From 78b4240e60dd14a5cb6049162d8b8a7a204f01f0 Mon Sep 17 00:00:00 2001 From: iampratik13 Date: Tue, 18 Aug 2026 19:52:13 +0530 Subject: [PATCH] feat: add stats/base/ndarray/dnanvariance --- type: pre_commit_static_analysis_report description: Results of running static analysis checks when committing changes. report: - task: lint_filenames status: passed - task: lint_editorconfig status: passed - task: lint_markdown_pkg_readmes status: passed - task: lint_markdown_docs status: na - task: lint_markdown status: na - task: lint_package_json status: passed - task: lint_repl_help status: passed - task: lint_javascript_src status: passed - task: lint_javascript_cli status: na - task: lint_javascript_examples status: passed - task: lint_javascript_tests status: passed - task: lint_javascript_benchmarks status: passed - task: lint_python status: na - task: lint_r status: na - task: lint_c_src status: na - task: lint_c_examples status: na - task: lint_c_benchmarks status: na - task: lint_c_tests_fixtures status: na - task: lint_shell status: na - task: lint_typescript_declarations status: passed - task: lint_typescript_tests status: passed - task: lint_license_headers status: passed --- --- .../stats/base/ndarray/dnanvariance/README.md | 196 +++++++++++++++ .../dnanvariance/benchmark/benchmark.js | 125 ++++++++++ .../docs/img/equation_sample_mean.svg | 43 ++++ .../base/ndarray/dnanvariance/docs/repl.txt | 50 ++++ .../dnanvariance/docs/types/index.d.ts | 57 +++++ .../ndarray/dnanvariance/docs/types/test.ts | 65 +++++ .../ndarray/dnanvariance/examples/index.js | 45 ++++ .../base/ndarray/dnanvariance/lib/index.js | 53 ++++ .../base/ndarray/dnanvariance/lib/main.js | 74 ++++++ .../base/ndarray/dnanvariance/package.json | 77 ++++++ .../base/ndarray/dnanvariance/test/test.js | 231 ++++++++++++++++++ 11 files changed, 1016 insertions(+) create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/README.md create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/benchmark/benchmark.js create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/docs/img/equation_sample_mean.svg create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/docs/repl.txt create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/docs/types/index.d.ts create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/docs/types/test.ts create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/examples/index.js create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/lib/index.js create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/lib/main.js create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/package.json create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/test/test.js diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/README.md b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/README.md new file mode 100644 index 000000000000..9ce2a62a3d31 --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/README.md @@ -0,0 +1,196 @@ + + +# dnanvariance + +> Calculate the [variance][variance] of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values. + +
+ +The population [variance][variance] of a finite size population of size `N` is given by + + + +```math +\sigma^2 = \frac{1}{N} \sum_{i=0}^{N-1} (x_i - \mu)^2 +``` + + + + + +where the population mean is given by + + + +```math +\mu = \frac{1}{N} \sum_{i=0}^{N-1} x_i +``` + + + + + +Often in the analysis of data, the true population [variance][variance] is not known _a priori_ and must be estimated from a sample drawn from the population distribution. If one attempts to use the formula for the population [variance][variance], the result is biased and yields an **uncorrected sample variance**. To compute a **corrected sample variance** for a sample of size `n`, + + + +```math +s^2 = \frac{1}{n-1} \sum_{i=0}^{n-1} (x_i - \bar{x})^2 +``` + + + + + +where the sample mean is given by + + + +```math +\bar{x} = \frac{1}{n} \sum_{i=0}^{n-1} x_i +``` + + + + + +The use of the term `n-1` is commonly referred to as Bessel's correction. Note, however, that applying Bessel's correction can increase the mean squared error between the sample variance and population variance. Depending on the characteristics of the population distribution, other correction factors (e.g., `n-1.5`, `n+1`, etc) can yield better estimators. + +
+ + + +
