From 47e39a19fae61af27395c1f04e4183ce078de0e4 Mon Sep 17 00:00:00 2001 From: DivyanshuVorrtex Date: Sat, 10 Jan 2026 18:46:17 +0000 Subject: [PATCH 1/4] feat(dvariancech): add a package stats/base/ndarray/dvariancech --- 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 status: passed - 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/dvariancech/README.md | 207 +++++++++++++++ .../dvariancech/benchmark/benchmark.js | 112 ++++++++ .../docs/img/equation_sample_mean.svg | 43 +++ .../base/ndarray/dvariancech/docs/repl.txt | 54 ++++ .../ndarray/dvariancech/docs/types/index.d.ts | 52 ++++ .../ndarray/dvariancech/docs/types/test.ts | 65 +++++ .../ndarray/dvariancech/examples/index.js | 37 +++ .../base/ndarray/dvariancech/lib/index.js | 55 ++++ .../base/ndarray/dvariancech/lib/main.js | 69 +++++ .../base/ndarray/dvariancech/package.json | 71 +++++ .../base/ndarray/dvariancech/test/test.js | 248 ++++++++++++++++++ 11 files changed, 1013 insertions(+) create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/README.md create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/benchmark/benchmark.js create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/img/equation_sample_mean.svg create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/repl.txt create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/types/index.d.ts create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/types/test.ts create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/examples/index.js create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/lib/index.js create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/lib/main.js create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/package.json create mode 100644 lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/test/test.js diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/README.md b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/README.md new file mode 100644 index 000000000000..20d0699a9810 --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/README.md @@ -0,0 +1,207 @@ + + +# dvariancech + +> Calculate the [variance][variance] of a one-dimensional double-precision floating-point ndarray using a one-pass trial mean algorithm. + +
+ +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 dvariancech = require( '@stdlib/stats/base/ndarray/dvariancech' ); +``` + +#### dvariancech( arrays ) + +Computes the [variance][variance] of a one-dimensional double-precision floating-point ndarray using a one-pass trial mean algorithm. + +```javascript +var ndarray = require( '@stdlib/ndarray/base/ctor' ); +var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' ); +var Float64Array = require( '@stdlib/array/float64' ); + +var opts = { + 'dtype': 'float64' +}; + +var xbuf = new Float64Array( [ 1.0, -2.0, 2.0 ] ); +var x = new ndarray( opts.dtype, xbuf, [ 3 ], [ 1 ], 0, 'row-major' ); +var correction = scalar2ndarray( 1.0, opts ); + +var v = dvariancech( [ x, correction ] ); +// returns ~4.3333 +``` + +The function has the following parameters: + +- **arrays**: array-like object containing two elements: a one-dimensional input ndarray and 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 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 elements in the input ndarray and `c` corresponds to the provided degrees of freedom adjustment), the function returns `NaN`. + +
+ + + +
+ +## Examples + + + +```javascript +var discreteUniform = require( '@stdlib/random/array/discrete-uniform' ); +var ndarray = require( '@stdlib/ndarray/base/ctor' ); +var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' ); +var ndarray2array = require( '@stdlib/ndarray/to-array' ); +var dvariancech = require( '@stdlib/stats/base/ndarray/dvariancech' ); + +var opts = { + 'dtype': 'float64' +}; + +var xbuf = discreteUniform( 10, -50, 50, opts ); +var x = new ndarray( opts.dtype, xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' ); +console.log( ndarray2array( x ) ); + +var correction = scalar2ndarray( 1.0, opts ); +var v = dvariancech( [ x, correction ] ); +console.log( v ); +``` + +
+ + + +* * * + +
