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feat: add stats/base/ndarray/dnanmeanpn
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feat: add stats/base/ndarray/dnanmeanpn
Orthodox-64 20d8b02
fix: lint
Orthodox-64 fda63f5
docs: update copy
kgryte e46d7a3
docs: update notes
kgryte df5bcdf
docs: add missing references
kgryte b367406
docs: fix references
kgryte 5ee7147
test: update descriptions
kgryte e2c223d
test: update description
kgryte d891747
style: remove whitespace
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138 changes: 138 additions & 0 deletions
138
lib/node_modules/@stdlib/stats/base/ndarray/dnanmeanpn/README.md
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| <!-- | ||
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| @license Apache-2.0 | ||
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| Copyright (c) 2025 The Stdlib Authors. | ||
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| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
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| http://www.apache.org/licenses/LICENSE-2.0 | ||
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| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
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| --> | ||
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| # dnanmeanpn | ||
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| > Compute the [arithmetic mean][arithmetic-mean] of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using a two-pass error correction algorithm. | ||
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| <section class="intro"> | ||
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| The [arithmetic mean][arithmetic-mean] is defined as | ||
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| <!-- <equation class="equation" label="eq:arithmetic_mean" align="center" raw="\mu = \frac{1}{n} \sum_{i=0}^{n-1} x_i" alt="Equation for the arithmetic mean."> --> | ||
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| ```math | ||
| \mu = \frac{1}{n} \sum_{i=0}^{n-1} x_i | ||
| ``` | ||
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| <!-- <div class="equation" align="center" data-raw-text="\mu = \frac{1}{n} \sum_{i=0}^{n-1} x_i" data-equation="eq:arithmetic_mean"> | ||
| <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@42d8f64d805113ab899c79c7c39d6c6bac7fe25c/lib/node_modules/@stdlib/stats/base/ndarray/dnanmeanpn/docs/img/equation_arithmetic_mean.svg" alt="Equation for the arithmetic mean."> | ||
| <br> | ||
| </div> --> | ||
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| <!-- </equation> --> | ||
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| </section> | ||
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| <!-- /.intro --> | ||
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| <section class="usage"> | ||
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| ## Usage | ||
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| ```javascript | ||
| var dnanmeanpn = require( '@stdlib/stats/base/ndarray/dnanmeanpn' ); | ||
| ``` | ||
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| #### dnanmeanpn( arrays ) | ||
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| Computes the [arithmetic mean][arithmetic-mean] of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using a two-pass error correction algorithm. | ||
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| ```javascript | ||
| var Float64Array = require( '@stdlib/array/float64' ); | ||
| var ndarray = require( '@stdlib/ndarray/base/ctor' ); | ||
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| var xbuf = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] ); | ||
| var x = new ndarray( 'float64', xbuf, [ 4 ], [ 1 ], 0, 'row-major' ); | ||
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| var v = dnanmeanpn( [ x ] ); | ||
| // returns ~0.3333 | ||
| ``` | ||
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| The function has the following parameters: | ||
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| - **arrays**: array-like object containing a one-dimensional input ndarray. | ||
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| </section> | ||
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| <!-- /.usage --> | ||
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| <section class="notes"> | ||
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| ## Notes | ||
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| - If provided an empty one-dimensional ndarray, the function returns `NaN`. | ||
| - `NaN` values are ignored (i.e., they do not contribute to the mean nor the count of values). | ||
| - Uses a two-pass error correction algorithm which first computes an initial estimate of the mean and then an error term to refine the estimate. | ||
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| </section> | ||
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| <!-- /.notes --> | ||
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| <section class="examples"> | ||
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| ## Examples | ||
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| <!-- eslint no-undef: "error" --> | ||
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| ```javascript | ||
| var uniform = require( '@stdlib/random/base/uniform' ); | ||
| var bernoulli = require( '@stdlib/random/base/bernoulli' ); | ||
| var filledarrayBy = require( '@stdlib/array/filled-by' ); | ||
| var ndarray = require( '@stdlib/ndarray/base/ctor' ); | ||
| var ndarray2array = require( '@stdlib/ndarray/to-array' ); | ||
| var dnanmeanpn = require( '@stdlib/stats/base/ndarray/dnanmeanpn' ); | ||
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| function rand() { | ||
| if ( bernoulli( 0.8 ) < 1 ) { | ||
| return NaN; | ||
| } | ||
| return uniform( -50.0, 50.0 ); | ||
| } | ||
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| var xbuf = filledarrayBy( 10, 'float64', rand ); | ||
| var x = new ndarray( 'float64', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' ); | ||
| console.log( ndarray2array( x ) ); | ||
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| var v = dnanmeanpn( [ x ] ); | ||
| console.log( v ); | ||
| ``` | ||
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| </section> | ||
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| <!-- /.examples --> | ||
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| <!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. --> | ||
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| <section class="related"> | ||
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| </section> | ||
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| <!-- /.related --> | ||