+ +## Usage + +```javascript +var dnanvariance = require( '@stdlib/stats/base/ndarray/dnanvariance' ); +``` + +#### dnanvariance( arrays ) + +Computes the [variance][variance] of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values. + +```javascript +var Float64Vector = require( '@stdlib/ndarray/vector/float64' ); +var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' ); + +var opts = { + 'dtype': 'float64' +}; + +var x = new Float64Vector( [ 1.0, -2.0, NaN, 2.0 ] ); +var correction = scalar2ndarray( 1.0, opts ); + +var v = dnanvariance( [ x, correction ] ); +// returns ~4.3333 +``` + +The function has the following parameters: + +- **arrays**: array-like object containing the following ndarrays: + + - a one-dimensional input ndarray. + - a zero-dimensional ndarray specifying the degrees of freedom adjustment. Providing a non-zero degrees of freedom adjustment has the effect of adjusting the divisor during the calculation of the [variance][variance] according to `N-c` where `N` is the number of non-NaN elements in the input ndarray and `c` corresponds to the provided degrees of freedom adjustment. When computing the [variance][variance] of a population, setting this parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the corrected sample [variance][variance], setting this parameter to `1` is the standard choice (i.e., the provided array contains data sampled from a larger population; this is commonly referred to as Bessel's correction). + +
+ + + +
+ +## Notes + +- If provided an empty one-dimensional ndarray, the function returns `NaN`. +- If `N - c` is less than or equal to `0` (where `N` corresponds to the number of non-NaN elements in the input ndarray and `c` corresponds to the provided degrees of freedom adjustment), the function returns `NaN`. + +
+ + + +
+ +## Examples + + + +```javascript +var uniform = require( '@stdlib/random/base/uniform' ); +var bernoulli = require( '@stdlib/random/base/bernoulli' ); +var fillBy = require( '@stdlib/ndarray/fill-by' ); +var zeros = require( '@stdlib/ndarray/zeros' ); +var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' ); +var ndarray2array = require( '@stdlib/ndarray/to-array' ); +var dnanvariance = require( '@stdlib/stats/base/ndarray/dnanvariance' ); + +function rand() { + if ( bernoulli( 0.8 ) < 1 ) { + return NaN; + } + return uniform( -50.0, 50.0 ); +} + +var opts = { + 'dtype': 'float64' +}; + +var x = fillBy( zeros( [ 10 ], opts ), rand ); +console.log( ndarray2array( x ) ); + +var correction = scalar2ndarray( 1.0, opts ); +var v = dnanvariance( [ x, correction ] ); +console.log( v ); +``` + +
+ + + + + +
+ +
+ + + + + +
+ +[variance]: https://en.wikipedia.org/wiki/Variance + +
+ + diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/benchmark/benchmark.js b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/benchmark/benchmark.js new file mode 100644 index 000000000000..d3303a9e5a4e --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/benchmark/benchmark.js @@ -0,0 +1,125 @@ +/** +* @license Apache-2.0 +* +* Copyright (c) 2026 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. +*/ + +'use strict'; + +// MODULES // + +var bench = require( '@stdlib/bench' ); +var uniform = require( '@stdlib/random/base/uniform' ); +var bernoulli = require( '@stdlib/random/base/bernoulli' ); +var fillBy = require( '@stdlib/ndarray/fill-by' ); +var zeros = require( '@stdlib/ndarray/zeros' ); +var isnan = require( '@stdlib/math/base/assert/is-nan' ); +var pow = require( '@stdlib/math/base/special/pow' ); +var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' ); +var format = require( '@stdlib/string/format' ); +var pkg = require( './../package.json' ).name; +var dnanvariance = require( './../lib' ); + + +// VARIABLES // + +var options = { + 'dtype': 'float64' +}; + + +// FUNCTIONS // + +/** +* Returns a random value. +* +* @private +* @returns {number} random value +*/ +function rand() { + if ( bernoulli( 0.8 ) < 1 ) { + return NaN; + } + return uniform( -10.0, 10.0 ); +} + +/** +* Creates a benchmark function. +* +* @private +* @param {PositiveInteger} len - array length +* @returns {Function} benchmark function +*/ +function createBenchmark( len ) { + var correction; + var x; + + x = fillBy( zeros( [ len ], options ), rand ); + correction = scalar2ndarray( 1.0, options ); + + return benchmark; + + /** + * Benchmark function. + * + * @private + * @param {Benchmark} b - benchmark instance + */ + function benchmark( b ) { + var v; + var i; + + b.tic(); + for ( i = 0; i < b.iterations; i++ ) { + v = dnanvariance( [ x, correction ] ); + 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(); + } +} + + +// MAIN // + +/** +* Main execution sequence. +* +* @private +*/ +function main() { + var len; + var min; + var max; + var f; + var i; + + min = 1; // 10^min + max = 6; // 10^max + + for ( i = min; i <= max; i++ ) { + len = pow( 10, i ); + f = createBenchmark( len ); + bench( format( '%s:len=%d', pkg, len ), f ); + } +} + +main(); diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/docs/img/equation_sample_mean.svg b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/docs/img/equation_sample_mean.svg new file mode 100644 index 000000000000..aea7a5f6687a --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/docs/img/equation_sample_mean.svg @@ -0,0 +1,43 @@ + +x overbar equals StartFraction 1 Over n EndFraction sigma-summation Underscript i equals 0 Overscript n minus 1 Endscripts x Subscript i + + + \ No newline at end of file diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/docs/repl.txt b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/docs/repl.txt new file mode 100644 index 000000000000..8b44e462535a --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/docs/repl.txt @@ -0,0 +1,50 @@ + +{{alias}}( arrays ) + Computes the variance of a one-dimensional double-precision floating-point + ndarray, ignoring `NaN` values. + + If provided an empty one-dimensional ndarray, the function returns `NaN`. + + If `N - c` is less than or equal to `0` (where `N` corresponds to the number + of non-NaN elements in the input ndarray and `c` corresponds to the provided + degrees of freedom adjustment), the function returns `NaN`. + + Parameters + ---------- + arrays: ArrayLikeObject + Array-like object containing the following ndarrays: + + - a one-dimensional input ndarray. + - a zero-dimensional ndarray specifying the degrees of freedom + adjustment. Providing a non-zero degrees of freedom adjustment has the + effect of adjusting the divisor during the calculation of the variance + according to `N-c` where `N` is the number of non-NaN elements in the + input ndarray and `c` corresponds to the provided degrees of freedom + adjustment. When computing the variance of a population, setting this + parameter to `0` is the standard choice (i.e., the provided array + contains data constituting an entire population). When computing the + corrected sample variance, setting this parameter to `1` is the standard + choice (i.e., the provided array contains data sampled from a larger + population; this is commonly referred to as Bessel's correction). + + Returns + ------- + out: number + The variance. + + Examples + -------- + // Create the input ndarray: + > var x = new {{alias:@stdlib/ndarray/vector/float64}}( [ 1.0, -2.0, NaN, 2.0 ] ); + + // Create the correction ndarray: + > var opts = { 'dtype': 'float64' }; + > var correction = {{alias:@stdlib/ndarray/from-scalar}}( 1.0, opts ); + + // Compute the variance: + > {{alias}}( [ x, correction ] ) + ~4.3333 + + See Also + -------- + diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/docs/types/index.d.ts new file mode 100644 index 000000000000..1abc23d43a83 --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/docs/types/index.d.ts @@ -0,0 +1,57 @@ +/* +* @license Apache-2.0 +* +* Copyright (c) 2026 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. +*/ + +// TypeScript Version: 4.1 + +/// + +import { float64ndarray, typedndarray } from '@stdlib/types/ndarray'; + +/** +* Computes the variance of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values. +* +* ## Notes +* +* - The function expects the following ndarrays: +* +* - a one-dimensional input ndarray. +* - a zero-dimensional ndarray specifying the degrees of freedom adjustment. +* +* @param arrays - array-like object containing ndarrays +* @returns variance +* +* @example +* var Float64Vector = require( '@stdlib/ndarray/vector/float64' ); +* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' ); +* +* var opts = { +* 'dtype': 'float64' +* }; +* +* var x = new Float64Vector( [ 1.0, -2.0, NaN, 2.0 ] ); +* var correction = scalar2ndarray( 1.0, opts ); +* +* var v = dnanvariance( [ x, correction ] ); +* // returns ~4.3333 +*/ +declare function dnanvariance( arrays: [ float64ndarray, typedndarray ] ): number; + + +// EXPORTS // + +export = dnanvariance; diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/docs/types/test.ts b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/docs/types/test.ts new file mode 100644 index 000000000000..58a0c0cf17e2 --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/docs/types/test.ts @@ -0,0 +1,65 @@ +/* +* @license Apache-2.0 +* +* Copyright (c) 2026 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. +*/ + +/* eslint-disable space-in-parens */ + +import zeros = require( '@stdlib/ndarray/zeros' ); +import scalar2ndarray = require( '@stdlib/ndarray/from-scalar' ); +import dnanvariance = require( './index' ); + + +// TESTS // + +// The function returns a number... +{ + const x = zeros( [ 10 ], { + 'dtype': 'float64' + }); + const correction = scalar2ndarray( 1.0, { + 'dtype': 'float64' + }); + + dnanvariance( [ x, correction ] ); // $ExpectType number +} + +// The compiler throws an error if the function is provided a first argument which is not an array of ndarrays... +{ + dnanvariance( '10' ); // $ExpectError + dnanvariance( 10 ); // $ExpectError + dnanvariance( true ); // $ExpectError + dnanvariance( false ); // $ExpectError + dnanvariance( null ); // $ExpectError + dnanvariance( undefined ); // $ExpectError + dnanvariance( [] ); // $ExpectError + dnanvariance( {} ); // $ExpectError + dnanvariance( ( x: number ): number => x ); // $ExpectError +} + +// The compiler throws an error if the function is provided an unsupported number of arguments... +{ + const x = zeros( [ 10 ], { + 'dtype': 'float64' + }); + const correction = scalar2ndarray( 1.0, { + 'dtype': 'float64' + }); + + dnanvariance(); // $ExpectError + dnanvariance( [ x, correction ], 10 ); // $ExpectError +} + diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/examples/index.js b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/examples/index.js new file mode 100644 index 000000000000..7ace64310715 --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/examples/index.js @@ -0,0 +1,45 @@ +/** +* @license Apache-2.0 +* +* Copyright (c) 2026 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. +*/ + +'use strict'; + +var uniform = require( '@stdlib/random/base/uniform' ); +var bernoulli = require( '@stdlib/random/base/bernoulli' ); +var fillBy = require( '@stdlib/ndarray/fill-by' ); +var zeros = require( '@stdlib/ndarray/zeros' ); +var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' ); +var ndarray2array = require( '@stdlib/ndarray/to-array' ); +var dnanvariance = require( './../lib' ); + +function rand() { + if ( bernoulli( 0.8 ) < 1 ) { + return NaN; + } + return uniform( -50.0, 50.0 ); +} + +var opts = { + 'dtype': 'float64' +}; + +var x = fillBy( zeros( [ 10 ], opts ), rand ); +console.log( ndarray2array( x ) ); + +var correction = scalar2ndarray( 1.0, opts ); +var v = dnanvariance( [ x, correction ] ); +console.log( v ); diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/lib/index.js b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/lib/index.js new file mode 100644 index 000000000000..48a85eca1501 --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/lib/index.js @@ -0,0 +1,53 @@ +/** +* @license Apache-2.0 +* +* Copyright (c) 2026 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. +*/ + +'use strict'; + +/** +* Compute the variance of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values. +* +* @module @stdlib/stats/base/ndarray/dnanvariance +* +* @example +* var Float64Vector = require( '@stdlib/ndarray/vector/float64' ); +* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' ); +* var dnanvariance = require( '@stdlib/stats/base/ndarray/dnanvariance' ); +* +* var opts = { +* 'dtype': 'float64' +* }; +* +* // Define a one-dimensional input ndarray: +* var x = new Float64Vector( [ 1.0, -2.0, NaN, 2.0 ] ); +* +* // Specify the degrees of freedom adjustment: +* var correction = scalar2ndarray( 1.0, opts ); +* +* // Compute the variance: +* var v = dnanvariance( [ x, correction ] ); +* // returns ~4.3333 +*/ + +// MODULES // + +var main = require( './main.js' ); + + +// EXPORTS // + +module.exports = main; diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/lib/main.js b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/lib/main.js