From 4f7cb74c85eb79b3ab569a3344798a94ac4d0e9b Mon Sep 17 00:00:00 2001 From: Rahul Kumar Date: Tue, 18 Mar 2025 21:44:18 +0000 Subject: [PATCH 01/10] feat: add accessor protocol support and refactor stats/base/nanvarinacech --- 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: na - 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/nanvariancech/README.md | 53 ++--- .../base/nanvariancech/benchmark/benchmark.js | 29 ++- .../benchmark/benchmark.ndarray.js | 29 ++- .../stats/base/nanvariancech/docs/repl.txt | 38 ++-- .../base/nanvariancech/docs/types/index.d.ts | 19 +- .../base/nanvariancech/docs/types/test.ts | 6 +- .../base/nanvariancech/examples/index.js | 21 +- .../stats/base/nanvariancech/lib/accessors.js | 122 ++++++++++ .../base/nanvariancech/lib/nanvariancech.js | 70 +----- .../stats/base/nanvariancech/lib/ndarray.js | 34 +-- .../nanvariancech/test/test.nanvariancech.js | 191 +++++++++++++++- .../base/nanvariancech/test/test.ndarray.js | 214 +++++++++++++++++- 12 files changed, 636 insertions(+), 190 deletions(-) create mode 100644 lib/node_modules/@stdlib/stats/base/nanvariancech/lib/accessors.js diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md b/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md index 479cf68094bd..4711fecb45a5 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md @@ -98,7 +98,7 @@ The use of the term `n-1` is commonly referred to as Bessel's correction. Note, var nanvariancech = require( '@stdlib/stats/base/nanvariancech' ); ``` -#### nanvariancech( N, correction, x, stride ) +#### nanvariancech( N, correction, x, strideX ) Computes the [variance][variance] of a strided array `x` ignoring `NaN` values and using a one-pass trial mean algorithm. @@ -114,17 +114,14 @@ The function has the following parameters: - **N**: number of indexed elements. - **correction**: degrees of freedom adjustment. Setting this parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the [variance][variance] according to `n-c` where `c` corresponds to the provided degrees of freedom adjustment and `n` corresponds to the number of non-`NaN` indexed elements. 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 unbiased 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). - **x**: input [`Array`][mdn-array] or [`typed array`][mdn-typed-array]. -- **stride**: index increment for `x`. +- **strideX**: stride length for `x`. -The `N` and `stride` parameters determine which elements in `x` are accessed at runtime. For example, to compute the [variance][variance] of every other element in `x`, +The `N` and stride parameters determine which elements in the stided array are accessed at runtime. For example, to compute the [variance][variance] of every other element in `x`, ```javascript -var floor = require( '@stdlib/math/base/special/floor' ); - var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN ]; -var N = floor( x.length / 2 ); -var v = nanvariancech( N, 1, x, 2 ); +var v = nanvariancech( 4, 1, x, 2 ); // returns 6.25 ``` @@ -134,41 +131,35 @@ Note that indexing is relative to the first index. To introduce an offset, use [ ```javascript var Float64Array = require( '@stdlib/array/float64' ); -var floor = require( '@stdlib/math/base/special/floor' ); var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN ] ); var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element -var N = floor( x0.length / 2 ); - -var v = nanvariancech( N, 1, x1, 2 ); +var v = nanvariancech( 4, 1, x1, 2 ); // returns 6.25 ``` -#### nanvariancech.ndarray( N, correction, x, stride, offset ) +#### nanvariancech.ndarray( N, correction, x, strideX, offsetX ) Computes the [variance][variance] of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm and alternative indexing semantics. ```javascript var x = [ 1.0, -2.0, NaN, 2.0 ]; -var v = nanvariancech.ndarray( x.length, 1, x, 1, 0 ); +var v = nanvariancech.ndarray( 4, 1, x, 1, 0 ); // returns ~4.33333 ``` The function has the following additional parameters: -- **offset**: starting index for `x`. +- **offsetX**: starting index for `x`. -While [`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 [variance][variance] for every other value in `x` starting from the second value +While [`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 [variance][variance] for every other element in the strided array starting from the second element ```javascript -var floor = require( '@stdlib/math/base/special/floor' ); +var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ]; -var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ]; -var N = floor( x.length / 2 ); - -var v = nanvariancech.ndarray( N, 1, x, 2, 1 ); +var v = nanvariancech.ndarray( 5, 1, x, 2, 1 ); // returns 6.25 ``` @@ -183,6 +174,7 @@ var v = nanvariancech.ndarray( N, 1, x, 2, 1 ); - If `N <= 0`, both functions return `NaN`. - If `n - c` is less than or equal to `0` (where `c` corresponds to the provided degrees of freedom adjustment and `n` corresponds to the number of non-`NaN` indexed elements), both functions return `NaN`. - The underlying algorithm is a specialized case of Neely's two-pass algorithm. As the variance is invariant with respect to changes in the location parameter, the underlying algorithm uses the first non-`NaN` strided array element as a trial mean to shift subsequent data values and thus mitigate catastrophic cancellation. Accordingly, the algorithm's accuracy is best when data is **unordered** (i.e., the data is **not** sorted in either ascending or descending order such that the first value is an "extreme" value). +- Both functions support array-like objects having getter and setter accessors for array element access (e.g., [`@stdlib/array/base/accessor`][@stdlib/array/base/accessor]). - Depending on the environment, the typed versions ([`dnanvariancech`][@stdlib/stats/base/dnanvariancech], [`snanvariancech`][@stdlib/stats/base/snanvariancech], etc.) are likely to be significantly more performant. @@ -196,18 +188,19 @@ var v = nanvariancech.ndarray( N, 1, x, 2, 1 ); ```javascript -var randu = require( '@stdlib/random/base/randu' ); -var round = require( '@stdlib/math/base/special/round' ); -var Float64Array = require( '@stdlib/array/float64' ); +var uniform = require( '@stdlib/random/base/uniform' ); +var filledarrayBy = require( '@stdlib/array/filled-by' ); +var bernoulli = require( '@stdlib/random/base/bernoulli' ); var nanvariancech = require( '@stdlib/stats/base/nanvariancech' ); -var x; -var i; - -x = new Float64Array( 10 ); -for ( i = 0; i < x.length; i++ ) { - x[ i ] = round( (randu()*100.0) - 50.0 ); +function rand() { + if ( bernoulli( 0.8 ) < 1 ) { + return NaN; + } + return uniform( -50.0, 50.0 ); } + +var x = filledarrayBy( 10, 'generic', rand ); console.log( x ); var v = nanvariancech( x.length, 1, x, 1 ); @@ -281,6 +274,8 @@ console.log( v ); [@stdlib/stats/base/variancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/variancech +[@stdlib/array/base/accessor]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/array/base/accessor + diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/benchmark/benchmark.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/benchmark/benchmark.js index 90544257d2e3..fd0a1451d9d2 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/benchmark/benchmark.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/benchmark/benchmark.js @@ -21,7 +21,9 @@ // MODULES // var bench = require( '@stdlib/bench' ); -var randu = require( '@stdlib/random/base/randu' ); +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 pkg = require( './../package.json' ).name; @@ -30,6 +32,19 @@ var nanvariancech = require( './../lib/nanvariancech.js' ); // FUNCTIONS // +/** +* Returns a random value or `NaN`. +* +* @private +* @returns {number} random number or `NaN` +*/ +function rand() { + if ( bernoulli( 0.8 ) < 1 ) { + return NaN; + } + return uniform( -10.0, 10.0 ); +} + /** * Creates a benchmark function. * @@ -38,17 +53,7 @@ var nanvariancech = require( './../lib/nanvariancech.js' ); * @returns {Function} benchmark function */ function createBenchmark( len ) { - var x; - var i; - - x = []; - for ( i = 0; i < len; i++ ) { - if ( randu() < 0.2 ) { - x.push( NaN ); - } else { - x.push( ( randu()*20.0 ) - 10.0 ); - } - } + var x = filledarrayBy( len, 'generic', rand ); return benchmark; function benchmark( b ) { diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/benchmark/benchmark.ndarray.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/benchmark/benchmark.ndarray.js index 070399adcdfa..d4183a1c2cd7 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/benchmark/benchmark.ndarray.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/benchmark/benchmark.ndarray.js @@ -21,7 +21,9 @@ // MODULES // var bench = require( '@stdlib/bench' ); -var randu = require( '@stdlib/random/base/randu' ); +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 pkg = require( './../package.json' ).name; @@ -30,6 +32,19 @@ var nanvariancech = require( './../lib/ndarray.js' ); // FUNCTIONS // +/** +* Returns a random value or `NaN`. +* +* @private +* @returns {number} random number or `NaN` +*/ +function rand() { + if ( bernoulli( 0.8 ) < 1 ) { + return NaN; + } + return uniform( -10.0, 10.0 ); +} + /** * Creates a benchmark function. * @@ -38,17 +53,7 @@ var nanvariancech = require( './../lib/ndarray.js' ); * @returns {Function} benchmark function */ function createBenchmark( len ) { - var x; - var i; - - x = []; - for ( i = 0; i < len; i++ ) { - if ( randu() < 0.2 ) { - x.push( NaN ); - } else { - x.push( ( randu()*20.0 ) - 10.0 ); - } - } + var x = filledarrayBy( len, 'generic', rand ); return benchmark; function benchmark( b ) { diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/repl.txt b/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/repl.txt index 2b19376b9f9d..27fa23f225d5 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/repl.txt +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/repl.txt @@ -1,10 +1,10 @@ -{{alias}}( N, correction, x, stride ) +{{alias}}( N, correction, x, strideX ) Computes the variance of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm. - The `N` and `stride` parameters determine which elements in `x` are accessed - at runtime. + The `N` and stride parameters determine which elements in the strided arrays + are accessed at runtime. Indexing is relative to the first index. To introduce an offset, use a typed array view. @@ -34,8 +34,8 @@ x: Array|TypedArray Input array. - stride: integer - Index increment. + strideX: integer + Stride length. Returns ------- @@ -46,31 +46,28 @@ -------- // Standard Usage: > var x = [ 1.0, -2.0, NaN, 2.0 ]; - > {{alias}}( x.length, 1, x, 1 ) + > {{alias}}( 4, 1, x, 1 ) ~4.3333 // Using `N` and `stride` parameters: > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ]; - > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 ); - > var stride = 2; - > {{alias}}( N, 1, x, stride ) + > {{alias}}( 3, 1, x, 2 ) ~4.3333 // Using view offsets: > var x0 = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] ); > var x1 = new {{alias:@stdlib/array/float64}}( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); - > N = {{alias:@stdlib/math/base/special/floor}}( x0.length / 2 ); - > stride = 2; - > {{alias}}( N, 1, x1, stride ) + > {{alias}}( 3, 1, x1, 2 ) ~4.3333 -{{alias}}.ndarray( N, correction, x, stride, offset ) + +{{alias}}.ndarray( N, correction, x, strideX, offsetX ) Computes the variance of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm and alternative indexing semantics. While typed array views mandate a view offset based on the underlying - buffer, the `offset` parameter supports indexing semantics based on a - starting index. + buffer, the offset parameters support indexing semantics based on a + starting indices. Parameters ---------- @@ -93,10 +90,10 @@ x: Array|TypedArray Input array. - stride: integer - Index increment. + strideX: integer + Stride length. - offset: integer + offsetX: integer Starting index. Returns @@ -108,13 +105,12 @@ -------- // Standard Usage: > var x = [ 1.0, -2.0, NaN, 2.0 ]; - > {{alias}}.ndarray( x.length, 1, x, 1, 0 ) + > {{alias}}.ndarray( 4, 1, x, 1, 0 ) ~4.3333 // Using offset parameter: > var x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ]; - > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 ); - > {{alias}}.ndarray( N, 1, x, 2, 1 ) + > {{alias}}.ndarray( 3, 1, x, 2, 1 ) ~4.3333 See Also diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/types/index.d.ts index 81f681661e59..735228752d94 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/types/index.d.ts +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/types/index.d.ts @@ -20,7 +20,12 @@ /// -import { NumericArray } from '@stdlib/types/array'; +import { NumericArray, Collection, AccessorArrayLike } from '@stdlib/types/array'; + +/** +* Input array. +*/ +type InputArray = NumericArray | Collection | AccessorArrayLike; /** * Interface describing `nanvariancech`. @@ -32,7 +37,7 @@ interface Routine { * @param N - number of indexed elements * @param correction - degrees of freedom adjustment * @param x - input array - * @param stride - stride length + * @param strideX - stride length * @returns variance * * @example @@ -41,7 +46,7 @@ interface Routine { * var v = nanvariancech( x.length, 1, x, 1 ); * // returns ~4.3333 */ - ( N: number, correction: number, x: NumericArray, stride: number ): number; + ( N: number, correction: number, x: InputArray, strideX: number ): number; /** * Computes the variance of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm and alternative indexing semantics. @@ -49,8 +54,8 @@ interface Routine { * @param N - number of indexed elements * @param correction - degrees of freedom adjustment * @param x - input array - * @param stride - stride length - * @param offset - starting index + * @param strideX - stride length + * @param offsetX - starting index * @returns variance * * @example @@ -59,7 +64,7 @@ interface Routine { * var v = nanvariancech.ndarray( x.length, 1, x, 1, 0 ); * // returns ~4.3333 */ - ndarray( N: number, correction: number, x: NumericArray, stride: number, offset: number ): number; + ndarray( N: number, correction: number, x: InputArray, strideX: number, offset: number ): number; } /** @@ -68,7 +73,7 @@ interface Routine { * @param N - number of indexed elements * @param correction - degrees of freedom adjustment * @param x - input array -* @param stride - stride length +* @param strideX - stride length * @returns variance * * @example diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/types/test.ts b/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/types/test.ts index 4722f6fe9412..8c5a2fcd87f2 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/types/test.ts +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/types/test.ts @@ -16,16 +16,16 @@ * limitations under the License. */ +import AccessorArray = require( '@stdlib/array/base/accessor' ); import nanvariancech = require( './index' ); - // TESTS // // The function returns a number... { const x = new Float64Array( 10 ); - nanvariancech( x.length, 1, x, 1 ); // $ExpectType number + nanvariancech( x.length, 1, new AccessorArray( x ), 1 ); // $ExpectType number } // The compiler throws an error if the function is provided a first argument which is not a number... @@ -100,7 +100,7 @@ import nanvariancech = require( './index' ); { const x = new Float64Array( 10 ); - nanvariancech.ndarray( x.length, 1, x, 1, 0 ); // $ExpectType number + nanvariancech.ndarray( x.length, 1, new AccessorArray( x ), 1, 0 ); // $ExpectType number } // The compiler throws an error if the `ndarray` method is provided a first argument which is not a number... diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/examples/index.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/examples/index.js index d49eb9c49d89..e2c1345ccc80 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/examples/index.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/examples/index.js @@ -18,22 +18,19 @@ 'use strict'; -var randu = require( '@stdlib/random/base/randu' ); -var round = require( '@stdlib/math/base/special/round' ); -var Float64Array = require( '@stdlib/array/float64' ); +var uniform = require( '@stdlib/random/base/uniform' ); +var filledarrayBy = require( '@stdlib/array/filled-by' ); +var bernoulli = require( '@stdlib/random/base/bernoulli' ); var nanvariancech = require( './../lib' ); -var x; -var i; - -x = new Float64Array( 10 ); -for ( i = 0; i < x.length; i++ ) { - if ( randu() < 0.2 ) { - x[ i ] = NaN; - } else { - x[ i ] = round( (randu()*100.0) - 50.0 ); +function rand() { + if ( bernoulli( 0.8 ) > 1 ) { + return NaN; } + return uniform( -50.0, 50.0 ); } + +var x = filledarrayBy( 10, 'generic', rand ); console.log( x ); var v = nanvariancech( x.length, 1, x, 1 ); diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/accessors.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/accessors.js new file mode 100644 index 000000000000..18b36a833e0e --- /dev/null +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/accessors.js @@ -0,0 +1,122 @@ +/** +* @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. +*/ + +'use strict'; + +// MAIN // + +/** +* Computes the variance of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm. +* +* ## Method +* +* - This implementation uses a one-pass trial mean approach, as suggested by Chan et al (1983). +* +* ## 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](https://doi.org/10.1145/365719.365958). +* - Ling, Robert F. 1974. "Comparison of Several Algorithms for Computing Sample Means and Variances." _Journal of the American Statistical Association_ 69 (348). American Statistical Association, Taylor & Francis, Ltd.: 859–66. doi:[10.2307/2286154](https://doi.org/10.2307/2286154). +* - 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, Ltd.: 242–47. doi:[10.1080/00031305.1983.10483115](https://doi.org/10.1080/00031305.1983.10483115). +* - Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co-)Variance." In _Proceedings of the 30th International Conference on Scientific and Statistical Database Management_. New York, NY, USA: Association for Computing Machinery. doi:[10.1145/3221269.3223036](https://doi.org/10.1145/3221269.3223036). +* +* @param {PositiveInteger} N - number of indexed elements +* @param {number} correction - degrees of freedom adjustment +* @param {Object} x - input array object +* @param {Collection} x.data - input array data +* @param {Array} x.accessors - array element accessors +* @param {integer} strideX - stride length +* @param {NonNegativeInteger} offsetX - starting index +* @returns {number} variance +* +* @example +* var arraylike2object = require( '@stdlib/array/base/arraylike2object' ); +* var toAccessorArray = require( '@stdlib/array/base/to-accessor-array' ); +* +* var x = toAccessorArray( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ] ); +* +* var v = nanvariancech( 5, 1, arraylike2object( x ), 2, 1 ); +* // returns 6.25 +*/ +function nanvariancech( N, correction, x, strideX, offsetX ) { + var xget; + var xbuf; + var mu; + var ix; + var M2; + var nc; + var M; + var d; + var v; + var n; + var i; + + // Cache references to array data: + xbuf = x.data; + + // Cache references to element accessors: + xget = x.accessors[ 0 ]; + + if ( N === 1 || strideX === 0 ) { + v = xget( xbuf, offsetX ); + if ( v === v && N-correction > 0.0 ) { + return 0.0; + } + return NaN; + } + ix = offsetX; + + // Find an estimate for the mean... + for ( i = 0; i < N; i++ ) { + v = xget( xbuf, ix ); + if ( v === v ) { + mu = v; + break; + } + ix += strideX; + } + if ( i === N ) { + return NaN; + } + ix += strideX; + i += 1; + + // Compute the variance... + M2 = 0.0; + M = 0.0; + n = 1; + for ( i; i < N; i++ ) { + v = xget( xbuf, ix ); + if ( v === v ) { + d = v - mu; + M2 += d * d; + M += d; + n += 1; + } + ix += strideX; + } + nc = n - correction; + if ( nc <= 0.0 ) { + return NaN; + } + return (M2/nc) - ((M/n)*(M/nc)); +} + + +// EXPORTS // + +module.exports = nanvariancech; diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/nanvariancech.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/nanvariancech.js index a69d93ed3990..04c366a9775c 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/nanvariancech.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/nanvariancech.js @@ -18,6 +18,12 @@ 'use strict'; +// MODULES // + +var stride2offset = require( '@stdlib/strided/base/stride2offset' ); +var ndarray = require( './ndarray.js' ); + + // MAIN // /** @@ -37,7 +43,7 @@ * @param {PositiveInteger} N - number of indexed elements * @param {number} correction - degrees of freedom adjustment * @param {NumericArray} x - input array -* @param {integer} stride - stride length +* @param {integer} strideX - stride length * @returns {number} variance * * @example @@ -46,66 +52,8 @@ * var v = nanvariancech( x.length, 1, x, 1 ); * // returns ~4.3333 */ -function nanvariancech( N, correction, x, stride ) { - var mu; - var ix; - var M2; - var nc; - var M; - var d; - var v; - var n; - var i; - - if ( N <= 0 ) { - return NaN; - } - if ( N === 1 || stride === 0 ) { - v = x[ 0 ]; - if ( v === v && N-correction > 0.0 ) { - return 0.0; - } - return NaN; - } - if ( stride < 0 ) { - ix = (1-N) * stride; - } else { - ix = 0; - } - // Find an estimate for the mean... - for ( i = 0; i < N; i++ ) { - v = x[ ix ]; - if ( v === v ) { - mu = v; - break; - } - ix += stride; - } - if ( i === N ) { - return NaN; - } - ix += stride; - i += 1; - - // Compute the variance... - M2 = 0.0; - M = 0.0; - n = 1; - for ( i; i < N; i++ ) { - v = x[ ix ]; - if ( v === v ) { - d = v - mu; - M2 += d * d; - M += d; - n += 1; - } - ix += stride; - } - nc = n - correction; - if ( nc <= 0.0 ) { - return NaN; - } - return (M2/nc) - ((M/n)*(M/nc)); +function nanvariancech( N, correction, x, strideX ) { + return ndarray( N, correction, x, strideX, stride2offset( N, strideX) ); } diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/ndarray.