diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancetk/README.md b/lib/node_modules/@stdlib/stats/base/nanvariancetk/README.md index 48ef01f74076..c71acce9fe54 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancetk/README.md +++ b/lib/node_modules/@stdlib/stats/base/nanvariancetk/README.md @@ -98,9 +98,9 @@ The use of the term `n-1` is commonly referred to as Bessel's correction. Note, var nanvariancetk = require( '@stdlib/stats/base/nanvariancetk' ); ``` -#### nanvariancetk( N, correction, x, stride ) +#### nanvariancetk( N, correction, x, strideX ) -Computes the [variance][variance] of a strided array `x` ignoring `NaN` values and using a one-pass textbook algorithm. +Computes the [variance][variance] of a strided array ignoring `NaN` values and using a one-pass textbook algorithm. ```javascript var x = [ 1.0, -2.0, NaN, 2.0 ]; @@ -114,38 +114,32 @@ 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 strided 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 = nanvariancetk( N, 1, x, 2 ); +var v = nanvariancetk( 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 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 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 N = floor( x0.length / 2 ); - -var v = nanvariancetk( N, 1, x1, 2 ); +var v = nanvariancetk( 5, 1, x1, 2 ); // returns 6.25 ``` -#### nanvariancetk.ndarray( N, correction, x, stride, offset ) +#### nanvariancetk.ndarray( N, correction, x, strideX, offsetX ) Computes the [variance][variance] of a strided array ignoring `NaN` values and using a one-pass textbook algorithm and alternative indexing semantics. @@ -158,17 +152,14 @@ var v = nanvariancetk.ndarray( x.length, 1, x, 1, 0 ); 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 = nanvariancetk.ndarray( N, 1, x, 2, 1 ); +var v = nanvariancetk.ndarray( 5, 1, x, 2, 1 ); // returns 6.25 ``` @@ -181,6 +172,7 @@ var v = nanvariancetk.ndarray( N, 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`. - Some caution should be exercised when using the one-pass textbook algorithm. Literature overwhelmingly discourages the algorithm's use for two reasons: 1) the lack of safeguards against underflow and overflow and 2) the risk of catastrophic cancellation when subtracting the two sums if the sums are large and the variance small. These concerns have merit; however, the one-pass textbook algorithm should not be dismissed outright. For data distributions with a moderately large standard deviation to mean ratio (i.e., **coefficient of variation**), the one-pass textbook algorithm may be acceptable, especially when performance is paramount and some precision loss is acceptable (including a risk of returning a negative variance due to floating-point rounding errors!). In short, no single "best" algorithm for computing the variance exists. The "best" algorithm depends on the underlying data distribution, your performance requirements, and your minimum precision requirements. When evaluating which algorithm to use, consider the relative pros and cons, and choose the algorithm which best serves your needs. - Depending on the environment, the typed versions ([`dnanvariancetk`][@stdlib/stats/base/dnanvariancetk], [`snanvariancetk`][@stdlib/stats/base/snanvariancetk], etc.) are likely to be significantly more performant. @@ -196,18 +188,19 @@ var v = nanvariancetk.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 nanvariancetk = require( '@stdlib/stats/base/nanvariancetk' ); +var bernoulli = require( '@stdlib/random/base/bernoulli' ); -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, 'float64', rand ); console.log( x ); var v = nanvariancetk( x.length, 1, x, 1 ); @@ -258,6 +251,8 @@ console.log( v ); [mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray +[@stdlib/array/base/accessor]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/array/base/accessor + [@ling:1974a]: https://doi.org/10.2307/2286154 diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancetk/benchmark/benchmark.js b/lib/node_modules/@stdlib/stats/base/nanvariancetk/benchmark/benchmark.js index edfdec84e650..22cdbadddc45 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancetk/benchmark/benchmark.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancetk/benchmark/benchmark.js @@ -21,15 +21,30 @@ // 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; -var nanvariancetk = require( './../lib/nanvariancetk.js' ); +var nanvariancetk = require( './../lib/main.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 nanvariancetk = require( './../lib/nanvariancetk.