vega-statistics
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vega-statistics - npm Package Compare versions
Comparing version 1.7.8 to 1.7.9
@@ -7,3 +7,3 @@ (function (global, factory) { | ||
| function* numbers(values, valueof) { | ||
| function* numbers (values, valueof) { | ||
| if (valueof == null) { | ||
@@ -17,4 +17,6 @@ for (let value of values) { | ||
| let index = -1; | ||
| for (let value of values) { | ||
| value = valueof(value, ++index, values); | ||
| if (value != null && value !== '' && (value = +value) >= value) { | ||
@@ -27,19 +29,17 @@ yield value; | ||
| function quantiles(array, p, f) { | ||
| const values = Float64Array.from(numbers(array, f)); | ||
| function quantiles (array, p, f) { | ||
| const values = Float64Array.from(numbers(array, f)); // don't depend on return value from typed array sort call | ||
| // protects against undefined sort results in Safari (vega/vega-lite#4964) | ||
| // don't depend on return value from typed array sort call | ||
| // protects against undefined sort results in Safari (vega/vega-lite#4964) | ||
| values.sort(d3Array.ascending); | ||
| return p.map(_ => d3Array.quantileSorted(values, _)); | ||
| } | ||
| function quartiles(array, f) { | ||
| function quartiles (array, f) { | ||
| return quantiles(array, [0.25, 0.50, 0.75], f); | ||
| } | ||
| // Scott, D. W. (1992) Multivariate Density Estimation: | ||
| // Theory, Practice, and Visualization. Wiley. | ||
| function estimateBandwidth(array, f) { | ||
| function estimateBandwidth (array, f) { | ||
| const n = array.length, | ||
@@ -53,3 +53,3 @@ d = d3Array.deviation(array, f), | ||
| function bin(_) { | ||
| function bin (_) { | ||
| // determine range | ||
@@ -59,10 +59,13 @@ const maxb = _.maxbins || 20, | ||
| logb = Math.log(base), | ||
| div = _.divide || [5, 2]; | ||
| div = _.divide || [5, 2]; | ||
| let min = _.extent[0], | ||
| max = _.extent[1], | ||
| step, | ||
| level, | ||
| minstep, | ||
| v, | ||
| i, | ||
| n; | ||
| const span = _.span || max - min || Math.abs(min) || 1; | ||
| let min = _.extent[0], | ||
| max = _.extent[1], | ||
| step, level, minstep, v, i, n; | ||
| const span = _.span || (max - min) || Math.abs(min) || 1; | ||
| if (_.step) { | ||
@@ -74,4 +77,6 @@ // if step size is explicitly given, use that | ||
| v = span / maxb; | ||
| for (i=0, n=_.steps.length; i < n && _.steps[i] < v; ++i); | ||
| step = _.steps[Math.max(0, i-1)]; | ||
| for (i = 0, n = _.steps.length; i < n && _.steps[i] < v; ++i); | ||
| step = _.steps[Math.max(0, i - 1)]; | ||
| } else { | ||
@@ -81,21 +86,20 @@ // else use span to determine step size | ||
| minstep = _.minstep || 0; | ||
| step = Math.max( | ||
| minstep, | ||
| Math.pow(base, Math.round(Math.log(span) / logb) - level) | ||
| ); | ||
| step = Math.max(minstep, Math.pow(base, Math.round(Math.log(span) / logb) - level)); // increase step size if too many bins | ||
| // increase step size if too many bins | ||
| while (Math.ceil(span/step) > maxb) { step *= base; } | ||
| while (Math.ceil(span / step) > maxb) { | ||
| step *= base; | ||
| } // decrease step size if allowed | ||
| // decrease step size if allowed | ||
| for (i=0, n=div.length; i<n; ++i) { | ||
| for (i = 0, n = div.length; i < n; ++i) { | ||
| v = step / div[i]; | ||
| if (v >= minstep && span / v <= maxb) step = v; | ||
| } | ||
| } | ||
| } // update precision, min and max | ||
| // update precision, min and max | ||
| v = Math.log(step); | ||
| const precision = v >= 0 ? 0 : ~~(-v / logb) + 1, | ||
| eps = Math.pow(base, -precision - 1); | ||
| if (_.nice || _.nice === undefined) { | ||
@@ -109,4 +113,4 @@ v = Math.floor(min / step + eps) * step; | ||
| start: min, | ||
| stop: max === min ? min + step : max, | ||
| step: step | ||
| stop: max === min ? min + step : max, | ||
| step: step | ||
| }; | ||
@@ -116,3 +120,2 @@ } | ||