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/ndarray.js index c7a63df90567..58ee81d6bba4 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/ndarray.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/ndarray.js @@ -18,6 +18,12 @@ 'use strict'; +// MODULES // + +var arraylike2object = require( '@stdlib/array/base/arraylike2object' ); +var accessors = require( './accessors.js' ); + + // MAIN // /** @@ -37,20 +43,17 @@ * @param {PositiveInteger} N - number of indexed elements * @param {number} correction - degrees of freedom adjustment * @param {NumericArray} x - input array -* @param {integer} stride - stride length -* @param {NonNegativeInteger} offset - starting index +* @param {integer} strideX - stride length +* @param {NonNegativeInteger} offsetX - starting index * @returns {number} variance * * @example -* var floor = require( '@stdlib/math/base/special/floor' ); -* * var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ]; -* var N = floor( x.length / 2 ); * -* var v = nanvariancech( N, 1, x, 2, 1 ); +* var v = nanvariancech( 5, 1, x, 2, 1 ); * // returns 6.25 */ -function nanvariancech( N, correction, x, stride, offset ) { +function nanvariancech( N, correction, x, strideX, offsetX ) { var mu; var ix; var M2; @@ -60,18 +63,23 @@ function nanvariancech( N, correction, x, stride, offset ) { var v; var n; var i; + var o; if ( N <= 0 ) { return NaN; } - if ( N === 1 || stride === 0 ) { - v = x[ offset ]; + o = arraylike2object( x ); + if ( o.accessorProtocol ) { + return accessors( N, correction, o, strideX, offsetX ); + } + if ( N === 1 || strideX === 0 ) { + v = x[ offsetX ]; if ( v === v && N-correction > 0.0 ) { return 0.0; } return NaN; } - ix = offset; + ix = offsetX; // Find an estimate for the mean... for ( i = 0; i < N; i++ ) { @@ -80,12 +88,12 @@ function nanvariancech( N, correction, x, stride, offset ) { mu = v; break; } - ix += stride; + ix += strideX; } if ( i === N ) { return NaN; } - ix += stride; + ix += strideX; i += 1; // Compute the variance... @@ -100,7 +108,7 @@ function nanvariancech( N, correction, x, stride, offset ) { M += d; n += 1; } - ix += stride; + ix += strideX; } nc = n - correction; if ( nc <= 0.0 ) { diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.nanvariancech.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.nanvariancech.js index ec54970e47f7..ed1dd9a617f6 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.nanvariancech.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.nanvariancech.js @@ -21,9 +21,9 @@ // MODULES // var tape = require( 'tape' ); -var floor = require( '@stdlib/math/base/special/floor' ); var isnan = require( '@stdlib/math/base/assert/is-nan' ); var Float64Array = require( '@stdlib/array/float64' ); +var toAccessorArray = require( '@stdlib/array/base/to-accessor-array' ); var nanvariancech = require( './../lib/nanvariancech.js' ); @@ -148,6 +148,114 @@ tape( 'the function calculates the sample variance of a strided array (ignoring t.end(); }); +tape( 'the function calculates the population variance of a strided array (accessors) (ignoring `NaN` values)', function test( t ) { + var x; + var v; + var i; + + x = [ 1.0, -2.0, -4.0, 5.0, NaN, 0.0, 3.0 ]; + + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); + t.strictEqual( v, 53.5/(x.length-1), 'returns expected value' ); + + x = [ 1.0, NaN, NaN, -2.0, NaN, -4.0, NaN, 5.0, NaN, 0.0, 3.0, NaN ]; + + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); + t.strictEqual( v, 53.5/(x.length-6), 'returns expected value' ); + + x = [ -4.0, NaN ]; + + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); + t.strictEqual( v, 0.0, 'returns expected value' ); + + x = [ NaN, NaN ]; + + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ NaN ]; + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ 4.0 ]; + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); + t.strictEqual( v, 0.0, 'returns expected value' ); + + x = []; + for ( i = 0; i < 1e3; i++ ) { + x.push( 100.0 ); + } + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); + t.strictEqual( v, 0.0, 'returns expected value' ); + + x = [ NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN ]; + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = []; + for ( i = 0; i < 1e3; i++ ) { + x.push( NaN ); + } + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + t.end(); +}); + +tape( 'the function calculates the sample variance of a strided array (accessors) (ignoring `NaN` values)', function test( t ) { + var x; + var v; + var i; + + x = [ 1.0, -2.0, -4.0, 5.0, NaN, 0.0, 3.0 ]; + + v = nanvariancech( x.length, 1, toAccessorArray( x ), 1 ); + t.strictEqual( v, 53.5/(x.length-2), 'returns expected value' ); + + x = [ 1.0, NaN, NaN, -2.0, NaN, -4.0, NaN, 5.0, NaN, 0.0, 3.0, NaN ]; + + v = nanvariancech( x.length, 1, toAccessorArray( x ), 1 ); + t.strictEqual( v, 53.5/(x.length-7), 'returns expected value' ); + + x = [ -4.0, NaN ]; + + v = nanvariancech( x.length, 1, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ NaN, NaN ]; + + v = nanvariancech( x.length, 1, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ NaN ]; + v = nanvariancech( x.length, 1, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ 4.0 ]; + v = nanvariancech( x.length, 1, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = []; + for ( i = 0; i < 1e3; i++ ) { + x.push( 100.0 ); + } + v = nanvariancech( x.length, 1, toAccessorArray( x ), 1 ); + t.strictEqual( v, 0.0, 'returns expected value' ); + + x = [ NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN ]; + v = nanvariancech( x.length, 1, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = []; + for ( i = 0; i < 1e3; i++ ) { + x.push( NaN ); + } + v = nanvariancech( x.length, 1, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + t.end(); +}); + tape( 'if provided an `N` parameter less than or equal to `0`, the function returns `NaN`', function test( t ) { var x; var v; @@ -163,6 +271,21 @@ tape( 'if provided an `N` parameter less than or equal to `0`, the function retu t.end(); }); +tape( 'if provided an `N` parameter less than or equal to `0`, the function returns `NaN`', function test( t ) { + var x; + var v; + + x = [ 1.0, -2.0, -4.0, 5.0, 3.0 ]; + + v = nanvariancech( 0, 1, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + v = nanvariancech( -1, 1, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + t.end(); +}); + tape( 'if provided an `N` parameter equal to `1`, the function returns a population variance of `0` provided the first element is not `NaN`', function test( t ) { var x; var v; @@ -213,7 +336,6 @@ tape( 'if provided a `correction` parameter yielding a correction term less than }); tape( 'the function supports a `stride` parameter', function test( t ) { - var N; var x; var v; @@ -230,15 +352,36 @@ tape( 'the function supports a `stride` parameter', function test( t ) { NaN ]; - N = floor( x.length / 2 ); - v = nanvariancech( N, 1, x, 2 ); + v = nanvariancech( 4, 1, x, 2 ); + + t.strictEqual( v, 6.25, 'returns expected value' ); + t.end(); +}); + +tape( 'the function supports a `stride` parameter (accessors)', function test( t ) { + var x; + var v; + + x = [ + 1.0, // 0 + 2.0, + 2.0, // 1 + -7.0, + -2.0, // 2 + 3.0, + 4.0, // 3 + 2.0, + NaN, // 4 + NaN + ]; + + v = nanvariancech( 4, 1, toAccessorArray( x ), 2 ); t.strictEqual( v, 6.25, 'returns expected value' ); t.end(); }); tape( 'the function supports a negative `stride` parameter', function test( t ) { - var N; var x; var v; var i; @@ -255,9 +398,8 @@ tape( 'the function supports a negative `stride` parameter', function test( t ) 4.0, // 0 2.0 ]; - N = floor( x.length / 2 ); - v = nanvariancech( N, 1, x, -2 ); + v = nanvariancech( 5, 1, x, -2 ); t.strictEqual( v, 6.25, 'returns expected value' ); x = []; @@ -270,6 +412,37 @@ tape( 'the function supports a negative `stride` parameter', function test( t ) t.end(); }); +tape( 'the function supports a negative `stride` parameter (accessors)', function test( t ) { + var x; + var v; + var i; + + x = [ + NaN, // 4 + NaN, + 1.0, // 3 + 2.0, + 2.0, // 2 + -7.0, + -2.0, // 1 + 3.0, + 4.0, // 0 + 2.0 + ]; + + v = nanvariancech( 5, 1, toAccessorArray( x ), -2 ); + t.strictEqual( v, 6.25, 'returns expected value' ); + + x = []; + for ( i = 0; i < 1e3; i++ ) { + x.push( 100.0 ); + } + v = nanvariancech( x.length, 1, toAccessorArray( x ), -1 ); + t.strictEqual( v, 0.0, 'returns expected value' ); + + t.end(); +}); + tape( 'if provided a `stride` parameter equal to `0`, the function returns `0` provided the correction term is not less than `0` and the first element is not `NaN`', function test( t ) { var x; var v; @@ -295,7 +468,6 @@ tape( 'if provided a `stride` parameter equal to `0`, the function returns `0` p tape( 'the function supports view offsets', function test( t ) { var x0; var x1; - var N; var v; x0 = new Float64Array([ @@ -313,9 +485,8 @@ tape( 'the function supports view offsets', function test( t ) { ]); x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element - N = floor(x1.length / 2); - v = nanvariancech( N, 1, x1, 2 ); + v = nanvariancech( 4, 1, x1, 2 ); t.strictEqual( v, 6.25, 'returns expected value' ); t.end(); diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.ndarray.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.ndarray.js index 8a66200f7f9f..8cb11920554b 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.ndarray.