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, 'float64', rand ); return benchmark; function benchmark( b ) { diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancetk/benchmark/benchmark.ndarray.js b/lib/node_modules/@stdlib/stats/base/nanvariancetk/benchmark/benchmark.ndarray.js index 41ffcd84bec2..a77a9682b441 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancetk/benchmark/benchmark.ndarray.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancetk/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 nanvariancetk = 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 nanvariancetk = 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, 'float64', rand ); return benchmark; function benchmark( b ) { diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancetk/docs/repl.txt b/lib/node_modules/@stdlib/stats/base/nanvariancetk/docs/repl.txt index ca8cd7cd9314..ef49c823e81e 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancetk/docs/repl.txt +++ b/lib/node_modules/@stdlib/stats/base/nanvariancetk/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 textbook 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 + array 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 ------- @@ -49,22 +49,19 @@ > {{alias}}( x.length, 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 ) + // Using `N` and stride parameters: + > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0, 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 ); - > N = {{alias:@stdlib/math/base/special/floor}}( x0.length / 2 ); - > stride = 2; - > {{alias}}( N, 1, x1, stride ) + > {{alias}}( 4, 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 textbook algorithm and alternative indexing semantics. @@ -93,10 +90,10 @@ x: Array|TypedArray Input array. - stride: integer - Index increment. + strideX: integer + Stride length. - offset: integer + offsetX: integer Starting index. Returns @@ -112,9 +109,8 @@ ~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 ) + > 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/nanvariancetk/docs/types/index.d.ts b/lib/node_modules/@stdlib/stats/base/nanvariancetk/docs/types/index.d.ts index 9f4a400872cb..55c7be248634 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancetk/docs/types/index.d.ts +++ b/lib/node_modules/@stdlib/stats/base/nanvariancetk/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 `nanvariancetk`. @@ -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 = nanvariancetk( 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 textbook 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 = nanvariancetk.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/nanvariancetk/docs/types/test.ts b/lib/node_modules/@stdlib/stats/base/nanvariancetk/docs/types/test.ts index d617034071f2..4774d9aea6a7 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancetk/docs/types/test.ts +++ b/lib/node_modules/@stdlib/stats/base/nanvariancetk/docs/types/test.ts @@ -16,6 +16,7 @@ * limitations under the License. */ +import AccessorArray = require( '@stdlib/array/base/accessor' ); import nanvariancetk = require( './index' ); @@ -26,6 +27,7 @@ import nanvariancetk = require( './index' ); const x = new Float64Array( 10 ); nanvariancetk( x.length, 1, x, 1 ); // $ExpectType number + nanvariancetk( 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... @@ -101,6 +103,7 @@ import nanvariancetk = require( './index' ); const x = new Float64Array( 10 ); nanvariancetk.ndarray( x.length, 1, x, 1, 0 ); // $ExpectType number + nanvariancetk.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/nanvariancetk/examples/index.js b/lib/node_modules/@stdlib/stats/base/nanvariancetk/examples/index.js index 08949831effa..60477a966996 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancetk/examples/index.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancetk/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 nanvariancetk = 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, 'float64', rand ); console.log( x ); var v = nanvariancetk( x.length, 1, x, 1 ); diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancetk/lib/nanvariancetk.js b/lib/node_modules/@stdlib/stats/base/nanvariancetk/lib/accessors.js similarity index 58% rename from lib/node_modules/@stdlib/stats/base/nanvariancetk/lib/nanvariancetk.js rename to lib/node_modules/@stdlib/stats/base/nanvariancetk/lib/accessors.js index d8c224cd516d..a923c3b65d52 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancetk/lib/nanvariancetk.