| exports.random = Math.random; | ||
| function setRandom(r) { | ||
@@ -122,5 +125,4 @@ exports.random = r; | ||
| function bootstrapCI(array, samples, alpha, f) { | ||
| function bootstrapCI (array, samples, alpha, f) { | ||
| if (!array.length) return [undefined, undefined]; | ||
| const values = Float64Array.from(numbers(array, f)), | ||
@@ -131,6 +133,7 @@ n = values.length, | ||
| for (j=0, mu=Array(m); j<m; ++j) { | ||
| for (a=0, i=0; i<n; ++i) { | ||
| for (j = 0, mu = Array(m); j < m; ++j) { | ||
| for (a = 0, i = 0; i < n; ++i) { | ||
| a += values[~~(exports.random() * n)]; | ||
| } | ||
| mu[j] = a / n; | ||
@@ -140,7 +143,3 @@ } | ||
| mu.sort(d3Array.ascending); | ||
| return [ | ||
| d3Array.quantile(mu, alpha/2), | ||
| d3Array.quantile(mu, 1-(alpha/2)) | ||
| ]; | ||
| return [d3Array.quantile(mu, alpha / 2), d3Array.quantile(mu, 1 - alpha / 2)]; | ||
| } | ||
@@ -151,3 +150,3 @@ | ||
| // https://www.cs.uic.edu/~wilkinson/Publications/dotplots.pdf | ||
| function dotbin(array, step, smooth, f) { | ||
| function dotbin (array, step, smooth, f) { | ||
| f = f || (_ => _); | ||
@@ -157,4 +156,4 @@ | ||
| v = new Float64Array(n); | ||
| let i = 0, j = 1, | ||
| let i = 0, | ||
| j = 1, | ||
| a = f(array[0]), | ||
@@ -165,10 +164,14 @@ b = a, | ||
| for (; j<n; ++j) { | ||
| for (; j < n; ++j) { | ||
| x = f(array[j]); | ||
| if (x >= w) { | ||
| b = (a + b) / 2; | ||
| for (; i<j; ++i) v[i] = b; | ||
| for (; i < j; ++i) v[i] = b; | ||
| w = x + step; | ||
| a = x; | ||
| } | ||
| b = x; | ||
@@ -178,10 +181,10 @@ } | ||
| b = (a + b) / 2; | ||
| for (; i<j; ++i) v[i] = b; | ||
| for (; i < j; ++i) v[i] = b; | ||
| return smooth ? smoothing(v, step + step / 4) : v; | ||
| } | ||
| // perform smoothing to reduce variance | ||
| } // perform smoothing to reduce variance | ||
| // swap points between "adjacent" stacks | ||
| // Wilkinson defines adjacent as within step/4 units | ||
| function smoothing(v, thresh) { | ||
@@ -191,5 +194,5 @@ const n = v.length; | ||
| b = 1, | ||
| c, d; | ||
| c, | ||
| d; // get left stack | ||
| // get left stack | ||
| while (v[a] === v[b]) ++b; | ||
@@ -200,13 +203,16 @@ | ||
| c = b + 1; | ||
| while (v[b] === v[c]) ++c; | ||
| // are stacks adjacent? | ||
| while (v[b] === v[c]) ++c; // are stacks adjacent? | ||
| // if so, compare sizes and swap as needed | ||
| if (v[b] - v[b-1] < thresh) { | ||
| d = b + ((a + c - b - b) >> 1); | ||
| if (v[b] - v[b - 1] < thresh) { | ||
| d = b + (a + c - b - b >> 1); | ||
| while (d < b) v[d++] = v[b]; | ||
| while (d > b) v[d--] = v[a]; | ||
| } | ||
| } // update left stack indices | ||
| // update left stack indices | ||
| a = b; | ||
@@ -219,6 +225,6 @@ b = c; | ||
| function lcg(seed) { | ||
| function lcg (seed) { | ||
| // Random numbers using a Linear Congruential Generator with seed value | ||
| // Uses glibc values from https://en.wikipedia.org/wiki/Linear_congruential_generator | ||
| return function() { | ||
| return function () { | ||
| seed = (1103515245 * seed + 12345) % 2147483647; | ||
@@ -229,3 +235,3 @@ return seed / 2147483647; | ||
| function integer(min, max) { | ||
| function integer (min, max) { | ||
| if (max == null) { | ||
@@ -237,3 +243,2 @@ max = min; | ||
| let a, b, d; | ||
| const dist = { | ||
@@ -249,2 +254,3 @@ min(_) { | ||
| }, | ||
| max(_) { | ||
@@ -259,8 +265,11 @@ if (arguments.length) { | ||
| }, | ||
| sample() { | ||
| return a + Math.floor(d * exports.random()); | ||
| }, | ||
| pdf(x) { | ||
| return (x === Math.floor(x) && x >= a && x < b) ? 1 / d : 0; | ||
| return x === Math.floor(x) && x >= a && x < b ? 1 / d : 0; | ||
| }, | ||
| cdf(x) { | ||
@@ -270,7 +279,8 @@ const v = Math.floor(x); | ||
| }, | ||
| icdf(p) { | ||
| return (p >= 0 && p <= 1) ? a - 1 + Math.floor(p * d) : NaN; | ||
| return p >= 0 && p <= 1 ? a - 1 + Math.floor(p * d) : NaN; | ||