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.ndarray.js @@ -21,7 +21,7 @@ // MODULES // var tape = require( 'tape' ); -var floor = require( '@stdlib/math/base/special/floor' ); +var toAccessorArray = require( '@stdlib/array/base/to-accessor-array' ); var isnan = require( '@stdlib/math/base/assert/is-nan' ); var nanvariancech = require( './../lib/ndarray.js' ); @@ -147,6 +147,114 @@ tape( 'the function calculates the sample variance of a strided array (ignoring t.end(); }); +tape( 'the function calculates the population variance of a strided array (accessors) (ignoring `NaN` values)', function test( t ) { + var x; + var v; + var i; + + x = [ 1.0, -2.0, -4.0, 5.0, NaN, 0.0, 3.0 ]; + + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); + t.strictEqual( v, 53.5/(x.length-1), 'returns expected value' ); + + x = [ 1.0, NaN, NaN, -2.0, NaN, -4.0, NaN, 5.0, NaN, 0.0, 3.0, NaN ]; + + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); + t.strictEqual( v, 53.5/(x.length-6), 'returns expected value' ); + + x = [ -4.0, NaN ]; + + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); + t.strictEqual( v, 0.0, 'returns expected value' ); + + x = [ NaN, NaN ]; + + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ NaN ]; + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ 4.0 ]; + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); + t.strictEqual( v, 0.0, 'returns expected value' ); + + x = []; + for ( i = 0; i < 1e3; i++ ) { + x.push( 100.0 ); + } + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); + t.strictEqual( v, 0.0, 'returns expected value' ); + + x = [ NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN ]; + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = []; + for ( i = 0; i < 1e3; i++ ) { + x.push( NaN ); + } + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + t.end(); +}); + +tape( 'the function calculates the sample variance of a strided array (accessors) (ignoring `NaN` values)', function test( t ) { + var x; + var v; + var i; + + x = [ 1.0, -2.0, -4.0, 5.0, NaN, 0.0, 3.0 ]; + + v = nanvariancech( x.length, 1, toAccessorArray( x ), 1, 0 ); + t.strictEqual( v, 53.5/(x.length-2), 'returns expected value' ); + + x = [ 1.0, NaN, NaN, -2.0, NaN, -4.0, NaN, 5.0, NaN, 0.0, 3.0, NaN ]; + + v = nanvariancech( x.length, 1, toAccessorArray( x ), 1, 0 ); + t.strictEqual( v, 53.5/(x.length-7), 'returns expected value' ); + + x = [ -4.0, NaN ]; + + v = nanvariancech( x.length, 1, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ NaN, NaN ]; + + v = nanvariancech( x.length, 1, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ NaN ]; + v = nanvariancech( x.length, 1, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ 4.0 ]; + v = nanvariancech( x.length, 1, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = []; + for ( i = 0; i < 1e3; i++ ) { + x.push( 100.0 ); + } + v = nanvariancech( x.length, 1, toAccessorArray( x ), 1, 0 ); + t.strictEqual( v, 0.0, 'returns expected value' ); + + x = [ NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN ]; + v = nanvariancech( x.length, 1, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = []; + for ( i = 0; i < 1e3; i++ ) { + x.push( NaN ); + } + v = nanvariancech( x.length, 1, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + t.end(); +}); + tape( 'if provided an `N` parameter less than or equal to `0`, the function returns `NaN`', function test( t ) { var x; var v; @@ -162,6 +270,21 @@ tape( 'if provided an `N` parameter less than or equal to `0`, the function retu t.end(); }); +tape( 'if provided an `N` parameter less than or equal to `0`, the function returns `NaN` (accessors)', function test( t ) { + var x; + var v; + + x = [ 1.0, -2.0, -4.0, 5.0, 3.0 ]; + + v = nanvariancech( 0, 1, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + v = nanvariancech( -1, 1, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + t.end(); +}); + tape( 'if provided an `N` parameter equal to `1`, the function returns a population variance of `0` provided the first element is not `NaN`', function test( t ) { var x; var v; @@ -212,7 +335,6 @@ tape( 'if provided a `correction` parameter yielding a correction term less than }); tape( 'the function supports a `stride` parameter', function test( t ) { - var N; var x; var v; @@ -229,15 +351,13 @@ tape( 'the function supports a `stride` parameter', function test( t ) { NaN ]; - N = floor( x.length / 2 ); - v = nanvariancech( N, 1, x, 2, 0 ); + v = nanvariancech( 4, 1, x, 2, 0 ); t.strictEqual( v, 6.25, 'returns expected value' ); t.end(); }); tape( 'the function supports a negative `stride` parameter', function test( t ) { - var N; var x; var v; var i; @@ -254,9 +374,8 @@ tape( 'the function supports a negative `stride` parameter', function test( t ) 4.0, // 0 2.0 ]; - N = floor( x.length / 2 ); - v = nanvariancech( N, 1, x, -2, 8 ); + v = nanvariancech( 4, 1, x, -2, 8 ); t.strictEqual( v, 6.25, 'returns expected value' ); x = []; @@ -269,6 +388,60 @@ tape( 'the function supports a negative `stride` parameter', function test( t ) t.end(); }); +tape( 'the function supports a `stride` parameter (accessors)', function test( t ) { + var x; + var v; + + x = [ + 1.0, // 0 + 2.0, + 2.0, // 1 + -7.0, + -2.0, // 2 + 3.0, + 4.0, // 3 + 2.0, + NaN, // 4 + NaN + ]; + + v = nanvariancech( 4, 1, toAccessorArray( x ), 2, 0 ); + + t.strictEqual( v, 6.25, 'returns expected value' ); + t.end(); +}); + +tape( 'the function supports a negative `stride` parameter (accessors)', function test( t ) { + var x; + var v; + var i; + + x = [ + NaN, // 4 + NaN, + 1.0, // 3 + 2.0, + 2.0, // 2 + -7.0, + -2.0, // 1 + 3.0, + 4.0, // 0 + 2.0 + ]; + + v = nanvariancech( 4, 1, toAccessorArray( x ), -2, 8 ); + t.strictEqual( v, 6.25, 'returns expected value' ); + + x = []; + for ( i = 0; i < 1e3; i++ ) { + x.push( 100.0 ); + } + v = nanvariancech( x.length, 1, toAccessorArray( x ), -1, x.length-1 ); + t.strictEqual( v, 0.0, 'returns expected value' ); + + t.end(); +}); + tape( 'if provided a `stride` parameter equal to `0`, the function returns `0` provided the correction term is not less than `0` and the first element is not `NaN`', function test( t ) { var x; var v; @@ -292,7 +465,6 @@ tape( 'if provided a `stride` parameter equal to `0`, the function returns `0` p }); tape( 'the function supports an `offset` parameter', function test( t ) { - var N; var x; var v; @@ -308,9 +480,31 @@ tape( 'the function supports an `offset` parameter', function test( t ) { NaN, NaN // 4 ]; - N = floor( x.length / 2 ); - v = nanvariancech( N, 1, x, 2, 1 ); + v = nanvariancech( 4, 1, x, 2, 1 ); + t.strictEqual( v, 6.25, 'returns expected value' ); + + t.end(); +}); + +tape( 'the function supports an `offset` parameter (accessors)', function test( t ) { + var x; + var v; + + x = [ + 2.0, + 1.0, // 0 + 2.0, + -2.0, // 1 + -2.0, + 2.0, // 2 + 3.0, + 4.0, // 3 + NaN, + NaN // 4 + ]; + + v = nanvariancech( 4, 1, toAccessorArray( x ), 2, 1 ); t.strictEqual( v, 6.25, 'returns expected value' ); t.end(); From da0f9d55588f4d90bfba299bfca41efac173f779 Mon Sep 17 00:00:00 2001 From: Rahul Kumar Date: Fri, 4 Apr 2025 06:16:19 +0000 Subject: [PATCH 02/10] refactor: add requested changes --- 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: 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: na - task: lint_typescript_tests status: na - task: lint_license_headers status: passed --- --- .../@stdlib/stats/base/nanvariancech/README.md | 16 ++++++++-------- .../stats/base/nanvariancech/docs/repl.txt | 18 +++++++++--------- .../nanvariancech/test/test.nanvariancech.js | 6 +++--- .../base/nanvariancech/test/test.ndarray.js | 12 ++++++------ 4 files changed, 26 insertions(+), 26 deletions(-) diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md b/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md index 4711fecb45a5..720b2cee06f9 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md @@ -119,23 +119,23 @@ The function has the following parameters: The `N` and stride parameters determine which elements in the stided array are accessed at runtime. For example, to compute the [variance][variance] of every other element in `x`, ```javascript -var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN ]; +var x = [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0, NaN, NaN ]; -var v = nanvariancech( 4, 1, x, 2 ); +var v = nanvariancech( 5, 1, x, 2 ); // returns 6.25 ``` Note that indexing is relative to the first index. To introduce an offset, use [`typed 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, NaN ] ); +var x0 = new Float64Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ] ); var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element -var v = nanvariancech( 4, 1, x1, 2 ); +var v = nanvariancech( 5, 1, x1, 2 ); // returns 6.25 ``` @@ -175,7 +175,7 @@ var v = nanvariancech.ndarray( 5, 1, x, 2, 1 ); - If `n - c` is less than or equal to `0` (where `c` corresponds to the provided degrees of freedom adjustment and `n` corresponds to the number of non-`NaN` indexed elements), both functions return `NaN`. - The underlying algorithm is a specialized case of Neely's two-pass algorithm. As the variance is invariant with respect to changes in the location parameter, the underlying algorithm uses the first non-`NaN` strided array element as a trial mean to shift subsequent data values and thus mitigate catastrophic cancellation. Accordingly, the algorithm's accuracy is best when data is **unordered** (i.e., the data is **not** sorted in either ascending or descending order such that the first value is an "extreme" value). - Both functions support array-like objects having getter and setter accessors for array element access (e.g., [`@stdlib/array/base/accessor`][@stdlib/array/base/accessor]). -- Depending on the environment, the typed versions ([`dnanvariancech`][@stdlib/stats/base/dnanvariancech], [`snanvariancech`][@stdlib/stats/base/snanvariancech], etc.) are likely to be significantly more performant. +- Depending on the environment, the typed versions ([`dnanvariancech`][@stdlib/stats/strided/dnanvariancech], [`snanvariancech`][@stdlib/stats/base/snanvariancech], etc.) are likely to be significantly more performant. @@ -234,7 +234,7 @@ console.log( v ); ## See Also -- [`@stdlib/stats/base/dnanvariancech`][@stdlib/stats/base/dnanvariancech]: calculate the variance of a double-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm. +- [`@stdlib/stats/strided/dnanvariancech`][@stdlib/stats/strided/dnanvariancech]: calculate the variance of a double-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm. - [`@stdlib/stats/base/nanstdevch`][@stdlib/stats/base/nanstdevch]: calculate the standard deviation of a strided array ignoring NaN values and using a one-pass trial mean algorithm. - [`@stdlib/stats/base/nanvariance`][@stdlib/stats/base/nanvariance]: calculate the variance of a strided array ignoring NaN values. - [`@stdlib/stats/base/snanvariancech`][@stdlib/stats/base/snanvariancech]: calculate the variance of a single-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm. @@ -264,7 +264,7 @@ console.log( v ); -[@stdlib/stats/base/dnanvariancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/dnanvariancech +[@stdlib/stats/strided/dnanvariancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/strided/dnanvariancech [@stdlib/stats/base/nanstdevch]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/nanstdevch diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/repl.txt b/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/repl.txt index 27fa23f225d5..b055883df0e4 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/repl.txt +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/repl.txt @@ -3,7 +3,7 @@ Computes the variance of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm. - The `N` and stride parameters determine which elements in the strided arrays + The `N` and stride parameters determine which elements in the strided array are accessed at runtime. Indexing is relative to the first index. To introduce an offset, use a typed @@ -50,14 +50,14 @@ ~4.3333 // Using `N` and `stride` parameters: - > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ]; - > {{alias}}( 3, 1, x, 2 ) + > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN, NaN ]; + > {{alias}}( 4, 1, x, 2 ) ~4.3333 // Using view offsets: - > var x0 = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] ); + > var x0 = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ] ); > var x1 = new {{alias:@stdlib/array/float64}}( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); - > {{alias}}( 3, 1, x1, 2 ) + > {{alias}}( 4, 1, x1, 2 ) ~4.3333 @@ -66,8 +66,8 @@ one-pass trial mean algorithm and alternative indexing semantics. While typed array views mandate a view offset based on the underlying - buffer, the offset parameters support indexing semantics based on a - starting indices. + buffer, the offset parameter supports indexing semantics based on a + starting index. Parameters ---------- @@ -109,8 +109,8 @@ ~4.3333 // Using offset parameter: - > var x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ]; - > {{alias}}.ndarray( 3, 1, x, 2, 1 ) + > var x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ]; + > {{alias}}.ndarray( 4, 1, x, 2, 1 ) ~4.3333 See Also diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.nanvariancech.