new file mode 100644 index 000000000000..f0b3d303a242 --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/lib/main.js @@ -0,0 +1,74 @@ +/** +* @license Apache-2.0 +* +* Copyright (c) 2026 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. +*/ + +'use strict'; + +// MODULES // + +var numelDimension = require( '@stdlib/ndarray/base/numel-dimension' ); +var getStride = require( '@stdlib/ndarray/base/stride' ); +var getOffset = require( '@stdlib/ndarray/base/offset' ); +var getData = require( '@stdlib/ndarray/base/data-buffer' ); +var ndarraylike2scalar = require( '@stdlib/ndarray/base/ndarraylike2scalar' ); +var strided = require( '@stdlib/stats/strided/dnanvariance' ).ndarray; + + +// MAIN // + +/** +* Computes the variance of a one-dimensional double-precision floating-point ndarray, ignoring `NaN` values. +* +* ## Notes +* +* - The function expects the following ndarrays: +* +* - a one-dimensional input ndarray. +* - a zero-dimensional ndarray specifying the degrees of freedom adjustment. +* +* @param {ArrayLikeObject} arrays - array-like object containing ndarrays +* @returns {number} variance +* +* @example +* var Float64Vector = require( '@stdlib/ndarray/vector/float64' ); +* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' ); +* +* var opts = { +* 'dtype': 'float64' +* }; +* +* var x = new Float64Vector( [ 1.0, -2.0, NaN, 2.0 ] ); +* +* var correction = scalar2ndarray( 1.0, opts ); +* +* var v = dnanvariance( [ x, correction ] ); +* // returns ~4.3333 +*/ +function dnanvariance( arrays ) { + var correction; + var x; + + x = arrays[ 0 ]; + correction = ndarraylike2scalar( arrays[ 1 ] ); + + return strided( numelDimension( x, 0 ), correction, getData( x ), getStride( x, 0 ), getOffset( x ) ); // eslint-disable-line max-len +} + + +// EXPORTS // + +module.exports = dnanvariance; diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/package.json b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/package.json new file mode 100644 index 000000000000..ea70530b75c2 --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/package.json @@ -0,0 +1,77 @@ +{ + "name": "@stdlib/stats/base/ndarray/dnanvariance", + "version": "0.0.0", + "description": "Compute the variance of a one-dimensional double-precision floating-point ndarray, ignoring NaN values.", + "license": "Apache-2.0", + "author": { + "name": "The Stdlib Authors", + "url": "https://github.com/stdlib-js/stdlib/graphs/contributors" + }, + "contributors": [ + { + "name": "The Stdlib Authors", + "url": "https://github.com/stdlib-js/stdlib/graphs/contributors" + } + ], + "main": "./lib", + "directories": { + "benchmark": "./benchmark", + "doc": "./docs", + "example": "./examples", + "lib": "./lib", + "test": "./test" + }, + "types": "./docs/types", + "scripts": {}, + "homepage": "https://github.com/stdlib-js/stdlib", + "repository": { + "type": "git", + "url": "git://github.com/stdlib-js/stdlib.git" + }, + "bugs": { + "url": "https://github.com/stdlib-js/stdlib/issues" + }, + "dependencies": {}, + "devDependencies": {}, + "engines": { + "node": ">=0.10.0", + "npm": ">2.7.0" + }, + "os": [ + "aix", + "darwin", + "freebsd", + "linux", + "macos", + "openbsd", + "sunos", + "win32", + "windows" + ], + "keywords": [ + "stdlib", + "stdmath", + "statistics", + "stats", + "mathematics", + "math", + "var", + "deviation", + "dispersion", + "spread", + "sample variance", + "unbiased", + "variance", + "dnanvariance", + "std", + "nan", + "ndarray", + "float64", + "double", + "double-precision", + "typed", + "array", + "float64array" + ], + "__stdlib__": {} +} diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/test/test.js b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/test/test.js new file mode 100644 index 000000000000..d13b8ffaaaa0 --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dnanvariance/test/test.js @@ -0,0 +1,231 @@ +/** +* @license Apache-2.0 +* +* Copyright (c) 2026 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. +*/ + +'use strict'; + +// MODULES // + +var tape = require( 'tape' ); +var isnan = require( '@stdlib/math/base/assert/is-nan' ); +var Float64Array = require( '@stdlib/array/float64' ); +var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' ); +var ndarray = require( '@stdlib/ndarray/base/ctor' ); +var dnanvariance = require( './../lib' ); + + +// FUNCTIONS // + +/** +* Returns