| } | ||
| }; | ||
| return dist.min(min).max(max); | ||
@@ -283,8 +293,10 @@ } | ||
| let nextSample = NaN; | ||
| function sampleNormal(mean, stdev) { | ||
| mean = mean || 0; | ||
| stdev = stdev == null ? 1 : stdev; | ||
| let x = 0, | ||
| y = 0, | ||
| rds, | ||
| c; | ||
| let x = 0, y = 0, rds, c; | ||
| if (nextSample === nextSample) { | ||
@@ -299,9 +311,11 @@ x = nextSample; | ||
| } while (rds === 0 || rds > 1); | ||
| c = Math.sqrt(-2 * Math.log(rds) / rds); // Box-Muller transform | ||
| x *= c; | ||
| nextSample = y * c; | ||
| } | ||
| return mean + x * stdev; | ||
| } | ||
| function densityNormal(value, mean, stdev) { | ||
@@ -311,10 +325,8 @@ stdev = stdev == null ? 1 : stdev; | ||
| return Math.exp(-0.5 * z * z) / (stdev * SQRT2PI); | ||
| } | ||
| } // Approximation from West (2009) | ||
| // Better Approximations to Cumulative Normal Functions | ||
| // Approximation from West (2009) | ||
| // Better Approximations to Cumulative Normal Functions | ||
| function cumulativeNormal(value, mean, stdev) { | ||
| mean = mean || 0; | ||
| stdev = stdev == null ? 1 : stdev; | ||
| const z = (value - mean) / stdev, | ||
@@ -329,2 +341,3 @@ Z = Math.abs(z); | ||
| let sum; | ||
| if (Z < 7.07106781186547) { | ||
@@ -355,14 +368,13 @@ sum = 3.52624965998911e-02 * Z + 0.700383064443688; | ||
| } | ||
| return z > 0 ? 1 - cd : cd; | ||
| } | ||
| } // Approximation of Probit function using inverse error function. | ||
| // Approximation of Probit function using inverse error function. | ||
| function quantileNormal(p, mean, stdev) { | ||
| if (p < 0 || p > 1) return NaN; | ||
| return (mean || 0) + (stdev == null ? 1 : stdev) * SQRT2 * erfinv(2 * p - 1); | ||
| } | ||
| // Approximate inverse error function. Implementation from "Approximating | ||
| } // Approximate inverse error function. Implementation from "Approximating | ||
| // the erfinv function" by Mike Giles, GPU Computing Gems, volume 2, 2010. | ||
| // Ported from Apache Commons Math, http://www.apache.org/licenses/LICENSE-2.0 | ||
| function erfinv(x) { | ||
@@ -373,71 +385,72 @@ // beware that the logarithm argument must be | ||
| // would induce rounding errors near the boundaries +/-1 | ||
| let w = - Math.log((1 - x) * (1 + x)), p; | ||
| let w = -Math.log((1 - x) * (1 + x)), | ||
| p; | ||
| if (w < 6.25) { | ||
| w -= 3.125; | ||
| p = -3.6444120640178196996e-21; | ||
| p = -1.685059138182016589e-19 + p * w; | ||
| p = 1.2858480715256400167e-18 + p * w; | ||
| p = 1.115787767802518096e-17 + p * w; | ||
| p = -1.333171662854620906e-16 + p * w; | ||
| p = 2.0972767875968561637e-17 + p * w; | ||
| p = 6.6376381343583238325e-15 + p * w; | ||
| p = -4.0545662729752068639e-14 + p * w; | ||
| p = -8.1519341976054721522e-14 + p * w; | ||
| p = 2.6335093153082322977e-12 + p * w; | ||
| p = -1.2975133253453532498e-11 + p * w; | ||
| p = -5.4154120542946279317e-11 + p * w; | ||
| p = 1.051212273321532285e-09 + p * w; | ||
| p = -4.1126339803469836976e-09 + p * w; | ||
| p = -2.9070369957882005086e-08 + p * w; | ||
| p = 4.2347877827932403518e-07 + p * w; | ||
| p = -1.3654692000834678645e-06 + p * w; | ||
| p = -1.3882523362786468719e-05 + p * w; | ||
| p = 0.0001867342080340571352 + p * w; | ||
| p = -0.00074070253416626697512 + p * w; | ||
| p = -0.0060336708714301490533 + p * w; | ||
| p = 0.24015818242558961693 + p * w; | ||
| p = 1.6536545626831027356 + p * w; | ||
| w -= 3.125; | ||
| p = -3.6444120640178196996e-21; | ||
| p = -1.685059138182016589e-19 + p * w; | ||
| p = 1.2858480715256400167e-18 + p * w; | ||
| p = 1.115787767802518096e-17 + p * w; | ||
| p = -1.333171662854620906e-16 + p * w; | ||
| p = 2.0972767875968561637e-17 + p * w; | ||
| p = 6.6376381343583238325e-15 + p * w; | ||
| p = -4.0545662729752068639e-14 + p * w; | ||
| p = -8.1519341976054721522e-14 + p * w; | ||