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.nanvariancech.js index ed1dd9a617f6..ab418dc8faea 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.nanvariancech.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.nanvariancech.js @@ -352,7 +352,7 @@ tape( 'the function supports a `stride` parameter', function test( t ) { NaN ]; - v = nanvariancech( 4, 1, x, 2 ); + v = nanvariancech( 5, 1, x, 2 ); t.strictEqual( v, 6.25, 'returns expected value' ); t.end(); @@ -375,7 +375,7 @@ tape( 'the function supports a `stride` parameter (accessors)', function test( t NaN ]; - v = nanvariancech( 4, 1, toAccessorArray( x ), 2 ); + v = nanvariancech( 5, 1, toAccessorArray( x ), 2 ); t.strictEqual( v, 6.25, 'returns expected value' ); t.end(); @@ -486,7 +486,7 @@ tape( 'the function supports view offsets', function test( t ) { x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element - v = nanvariancech( 4, 1, x1, 2 ); + v = nanvariancech( 5, 1, x1, 2 ); t.strictEqual( v, 6.25, 'returns expected value' ); t.end(); diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.ndarray.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.ndarray.js index 8cb11920554b..62e08c0e8a8d 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.ndarray.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.ndarray.js @@ -351,7 +351,7 @@ tape( 'the function supports a `stride` parameter', function test( t ) { NaN ]; - v = nanvariancech( 4, 1, x, 2, 0 ); + v = nanvariancech( 5, 1, x, 2, 0 ); t.strictEqual( v, 6.25, 'returns expected value' ); t.end(); @@ -375,7 +375,7 @@ tape( 'the function supports a negative `stride` parameter', function test( t ) 2.0 ]; - v = nanvariancech( 4, 1, x, -2, 8 ); + v = nanvariancech( 5, 1, x, -2, 8 ); t.strictEqual( v, 6.25, 'returns expected value' ); x = []; @@ -405,7 +405,7 @@ tape( 'the function supports a `stride` parameter (accessors)', function test( t NaN ]; - v = nanvariancech( 4, 1, toAccessorArray( x ), 2, 0 ); + v = nanvariancech( 5, 1, toAccessorArray( x ), 2, 0 ); t.strictEqual( v, 6.25, 'returns expected value' ); t.end(); @@ -429,7 +429,7 @@ tape( 'the function supports a negative `stride` parameter (accessors)', functio 2.0 ]; - v = nanvariancech( 4, 1, toAccessorArray( x ), -2, 8 ); + v = nanvariancech( 5, 1, toAccessorArray( x ), -2, 8 ); t.strictEqual( v, 6.25, 'returns expected value' ); x = []; @@ -481,7 +481,7 @@ tape( 'the function supports an `offset` parameter', function test( t ) { NaN // 4 ]; - v = nanvariancech( 4, 1, x, 2, 1 ); + v = nanvariancech( 5, 1, x, 2, 1 ); t.strictEqual( v, 6.25, 'returns expected value' ); t.end(); @@ -504,7 +504,7 @@ tape( 'the function supports an `offset` parameter (accessors)', function test( NaN // 4 ]; - v = nanvariancech( 4, 1, toAccessorArray( x ), 2, 1 ); + v = nanvariancech( 5, 1, toAccessorArray( x ), 2, 1 ); t.strictEqual( v, 6.25, 'returns expected value' ); t.end(); From 418d3ecabd72a6594ab42eeb6baeb44ef82d211d Mon Sep 17 00:00:00 2001 From: Rahul Kumar Date: Sun, 6 Apr 2025 17:35:06 +0000 Subject: [PATCH 03/10] refactor: refactor the lib folder's files to align with existing style --- 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: 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: 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: na - task: lint_typescript_tests status: na - task: lint_license_headers status: passed --- --- .../stats/base/nanvariancech/README.md | 3 - .../base/nanvariancech/benchmark/benchmark.js | 2 +- .../stats/base/nanvariancech/lib/index.js | 13 ++-- .../stats/base/nanvariancech/lib/main.js | 33 +++++++++- .../base/nanvariancech/lib/nanvariancech.js | 62 ------------------- .../{test.nanvariancech.js => test.main.js} | 2 +- 6 files changed, 41 insertions(+), 74 deletions(-) delete mode 100644 lib/node_modules/@stdlib/stats/base/nanvariancech/lib/nanvariancech.js rename lib/node_modules/@stdlib/stats/base/nanvariancech/test/{test.nanvariancech.js => test.main.js} (99%) diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md b/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md index 720b2cee06f9..93a74062d99c 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md @@ -174,7 +174,6 @@ var v = nanvariancech.ndarray( 5, 1, x, 2, 1 ); - If `N <= 0`, both functions return `NaN`. - If `n - c` is less than or equal to `0` (where `c` corresponds to the provided degrees of freedom adjustment and `n` corresponds to the number of non-`NaN` indexed elements), both functions return `NaN`. - The underlying algorithm is a specialized case of Neely's two-pass algorithm. As the variance is invariant with respect to changes in the location parameter, the underlying algorithm uses the first non-`NaN` strided array element as a trial mean to shift subsequent data values and thus mitigate catastrophic cancellation. Accordingly, the algorithm's accuracy is best when data is **unordered** (i.e., the data is **not** sorted in either ascending or descending order such that the first value is an "extreme" value). -- Both functions support array-like objects having getter and setter accessors for array element access (e.g., [`@stdlib/array/base/accessor`][@stdlib/array/base/accessor]). - Depending on the environment, the typed versions ([`dnanvariancech`][@stdlib/stats/strided/dnanvariancech], [`snanvariancech`][@stdlib/stats/base/snanvariancech], etc.) are likely to be significantly more performant. @@ -274,8 +273,6 @@ console.log( v ); [@stdlib/stats/base/variancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/variancech -[@stdlib/array/base/accessor]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/array/base/accessor - diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/benchmark/benchmark.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/benchmark/benchmark.js index fd0a1451d9d2..d17915d8d584 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/benchmark/benchmark.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/benchmark/benchmark.js @@ -27,7 +27,7 @@ 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 pkg = require( './../package.json' ).name; -var nanvariancech = require( './../lib/nanvariancech.js' ); +var nanvariancech = require( './../lib/main.js' ); // FUNCTIONS // diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/index.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/index.js index e76a3eb2b5c7..c2c03f437d9b 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/index.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/index.js @@ -28,23 +28,28 @@ * * var x = [ 1.0, -2.0, NaN, 2.0 ]; * -* var v = nanvariancech( x.length, 1, x, 1 ); +* var v = nanvariancech( 4, 1, x, 1 ); * // returns ~4.3333 * * @example -* var floor = require( '@stdlib/math/base/special/floor' ); * var nanvariancech = require( '@stdlib/stats/base/nanvariancech' ); * * var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ]; -* var N = floor( x.length / 2 ); * -* var v = nanvariancech.ndarray( N, 1, x, 2, 1 ); +* var v = nanvariancech.ndarray( 5, 1, x, 2, 1 ); * // returns 6.25 */ // MODULES // +var setReadOnly = require( '@stdlib/utils/define-nonenumerable-read-only-property' ); var main = require( './main.js' ); +var ndarray = require( './ndarray.js' ); + + +// MAIN // + +setReadOnly( main, 'ndarray', ndarray ); // EXPORTS // diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/main.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/main.js index a61e1835e474..ec49e6d56c93 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/main.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/main.js @@ -20,14 +20,41 @@ // MODULES // -var setReadOnly = require( '@stdlib/utils/define-nonenumerable-read-only-property' ); -var nanvariancech = require( './nanvariancech.js' ); +var stride2offset = require( '@stdlib/strided/base/stride2offset' ); var ndarray = require( './ndarray.js' ); // MAIN // -setReadOnly( nanvariancech, 'ndarray', ndarray ); +/** +* Computes the variance of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm. +* +* ## Method +* +* - This implementation uses a one-pass trial mean approach, as suggested by Chan et al (1983). +* +* ## 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](https://doi.org/10.1145/365719.365958). +* - Ling, Robert F. 1974. "Comparison of Several Algorithms for Computing Sample Means and Variances." _Journal of the American Statistical Association_ 69 (348). American Statistical Association, Taylor & Francis, Ltd.: 859–66. doi:[10.2307/2286154](https://doi.org/10.2307/2286154). +* - 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, Ltd.: 242–47. doi:[10.1080/00031305.1983.10483115](https://doi.org/10.1080/00031305.1983.10483115). +* - Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co-)Variance." In _Proceedings of the 30th International Conference on Scientific and Statistical Database Management_. New York, NY, USA: Association for Computing Machinery. doi:[10.1145/3221269.3223036](https://doi.org/10.1145/3221269.3223036). +* +* @param {PositiveInteger} N - number of indexed elements +* @param {number} correction - degrees of freedom adjustment +* @param {NumericArray} x - input array +* @param {integer} strideX - stride length +* @returns {number} variance +* +* @example +* var x = [ 1.0, -2.0, NaN, 2.0 ]; +* +* var v = nanvariancech( 4, 1, x, 1 ); +* // returns ~4.3333 +*/ +function nanvariancech( N, correction, x, strideX ) { + return ndarray( N, correction, x, strideX, stride2offset( N, strideX) ); +} // EXPORTS // diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/nanvariancech.