From 43e7a3386606e54475e872d24ddf11fa6c122c42 Mon Sep 17 00:00:00 2001 From: Philipp Burckhardt Date: Sat, 12 Oct 2024 13:37:18 -0400 Subject: [PATCH] chore: minor clean-up after code review --- .../assert/is-same-typed-array-like/README.md | 2 +- .../docs/types/index.d.ts | 2 + .../@stdlib/iter/cunone/README.md | 5 +-- .../@stdlib/stats/base/dists/chi/README.md | 40 +++++++++---------- .../stats/base/dists/chi/examples/index.js | 40 +++++++++---------- .../stats/base/dists/exponential/README.md | 6 +-- .../base/dists/exponential/examples/index.js | 6 +-- .../stats/base/dists/geometric/README.md | 38 +++++++++--------- .../base/dists/geometric/examples/index.js | 38 +++++++++--------- 9 files changed, 87 insertions(+), 90 deletions(-) diff --git a/lib/node_modules/@stdlib/assert/is-same-typed-array-like/README.md b/lib/node_modules/@stdlib/assert/is-same-typed-array-like/README.md index 187ade975de..b5fc51528ef 100644 --- a/lib/node_modules/@stdlib/assert/is-same-typed-array-like/README.md +++ b/lib/node_modules/@stdlib/assert/is-same-typed-array-like/README.md @@ -18,7 +18,7 @@ limitations under the License. --> -# isSameArrayLike +# isSameTypedArrayLike > Test if two arguments are both typed-array-like objects and have the [same values][@stdlib/assert/is-same-value]. diff --git a/lib/node_modules/@stdlib/assert/is-same-typed-array-like/docs/types/index.d.ts b/lib/node_modules/@stdlib/assert/is-same-typed-array-like/docs/types/index.d.ts index 2485bfd39cc..4d439e42331 100644 --- a/lib/node_modules/@stdlib/assert/is-same-typed-array-like/docs/types/index.d.ts +++ b/lib/node_modules/@stdlib/assert/is-same-typed-array-like/docs/types/index.d.ts @@ -28,6 +28,7 @@ * @example * var Int8Array = require( '@stdlib/array/int8' ); * var Int16Array = require( '@stdlib/array/int16' ); +* * var x = new Int8Array( [ 1.0, 2.0, 3.0 ] ); * var y = new Int16Array( [ 1.0, 2.0, 3.0 ] ); * @@ -36,6 +37,7 @@ * * @example * var Int8Array = require( '@stdlib/array/int8' ); +* * var x = new Int8Array( [ 1.0, 2.0, 3.0 ] ); * var y = new Int8Array( [ 1.0, 2.0, 4.0 ] ); * diff --git a/lib/node_modules/@stdlib/iter/cunone/README.md b/lib/node_modules/@stdlib/iter/cunone/README.md index 34bbac5bd3d..0145cb4ca50 100644 --- a/lib/node_modules/@stdlib/iter/cunone/README.md +++ b/lib/node_modules/@stdlib/iter/cunone/README.md @@ -42,8 +42,7 @@ var iterCuNone = require( '@stdlib/iter/cunone' ); #### iterCuNone( iterator ) -Returns an [iterator][mdn-iterator-protocol] which cumulatively tests whether every -iterated value is falsy. +Returns an [iterator][mdn-iterator-protocol] which cumulatively tests whether every iterated value is falsy. ```javascript var array2iterator = require( '@stdlib/array/to-iterator' ); @@ -115,7 +114,7 @@ var riter = randu( opts ); // Create an iterator which applies a threshold to generated numbers: var miter = iterMap( riter, threshold ); -// Create an iterator which cumulatively tests whether every iterated value is falsy. +// Create an iterator which cumulatively tests whether every iterated value is falsy: var it = iterCuNone( miter ); // Perform manual iteration... diff --git a/lib/node_modules/@stdlib/stats/base/dists/chi/README.md b/lib/node_modules/@stdlib/stats/base/dists/chi/README.md index 15972576c5a..7a194f7eaed 100644 --- a/lib/node_modules/@stdlib/stats/base/dists/chi/README.md +++ b/lib/node_modules/@stdlib/stats/base/dists/chi/README.md @@ -109,8 +109,10 @@ var filledarrayBy = require( '@stdlib/array/filled-by' ); var variance = require( '@stdlib/stats/base/variance' ); var linspace = require( '@stdlib/array/base/linspace' ); var rayleigh = require( '@stdlib/stats/base/dists/rayleigh' ); +var absdiff = require( '@stdlib/math/base/utils/absolute-difference' ); var mean = require( '@stdlib/stats/base/mean' ); var abs = require( '@stdlib/math/base/special/abs' ); +var max = require( '@stdlib/math/base/special/max' ); var chi = require( '@stdlib/stats/base/dists/chi' ); // Define the degrees of freedom parameter: @@ -128,9 +130,9 @@ var chiCDF = chi.cdf.factory( k ); var cdf = filledarrayBy( x.length, 'float64', chiCDF ); // Output the PDF and CDF values: -console.log( 'x values:', x ); -console.log( 'PDF values:', pdf ); -console.log( 'CDF values:', cdf ); +console.log( 'x values: ', x ); +console.log( 'PDF values: ', pdf ); +console.log( 'CDF values: ', cdf ); // Compute statistical properties: var theoreticalMean = chi.mean( k ); @@ -138,10 +140,10 @@ var theoreticalVariance = chi.variance( k ); var theoreticalSkewness = chi.skewness( k ); var theoreticalKurtosis = chi.kurtosis( k ); -console.log( 'Theoretical Mean:', theoreticalMean ); -console.log( 'Theoretical Variance:', theoreticalVariance ); -console.log( 'Skewness:', theoreticalSkewness ); -console.log( 'Kurtosis:', theoreticalKurtosis ); +console.log( 'Theoretical Mean: ', theoreticalMean ); +console.log( 'Theoretical Variance: ', theoreticalVariance ); +console.log( 'Skewness: ', theoreticalSkewness ); +console.log( 'Kurtosis: ', theoreticalKurtosis ); // Generate random samples from the Chi distribution: var rchi = chiRandomFactory( k ); @@ -152,12 +154,12 @@ var samples = filledarrayBy( n, 'float64', rchi ); var sampleMean = mean( n, samples, 1 ); var sampleVariance = variance( n, 1, samples, 1 ); -console.log( 'Sample Mean:', sampleMean ); -console.log( 'Sample Variance:', sampleVariance ); +console.log( 'Sample Mean: ', sampleMean ); +console.log( 'Sample Variance: ', sampleVariance ); // Compare sample statistics to theoretical values: -console.log( 'Difference in Mean:', abs( theoreticalMean - sampleMean ) ); -console.log( 'Difference in Variance:', abs( theoreticalVariance - sampleVariance ) ); +console.log( 'Difference in Mean: ', abs( theoreticalMean - sampleMean ) ); +console.log( 'Difference in Variance: ', abs( theoreticalVariance - sampleVariance ) ); // Demonstrate the relationship with the Rayleigh distribution when k=2: var rayleighPDF = rayleigh.pdf.factory( 1.0 ); @@ -175,17 +177,13 @@ var diffPDF; var diffCDF; var i; for ( i = 0; i < x.length; i++ ) { - diffPDF = abs( pdf[ i ] - rayleighPDFValues[ i ] ); - if ( diffPDF > maxDiffPDF ) { - maxDiffPDF = diffPDF; - } - diffCDF = abs( cdf[ i ] - rayleighCDFValues[ i ] ); - if ( diffCDF > maxDiffCDF ) { - maxDiffCDF = diffCDF; - } + diffPDF = absdiff( pdf[ i ], rayleighPDFValues[ i ] ); + maxDiffPDF = max( maxDiffPDF, diffPDF ); + diffCDF = absdiff( cdf[ i ], rayleighCDFValues[ i ] ); + maxDiffCDF = max( maxDiffCDF, diffCDF ); } -console.log( 'Maximum difference between Chi(k=2) PDF and Rayleigh PDF:', maxDiffPDF ); -console.log( 'Maximum difference between Chi(k=2) CDF and Rayleigh CDF:', maxDiffCDF ); +console.log( 'Maximum difference between