| p = 2.6335093153082322977e-12 + p * w; | ||
| p = -1.2975133253453532498e-11 + p * w; | ||
| p = -5.4154120542946279317e-11 + p * w; | ||
| p = 1.051212273321532285e-09 + p * w; | ||
| p = -4.1126339803469836976e-09 + p * w; | ||
| p = -2.9070369957882005086e-08 + p * w; | ||
| p = 4.2347877827932403518e-07 + p * w; | ||
| p = -1.3654692000834678645e-06 + p * w; | ||
| p = -1.3882523362786468719e-05 + p * w; | ||
| p = 0.0001867342080340571352 + p * w; | ||
| p = -0.00074070253416626697512 + p * w; | ||
| p = -0.0060336708714301490533 + p * w; | ||
| p = 0.24015818242558961693 + p * w; | ||
| p = 1.6536545626831027356 + p * w; | ||
| } else if (w < 16.0) { | ||
| w = Math.sqrt(w) - 3.25; | ||
| p = 2.2137376921775787049e-09; | ||
| p = 9.0756561938885390979e-08 + p * w; | ||
| p = -2.7517406297064545428e-07 + p * w; | ||
| p = 1.8239629214389227755e-08 + p * w; | ||
| p = 1.5027403968909827627e-06 + p * w; | ||
| p = -4.013867526981545969e-06 + p * w; | ||
| p = 2.9234449089955446044e-06 + p * w; | ||
| p = 1.2475304481671778723e-05 + p * w; | ||
| p = -4.7318229009055733981e-05 + p * w; | ||
| p = 6.8284851459573175448e-05 + p * w; | ||
| p = 2.4031110387097893999e-05 + p * w; | ||
| p = -0.0003550375203628474796 + p * w; | ||
| p = 0.00095328937973738049703 + p * w; | ||
| p = -0.0016882755560235047313 + p * w; | ||
| p = 0.0024914420961078508066 + p * w; | ||
| p = -0.0037512085075692412107 + p * w; | ||
| p = 0.005370914553590063617 + p * w; | ||
| p = 1.0052589676941592334 + p * w; | ||
| p = 3.0838856104922207635 + p * w; | ||
| w = Math.sqrt(w) - 3.25; | ||
| p = 2.2137376921775787049e-09; | ||
| p = 9.0756561938885390979e-08 + p * w; | ||
| p = -2.7517406297064545428e-07 + p * w; | ||
| p = 1.8239629214389227755e-08 + p * w; | ||
| p = 1.5027403968909827627e-06 + p * w; | ||
| p = -4.013867526981545969e-06 + p * w; | ||
| p = 2.9234449089955446044e-06 + p * w; | ||
| p = 1.2475304481671778723e-05 + p * w; | ||
| p = -4.7318229009055733981e-05 + p * w; | ||
| p = 6.8284851459573175448e-05 + p * w; | ||
| p = 2.4031110387097893999e-05 + p * w; | ||
| p = -0.0003550375203628474796 + p * w; | ||
| p = 0.00095328937973738049703 + p * w; | ||
| p = -0.0016882755560235047313 + p * w; | ||
| p = 0.0024914420961078508066 + p * w; | ||
| p = -0.0037512085075692412107 + p * w; | ||
| p = 0.005370914553590063617 + p * w; | ||
| p = 1.0052589676941592334 + p * w; | ||
| p = 3.0838856104922207635 + p * w; | ||
| } else if (Number.isFinite(w)) { | ||
| w = Math.sqrt(w) - 5.0; | ||
| p = -2.7109920616438573243e-11; | ||
| p = -2.5556418169965252055e-10 + p * w; | ||
| p = 1.5076572693500548083e-09 + p * w; | ||
| p = -3.7894654401267369937e-09 + p * w; | ||
| p = 7.6157012080783393804e-09 + p * w; | ||
| p = -1.4960026627149240478e-08 + p * w; | ||
| p = 2.9147953450901080826e-08 + p * w; | ||
| p = -6.7711997758452339498e-08 + p * w; | ||
| p = 2.2900482228026654717e-07 + p * w; | ||
| p = -9.9298272942317002539e-07 + p * w; | ||
| p = 4.5260625972231537039e-06 + p * w; | ||
| p = -1.9681778105531670567e-05 + p * w; | ||
| p = 7.5995277030017761139e-05 + p * w; | ||
| p = -0.00021503011930044477347 + p * w; | ||
| p = -0.00013871931833623122026 + p * w; | ||
| p = 1.0103004648645343977 + p * w; | ||
| p = 4.8499064014085844221 + p * w; | ||
| w = Math.sqrt(w) - 5.0; | ||
| p = -2.7109920616438573243e-11; | ||
| p = -2.5556418169965252055e-10 + p * w; | ||
| p = 1.5076572693500548083e-09 + p * w; | ||
| p = -3.7894654401267369937e-09 + p * w; | ||
| p = 7.6157012080783393804e-09 + p * w; | ||
| p = -1.4960026627149240478e-08 + p * w; | ||
| p = 2.9147953450901080826e-08 + p * w; | ||
| p = -6.7711997758452339498e-08 + p * w; | ||
| p = 2.2900482228026654717e-07 + p * w; | ||
| p = -9.9298272942317002539e-07 + p * w; | ||
| p = 4.5260625972231537039e-06 + p * w; | ||
| p = -1.9681778105531670567e-05 + p * w; | ||