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancetk/lib/accessors.js @@ -1,7 +1,7 @@ /** * @license Apache-2.0 * -* Copyright (c) 2020 The Stdlib Authors. +* 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. @@ -23,19 +23,28 @@ /** * Computes the variance of a strided array ignoring `NaN` values and using a one-pass textbook algorithm. * +* @private * @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 {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 x = [ 1.0, -2.0, NaN, 2.0 ]; +* var toAccessorArray = require( '@stdlib/array/base/to-accessor-array' ); +* var arraylike2object = require( '@stdlib/array/base/arraylike2object' ); * -* var v = nanvariancetk( x.length, 1, x, 1 ); +* var x = toAccessorArray( [ 1.0, -2.0, NaN, 2.0 ] ); +* +* var v = nanvariancetk(x.length, 1, arraylike2object( x ), 1, 0 ); * // returns ~4.3333 */ -function nanvariancetk( N, correction, x, stride ) { +function nanvariancetk( N, correction, x, strideX, offsetX ) { + var xbuf; + var get; var S2; var ix; var nc; @@ -44,32 +53,32 @@ function nanvariancetk( N, correction, x, stride ) { var n; var i; - if ( N <= 0 ) { - return NaN; - } - if ( N === 1 || stride === 0 ) { - v = x[ 0 ]; + // Cache reference to array data: + xbuf = x.data; + + // Cache a reference to the element accessor: + get = x.accessors[ 0 ]; + + if ( N === 1 || strideX === 0 ) { + v = get( xbuf, offsetX ); if ( v === v && N-correction > 0.0 ) { return 0.0; } return NaN; } - if ( stride < 0 ) { - ix = (1-N) * stride; - } else { - ix = 0; - } + + ix = offsetX; S2 = 0.0; S = 0.0; n = 0; for ( i = 0; i < N; i++ ) { - v = x[ ix ]; + v = get( xbuf, ix ); if ( v === v ) { S2 += v * v; S += v; n += 1; } - ix += stride; + ix += strideX; } nc = n - correction; if ( nc <= 0.0 ) { diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancetk/lib/index.js b/lib/node_modules/@stdlib/stats/base/nanvariancetk/lib/index.js index ba38df0422c6..7efb98392cb7 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancetk/lib/index.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancetk/lib/index.js @@ -36,15 +36,21 @@ * var nanvariancetk = require( '@stdlib/stats/base/nanvariancetk' ); * * 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 = nanvariancetk.ndarray( N, 1, x, 2, 1 ); +* var v = nanvariancetk.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/nanvariancetk/lib/main.js b/lib/node_modules/@stdlib/stats/base/nanvariancetk/lib/main.js index e293cf36555e..e4f54dced526 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancetk/lib/main.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancetk/lib/main.js @@ -20,14 +20,30 @@ // MODULES // -var setReadOnly = require( '@stdlib/utils/define-nonenumerable-read-only-property' ); -var nanvariancetk = require( './nanvariancetk.js' ); +var stride2offset = require( '@stdlib/strided/base/stride2offset' ); var ndarray = require( './ndarray.js' ); // MAIN // -setReadOnly( nanvariancetk, 'ndarray', ndarray ); +/** +* Computes the variance of a strided array ignoring `NaN` values and using a one-pass textbook algorithm. +* +* @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 = nanvariancetk( 4, 1, x, 1 ); +* // returns ~4.3333 +*/ +function nanvariancetk( N, correction, x, strideX ) { + return ndarray( N, correction, x, strideX, stride2offset( N, strideX ) ); +} // EXPORTS // diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancetk/lib/ndarray.js b/lib/node_modules/@stdlib/stats/base/nanvariancetk/lib/ndarray.js index 68122f95b6cc..2a6b16b87221 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancetk/lib/ndarray.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancetk/lib/ndarray.js @@ -18,6 +18,12 @@ 'use strict'; +// MODULES // + +var arraylike2object = require( '@stdlib/array/base/arraylike2object' ); +var accessors = require( './accessors.js' ); + + // MAIN // /** @@ -26,23 +32,21 @@ * @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 = nanvariancetk( N, 1, x, 2, 1 ); +* var v = nanvariancetk( 5, 1, x, 2, 1 ); * // returns 6.25 */ -function nanvariancetk( N, correction, x, stride, offset ) { +function nanvariancetk( N, correction, x, strideX, offsetX ) { var S2; var ix; var nc; + var o; var S; var v; var n; @@ -51,14 +55,18 @@ function nanvariancetk( N, correction, x, stride, offset ) { 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; S2 = 0.0; S = 0.0; n = 0; @@ -69,7 +77,7 @@ function nanvariancetk( N, correction, x, stride, offset ) { S += v; n += 1; } - ix += stride; + ix += strideX; } nc = n - correction; if ( nc <= 0.0 ) { diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancetk/test/test.nanvariancetk.js b/lib/node_modules/@stdlib/stats/base/nanvariancetk/test/test.main.js similarity index 59% rename from lib/node_modules/@stdlib/stats/base/nanvariancetk/test/test.nanvariancetk.js rename to lib/node_modules/@stdlib/stats/base/nanvariancetk/test/test.main.js index 42f9cee5916b..53e063979308 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancetk/test/test.nanvariancetk.