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/nanvariancech.js deleted file mode 100644 index 04c366a9775c..000000000000 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/nanvariancech.js +++ /dev/null @@ -1,62 +0,0 @@ -/** -* @license Apache-2.0 -* -* Copyright (c) 2020 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 stride2offset = require( '@stdlib/strided/base/stride2offset' ); -var ndarray = require( './ndarray.js' ); - - -// MAIN // - -/** -* Computes the variance of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm. -* -* ## Method -* -* - This implementation uses a one-pass trial mean approach, as suggested by Chan et al (1983). -* -* ## 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](https://doi.org/10.1145/365719.365958). -* - Ling, Robert F. 1974. "Comparison of Several Algorithms for Computing Sample Means and Variances." _Journal of the American Statistical Association_ 69 (348). American Statistical Association, Taylor & Francis, Ltd.: 859–66. doi:[10.2307/2286154](https://doi.org/10.2307/2286154). -* - 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, Ltd.: 242–47. doi:[10.1080/00031305.1983.10483115](https://doi.org/10.1080/00031305.1983.10483115). -* - Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co-)Variance." In _Proceedings of the 30th International Conference on Scientific and Statistical Database Management_. New York, NY, USA: Association for Computing Machinery. doi:[10.1145/3221269.3223036](https://doi.org/10.1145/3221269.3223036). -* -* @param {PositiveInteger} N - number of indexed elements -* @param {number} correction - degrees of freedom adjustment -* @param {NumericArray} x - input array -* @param {integer} strideX - stride length -* @returns {number} variance -* -* @example -* var x = [ 1.0, -2.0, NaN, 2.0 ]; -* -* var v = nanvariancech( x.length, 1, x, 1 ); -* // returns ~4.3333 -*/ -function nanvariancech( N, correction, x, strideX ) { - return ndarray( N, correction, x, strideX, stride2offset( N, strideX) ); -} - - -// EXPORTS // - -module.exports = nanvariancech; diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.nanvariancech.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.main.js similarity index 99% rename from lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.nanvariancech.js rename to lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.main.js index ab418dc8faea..7c7b6d192787 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.nanvariancech.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.main.js @@ -24,7 +24,7 @@ var tape = require( 'tape' ); var isnan = require( '@stdlib/math/base/assert/is-nan' ); var Float64Array = require( '@stdlib/array/float64' ); var toAccessorArray = require( '@stdlib/array/base/to-accessor-array' ); -var nanvariancech = require( './../lib/nanvariancech.js' ); +var nanvariancech = require( './../lib/main.js' ); // TESTS // From 4481ce3220096a1fae4d15d31124e8a60eeb3584 Mon Sep 17 00:00:00 2001 From: Rahul Kumar Date: Thu, 10 Apr 2025 12:40:00 +0000 Subject: [PATCH 04/10] docs: add note about accessor array support --- 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: 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: na - task: lint_typescript_tests status: na - task: lint_license_headers status: passed --- --- lib/node_modules/@stdlib/stats/base/nanvariancech/README.md | 3 +++ lib/node_modules/@stdlib/stats/base/nanvariancech/lib/main.js | 2 +- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md b/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md index 93a74062d99c..8c3146b7638a 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md @@ -172,6 +172,7 @@ var v = nanvariancech.ndarray( 5, 1, x, 2, 1 ); ## Notes - If `N <= 0`, both functions return `NaN`. +- Both functions support array-like objects having getter and setter accessors for array element access (e.g., [`@stdlib/array/base/accessor`][@stdlib/array/base/accessor]). - If `n - c` is less than or equal to `0` (where `c` corresponds to the provided degrees of freedom adjustment and `n` corresponds to the number of non-`NaN` indexed elements), both functions return `NaN`. - The underlying algorithm is a specialized case of Neely's two-pass algorithm. As the variance is invariant with respect to changes in the location parameter, the underlying algorithm uses the first non-`NaN` strided array element as a trial mean to shift subsequent data values and thus mitigate catastrophic cancellation. Accordingly, the algorithm's accuracy is best when data is **unordered** (i.e., the data is **not** sorted in either ascending or descending order such that the first value is an "extreme" value). - Depending on the environment, the typed versions ([`dnanvariancech`][@stdlib/stats/strided/dnanvariancech], [`snanvariancech`][@stdlib/stats/base/snanvariancech], etc.) are likely to be significantly more performant. @@ -273,6 +274,8 @@ console.log( v ); [@stdlib/stats/base/variancech]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/stats/base/variancech +[@stdlib/array/base/accessor]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/array/base/accessor + diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/main.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/main.js index ec49e6d56c93..1344fd41ad25 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/main.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/main.js @@ -53,7 +53,7 @@ var ndarray = require( './ndarray.js' ); * // returns ~4.3333 */ function nanvariancech( N, correction, x, strideX ) { - return ndarray( N, correction, x, strideX, stride2offset( N, strideX) ); + return ndarray( N, correction, x, strideX, stride2offset( N, strideX ) ); } From b2c551e6410bf0f8f28c12ddd7662ba17d82a7d7 Mon Sep 17 00:00:00 2001 From: gururaj1512 Date: Thu, 12 Jun 2025 11:57:47 +0000 Subject: [PATCH 05/10] test: update variance tests to use accessors --- 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: 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: na - task: lint_typescript_tests status: na - task: lint_license_headers status: passed --- --- .../base/nanvariancech/test/test.main.js | 158 +++++++++++++--- .../base/nanvariancech/test/test.ndarray.js | 175 ++++++++++++------ 2 files changed, 251 insertions(+), 82 deletions(-) diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.main.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.main.js index 7c7b6d192787..14aa96bcfaa5 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.main.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.main.js @@ -94,115 +94,115 @@ tape( 'the function calculates the population variance of a strided array (ignor t.end(); }); -tape( 'the function calculates the sample variance of a strided array (ignoring `NaN` values)', function test( t ) { +tape( 'the function calculates the population variance of a strided array (ignoring `NaN` values) (accessors)', function test( t ) { var x; var v; var i; x = [ 1.0, -2.0, -4.0, 5.0, NaN, 0.0, 3.0 ]; - v = nanvariancech( x.length, 1, x, 1 ); - t.strictEqual( v, 53.5/(x.length-2), 'returns expected value' ); + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); + t.strictEqual( v, 53.5/(x.length-1), 'returns expected value' ); x = [ 1.0, NaN, NaN, -2.0, NaN, -4.0, NaN, 5.0, NaN, 0.0, 3.0, NaN ]; - v = nanvariancech( x.length, 1, x, 1 ); - t.strictEqual( v, 53.5/(x.length-7), 'returns expected value' ); + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); + t.strictEqual( v, 53.5/(x.length-6), 'returns expected value' ); x = [ -4.0, NaN ]; - v = nanvariancech( x.length, 1, x, 1 ); - t.strictEqual( isnan( v ), true, 'returns expected value' ); + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); + t.strictEqual( v, 0.0, 'returns expected value' ); x = [ NaN, NaN ]; - v = nanvariancech( x.length, 1, x, 1 ); + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); t.strictEqual( isnan( v ), true, 'returns expected value' ); x = [ NaN ]; - v = nanvariancech( x.length, 1, x, 1 ); + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); t.strictEqual( isnan( v ), true, 'returns expected value' ); x = [ 4.0 ]; - v = nanvariancech( x.length, 1, x, 1 ); - t.strictEqual( isnan( v ), true, 'returns expected value' ); + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); + t.strictEqual( v, 0.0, 'returns expected value' ); x = []; for ( i = 0; i < 1e3; i++ ) { x.push( 100.0 ); } - v = nanvariancech( x.length, 1, x, 1 ); + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); t.strictEqual( v, 0.0, 'returns expected value' ); x = [ NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN ]; - v = nanvariancech( x.length, 1, x, 1 ); + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); t.strictEqual( isnan( v ), true, 'returns expected value' ); x = []; for ( i = 0; i < 1e3; i++ ) { x.push( NaN ); } - v = nanvariancech( x.length, 1, x, 1 ); + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); t.strictEqual( isnan( v ), true, 'returns expected value' ); t.end(); }); -tape( 'the function calculates the population variance of a strided array (accessors) (ignoring `NaN` values)', function test( t ) { +tape( 'the function calculates the sample variance of a strided array (ignoring `NaN` values)', function test( t ) { var x; var v; var i; x = [ 1.0, -2.0, -4.0, 5.0, NaN, 0.0, 3.0 ]; - v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); - t.strictEqual( v, 53.5/(x.length-1), 'returns expected value' ); + v = nanvariancech( x.length, 1, x, 1 ); + t.strictEqual( v, 53.5/(x.length-2), 'returns expected value' ); x = [ 1.0, NaN, NaN, -2.0, NaN, -4.0, NaN, 5.0, NaN, 0.0, 3.0, NaN ]; - v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); - t.strictEqual( v, 53.5/(x.length-6), 'returns expected value' ); + v = nanvariancech( x.length, 1, x, 1 ); + t.strictEqual( v, 53.5/(x.length-7), 'returns expected value' ); x = [ -4.0, NaN ]; - v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); - t.strictEqual( v, 0.0, 'returns expected value' ); + v = nanvariancech( x.length, 1, x, 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); x = [ NaN, NaN ]; - v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); + v = nanvariancech( x.length, 1, x, 1 ); t.strictEqual( isnan( v ), true, 'returns expected value' ); x = [ NaN ]; - v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); + v = nanvariancech( x.length, 1, x, 1 ); t.strictEqual( isnan( v ), true, 'returns expected value' ); x = [ 4.0 ]; - v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); - t.strictEqual( v, 0.0, 'returns expected value' ); + v = nanvariancech( x.length, 1, x, 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); x = []; for ( i = 0; i < 1e3; i++ ) { x.push( 100.0 ); } - v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); + v = nanvariancech( x.length, 1, x, 1 ); t.strictEqual( v, 0.0, 'returns expected value' ); x = [ NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN ]; - v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); + v = nanvariancech( x.length, 1, x, 1 ); t.strictEqual( isnan( v ), true, 'returns expected value' ); x = []; for ( i = 0; i < 1e3; i++ ) { x.push( NaN ); } - v = nanvariancech( x.length, 0, toAccessorArray( x ), 1 ); + v = nanvariancech( x.length, 1, x, 1 ); t.strictEqual( isnan( v ), true, 'returns expected value' ); t.end(); }); -tape( 'the function calculates the sample variance of a strided array (accessors) (ignoring `NaN` values)', function test( t ) { +tape( 'the function calculates the sample variance of a strided array (ignoring `NaN` values) (accessors)', function test( t ) { var x; var v; var i; @@ -271,7 +271,7 @@ tape( 'if provided an `N` parameter less than or equal to `0`, the function retu t.end(); }); -tape( 'if provided an `N` parameter less than or equal to `0`, the function returns `NaN`', function test( t ) { +tape( 'if provided an `N` parameter less than or equal to `0`, the function returns `NaN` (accessors)', function test( t ) { var x; var v; @@ -303,6 +303,23 @@ tape( 'if provided an `N` parameter equal to `1`, the function returns a populat t.end(); }); +tape( 'if provided an `N` parameter equal to `1`, the function returns a population variance of `0` provided the first element is not `NaN` (accessors)', function test( t ) { + var x; + var v; + + x = [ 1.0, -2.0, -4.0, 5.0, 3.0 ]; + + v = nanvariancech( 1, 0, toAccessorArray( x ), 1 ); + t.strictEqual( v, 0.0, 'returns expected value' ); + + x = [ NaN, 1.0, -2.0, -4.0, 5.0, 3.0 ]; + + v = nanvariancech( 1, 0, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + t.end(); +}); + tape( 'if provided an `N` parameter equal to `1`, the function returns a sample variance equal to `NaN`', function test( t ) { var x; var v; @@ -320,6 +337,23 @@ tape( 'if provided an `N` parameter equal to `1`, the function returns a sample t.end(); }); +tape( 'if provided an `N` parameter equal to `1`, the function returns a sample variance equal to `NaN` (accessors)', function test( t ) { + var x; + var v; + + x = [ 1.0, -2.0, -4.0, 5.0, 3.0 ]; + + v = nanvariancech( 1, 1, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ NaN, 1.0, -2.0, -4.0, 5.0, 3.0 ]; + + v = nanvariancech( 1, 1, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + t.end(); +}); + tape( 'if provided a `correction` parameter yielding a correction term less than or equal to `0`, the function returns `NaN`', function test( t ) { var x; var v; @@ -335,6 +369,21 @@ tape( 'if provided a `correction` parameter yielding a correction term less than t.end(); }); +tape( 'if provided a `correction` parameter yielding a correction term less than or equal to `0`, the function returns `NaN` (accessors)', function test( t ) { + var x; + var v; + + x = [ 1.0, -2.0, -4.0, 5.0, 3.0 ]; + + v = nanvariancech( x.length, x.length, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + v = nanvariancech( x.length, x.length+1, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + t.end(); +}); + tape( 'the function supports a `stride` parameter', function test( t ) { var x; var v; @@ -465,6 +514,28 @@ tape( 'if provided a `stride` parameter equal to `0`, the function returns `0` p t.end(); }); +tape( 'if provided a `stride` parameter equal to `0`, the function returns `0` provided the correction term is not less than `0` and the first element is not `NaN` (accessors)', function test( t ) { + var x; + var v; + + x = [ 1.0, -2.0, -4.0, 5.0, 3.0 ]; + + v = nanvariancech( x.length, 1, toAccessorArray( x ), 0 ); + t.strictEqual( v, 0.0, 'returns expected value' ); + + x = [ NaN, 1.0, -2.0, -4.0, 5.0, 3.0 ]; + + v = nanvariancech( x.length, 1, toAccessorArray( x ), 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ 1.0, -2.0, -4.0, 5.0, 3.0 ]; + + v = nanvariancech( x.length, x.length, toAccessorArray( x ), 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + t.end(); +}); + tape( 'the function supports view offsets', function test( t ) { var x0; var x1; @@ -491,3 +562,30 @@ tape( 'the function supports view offsets', function test( t ) { t.end(); }); + +tape( 'the function supports view offsets (accessors)', function test( t ) { + var x0; + var x1; + var v; + + x0 = new Float64Array([ + 2.0, + 1.0, // 0 + 2.0, + -2.0, // 1 + -2.0, + 2.0, // 2 + 3.0, + 4.0, // 3 + 6.0, + NaN, // 4 + NaN + ]); + + x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element + + v = nanvariancech( 5, 1, toAccessorArray( x1 ), 2 ); + t.strictEqual( v, 6.25, 'returns expected value' ); + + t.end(); +}); diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.ndarray.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.ndarray.js index 62e08c0e8a8d..ecacc938843c 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.ndarray.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/test/test.ndarray.js @@ -93,115 +93,115 @@ tape( 'the function calculates the population variance of a strided array (ignor t.end(); }); -tape( 'the function calculates the sample variance of a strided array (ignoring `NaN` values)', function test( t ) { +tape( 'the function calculates the population variance of a strided array (ignoring `NaN` values) (accessors)', function test( t ) { var x; var v; var i; x = [ 1.0, -2.0, -4.0, 5.0, NaN, 0.0, 3.0 ]; - v = nanvariancech( x.length, 1, x, 1, 0 ); - t.strictEqual( v, 53.5/(x.length-2), 'returns expected value' ); + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); + t.strictEqual( v, 53.5/(x.length-1), 'returns expected value' ); x = [ 1.0, NaN, NaN, -2.0, NaN, -4.0, NaN, 5.0, NaN, 0.0, 3.0, NaN ]; - v = nanvariancech( x.length, 1, x, 1, 0 ); - t.strictEqual( v, 53.5/(x.length-7), 'returns expected value' ); + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); + t.strictEqual( v, 53.5/(x.length-6), 'returns expected value' ); x = [ -4.0, NaN ]; - v = nanvariancech( x.length, 1, x, 1, 0 ); - t.strictEqual( isnan( v ), true, 'returns expected value' ); + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); + t.strictEqual( v, 0.0, 'returns expected value' ); x = [ NaN, NaN ]; - v = nanvariancech( x.length, 1, x, 1, 0 ); + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); t.strictEqual( isnan( v ), true, 'returns expected value' ); x = [ NaN ]; - v = nanvariancech( x.length, 1, x, 1, 0 ); + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); t.strictEqual( isnan( v ), true, 'returns expected value' ); x = [ 4.0 ]; - v = nanvariancech( x.length, 1, x, 1, 0 ); - t.strictEqual( isnan( v ), true, 'returns expected value' ); + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); + t.strictEqual( v, 0.0, 'returns expected value' ); x = []; for ( i = 0; i < 1e3; i++ ) { x.push( 100.0 ); } - v = nanvariancech( x.length, 1, x, 1, 0 ); + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); t.strictEqual( v, 0.0, 'returns expected value' ); x = [ NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN ]; - v = nanvariancech( x.length, 1, x, 1, 0 ); + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); t.strictEqual( isnan( v ), true, 'returns expected value' ); x = []; for ( i = 0; i < 1e3; i++ ) { x.push( NaN ); } - v = nanvariancech( x.length, 1, x, 1, 0 ); + v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); t.strictEqual( isnan( v ), true, 'returns expected value' ); t.end(); }); -tape( 'the function calculates the population variance of a strided array (accessors) (ignoring `NaN` values)', function test( t ) { +tape( 'the function calculates the sample variance of a strided array (ignoring `NaN` values)', function test( t ) { var x; var v; var i; x = [ 1.0, -2.0, -4.0, 5.0, NaN, 0.0, 3.0 ]; - v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); - t.strictEqual( v, 53.5/(x.length-1), 'returns expected value' ); + v = nanvariancech( x.length, 1, x, 1, 0 ); + t.strictEqual( v, 53.5/(x.length-2), 'returns expected value' ); x = [ 1.0, NaN, NaN, -2.0, NaN, -4.0, NaN, 5.0, NaN, 0.0, 3.0, NaN ]; - v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); - t.strictEqual( v, 53.5/(x.length-6), 'returns expected value' ); + v = nanvariancech( x.length, 1, x, 1, 0 ); + t.strictEqual( v, 53.5/(x.length-7), 'returns expected value' ); x = [ -4.0, NaN ]; - v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); - t.strictEqual( v, 0.0, 'returns expected value' ); + v = nanvariancech( x.length, 1, x, 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); x = [ NaN, NaN ]; - v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); + v = nanvariancech( x.length, 1, x, 1, 0 ); t.strictEqual( isnan( v ), true, 'returns expected value' ); x = [ NaN ]; - v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); + v = nanvariancech( x.length, 1, x, 1, 0 ); t.strictEqual( isnan( v ), true, 'returns expected value' ); x = [ 4.0 ]; - v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); - t.strictEqual( v, 0.0, 'returns expected value' ); + v = nanvariancech( x.length, 1, x, 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); x = []; for ( i = 0; i < 1e3; i++ ) { x.push( 100.0 ); } - v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); + v = nanvariancech( x.length, 1, x, 1, 0 ); t.strictEqual( v, 0.0, 'returns expected value' ); x = [ NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN ]; - v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); + v = nanvariancech( x.length, 1, x, 1, 0 ); t.strictEqual( isnan( v ), true, 'returns expected value' ); x = []; for ( i = 0; i < 1e3; i++ ) { x.push( NaN ); } - v = nanvariancech( x.length, 0, toAccessorArray( x ), 1, 0 ); + v = nanvariancech( x.length, 1, x, 1, 0 ); t.strictEqual( isnan( v ), true, 'returns expected value' ); t.end(); }); -tape( 'the function calculates the sample variance of a strided array (accessors) (ignoring `NaN` values)', function test( t ) { +tape( 'the function calculates the sample variance of a strided array (ignoring `NaN` values) (accessors)', function test( t ) { var x; var v; var i; @@ -302,6 +302,23 @@ tape( 'if provided an `N` parameter equal to `1`, the function returns a populat t.end(); }); +tape( 'if provided an `N` parameter equal to `1`, the function returns a population variance of `0` provided the first element is not `NaN` (accessors)', function test( t ) { + var x; + var v; + + x = [ 1.0, -2.0, -4.0, 5.0, 3.0 ]; + + v = nanvariancech( 1, 0, toAccessorArray( x ), 1, 0 ); + t.strictEqual( v, 0.0, 'returns expected value' ); + + x = [ NaN, 1.0, -2.0, -4.0, 5.0, 3.0 ]; + + v = nanvariancech( 1, 0, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + t.end(); +}); + tape( 'if provided an `N` parameter equal to `1`, the function returns a sample variance equal to `NaN`', function test( t ) { var x; var v; @@ -319,6 +336,23 @@ tape( 'if provided an `N` parameter equal to `1`, the function returns a sample t.end(); }); +tape( 'if provided an `N` parameter equal to `1`, the function returns a sample variance equal to `NaN` (accessors)', function test( t ) { + var x; + var v; + + x = [ 1.0, -2.0, -4.0, 5.0, 3.0 ]; + + v = nanvariancech( 1, 1, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ NaN, 1.0, -2.0, -4.0, 5.0, 3.0 ]; + + v = nanvariancech( 1, 1, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + t.end(); +}); + tape( 'if provided