| p = 7.5995277030017761139e-05 + p * w; | ||
| p = -0.00021503011930044477347 + p * w; | ||
| p = -0.00013871931833623122026 + p * w; | ||
| p = 1.0103004648645343977 + p * w; | ||
| p = 4.8499064014085844221 + p * w; | ||
| } else { | ||
| p = Infinity; | ||
| p = Infinity; | ||
| } | ||
@@ -448,5 +461,4 @@ | ||
| function gaussian(mean, stdev) { | ||
| function gaussian (mean, stdev) { | ||
| let mu, sigma; | ||
| const dist = { | ||
@@ -461,2 +473,3 @@ mean(_) { | ||
| }, | ||
| stdev(_) { | ||
@@ -470,2 +483,3 @@ if (arguments.length) { | ||
| }, | ||
| sample: () => sampleNormal(mu, sigma), | ||
@@ -476,10 +490,8 @@ pdf: value => densityNormal(value, mu, sigma), | ||
| }; | ||
| return dist.mean(mean).stdev(stdev); | ||
| } | ||
| function kde(support, bandwidth) { | ||
| function kde (support, bandwidth) { | ||
| const kernel = gaussian(); | ||
| let n = 0; | ||
| const dist = { | ||
@@ -508,6 +520,9 @@ data(_) { | ||
| pdf(x) { | ||
| let y = 0, i = 0; | ||
| for (; i<n; ++i) { | ||
| let y = 0, | ||
| i = 0; | ||
| for (; i < n; ++i) { | ||
| y += kernel.pdf((x - support[i]) / bandwidth); | ||
| } | ||
| return y / bandwidth / n; | ||
@@ -517,6 +532,9 @@ }, | ||
| cdf(x) { | ||
| let y = 0, i = 0; | ||
| for (; i<n; ++i) { | ||
| let y = 0, | ||
| i = 0; | ||
| for (; i < n; ++i) { | ||
| y += kernel.cdf((x - support[i]) / bandwidth); | ||
| } | ||
| return y / n; | ||
@@ -528,4 +546,4 @@ }, | ||
| } | ||
| }; | ||
| return dist.data(support); | ||
@@ -539,3 +557,2 @@ } | ||
| } | ||
| function densityLogNormal(value, mean, stdev) { | ||
@@ -548,14 +565,10 @@ if (value <= 0) return 0; | ||
| } | ||
| function cumulativeLogNormal(value, mean, stdev) { | ||
| return cumulativeNormal(Math.log(value), mean, stdev); | ||
| } | ||
| function quantileLogNormal(p, mean, stdev) { | ||
| return Math.exp(quantileNormal(p, mean, stdev)); | ||
| } | ||
| function lognormal(mean, stdev) { | ||
| function lognormal (mean, stdev) { | ||
| let mu, sigma; | ||
| const dist = { | ||
@@ -570,2 +583,3 @@ mean(_) { | ||
| }, | ||
| stdev(_) { | ||
@@ -579,2 +593,3 @@ if (arguments.length) { | ||
| }, | ||
| sample: () => sampleLogNormal(mu, sigma), | ||
@@ -585,14 +600,22 @@ pdf: value => densityLogNormal(value, mu, sigma), | ||
| }; | ||
| return dist.mean(mean).stdev(stdev); | ||
| } | ||
| function mixture(dists, weights) { | ||
| let m = 0, w; | ||
| function mixture (dists, weights) { | ||
| let m = 0, | ||
| w; | ||
| function normalize(x) { | ||
| const w = []; | ||
| let sum = 0, i; | ||
| for (i = 0; i < m; ++i) { sum += (w[i] = (x[i]==null ? 1 : +x[i])); } | ||
| for (i = 0; i < m; ++i) { w[i] /= sum; } | ||
| let sum = 0, | ||
| i; | ||
| for (i = 0; i < m; ++i) { | ||
| sum += w[i] = x[i] == null ? 1 : +x[i]; | ||
| } | ||
| for (i = 0; i < m; ++i) { | ||
| w[i] /= sum; | ||
| } | ||
| return w; | ||
@@ -604,5 +627,6 @@ } | ||
| if (arguments.length) { | ||
| w = normalize(weights = (_ || [])); | ||
| w = normalize(weights = _ || []); | ||
| return dist; | ||
| } | ||
| return weights; | ||
@@ -620,4 +644,6 @@ }, | ||
| } | ||
| return dist.weights(weights); | ||
| } | ||
| return dists; | ||
@@ -628,11 +654,14 @@ }, | ||
| const r = exports.random(); | ||
| let d = dists[m-1], | ||
| let d = dists[m - 1], | ||
| v = w[0], | ||
| i = 0; | ||
| i = 0; // first select distribution | ||
| // first select distribution | ||
| for (; i<m-1; v += w[++i]) { | ||
| if (r < v) { d = dists[i]; break; } | ||
| } | ||
| // then sample from it | ||
| for (; i < m - 1; v += w[++i]) { | ||
| if (r < v) { | ||
| d = dists[i]; | ||
| break; | ||
| } | ||
| } // then sample from it | ||
| return d.sample(); | ||
@@ -642,6 +671,9 @@ }, | ||
| pdf(x) { | ||
| let p = 0, i = 0; | ||
| for (; i<m; ++i) { | ||
| let p = 0, | ||
| i = 0; | ||
| for (; i < m; ++i) { | ||
| p += w[i] * dists[i].pdf(x); | ||