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancetk/test/test.main.js @@ -21,10 +21,10 @@ // MODULES // var tape = require( 'tape' ); -var floor = require( '@stdlib/math/base/special/floor' ); var isnan = require( '@stdlib/math/base/assert/is-nan' ); +var toAccessorArray = require( '@stdlib/array/base/to-accessor-array' ); var Float64Array = require( '@stdlib/array/float64' ); -var nanvariancetk = require( './../lib/nanvariancetk.js' ); +var nanvariancetk = require( './../lib/main.js' ); // TESTS // @@ -40,7 +40,7 @@ tape( 'the function has an arity of 4', function test( t ) { t.end(); }); -tape( 'the function calculates the population variance of a strided array (ignoring `NaN` values)', function test( t ) { +tape( 'the function calculates the population variance of a strided array', function test( t ) { var x; var v; var i; @@ -94,7 +94,61 @@ 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 (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 = nanvariancetk( 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 = nanvariancetk( x.length, 0, toAccessorArray( x ), 1 ); + t.strictEqual( v, 53.5/(x.length-6), 'returns expected value' ); + + x = [ -4.0, NaN ]; + + v = nanvariancetk( x.length, 0, toAccessorArray( x ), 1 ); + t.strictEqual( v, 0.0, 'returns expected value' ); + + x = [ NaN, NaN ]; + + v = nanvariancetk( x.length, 0, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ NaN ]; + v = nanvariancetk( x.length, 0, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ 4.0 ]; + v = nanvariancetk( 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 = nanvariancetk( 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 = nanvariancetk( 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 = nanvariancetk( 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', function test( t ) { var x; var v; var i; @@ -148,6 +202,60 @@ tape( 'the function calculates the sample variance of a strided array (ignoring t.end(); }); +tape( 'the function calculates the sample variance of a strided array (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 = nanvariancetk( 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 = nanvariancetk( x.length, 1, toAccessorArray( x ), 1 ); + t.strictEqual( v, 53.5/(x.length-7), 'returns expected value' ); + + x = [ -4.0, NaN ]; + + v = nanvariancetk( x.length, 1, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ NaN, NaN ]; + + v = nanvariancetk( x.length, 1, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ NaN ]; + v = nanvariancetk( x.length, 1, toAccessorArray( x ), 1 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ 4.0 ]; + v = nanvariancetk( 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 = nanvariancetk( 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 = nanvariancetk( 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 = nanvariancetk( 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; @@ -213,7 +321,6 @@ 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 +337,36 @@ tape( 'the function supports a `stride` parameter', function test( t ) { NaN ]; - N = floor( x.length / 2 ); - v = nanvariancetk( N, 1, x, 2 ); + v = nanvariancetk( 5, 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 = nanvariancetk( 5, 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 +383,8 @@ tape( 'the function supports a negative `stride` parameter', function test( t ) 4.0, // 0 2.0 ]; - N = floor( x.length / 2 ); - v = nanvariancetk( N, 1, x, -2 ); + v = nanvariancetk( 5, 1, x, -2 ); t.strictEqual( v, 6.25, 'returns expected value' ); x = []; @@ -270,6 +397,37 @@ 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 = nanvariancetk( 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 = nanvariancetk( 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; @@ -292,10 +450,31 @@ 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 = nanvariancetk( 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 = nanvariancetk( 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 = nanvariancetk( 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; - var N; var v; x0 = new Float64Array([ @@ -313,9 +492,8 @@ 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 = nanvariancetk( N, 1, x1, 2 ); + v = nanvariancetk( 5, 1, x1, 2 ); t.strictEqual( v, 6.25, 'returns expected value' ); t.end(); diff --git a/lib/node_modules/@stdlib/stats/base/nanvariancetk/test/test.ndarray.js b/lib/node_modules/@stdlib/stats/base/nanvariancetk/test/test.ndarray.js index 593c466568a2..09720a400bab 100644 --- a/lib/node_modules/@stdlib/stats/base/nanvariancetk/test/test.ndarray.js +++ b/lib/node_modules/@stdlib/stats/base/nanvariancetk/test/test.ndarray.js @@ -21,8 +21,8 @@ // MODULES // var tape = require( 'tape' ); -var floor = require( '@stdlib/math/base/special/floor' ); var isnan = require( '@stdlib/math/base/assert/is-nan' ); +var toAccessorArray = require( '@stdlib/array/base/to-accessor-array' ); var nanvariancetk = require( './../lib/ndarray.js' ); @@ -39,7 +39,7 @@ tape( 'the function has an arity of 5', function test( t ) { t.end(); }); -tape( 'the function calculates the population variance of a strided array (ignoring `NaN` values)', function test( t ) { +tape( 'the function calculates the population variance of a strided array', function test( t ) { var x; var v; var i; @@ -93,7 +93,61 @@ 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 (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 = nanvariancetk( 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 = nanvariancetk( x.length, 0, toAccessorArray( x ), 1, 0 ); + t.strictEqual( v, 53.5/(x.length-6), 'returns expected value' ); + + x = [ -4.0, NaN ]; + + v = nanvariancetk( x.length, 0, toAccessorArray( x ), 1, 0 ); + t.strictEqual( v, 0.0, 'returns expected value' ); + + x = [ NaN, NaN ]; + + v = nanvariancetk( x.length, 0, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ NaN ]; + v = nanvariancetk( x.length, 0, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ 4.0 ]; + v = nanvariancetk( 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 = nanvariancetk( 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 = nanvariancetk( 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 = nanvariancetk( 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', function test( t ) { var x; var v; var i; @@ -147,6 +201,60 @@ tape( 'the function calculates the sample variance of a strided array (ignoring t.end(); }); +tape( 'the function calculates the sample variance of a strided array (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 = nanvariancetk( 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 = nanvariancetk( x.length, 1, toAccessorArray( x ), 1, 0 ); + t.strictEqual( v, 53.5/(x.length-7), 'returns expected value' ); + + x = [ -4.0, NaN ]; + + v = nanvariancetk( x.length, 1, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ NaN, NaN ]; + + v = nanvariancetk( x.length, 1, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ NaN ]; + v = nanvariancetk( x.length, 1, toAccessorArray( x ), 1, 0 ); + t.strictEqual( isnan( v ), true, 'returns expected value' ); + + x = [ 4.0 ]; + v = nanvariancetk( 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 = nanvariancetk( 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 = nanvariancetk( 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 = nanvariancetk( 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; @@ -212,7 +320,6 @@ 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 +336,36 @@ tape( 'the function supports a `stride` parameter', function test( t ) { NaN ]; - N = floor( x.length / 2 ); - v = nanvariancetk( N, 1, x, 2, 0 ); + v = nanvariancetk( 5, 1, x, 2, 0 ); + + 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 = nanvariancetk( 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 N; var x; var v; var i; @@ -254,9 +382,8 @@ tape( 'the function supports a negative `stride` parameter', function test( t ) 4.0, // 0 2.0 ]; - N = floor( x.length / 2 ); - v = nanvariancetk( N, 1, x, -2, 8 ); + v = nanvariancetk( 5, 1, x, -2, 8 ); t.strictEqual( v, 6.25, 'returns expected value' ); x = []; @@ -269,6 +396,37 @@ tape( 'the function supports a negative `stride` parameter', function test( t ) t.end(); }); +tape( 'the function supports a negative `stride` parameter', 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 = nanvariancetk( 5, 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 = nanvariancetk( 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; @@ -291,8 +449,29 @@ 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 = nanvariancetk( 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 = nanvariancetk( 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 = nanvariancetk( 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 N; var x; var v; @@ -308,9 +487,31 @@ tape( 'the function supports an `offset` parameter', function test( t ) { NaN, NaN // 4 ]; - N = floor( x.length / 2 ); - v = nanvariancetk( N, 1, x, 2, 1 ); + v = nanvariancetk( 5, 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 = nanvariancetk( 5, 1, toAccessorArray( x ), 2, 1 ); t.strictEqual( v, 6.25, 'returns expected value' ); t.end();