a `correction` parameter yielding a correction term less than or equal to `0`, the function returns `NaN`', function test( t ) { var x; var v; @@ -334,6 +368,21 @@ tape( 'if provided a `correction` parameter yielding a correction term less than t.end(); }); +tape( 'if provided a `correction` parameter yielding a correction term less than or equal to `0`, the function returns `NaN` (accessors)', function test( t ) { + var x; + var v; + + x = [ 1.0, -2.0, -4.0, 5.0, 3.0 ]; + + v = nanvariancech( x.length, x.length, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + v = nanvariancech( x.length, x.length+1, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + t.end(); +}); + tape( 'the function supports a `stride` parameter', function test( t ) { var x; var v; @@ -357,6 +406,29 @@ tape( 'the function supports a `stride` parameter', function test( t ) { t.end(); }); +tape( 'the function supports a `stride` parameter (accessors)', function test( t ) { + var x; + var v; + + x = [ + 1.0, // 0 + 2.0, + 2.0, // 1 + -7.0, + -2.0, // 2 + 3.0, + 4.0, // 3 + 2.0, + NaN, // 4 + NaN + ]; + + v = nanvariancech( 5, 1, toAccessorArray( x ), 2, 0 ); + + t.strictEqual( v, 6.25, 'returns expected value' ); + t.end(); +}); + tape( 'the function supports a negative `stride` parameter', function test( t ) { var x; var v; @@ -388,29 +460,6 @@ tape( 'the function supports a negative `stride` parameter', function test( t ) t.end(); }); -tape( 'the function supports a `stride` parameter (accessors)', function test( t ) { - var x; - var v; - - x = [ - 1.0, // 0 - 2.0, - 2.0, // 1 - -7.0, - -2.0, // 2 - 3.0, - 4.0, // 3 - 2.0, - NaN, // 4 - NaN - ]; - - v = nanvariancech( 5, 1, toAccessorArray( x ), 2, 0 ); - - t.strictEqual( v, 6.25, 'returns expected value' ); - t.end(); -}); - tape( 'the function supports a negative `stride` parameter (accessors)', function test( t ) { var x; var v; @@ -464,6 +513,28 @@ tape( 'if provided a `stride` parameter equal to `0`, the function returns `0` p t.end(); }); +tape( 'if provided a `stride` parameter equal to `0`, the function returns `0` provided the correction term is not less than `0` and the first element is not `NaN` (accessors)', function test( t ) { + var x; + var v; + + x = [ 1.0, -2.0, -4.0, 5.0, 3.0 ]; + + v = nanvariancech( x.length, 1, toAccessorArray( x ), 0, 0 ); + t.strictEqual( v, 0.0, 'returns expected value' ); + + x = [ NaN, 1.0, -2.0, -4.0, 5.0, 3.0 ]; + + v = nanvariancech( x.length, 1, toAccessorArray( x ), 0, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ 1.0, -2.0, -4.0, 5.0, 3.0 ]; + + v = nanvariancech( x.length, x.length, toAccessorArray( x ), 0, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + t.end(); +}); + tape( 'the function supports an `offset` parameter', function test( t ) { var x; var v; From 40d18bbf543ba691c3cbe4c36cf6c29f874a4c27 Mon Sep 17 00:00:00 2001 From: gururaj1512 Date: Thu, 12 Jun 2025 12:01:37 +0000 Subject: [PATCH 06/10] fix: update nanvariancech example --- 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: na - task: lint_typescript_tests status: na - task: lint_license_headers status: passed --- --- .../@stdlib/stats/base/nanvariancech/lib/accessors.js | 10 +++++----- .../@stdlib/stats/base/nanvariancech/lib/index.js | 2 +- .../@stdlib/stats/base/nanvariancech/lib/main.js | 2 +- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/accessors.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/accessors.js index 18b36a833e0e..b6c0d185bd4e 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/accessors.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/accessors.js @@ -53,8 +53,8 @@ * // returns 6.25 */ function nanvariancech( N, correction, x, strideX, offsetX ) { - var xget; var xbuf; + var get; var mu; var ix; var M2; @@ -69,10 +69,10 @@ function nanvariancech( N, correction, x, strideX, offsetX ) { xbuf = x.data; // Cache references to element accessors: - xget = x.accessors[ 0 ]; + get = x.accessors[ 0 ]; if ( N === 1 || strideX === 0 ) { - v = xget( xbuf, offsetX ); + v = get( xbuf, offsetX ); if ( v === v && N-correction > 0.0 ) { return 0.0; } @@ -82,7 +82,7 @@ function nanvariancech( N, correction, x, strideX, offsetX ) { // Find an estimate for the mean... for ( i = 0; i < N; i++ ) { - v = xget( xbuf, ix ); + v = get( xbuf, ix ); if ( v === v ) { mu = v; break; @@ -100,7 +100,7 @@ function nanvariancech( N, correction, x, strideX, offsetX ) { M = 0.0; n = 1; for ( i; i < N; i++ ) { - v = xget( xbuf, ix ); + v = get( xbuf, ix ); if ( v === v ) { d = v - mu; M2 += d * d; diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/index.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/index.js index c2c03f437d9b..824340dc8d80 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/index.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/index.js @@ -28,7 +28,7 @@ * * var x = [ 1.0, -2.0, NaN, 2.0 ]; * -* var v = nanvariancech( 4, 1, x, 1 ); +* var v = nanvariancech( x.length, 1, x, 1 ); * // returns ~4.3333 * * @example diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/main.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/main.js index 1344fd41ad25..571d98f02b30 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/main.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/main.js @@ -49,7 +49,7 @@ var ndarray = require( './ndarray.js' ); * @example * var x = [ 1.0, -2.0, NaN, 2.0 ]; * -* var v = nanvariancech( 4, 1, x, 1 ); +* var v = nanvariancech( x.length, 1, x, 1 ); * // returns ~4.3333 */ function nanvariancech( N, correction, x, strideX ) { From ef20dda58768cf969a77e5af7d12f6239e858f90 Mon Sep 17 00:00:00 2001 From: gururaj1512 Date: Thu, 12 Jun 2025 12:02:47 +0000 Subject: [PATCH 07/10] fix: update examples in README and REPL for nanvariancech --- 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: 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: 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: na - task: lint_typescript_tests status: na - task: lint_license_headers status: passed --- --- lib/node_modules/@stdlib/stats/base/nanvariancech/README.md | 4 ++-- .../@stdlib/stats/base/nanvariancech/docs/repl.txt | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md b/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md index 8c3146b7638a..e07ab16f155c 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md @@ -100,7 +100,7 @@ var nanvariancech = require( '@stdlib/stats/base/nanvariancech' ); #### nanvariancech( N, correction, x, strideX ) -Computes the [variance][variance] of a strided array `x` ignoring `NaN` values and using a one-pass trial mean algorithm. +Computes the [variance][variance] of a strided array ignoring `NaN` values and using a one-pass trial mean algorithm. ```javascript var x = [ 1.0, -2.0, NaN, 2.0 ]; @@ -146,7 +146,7 @@ Computes the [variance][variance] of a strided array ignoring `NaN` values and u ```javascript var x = [ 1.0, -2.0, NaN, 2.0 ]; -var v = nanvariancech.ndarray( 4, 1, x, 1, 0 ); +var v = nanvariancech.ndarray( x.length, 1, x, 1, 0 ); // returns ~4.33333 ``` diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/repl.txt b/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/repl.txt index b055883df0e4..dae3224e1373 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/repl.txt +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/repl.txt @@ -46,10 +46,10 @@ -------- // Standard Usage: > var x = [ 1.0, -2.0, NaN, 2.0 ]; - > {{alias}}( 4, 1, x, 1 ) + > {{alias}}( x.length, 1, x, 1 ) ~4.3333 - // Using `N` and `stride` parameters: + // Using `N` and stride parameters: > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, NaN, NaN ]; > {{alias}}( 4, 1, x, 2 ) ~4.3333 @@ -105,7 +105,7 @@ -------- // Standard Usage: > var x = [ 1.0, -2.0, NaN, 2.0 ]; - > {{alias}}.ndarray( 4, 1, x, 1, 0 ) + > {{alias}}.ndarray( x.length, 1, x, 1, 0 ) ~4.3333 // Using offset parameter: From 0be5d17a55bde84bbf3a2d103cd460c981eea99c Mon Sep 17 00:00:00 2001 From: Athan Date: Fri, 13 Jun 2025 00:10:29 -0700 Subject: [PATCH 08/10] docs: remove backticks Signed-off-by: Athan --- lib/node_modules/@stdlib/stats/base/nanvariancech/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md b/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md index e07ab16f155c..0cdcc7a11a11 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/README.md @@ -154,7 +154,7 @@ The function has the following additional parameters: - **offsetX**: starting index for `x`. -While [`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 [variance][variance] for every other element in the strided array starting from the second element +While [`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 [variance][variance] for every other element in the strided array starting from the second element ```javascript var x = [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0, NaN, NaN ]; From e0a6393ed5bd31f219c6516600c22d2623fbef70 Mon Sep 17 00:00:00 2001 From: Athan Date: Fri, 13 Jun 2025 00:11:39 -0700 Subject: [PATCH 09/10] docs: avoid duplicate declaration Signed-off-by: Athan --- lib/node_modules/@stdlib/stats/base/nanvariancech/docs/repl.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/repl.txt b/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/repl.txt index dae3224e1373..5e027a793b8c 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/repl.txt +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/docs/repl.txt @@ -109,7 +109,7 @@ ~4.3333 // Using offset parameter: - > var x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ]; + > x = [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0, NaN, NaN ]; > {{alias}}.ndarray( 4, 1, x, 2, 1 ) ~4.3333 From a43280b139b3b3679005d010156b3752767df157 Mon Sep 17 00:00:00 2001 From: Athan Date: Fri, 13 Jun 2025 00:12:45 -0700 Subject: [PATCH 10/10] docs: add missing private annotation Signed-off-by: Athan --- .../@stdlib/stats/base/nanvariancech/lib/accessors.js | 1 + 1 file changed, 1 insertion(+) diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/accessors.js b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/accessors.js index b6c0d185bd4e..08fa12e7bc48 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/accessors.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancech/lib/accessors.js @@ -34,6 +34,7 @@ * - 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, Ltd.: 242–47. doi:[10.1080/00031305.1983.10483115](https://doi.org/10.1080/00031305.1983.10483115). * - Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co-)Variance." In _Proceedings of the 30th International Conference on Scientific and Statistical Database Management_. New York, NY, USA: Association for Computing Machinery. doi:[10.1145/3221269.3223036](https://doi.org/10.1145/3221269.3223036). * +* @private * @param {PositiveInteger} N - number of indexed elements * @param {number} correction - degrees of freedom adjustment * @param {Object} x - input array object