| } | ||
| return p; | ||
@@ -651,6 +683,9 @@ }, | ||
| cdf(x) { | ||
| let p = 0, i = 0; | ||
| for (; i<m; ++i) { | ||
| let p = 0, | ||
| i = 0; | ||
| for (; i < m; ++i) { | ||
| p += w[i] * dists[i].cdf(x); | ||
| } | ||
| return p; | ||
@@ -662,4 +697,4 @@ }, | ||
| } | ||
| }; | ||
| return dist.distributions(dists).weights(weights); | ||
@@ -670,35 +705,34 @@ } | ||
| if (max == null) { | ||
| max = (min == null ? 1 : min); | ||
| max = min == null ? 1 : min; | ||
| min = 0; | ||
| } | ||
| return min + (max - min) * exports.random(); | ||
| } | ||
| function densityUniform(value, min, max) { | ||
| if (max == null) { | ||
| max = (min == null ? 1 : min); | ||
| max = min == null ? 1 : min; | ||
| min = 0; | ||
| } | ||
| return (value >= min && value <= max) ? 1 / (max - min) : 0; | ||
| return value >= min && value <= max ? 1 / (max - min) : 0; | ||
| } | ||
| function cumulativeUniform(value, min, max) { | ||
| if (max == null) { | ||
| max = (min == null ? 1 : min); | ||
| max = min == null ? 1 : min; | ||
| min = 0; | ||
| } | ||
| return value < min ? 0 : value > max ? 1 : (value - min) / (max - min); | ||
| } | ||
| function quantileUniform(p, min, max) { | ||
| if (max == null) { | ||
| max = (min == null ? 1 : min); | ||
| max = min == null ? 1 : min; | ||
| min = 0; | ||
| } | ||
| return (p >= 0 && p <= 1) ? min + p * (max - min) : NaN; | ||
| return p >= 0 && p <= 1 ? min + p * (max - min) : NaN; | ||
| } | ||
| function uniform(min, max) { | ||
| function uniform (min, max) { | ||
| let a, b; | ||
| const dist = { | ||
@@ -713,2 +747,3 @@ min(_) { | ||
| }, | ||
| max(_) { | ||
@@ -722,2 +757,3 @@ if (arguments.length) { | ||
| }, | ||
| sample: () => sampleUniform(a, b), | ||
@@ -730,5 +766,6 @@ pdf: value => densityUniform(value, a, b), | ||
| if (max == null) { | ||
| max = (min == null ? 1 : min); | ||
| max = min == null ? 1 : min; | ||
| min = 0; | ||
| } | ||
| return dist.min(min).max(max); | ||
@@ -738,7 +775,6 @@ } | ||
| // Ordinary Least Squares | ||
| function ols(uX, uY, uXY, uX2) { | ||
| function ols (uX, uY, uXY, uX2) { | ||
| const delta = uX2 - uX * uX, | ||
| slope = Math.abs(delta) < 1e-24 ? 0 : (uXY - uX * uY) / delta, | ||
| intercept = uY - slope * uX; | ||
| return [intercept, slope]; | ||
@@ -749,3 +785,4 @@ } | ||
| data = data.filter(d => { | ||
| let u = x(d), v = y(d); | ||
| let u = x(d), | ||
| v = y(d); | ||
| return u != null && (u = +u) >= u && v != null && (v = +v) >= v; | ||
@@ -760,6 +797,11 @@ }); | ||
| X = new Float64Array(n), | ||
| Y = new Float64Array(n); | ||
| Y = new Float64Array(n); // extract values, calculate means | ||
| // extract values, calculate means | ||
| let i = 0, ux = 0, uy = 0, xv, yv, d; | ||
| let i = 0, | ||
| ux = 0, | ||
| uy = 0, | ||
| xv, | ||
| yv, | ||
| d; | ||
| for (d of data) { | ||
@@ -771,6 +813,6 @@ X[i] = xv = +x(d); | ||
| uy += (yv - uy) / i; | ||
| } | ||
| } // mean center the data | ||
| // mean center the data | ||
| for (i=0; i<n; ++i) { | ||
| for (i = 0; i < n; ++i) { | ||
| X[i] -= ux; | ||
@@ -782,5 +824,6 @@ Y[i] -= uy; | ||
| } | ||
| function visitPoints(data, x, y, callback) { | ||
| let i = -1, u, v; | ||
| let i = -1, | ||
| u, | ||
| v; | ||
@@ -790,2 +833,3 @@ for (const d of data) { | ||
| v = y(d); | ||
| if (u != null && (u = +u) >= u && v != null && (v = +v) >= v) { | ||
@@ -797,23 +841,24 @@ callback(u, v, ++i); | ||
| // Adapted from d3-regression by Harry Stevens | ||
| // License: https://github.com/HarryStevens/d3-regression/blob/master/LICENSE | ||
| function rSquared(data, x, y, uY, predict) { | ||
| let SSE = 0, SST = 0; | ||
| function rSquared (data, x, y, uY, predict) { | ||
| let SSE = 0, | ||
| SST = 0; | ||
| visitPoints(data, x, y, (dx, dy) => { | ||
| const sse = dy - predict(dx), | ||
| sst = dy - uY; | ||
| SSE += sse * sse; | ||