Chi(k=2) PDF and Rayleigh PDF: ', maxDiffPDF ); +console.log( 'Maximum difference between Chi(k=2) CDF and Rayleigh CDF: ', maxDiffCDF ); ``` diff --git a/lib/node_modules/@stdlib/stats/base/dists/chi/examples/index.js b/lib/node_modules/@stdlib/stats/base/dists/chi/examples/index.js index 6857d460c06..dfb3ad3828e 100644 --- a/lib/node_modules/@stdlib/stats/base/dists/chi/examples/index.js +++ b/lib/node_modules/@stdlib/stats/base/dists/chi/examples/index.js @@ -23,8 +23,10 @@ var filledarrayBy = require( '@stdlib/array/filled-by' ); var variance = require( '@stdlib/stats/base/variance' ); var linspace = require( '@stdlib/array/base/linspace' ); var rayleigh = require( '@stdlib/stats/base/dists/rayleigh' ); +var absdiff = require( '@stdlib/math/base/utils/absolute-difference' ); var mean = require( '@stdlib/stats/base/mean' ); var abs = require( '@stdlib/math/base/special/abs' ); +var max = require( '@stdlib/math/base/special/max' ); var chi = require( './../lib' ); // Define the degrees of freedom parameter: @@ -42,9 +44,9 @@ var chiCDF = chi.cdf.factory( k ); var cdf = filledarrayBy( x.length, 'float64', chiCDF ); // Output the PDF and CDF values: -console.log( 'x values:', x ); -console.log( 'PDF values:', pdf ); -console.log( 'CDF values:', cdf ); +console.log( 'x values: ', x ); +console.log( 'PDF values: ', pdf ); +console.log( 'CDF values: ', cdf ); // Compute statistical properties: var theoreticalMean = chi.mean( k ); @@ -52,10 +54,10 @@ var theoreticalVariance = chi.variance( k ); var theoreticalSkewness = chi.skewness( k ); var theoreticalKurtosis = chi.kurtosis( k ); -console.log( 'Theoretical Mean:', theoreticalMean ); -console.log( 'Theoretical Variance:', theoreticalVariance ); -console.log( 'Skewness:', theoreticalSkewness ); -console.log( 'Kurtosis:', theoreticalKurtosis ); +console.log( 'Theoretical Mean: ', theoreticalMean ); +console.log( 'Theoretical Variance: ', theoreticalVariance ); +console.log( 'Skewness: ', theoreticalSkewness ); +console.log( 'Kurtosis: ', theoreticalKurtosis ); // Generate random samples from the Chi distribution: var rchi = chiRandomFactory( k ); @@ -66,12 +68,12 @@ var samples = filledarrayBy( n, 'float64', rchi ); var sampleMean = mean( n, samples, 1 ); var sampleVariance = variance( n, 1, samples, 1 ); -console.log( 'Sample Mean:', sampleMean ); -console.log( 'Sample Variance:', sampleVariance ); +console.log( 'Sample Mean: ', sampleMean ); +console.log( 'Sample Variance: ', sampleVariance ); // Compare sample statistics to theoretical values: -console.log( 'Difference in Mean:', abs( theoreticalMean - sampleMean ) ); -console.log( 'Difference in Variance:', abs( theoreticalVariance - sampleVariance ) ); +console.log( 'Difference in Mean: ', abs( theoreticalMean - sampleMean ) ); +console.log( 'Difference in Variance: ', abs( theoreticalVariance - sampleVariance ) ); // Demonstrate the relationship with the Rayleigh distribution when k=2: var rayleighPDF = rayleigh.pdf.factory( 1.0 ); @@ -89,14 +91,10 @@ var diffPDF; var diffCDF; var i; for ( i = 0; i < x.length; i++ ) { - diffPDF = abs( pdf[ i ] - rayleighPDFValues[ i ] ); - if ( diffPDF > maxDiffPDF ) { - maxDiffPDF = diffPDF; - } - diffCDF = abs( cdf[ i ] - rayleighCDFValues[ i ] ); - if ( diffCDF > maxDiffCDF ) { - maxDiffCDF = diffCDF; - } + diffPDF = absdiff( pdf[ i ], rayleighPDFValues[ i ] ); + maxDiffPDF = max( maxDiffPDF, diffPDF ); + diffCDF = absdiff( cdf[ i ], rayleighCDFValues[ i ] ); + maxDiffCDF = max( maxDiffCDF, diffCDF ); } -console.log( 'Maximum difference between Chi(k=2) PDF and Rayleigh PDF:', maxDiffPDF ); -console.log( 'Maximum difference between Chi(k=2) CDF and Rayleigh CDF:', maxDiffCDF ); +console.log( 'Maximum difference between Chi(k=2) PDF and Rayleigh PDF: ', maxDiffPDF ); +console.log( 'Maximum difference between Chi(k=2) CDF and Rayleigh CDF: ', maxDiffCDF ); diff --git a/lib/node_modules/@stdlib/stats/base/dists/exponential/README.md b/lib/node_modules/@stdlib/stats/base/dists/exponential/README.md index 6be2ae17e1f..5161e75cbd4 100644 --- a/lib/node_modules/@stdlib/stats/base/dists/exponential/README.md +++ b/lib/node_modules/@stdlib/stats/base/dists/exponential/README.md @@ -123,14 +123,14 @@ var interarrivalTimes = randomExponential( numCustomers, lambda, { 'dtype': 'float64' }); -console.log( 'Simulated interarrival times for ' + numCustomers + ' customers:' ); +console.log( 'Simulated interarrival times for ' + numCustomers + ' customers: ' ); console.log( interarrivalTimes ); // Calculate cumulative arrival times by computing the cumulative sum of interarrival times: var arrivalTimes = new Float64Array( interarrivalTimes.length ); dcusum( interarrivalTimes.length, 0.0, interarrivalTimes, 1, arrivalTimes, 1 ); -console.log( '\nCustomer arrival times:' ); +console.log( '\nCustomer arrival times: ' ); console.log( arrivalTimes ); // Probability that a customer arrives within two minutes: @@ -153,7 +153,7 @@ console.log( 'PDF at x = 1: ' + out.toFixed(4) ); // Evaluate the MGF at t = 0.1: out = dist.mgf( 0.1 ); -console.log( 'MGF at t = 0.5: ' + out.toFixed(4) ); +console.log( 'MGF at t = 0.1: ' + out.toFixed(4) ); ``` diff --git a/lib/node_modules/@stdlib/stats/base/dists/exponential/examples/index.js b/lib/node_modules/@stdlib/stats/base/dists/exponential/examples/index.js index ed12ae6c03e..1c3e016b1ba 100644 --- a/lib/node_modules/@stdlib/stats/base/dists/exponential/examples/index.js +++ b/lib/node_modules/@stdlib/stats/base/dists/exponential/examples/index.js @@ -32,14 +32,14 @@ var interarrivalTimes = randomExponential( numCustomers, lambda, { 'dtype': 'float64' }); -console.log( 'Simulated interarrival times for ' + numCustomers + ' customers:' ); +console.log( 'Simulated interarrival times for ' + numCustomers + ' customers: ' ); console.log( interarrivalTimes ); // Calculate cumulative arrival times by computing the cumulative sum of interarrival times: var arrivalTimes = new Float64Array( interarrivalTimes.length ); dcusum( interarrivalTimes.length, 0.0, interarrivalTimes, 1, arrivalTimes, 1 ); -console.log( '\nCustomer arrival times:' ); +console.log( '\nCustomer arrival times: ' ); console.log( arrivalTimes ); // Probability that a customer arrives within two minutes: @@ -62,4 +62,4 @@ console.log( 'PDF at x = 1: ' + out.toFixed(4) ); // Evaluate the MGF at t = 0.1: out = dist.mgf( 0.1 ); -console.log( 'MGF at t = 0.5: ' + out.toFixed(4) ); +console.log( 'MGF at t = 0.1: ' + out.toFixed(4) ); diff --git a/lib/node_modules/@stdlib/stats/base/dists/geometric/README.md b/lib/node_modules/@stdlib/stats/base/dists/geometric/README.md index fd921a584bb..393445cb54f 100644 --- a/lib/node_modules/@stdlib/stats/base/dists/geometric/README.md +++ b/lib/node_modules/@stdlib/stats/base/dists/geometric/README.md @@ -136,9 +136,9 @@ var geometricCDF = geometric.cdf.factory( p ); var cdf = filledarrayBy( x.length, 'float64', geometricCDF ); // Output the PMF and CDF values: -console.log( 'x values:', x ); -console.log( 'PMF values:', pmf ); -console.log( 'CDF values:', cdf ); +console.log( 'x values: ', x ); +console.log( 'PMF values: ', pmf ); +console.log( 'CDF values: ', cdf ); // Compute statistical properties: var theoreticalMean = geometric.mean( p ); @@ -146,10 +146,10 @@ var theoreticalVariance = geometric.variance( p ); var theoreticalSkewness = geometric.skewness( p ); var theoreticalKurtosis = geometric.kurtosis( p ); -console.log( 'Theoretical Mean:', theoreticalMean ); -console.log( 'Theoretical Variance:', theoreticalVariance ); -console.log( 'Skewness:', theoreticalSkewness ); -console.log( 'Kurtosis:', theoreticalKurtosis ); +console.log( 'Theoretical Mean: ', theoreticalMean ); +console.log( 'Theoretical Variance: ', theoreticalVariance ); +console.log( 'Skewness: ', theoreticalSkewness ); +console.log( 'Kurtosis: ', theoreticalKurtosis ); // Generate random samples from the geometric distribution: var rgeom = geometricRandomFactory( p ); @@ -160,19 +160,19 @@ var samples = filledarrayBy( n, 'float64', rgeom ); var sampleMean = mean( n, samples, 1 ); var sampleVariance = variance( n, 1, samples, 1 ); -console.log( 'Sample Mean:', sampleMean ); -console.log( 'Sample Variance:', sampleVariance ); +console.log( 'Sample Mean: ', sampleMean ); +console.log( 'Sample Variance: ', sampleVariance ); // Demonstrate the memoryless property: var s = 2.0; var t = 3.0; var prob1 = ( 1.0 - geometric.cdf( s + t - 1.0, p ) ) / - ( 1.0 - geometric.cdf( s - 1.0, p )); + ( 1.0 - geometric.cdf( s - 1.0, p ) ); var prob2 = 1.0 - geometric.cdf( t - 1.0, p ); -console.log( 'P(X > s + t | X > s):', prob1 ); -console.log( 'P(X > t):', prob2 ); -console.log( 'Difference:', abs( prob1 - prob2 ) ); +console.log( 'P(X > s + t | X > s): ', prob1 ); +console.log( 'P(X > t): ', prob2 ); +console.log( 'Difference: ', abs( prob1 - prob2 ) ); // Demonstrate that the sum of k independent geometric random variables follows a negative binomial distribution: var k = 5; @@ -194,14 +194,14 @@ var sumSampleVariance = variance( n, 1, sumSamples, 1 ); var nbMean = negativeBinomial.mean( k, p ); var nbVariance = negativeBinomial.variance( k, p ); -console.log( 'Sum Sample Mean:', sumSampleMean ); -console.log( 'Sum Sample Variance:', sumSampleVariance ); -console.log( 'Negative Binomial Mean:', nbMean ); -console.log( 'Negative Binomial Variance:', nbVariance ); +console.log( 'Sum Sample Mean: ', sumSampleMean ); +console.log( 'Sum Sample Variance: ', sumSampleVariance ); +console.log( 'Negative Binomial Mean: ', nbMean ); +console.log( 'Negative Binomial Variance: ', nbVariance ); // Compare sample statistics to theoretical values: -console.log( 'Difference in Mean:', abs( nbMean - sumSampleMean ) ); -console.log( 'Difference in Variance:', abs( nbVariance - sumSampleVariance ) ); +console.log( 'Difference in Mean: ', abs( nbMean - sumSampleMean ) ); +console.log( 'Difference in Variance: ', abs( nbVariance - sumSampleVariance ) ); ``` diff --git a/lib/node_modules/@stdlib/stats/base/dists/geometric/examples/index.js b/lib/node_modules/@stdlib/stats/base/dists/geometric/examples/index.js