a one-dimensional ndarray. +* +* @private +* @param {Collection} buffer - underlying data buffer +* @param {NonNegativeInteger} length - number of indexed elements +* @param {integer} stride - stride length +* @param {NonNegativeInteger} offset - index offset +* @returns {ndarray} one-dimensional ndarray +*/ +function vector( buffer, length, stride, offset ) { + return new ndarray( 'float64', buffer, [ length ], [ stride ], offset, 'row-major' ); +} + + +// TESTS // + +tape( 'main export is a function', function test( t ) { + t.ok( true, __filename ); + t.strictEqual( typeof dnanvariance, 'function', 'main export is a function' ); + t.end(); +}); + +tape( 'the function has an arity of 1', function test( t ) { + t.strictEqual( dnanvariance.length, 1, 'has expected arity' ); + t.end(); +}); + +tape( 'the function calculates the variance of a one-dimensional ndarray, ignoring NaN values', function test( t ) { + var correction; + var expected; + var opts; + var x; + var v; + + opts = { + 'dtype': 'float64' + }; + + x = new Float64Array( [ 1.0, -2.0, -4.0, NaN, 5.0, 0.0, 3.0 ] ); + correction = scalar2ndarray( 1.0, opts ); + + v = dnanvariance( [ vector( x, x.length, 1, 0 ), correction ] ); + expected = 53.5 / 5; + t.strictEqual( v, expected, 'returns expected value' ); + + x = new Float64Array( [ -4.0, NaN, -5.0 ] ); + correction = scalar2ndarray( 1.0, opts ); + + v = dnanvariance( [ vector( x, x.length, 1, 0 ), correction ] ); + expected = 0.5; + t.strictEqual( v, expected, 'returns expected value' ); + + x = new Float64Array( [ NaN ] ); + correction = scalar2ndarray( 1.0, opts ); + + v = dnanvariance( [ vector( x, x.length, 1, 0 ), correction ] ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = new Float64Array( [ NaN, NaN ] ); + correction = scalar2ndarray( 1.0, opts ); + + v = dnanvariance( [ vector( x, x.length, 1, 0 ), correction ] ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + t.end(); +}); + +tape( 'if provided an empty ndarray, the function returns `NaN`', function test( t ) { + var correction; + var opts; + var x; + var v; + + opts = { + 'dtype': 'float64' + }; + + x = new Float64Array( [] ); + correction = scalar2ndarray( 1.0, opts ); + + v = dnanvariance( [ vector( x, 0, 1, 0 ), correction ] ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + t.end(); +}); + +tape( 'if provided a correction argument yielding `N-correction` less than or equal to `0`, the function returns `NaN`', function test( t ) { + var correction; + var opts; + var x; + var v; + + opts = { + 'dtype': 'float64' + }; + + x = new Float64Array( [ 1.0 ] ); + correction = scalar2ndarray( 1.0, opts ); + + v = dnanvariance( [ vector( x, 1, 1, 0 ), correction ] ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + t.end(); +}); + +tape( 'the function supports one-dimensional ndarrays having non-unit strides', function test( t ) { + var correction; + var expected; + var opts; + var x; + var v; + + opts = { + 'dtype': 'float64' + }; + + x = new Float64Array([ + 1.0, // 0 + 2.0, + -2.0, // 1 + -7.0, + 2.0, // 2 + 3.0, + 4.0, // 3 + 2.0, + NaN // 4 + ]); + correction = scalar2ndarray( 1.0, opts ); + + v = dnanvariance( [ vector( x, 5, 2, 0 ), correction ] ); + expected = 6.25; + t.strictEqual( v, expected, 'returns expected value' ); + + t.end(); +}); + +tape( 'the function supports one-dimensional ndarrays having negative strides', function test( t ) { + var correction; + var expected; + var opts; + var x; + var v; + + opts = { + 'dtype': 'float64' + }; + + x = new Float64Array([ + 1.0, // 4 + 2.0, + -2.0, // 3 + -7.0, + 2.0, // 2 + 3.0, + 4.0, // 1 + 2.0, + NaN // 0 + ]); + correction = scalar2ndarray( 1.0, opts ); + + v = dnanvariance( [ vector( x, 5, -2, 8 ), correction ] ); + expected = 6.25; + t.strictEqual( v, expected, 'returns expected value' ); + + t.end(); +}); + +tape( 'the function supports one-dimensional ndarrays having non-zero offsets', function test( t ) { + var correction; + var expected; + var opts; + var x; + var v; + + opts = { + 'dtype': 'float64' + }; + + x = new Float64Array([ + 2.0, + 1.0, // 0 + 2.0, + -2.0, // 1 + -2.0, + 2.0, // 2 + 3.0, + 4.0, // 3 + NaN, + NaN // 4 + ]); + correction = scalar2ndarray( 1.0, opts ); + + v = dnanvariance( [ vector( x, 5, 2, 1 ), correction ] ); + expected = 6.25; + t.strictEqual( v, expected, 'returns expected value' ); + + t.end(); +});