| SST += sst * sst; | ||
| }); | ||
| return 1 - SSE / SST; | ||
| } | ||
| // Adapted from d3-regression by Harry Stevens | ||
| // License: https://github.com/HarryStevens/d3-regression/blob/master/LICENSE | ||
| function linear(data, x, y) { | ||
| let X = 0, Y = 0, XY = 0, X2 = 0, n = 0; | ||
| function linear (data, x, y) { | ||
| let X = 0, | ||
| Y = 0, | ||
| XY = 0, | ||
| X2 = 0, | ||
| n = 0; | ||
| visitPoints(data, x, y, (dx, dy) => { | ||
@@ -837,7 +882,10 @@ ++n; | ||
| // Adapted from d3-regression by Harry Stevens | ||
| // License: https://github.com/HarryStevens/d3-regression/blob/master/LICENSE | ||
| function log(data, x, y) { | ||
| let X = 0, Y = 0, XY = 0, X2 = 0, n = 0; | ||
| function log (data, x, y) { | ||
| let X = 0, | ||
| Y = 0, | ||
| XY = 0, | ||
| X2 = 0, | ||
| n = 0; | ||
| visitPoints(data, x, y, (dx, dy) => { | ||
@@ -862,7 +910,13 @@ ++n; | ||
| function exp(data, x, y) { | ||
| function exp (data, x, y) { | ||
| // eslint-disable-next-line no-unused-vars | ||
| const [xv, yv, ux, uy] = points(data, x, y); | ||
| let YL = 0, XY = 0, XYL = 0, X2Y = 0, n = 0, dx, ly, xy; | ||
| let YL = 0, | ||
| XY = 0, | ||
| XYL = 0, | ||
| X2Y = 0, | ||
| n = 0, | ||
| dx, | ||
| ly, | ||
| xy; | ||
| visitPoints(data, x, y, (_, dy) => { | ||
@@ -872,3 +926,2 @@ dx = xv[n++]; | ||
| xy = dx * dy; | ||
| YL += (dy * ly - YL) / n; | ||
@@ -890,7 +943,11 @@ XY += (xy - XY) / n; | ||
| // Adapted from d3-regression by Harry Stevens | ||
| // License: https://github.com/HarryStevens/d3-regression/blob/master/LICENSE | ||
| function pow(data, x, y) { | ||
| let X = 0, Y = 0, XY = 0, X2 = 0, YS = 0, n = 0; | ||
| function pow (data, x, y) { | ||
| let X = 0, | ||
| Y = 0, | ||
| XY = 0, | ||
| X2 = 0, | ||
| YS = 0, | ||
| n = 0; | ||
| visitPoints(data, x, y, (dx, dy) => { | ||
@@ -911,3 +968,2 @@ const lx = Math.log(dx), | ||
| coef[0] = Math.exp(coef[0]); | ||
| return { | ||
@@ -920,10 +976,16 @@ coef: coef, | ||
| function quad(data, x, y) { | ||
| function quad (data, x, y) { | ||
| const [xv, yv, ux, uy] = points(data, x, y), | ||
| n = xv.length; | ||
| let X2 = 0, | ||
| X3 = 0, | ||
| X4 = 0, | ||
| XY = 0, | ||
| X2Y = 0, | ||
| i, | ||
| dx, | ||
| dy, | ||
| x2; | ||
| let X2 = 0, X3 = 0, X4 = 0, XY = 0, X2Y = 0, | ||
| i, dx, dy, x2; | ||
| for (i=0; i<n;) { | ||
| for (i = 0; i < n;) { | ||
| dx = xv[i]; | ||
@@ -939,4 +1001,4 @@ dy = yv[i++]; | ||
| const X2X2 = X4 - (X2 * X2), | ||
| d = (X2 * X2X2 - X3 * X3), | ||
| const X2X2 = X4 - X2 * X2, | ||
| d = X2 * X2X2 - X3 * X3, | ||
| a = (X2Y * X2 - XY * X3) / d, | ||
@@ -946,13 +1008,9 @@ b = (XY * X2X2 - X2Y * X3) / d, | ||
| predict = x => { | ||
| x = x - ux; | ||
| return a * x * x + b * x + c + uy; | ||
| }; | ||
| x = x - ux; | ||
| return a * x * x + b * x + c + uy; | ||
| }; // transform coefficients back from mean-centered space | ||
| // transform coefficients back from mean-centered space | ||
| return { | ||
| coef: [ | ||
| c - b * ux + a * ux * ux + uy, | ||
| b - 2 * a * ux, | ||
| a | ||
| ], | ||
| coef: [c - b * ux + a * ux * ux + uy, b - 2 * a * ux, a], | ||
| predict: predict, | ||
@@ -963,3 +1021,2 @@ rSquared: rSquared(data, x, y, uy, predict) | ||
| // Adapted from d3-regression by Harry Stevens | ||
| // License: https://github.com/HarryStevens/d3-regression/blob/master/LICENSE | ||
@@ -969,7 +1026,7 @@ // ... which was adapted from regression-js by Tom Alexander | ||
| // License: https://github.com/Tom-Alexander/regression-js/blob/master/LICENSE | ||
| function poly(data, x, y, order) { | ||
| function poly (data, x, y, order) { | ||
| // use more efficient methods for lower orders | ||
| if (order === 1) return linear(data, x, y); | ||
| if (order === 2) return quad(data, x, y); | ||
| const [xv, yv, ux, uy] = points(data, x, y), | ||
@@ -980,20 +1037,23 @@ n = xv.length, | ||