index edf38c65b64..ad503990e48 100644 --- a/lib/node_modules/@stdlib/stats/base/dists/geometric/examples/index.js +++ b/lib/node_modules/@stdlib/stats/base/dists/geometric/examples/index.js @@ -42,9 +42,9 @@ var geometricCDF = geometric.cdf.factory( p ); var cdf = filledarrayBy( x.length, 'float64', geometricCDF ); // Output the PMF and CDF values: -console.log( 'x values:', x ); -console.log( 'PMF values:', pmf ); -console.log( 'CDF values:', cdf ); +console.log( 'x values: ', x ); +console.log( 'PMF values: ', pmf ); +console.log( 'CDF values: ', cdf ); // Compute statistical properties: var theoreticalMean = geometric.mean( p ); @@ -52,10 +52,10 @@ var theoreticalVariance = geometric.variance( p ); var theoreticalSkewness = geometric.skewness( p ); var theoreticalKurtosis = geometric.kurtosis( p ); -console.log( 'Theoretical Mean:', theoreticalMean ); -console.log( 'Theoretical Variance:', theoreticalVariance ); -console.log( 'Skewness:', theoreticalSkewness ); -console.log( 'Kurtosis:', theoreticalKurtosis ); +console.log( 'Theoretical Mean: ', theoreticalMean ); +console.log( 'Theoretical Variance: ', theoreticalVariance ); +console.log( 'Skewness: ', theoreticalSkewness ); +console.log( 'Kurtosis: ', theoreticalKurtosis ); // Generate random samples from the geometric distribution: var rgeom = geometricRandomFactory( p ); @@ -66,19 +66,19 @@ var samples = filledarrayBy( n, 'float64', rgeom ); var sampleMean = mean( n, samples, 1 ); var sampleVariance = variance( n, 1, samples, 1 ); -console.log( 'Sample Mean:', sampleMean ); -console.log( 'Sample Variance:', sampleVariance ); +console.log( 'Sample Mean: ', sampleMean ); +console.log( 'Sample Variance: ', sampleVariance ); // Demonstrate the memoryless property: var s = 2.0; var t = 3.0; var prob1 = ( 1.0 - geometric.cdf( s + t - 1.0, p ) ) / - ( 1.0 - geometric.cdf( s - 1.0, p )); + ( 1.0 - geometric.cdf( s - 1.0, p ) ); var prob2 = 1.0 - geometric.cdf( t - 1.0, p ); -console.log( 'P(X > s + t | X > s):', prob1 ); -console.log( 'P(X > t):', prob2 ); -console.log( 'Difference:', abs( prob1 - prob2 ) ); +console.log( 'P(X > s + t | X > s): ', prob1 ); +console.log( 'P(X > t): ', prob2 ); +console.log( 'Difference: ', abs( prob1 - prob2 ) ); // Demonstrate that the sum of k independent geometric random variables follows a negative binomial distribution: var k = 5; @@ -100,11 +100,11 @@ var sumSampleVariance = variance( n, 1, sumSamples, 1 ); var nbMean = negativeBinomial.mean( k, p ); var nbVariance = negativeBinomial.variance( k, p ); -console.log( 'Sum Sample Mean:', sumSampleMean ); -console.log( 'Sum Sample Variance:', sumSampleVariance ); -console.log( 'Negative Binomial Mean:', nbMean ); -console.log( 'Negative Binomial Variance:', nbVariance ); +console.log( 'Sum Sample Mean: ', sumSampleMean ); +console.log( 'Sum Sample Variance: ', sumSampleVariance ); +console.log( 'Negative Binomial Mean: ', nbMean ); +console.log( 'Negative Binomial Variance: ', nbVariance ); // Compare sample statistics to theoretical values: -console.log( 'Difference in Mean:', abs( nbMean - sumSampleMean ) ); -console.log( 'Difference in Variance:', abs( nbVariance - sumSampleVariance ) ); +console.log( 'Difference in Mean: ', abs( nbMean - sumSampleMean ) ); +console.log( 'Difference in Variance: ', abs( nbVariance - sumSampleVariance ) );