYPE html>
<html lang="en">
<body>
<script type="module">
import randu from 'https://cdn.jsdelivr.net/gh/stdlib-js/random-base-randu@esm/index.mjs';
import Float64Array from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-float64@esm/index.mjs';
import ArrayBuffer from 'https://cdn.jsdelivr.net/gh/stdlib-js/array-buffer@esm/index.mjs';
import incrmmeanvar from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-incr-mmeanvar@esm/index.mjs';
var offset;
var acc;
var buf;
var out;
var mv;
var N;
var v;
var i;
var j;
// Define the number of accumulators:
N = 5;
// Create an array buffer for storing accumulator output:
buf = new ArrayBuffer( N*2*8 ); // 8 bytes per element
// Initialize accumulators:
acc = [];
for ( i = 0; i < N; i++ ) {
// Compute the byte offset:
offset = i * 2 * 8; // stride=2, bytes_per_element=8
// Create a new view for storing accumulated values:
out = new Float64Array( buf, offset, 2 );
// Initialize an accumulator which will write results to the view:
acc.push( incrmmeanvar( out, 5 ) );
}
// Simulate data and update the moving sample means and variances...
for ( i = 0; i < 100; i++ ) {
for ( j = 0; j < N; j++ ) {
v = randu() * 100.0 * (j+1);
acc[ j ]( v );
}
}
// Print the final results:
console.log( 'Mean\tVariance' );
for ( i = 0; i < N; i++ ) {
mv = acc[ i ]();
console.log( '%d\t%d', mv[ 0 ].toFixed( 3 ), mv[ 1 ].toFixed( 3 ) );
}
</script>
</body>
</html>@stdlib/stats-incr/meanvar: compute an arithmetic mean and unbiased sample variance incrementally.@stdlib/stats-incr/mmean: compute a moving arithmetic mean incrementally.@stdlib/stats-incr/mmeanstdev: compute a moving arithmetic mean and corrected sample standard deviation incrementally.@stdlib/stats-incr/mvariance: compute a moving unbiased sample variance incrementally.
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