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| <!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. --> | ||
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| <section class="links"> | ||
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| [arithmetic-mean]: https://en.wikipedia.org/wiki/Arithmetic_mean | ||
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| </section> | ||
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| <!-- /.links --> | ||
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110
lib/node_modules/@stdlib/stats/base/ndarray/dnanmeanpn/benchmark/benchmark.js
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| /** | ||
| * @license Apache-2.0 | ||
| * | ||
| * Copyright (c) 2025 The Stdlib Authors. | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
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| 'use strict'; | ||
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| // MODULES // | ||
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| var bench = require( '@stdlib/bench' ); | ||
| var uniform = require( '@stdlib/random/base/uniform' ); | ||
| var bernoulli = require( '@stdlib/random/base/bernoulli' ); | ||
| var filledarrayBy = require( '@stdlib/array/filled-by' ); | ||
| var isnan = require( '@stdlib/math/base/assert/is-nan' ); | ||
| var pow = require( '@stdlib/math/base/special/pow' ); | ||
| var ndarray = require( '@stdlib/ndarray/base/ctor' ); | ||
| var pkg = require( './../package.json' ).name; | ||
| var dnanmeanpn = require( './../lib' ); | ||
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| // FUNCTIONS // | ||
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| /** | ||
| * Returns a random number. | ||
| * | ||
| * @private | ||
| * @returns {number} random number or `NaN` | ||
| */ | ||
| function rand() { | ||
| if ( bernoulli( 0.8 ) < 1 ) { | ||
| return NaN; | ||
| } | ||
| return uniform( -10.0, 10.0 ); | ||
| } | ||
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| /** | ||
| * Creates a benchmark function. | ||
| * | ||
| * @private | ||
| * @param {PositiveInteger} len - array length | ||
| * @returns {Function} benchmark function | ||
| */ | ||
| function createBenchmark( len ) { | ||
| var xbuf; | ||
| var x; | ||
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| xbuf = filledarrayBy( len, 'float64', rand ); | ||
| x = new ndarray( 'float64', xbuf, [ len ], [ 1 ], 0, 'row-major' ); | ||
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| return benchmark; | ||
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| function benchmark( b ) { | ||
| var v; | ||
| var i; | ||
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| b.tic(); | ||
| for ( i = 0; i < b.iterations; i++ ) { | ||
| v = dnanmeanpn( [ x ] ); | ||
| if ( isnan( v ) ) { | ||
| b.fail( 'should not return NaN' ); | ||
| } | ||
| } | ||
| b.toc(); | ||
| if ( isnan( v ) ) { | ||
| b.fail( 'should not return NaN' ); | ||
| } | ||
| b.pass( 'benchmark finished' ); | ||
| b.end(); | ||
| } | ||
| } | ||
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| // MAIN // | ||
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| /** | ||
| * Main execution sequence. | ||
| * | ||
| * @private | ||
| */ | ||
| function main() { | ||
| var len; | ||
| var min; | ||
| var max; | ||
| var f; | ||
| var i; | ||
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| min = 1; // 10^min | ||
| max = 6; // 10^max | ||
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| for ( i = min; i <= max; i++ ) { | ||
| len = pow( 10, i ); | ||
| f = createBenchmark( len ); | ||
| bench( pkg+':len='+len, f ); | ||
| } | ||
| } | ||
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| main(); |
42 changes: 42 additions & 0 deletions
42
...les/@stdlib/stats/base/ndarray/dnanmeanpn/docs/img/equation_arithmetic_mean.svg
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32 changes: 32 additions & 0 deletions
32
lib/node_modules/@stdlib/stats/base/ndarray/dnanmeanpn/docs/repl.txt
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| {{alias}}( arrays ) | ||
| Computes the arithmetic mean of a one-dimensional double-precision floating- | ||
| point ndarray, ignoring `NaN` values and using a two-pass error correction | ||
| algorithm. | ||
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| If provided an empty ndarray, the function returns `NaN`. | ||
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| Parameters | ||
| ---------- | ||
| arrays: ArrayLikeObject<ndarray> | ||
| Array-like object containing a one-dimensional input ndarray. | ||
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| Returns | ||
| ------- | ||
| out: number | ||
| Arithmetic mean. | ||
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| Examples | ||
| -------- | ||
| > var xbuf = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, NaN, 2.0 ] ); | ||
| > var dt = 'float64'; | ||
| > var sh = [ xbuf.length ]; | ||
| > var sx = [ 1 ]; | ||
| > var ox = 0; | ||
| > var ord = 'row-major'; | ||
| > var x = new {{alias:@stdlib/ndarray/ctor}}( dt, xbuf, sh, sx, ox, ord ); | ||
| > {{alias}}( [ x ] ) | ||
| ~0.3333 | ||
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| See Also | ||
| -------- |
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46
lib/node_modules/@stdlib/stats/base/ndarray/dnanmeanpn/docs/types/index.d.ts
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| /* | ||
| * @license Apache-2.0 | ||
| * | ||
| * Copyright (c) 2025 The Stdlib Authors. | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
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| // TypeScript Version: 4.1 | ||
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| /// <reference types="@stdlib/types"/> | ||
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| import { float64ndarray } from '@stdlib/types/ndarray'; | ||
|
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| /** | ||
| * Computes the arithmetic mean of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values and using a two-pass error correction algorithm. | ||
| * | ||
| * @param arrays - array-like object containing an input ndarray | ||
| * @returns arithmetic mean | ||
| * | ||
| * @example | ||
| * var Float64Array = require( '@stdlib/array/float64' ); | ||
| * var ndarray = require( '@stdlib/ndarray/base/ctor' ); | ||
| * | ||
| * var xbuf = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] ); | ||
| * var x = new ndarray( 'float64', xbuf, [ 4 ], [ 1 ], 0, 'row-major' ); | ||
| * | ||
| * var v = dnanmeanpn( [ x ] ); | ||
| * // returns ~0.3333 | ||
| */ | ||
| declare function dnanmeanpn( arrays: [ float64ndarray ] ): number; | ||
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| // EXPORTS // | ||
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| export = dnanmeanpn; |
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