+ +## References + +- 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: 496–99. doi:[10.1145/365719.365958][@neely:1966a]. +- Chan, Tony F., Gene H. Golub, and Randall J. LeVeque. 1983. "Algorithms for Computing the Sample Variance: Analysis and Recommendations." _The American Statistician_ 37 (3). American Statistical Association, Taylor & Francis: 242–47. doi:[10.1080/00031305.1983.10483115][@chan:1983a]. +- Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co-)Variance." in _SSDBM '18: Proceedings of the 30th International Conference on Scientific and Statistical Database Management_. Association for Computing Machinery. doi:[10.1145/3221664.3221674][@schubert:2018a]. + +
+ + + + + +
+ +
+ + + + + +
+ +[variance]: https://en.wikipedia.org/wiki/Variance + +[@neely:1966a]: https://doi.org/10.1145/365719.365958 + +[@chan:1983a]: https://doi.org/10.1080/00031305.1983.10483115 + +[@schubert:2018a]: https://doi.org/10.1145/3221664.3221674 + +
+ + diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/benchmark/benchmark.js b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/benchmark/benchmark.js new file mode 100644 index 000000000000..2b9044e295fa --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/benchmark/benchmark.js @@ -0,0 +1,112 @@ +/** +* @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/array/uniform' ); +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 scalar2ndarray = require( '@stdlib/ndarray/from-scalar' ); +var format = require( '@stdlib/string/format' ); +var pkg = require( './../package.json' ).name; +var dvariancech = require( './../lib' ); + + +// VARIABLES // + +var options = { + 'dtype': 'float64' +}; + + +// FUNCTIONS // + +/** +* Creates a benchmark function. +* +* @private +* @param {PositiveInteger} len - array length +* @returns {Function} benchmark function +*/ +function createBenchmark( len ) { + var correction; + var xbuf; + var x; + + xbuf = uniform( len, -10.0, 10.0, options ); + x = new ndarray( options.dtype, xbuf, [ len ], [ 1 ], 0, 'row-major' ); + 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 = dvariancech( [ 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/dvariancech/docs/img/equation_sample_mean.svg b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/img/equation_sample_mean.svg new file mode 100644 index 000000000000..aea7a5f6687a --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/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/dvariancech/docs/repl.txt b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/repl.txt new file mode 100644 index 000000000000..f04dfe57d926 --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/repl.txt @@ -0,0 +1,54 @@ + +{{alias}}( arrays ) + Computes the variance of a one-dimensional single-precision floating-point + ndarray using a one-pass trial mean algorithm. + + 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 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 two elements: a one-dimensional input + ndarray and 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 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 input ndarray: + > var xbuf = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 2.0 ] ); + > var dt = 'float64'; + > var sh = [ xbuf.length ]; + > var st = [ 1 ]; + > var oo = 0; + > var ord = 'row-major'; + > var x = new {{alias:@stdlib/ndarray/ctor}}( dt, xbuf, sh, st, oo, ord ); + + // Create correction ndarray: + > var opts = { 'dtype': dt }; + > 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/dvariancech/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/types/index.d.ts new file mode 100644 index 000000000000..67b67bd96c99 --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/types/index.d.ts @@ -0,0 +1,52 @@ +/* +* @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 { typedndarray } from '@stdlib/types/ndarray'; + +/** +* Computes the variance of a one-dimensional double-precision floating-point ndarray using a one-pass trial mean algorithm. +* +* @param arrays - array-like object containing a one-dimensional input ndarray and a zero-dimensional ndarray specifying a degrees of freedom adjustment +* @returns variance +* +* @example +* var ndarray = require( '@stdlib/ndarray/base/ctor' ); +* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' ); +* var Float64Array = require( '@stdlib/array/float64' ); +* +* var opts = { +* 'dtype': 'float64' +* }; +* +* var xbuf = new Float64Array( [ 1.0, -2.0, 2.0 ] ); +* var x = new ndarray( opts.dtype, xbuf, [ 3 ], [ 1 ], 0, 'row-major' ); +* var correction = scalar2ndarray( 1.0, opts ); +* +* var v = dvariancech( [ x, correction ] ); +* // returns ~4.3333 +*/ +declare function dvariancech = typedndarray>( arrays: [ T, T ] ): number; + + +// EXPORTS // + +export = dvariancech; diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/types/test.ts b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/types/test.ts new file mode 100644 index 000000000000..19dbe1714dea --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/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 dvariancech = require( './index' ); + + +// TESTS // + +// The function returns a number... +{ + const x = zeros( [ 10 ], { + 'dtype': 'float64' + }); + const correction = scalar2ndarray( 1.0, { + 'dtype': 'float64' + }); + + dvariancech( [ x, correction ] ); // $ExpectType number +} + +// The compiler throws an error if the function is provided a first argument which is not an array of ndarrays... +{ + dvariancech( '10' ); // $ExpectError + dvariancech( 10 ); // $ExpectError + dvariancech( true ); // $ExpectError + dvariancech( false ); // $ExpectError + dvariancech( null ); // $ExpectError + dvariancech( undefined ); // $ExpectError + dvariancech( [] ); // $ExpectError + dvariancech( {} ); // $ExpectError + dvariancech( ( 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' + }); + + dvariancech(); // $ExpectError + dvariancech( [ x, correction ], 10 ); // $ExpectError +} + diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/examples/index.js b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/examples/index.js new file mode 100644 index 000000000000..0530d4fbd18e --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/examples/index.js @@ -0,0 +1,37 @@ +/** +* @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 discreteUniform = require( '@stdlib/random/array/discrete-uniform' ); +var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' ); +var ndarray = require( '@stdlib/ndarray/base/ctor' ); +var ndarray2array = require( '@stdlib/ndarray/to-array' ); +var dvariancech = require( './../lib' ); + +var opts = { + 'dtype': 'float64' +}; + +var xbuf = discreteUniform( 10, -50, 50, opts ); +var x = new ndarray( opts.dtype, xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' ); +console.log( ndarray2array( x ) ); + +var correction = scalar2ndarray( 1.0, opts ); +var v = dvariancech( [ x, correction ] ); +console.log( v ); diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/lib/index.js b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/lib/index.js new file mode 100644 index 000000000000..fa0d8357a28b --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/lib/index.js @@ -0,0 +1,55 @@ +/** +* @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 using a one-pass trial mean algorithm. +* +* @module @stdlib/stats/base/ndarray/dvariancech +* +* @example +* var Float64Array = require( '@stdlib/array/float64' ); +* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' ); +* var ndarray = require( '@stdlib/ndarray/ctor' ); +* var dvariancech = require( '@stdlib/stats/base/ndarray/dvariancech' ); +* +* var opts = { +* 'dtype': 'float64' +* }; +* +* // Define a one-dimensional input ndarray: +* var xbuf = new Float64Array( [ 1.0, -2.0, 2.0 ] ); +* var x = new ndarray( opts.dtype, xbuf, [ 3 ], [ 1 ], 0, 'row-major' ); +* +* // Specify the degrees of freedom adjustment: +* var correction = scalar2ndarray( 1.0, opts ); +* +* // Compute the variance: +* var v = dvariancech( [ 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/dvariancech/lib/main.js b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/lib/main.js new file mode 100644 index 000000000000..e683b0384220 --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/lib/main.js @@ -0,0 +1,69 @@ +/** +* @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/dvariancech' ).ndarray; + + +// MAIN // + +/** +* Computes the variance of a one-dimensional double-precision floating-point ndarray using a one-pass trial mean algorithm. +* +* @param {ArrayLikeObject} arrays - array-like object containing a one-dimensional input ndarray and a zero-dimensional ndarray specifying a degrees of freedom adjustment +* @returns {number} variance +* +* @example +* var Float64Array = require( '@stdlib/array/float64' ); +* var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' ); +* var ndarray = require( '@stdlib/ndarray/base/ctor' ); +* +* var opts = { +* 'dtype': 'float64' +* }; +* +* var xbuf = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] ); +* var x = new ndarray( opts.dtype, xbuf, [ 4 ], [ 2 ], 1, 'row-major' ); +* +* var correction = scalar2ndarray( 1.0, opts ); +* +* var v = variancech( [ x, correction ] ); +* // returns 6.25 +*/ +function variancech( 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 = variancech; diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/package.json b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/package.json new file mode 100644 index 000000000000..4947818eac98 --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/package.json @@ -0,0 +1,71 @@ +{ + "name": "@stdlib/stats/base/ndarray/dvariancech", + "version": "0.0.0", + "description": "Compute the variance of a one-dimensional double-precision floating-point ndarray using a one-pass trial mean algorithm.", + "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", + "std", + "ndarray", + "typed", + "array" + ], + "__stdlib__": {} +} diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/test/test.js b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/test/test.js new file mode 100644 index 000000000000..14cdcc4ee94b --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/test/test.js @@ -0,0 +1,248 @@ +/** +* @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 isnanf = require( '@stdlib/math/base/assert/is-nanf' ); +var float64Array = require( '@stdlib/array/float64' ); +var scalar2ndarray = require( '@stdlib/ndarray/from-scalar' ); +var ndarray = require( '@stdlib/ndarray/base/ctor' ); +var dvariancech = 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 dvariancech, 'function', 'main export is a function' ); + t.end(); +}); + +tape( 'the function has an arity of 1', function test( t ) { + t.strictEqual( dvariancech.length, 1, 'has expected arity' ); + t.end(); +}); + +tape( 'the function calculates the variance of a one-dimensional ndarray', 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, 5.0, 0.0, 3.0 ] ); + correction = scalar2ndarray( 1.0, opts ); + + v = dvariancech( [ vector( x, x.length, 1, 0 ), correction ] ); + expected = 53.5 / ( x.length - 1 ); + t.strictEqual( v, expected, 'returns expected value' ); + + x = new float64Array( [ -4.0, -5.0 ] ); + correction = scalar2ndarray( 1.0, opts ); + + v = dvariancech( [ 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 = dvariancech( [ vector( x, x.length, 1, 0 ), correction ] ); + t.strictEqual( isnanf( v ), true, 'returns expected value' ); + + x = new float64Array( [ NaN, NaN ] ); + correction = scalar2ndarray( 1.0, opts ); + + v = dvariancech( [ vector( x, x.length, 1, 0 ), correction ] ); + t.strictEqual( isnanf( 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 = dvariancech( [ vector( x, 0, 1, 0 ), correction ] ); + t.strictEqual( isnanf( 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 = dvariancech( [ vector( x, 1, 1, 0 ), correction ] ); + t.strictEqual( isnanf( 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 + ]); + correction = scalar2ndarray( 1.0, opts ); + + v = dvariancech( [ vector( x, 4, 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, // 3 + 2.0, + 2.0, // 2 + -7.0, + -2.0, // 1 + 3.0, + 4.0, // 0 + 2.0 + ]); + correction = scalar2ndarray( 1.0, opts ); + + v = dvariancech( [ vector( x, 4, -2, 6 ), 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 + ]); + correction = scalar2ndarray( 1.0, opts ); + + v = dvariancech( [ vector( x, 4, 2, 1 ), correction ] ); + expected = 6.25; + t.strictEqual( v, expected, 'returns expected value' ); + + t.end(); +}); + +tape( 'the function returns zero when all elements are equal', function test( t ) { + var correction; + var expected; + var opts; + var x; + var v; + + opts = { + 'dtype': 'float64' + }; + + x = new float64Array( [ 5.0, 5.0, 5.0, 5.0 ] ); + correction = scalar2ndarray( 1.0, opts ); + + v = dvariancech( [ vector( x, x.length, 1, 0 ), correction ] ); + expected = 0.0; + t.strictEqual( v, expected, 'returns expected value' ); + + t.end(); +}); From 43351f94491ce90dc3c9ad29943c92479650ef3e Mon Sep 17 00:00:00 2001 From: DivyanshuVorrtex Date: Sun, 11 Jan 2026 07:18:17 +0000 Subject: [PATCH 2/4] fix(dvariancech): fix a package stats/base/ndarray/dvariancech --- 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 status: na - task: lint_package_json status: na - task: lint_repl_help status: passed - task: lint_javascript_src status: na - task: lint_javascript_cli status: na - task: lint_javascript_examples status: na - task: lint_javascript_tests status: passed - task: lint_javascript_benchmarks status: na - 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: na - task: lint_license_headers status: passed --- --- .../base/ndarray/dvariancech/docs/repl.txt | 2 +- .../base/ndarray/dvariancech/test/test.js | 32 +++++++++---------- 2 files changed, 17 insertions(+), 17 deletions(-) diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/repl.txt b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/repl.txt index f04dfe57d926..04bd248d9515 100644 --- a/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/repl.txt +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/repl.txt @@ -1,6 +1,6 @@ {{alias}}( arrays ) - Computes the variance of a one-dimensional single-precision floating-point + Computes the variance of a one-dimensional double-precision floating-point ndarray using a one-pass trial mean algorithm. If provided an empty one-dimensional ndarray, the function returns `NaN`. diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/test/test.js b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/test/test.js index 14cdcc4ee94b..c0ecd92b86e1 100644 --- a/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/test/test.js +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/test/test.js @@ -21,8 +21,8 @@ // MODULES // var tape = require( 'tape' ); -var isnanf = require( '@stdlib/math/base/assert/is-nanf' ); -var float64Array = require( '@stdlib/array/float64' ); +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 dvariancech = require( './../lib' ); @@ -69,31 +69,31 @@ tape( 'the function calculates the variance of a one-dimensional ndarray', funct 'dtype': 'float64' }; - x = new float64Array( [ 1.0, -2.0, -4.0, 5.0, 0.0, 3.0 ] ); + x = new Float64Array( [ 1.0, -2.0, -4.0, 5.0, 0.0, 3.0 ] ); correction = scalar2ndarray( 1.0, opts ); v = dvariancech( [ vector( x, x.length, 1, 0 ), correction ] ); expected = 53.5 / ( x.length - 1 ); t.strictEqual( v, expected, 'returns expected value' ); - x = new float64Array( [ -4.0, -5.0 ] ); + x = new Float64Array( [ -4.0, -5.0 ] ); correction = scalar2ndarray( 1.0, opts ); v = dvariancech( [ vector( x, x.length, 1, 0 ), correction ] ); expected = 0.5; t.strictEqual( v, expected, 'returns expected value' ); - x = new float64Array( [ NaN ] ); + x = new Float64Array( [ NaN ] ); correction = scalar2ndarray( 1.0, opts ); v = dvariancech( [ vector( x, x.length, 1, 0 ), correction ] ); - t.strictEqual( isnanf( v ), true, 'returns expected value' ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); - x = new float64Array( [ NaN, NaN ] ); + x = new Float64Array( [ NaN, NaN ] ); correction = scalar2ndarray( 1.0, opts ); v = dvariancech( [ vector( x, x.length, 1, 0 ), correction ] ); - t.strictEqual( isnanf( v ), true, 'returns expected value' ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); t.end(); }); @@ -108,11 +108,11 @@ tape( 'if provided an empty ndarray, the function returns `NaN`', function test( 'dtype': 'float64' }; - x = new float64Array( [] ); + x = new Float64Array( [] ); correction = scalar2ndarray( 1.0, opts ); v = dvariancech( [ vector( x, 0, 1, 0 ), correction ] ); - t.strictEqual( isnanf( v ), true, 'returns expected value' ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); t.end(); }); @@ -127,11 +127,11 @@ tape( 'if provided a correction argument yielding `N-correction` less than or eq 'dtype': 'float64' }; - x = new float64Array( [ 1.0 ] ); + x = new Float64Array( [ 1.0 ] ); correction = scalar2ndarray( 1.0, opts ); v = dvariancech( [ vector( x, 1, 1, 0 ), correction ] ); - t.strictEqual( isnanf( v ), true, 'returns expected value' ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); t.end(); }); @@ -147,7 +147,7 @@ tape( 'the function supports one-dimensional ndarrays having non-unit strides', 'dtype': 'float64' }; - x = new float64Array([ + x = new Float64Array([ 1.0, // 0 2.0, 2.0, // 1 @@ -177,7 +177,7 @@ tape( 'the function supports one-dimensional ndarrays having negative strides', 'dtype': 'float64' }; - x = new float64Array([ + x = new Float64Array([ 1.0, // 3 2.0, 2.0, // 2 @@ -207,7 +207,7 @@ tape( 'the function supports one-dimensional ndarrays having non-zero offsets', 'dtype': 'float64' }; - x = new float64Array([ + x = new Float64Array([ 2.0, 1.0, // 0 2.0, @@ -237,7 +237,7 @@ tape( 'the function returns zero when all elements are equal', function test( t 'dtype': 'float64' }; - x = new float64Array( [ 5.0, 5.0, 5.0, 5.0 ] ); + x = new Float64Array( [ 5.0, 5.0, 5.0, 5.0 ] ); correction = scalar2ndarray( 1.0, opts ); v = dvariancech( [ vector( x, x.length, 1, 0 ), correction ] ); From 4e6f1d8df3e6734d9971bedc305c087f373eb95d Mon Sep 17 00:00:00 2001 From: DivyanshuVorrtex Date: Tue, 13 Jan 2026 08:33:09 +0000 Subject: [PATCH 3/4] fix: fixing files and naming --- 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 status: na - task: lint_package_json status: na - task: lint_repl_help status: na - task: lint_javascript_src status: passed - task: lint_javascript_cli status: na - task: lint_javascript_examples status: na - task: lint_javascript_tests status: na - task: lint_javascript_benchmarks status: na - 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/dvariancech/docs/types/index.d.ts | 4 ++-- .../@stdlib/stats/base/ndarray/dvariancech/lib/main.js | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/types/index.d.ts index 67b67bd96c99..b234d03e39c2 100644 --- a/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/types/index.d.ts +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/types/index.d.ts @@ -20,7 +20,7 @@ /// -import { typedndarray } from '@stdlib/types/ndarray'; +import { float64ndarray, typedndarray } from '@stdlib/types/ndarray'; /** * Computes the variance of a one-dimensional double-precision floating-point ndarray using a one-pass trial mean algorithm. @@ -44,7 +44,7 @@ import { typedndarray } from '@stdlib/types/ndarray'; * var v = dvariancech( [ x, correction ] ); * // returns ~4.3333 */ -declare function dvariancech = typedndarray>( arrays: [ T, T ] ): number; +declare function dvariancech = typedndarray>( arrays: [ float64ndarray, T ] ): number; // EXPORTS // diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/lib/main.js b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/lib/main.js index e683b0384220..99338665b2f2 100644 --- a/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/lib/main.js +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/lib/main.js @@ -50,10 +50,10 @@ var strided = require( '@stdlib/stats/strided/dvariancech' ).ndarray; * * var correction = scalar2ndarray( 1.0, opts ); * -* var v = variancech( [ x, correction ] ); +* var v = dvariancech( [ x, correction ] ); * // returns 6.25 */ -function variancech( arrays ) { +function dvariancech( arrays ) { var correction; var x; @@ -66,4 +66,4 @@ function variancech( arrays ) { // EXPORTS // -module.exports = variancech; +module.exports = dvariancech; From c165264d61cf73193ca07267053ddb6f2c8550d8 Mon Sep 17 00:00:00 2001 From: Divyanshu Date: Sun, 18 Jan 2026 09:46:04 +0530 Subject: [PATCH 4/4] Update test.ts --- .../@stdlib/stats/base/ndarray/dvariancech/docs/types/test.ts | 1 - 1 file changed, 1 deletion(-) diff --git a/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/types/test.ts b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/types/test.ts index 19dbe1714dea..e2695aa2807f 100644 --- a/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/types/test.ts +++ b/lib/node_modules/@stdlib/stats/base/ndarray/dvariancech/docs/types/test.ts @@ -62,4 +62,3 @@ import dvariancech = require( './index' ); dvariancech(); // $ExpectError dvariancech( [ x, correction ], 10 ); // $ExpectError } -