| k = order + 1; | ||
| let i, j, l, v, c; | ||
| for (i=0; i<k; ++i) { | ||
| for (l=0, v=0; l<n; ++l) { | ||
| for (i = 0; i < k; ++i) { | ||
| for (l = 0, v = 0; l < n; ++l) { | ||
| v += Math.pow(xv[l], i) * yv[l]; | ||
| } | ||
| lhs.push(v); | ||
| c = new Float64Array(k); | ||
| c = new Float64Array(k); | ||
| for (j=0; j<k; ++j) { | ||
| for (l=0, v=0; l<n; ++l) { | ||
| for (j = 0; j < k; ++j) { | ||
| for (l = 0, v = 0; l < n; ++l) { | ||
| v += Math.pow(xv[l], i + j); | ||
| } | ||
| c[j] = v; | ||
| } | ||
| rhs.push(c); | ||
| } | ||
| rhs.push(lhs); | ||
@@ -1003,8 +1063,10 @@ | ||
| predict = x => { | ||
| x -= ux; | ||
| let y = uy + coef[0] + coef[1] * x + coef[2] * x * x; | ||
| for (i=3; i<k; ++i) y += coef[i] * Math.pow(x, i); | ||
| return y; | ||
| }; | ||
| x -= ux; | ||
| let y = uy + coef[0] + coef[1] * x + coef[2] * x * x; | ||
| for (i = 3; i < k; ++i) y += coef[i] * Math.pow(x, i); | ||
| return y; | ||
| }; | ||
| return { | ||
@@ -1019,30 +1081,29 @@ coef: uncenter(k, coef, -ux, uy), | ||
| const z = Array(k); | ||
| let i, j, v, c; | ||
| let i, j, v, c; // initialize to zero | ||
| // initialize to zero | ||
| for (i=0; i<k; ++i) z[i] = 0; | ||
| for (i = 0; i < k; ++i) z[i] = 0; // polynomial expansion | ||
| // polynomial expansion | ||
| for (i=k-1; i>=0; --i) { | ||
| for (i = k - 1; i >= 0; --i) { | ||
| v = a[i]; | ||
| c = 1; | ||
| z[i] += v; | ||
| for (j=1; j<=i; ++j) { | ||
| for (j = 1; j <= i; ++j) { | ||
| c *= (i + 1 - j) / j; // binomial coefficent | ||
| z[i-j] += v * Math.pow(x, j) * c; | ||
| z[i - j] += v * Math.pow(x, j) * c; | ||
| } | ||
| } | ||
| } // bias term | ||
| // bias term | ||
| z[0] += y; | ||
| return z; | ||
| } | ||
| } // Given an array for a two-dimensional matrix and the polynomial order, | ||
| // solve A * x = b using Gaussian elimination. | ||
| // Given an array for a two-dimensional matrix and the polynomial order, | ||
| // solve A * x = b using Gaussian elimination. | ||
| function gaussianElimination(matrix) { | ||
| const n = matrix.length - 1, | ||
| coef = []; | ||
| let i, j, k, r, t; | ||
@@ -1052,2 +1113,3 @@ | ||
| r = i; // max row | ||
| for (j = i + 1; j < n; ++j) { | ||
@@ -1067,3 +1129,3 @@ if (Math.abs(matrix[i][j]) > Math.abs(matrix[i][r])) { | ||
| for (k = n; k >= i; k--) { | ||
| matrix[k][j] -= (matrix[k][i] * matrix[i][j]) / matrix[i][i]; | ||
| matrix[k][j] -= matrix[k][i] * matrix[i][j] / matrix[i][i]; | ||
| } | ||
@@ -1075,5 +1137,7 @@ } | ||
| t = 0; | ||
| for (k = j + 1; k < n; ++k) { | ||
| t += matrix[k][j] * coef[k]; | ||
| } | ||
| coef[j] = (matrix[n][j] - t) / matrix[j][j]; | ||
@@ -1086,16 +1150,16 @@ } | ||
| const maxiters = 2, | ||
| epsilon = 1e-12; | ||
| // Adapted from science.js by Jason Davies | ||
| epsilon = 1e-12; // Adapted from science.js by Jason Davies | ||
| // Source: https://github.com/jasondavies/science.js/blob/master/src/stats/loess.js | ||
| // License: https://github.com/jasondavies/science.js/blob/master/LICENSE | ||
| function loess(data, x, y, bandwidth) { | ||
| function loess (data, x, y, bandwidth) { | ||
| const [xv, yv, ux, uy] = points(data, x, y, true), | ||
| n = xv.length, | ||
| bw = Math.max(2, ~~(bandwidth * n)), // # nearest neighbors | ||
| yhat = new Float64Array(n), | ||
| bw = Math.max(2, ~~(bandwidth * n)), | ||
| // # nearest neighbors | ||
| yhat = new Float64Array(n), | ||
| residuals = new Float64Array(n), | ||
| robustWeights = new Float64Array(n).fill(1); | ||
| for (let iter = -1; ++iter <= maxiters; ) { | ||
| for (let iter = -1; ++iter <= maxiters;) { | ||
| const interval = [0, bw - 1]; | ||
@@ -1107,5 +1171,8 @@ | ||
| i1 = interval[1], | ||
| edge = (dx - xv[i0]) > (xv[i1] - dx) ? i0 : i1; | ||
| let W = 0, X = 0, Y = 0, XY = 0, X2 = 0; | ||
| edge = dx - xv[i0] > xv[i1] - dx ? i0 : i1; | ||
| let W = 0, | ||
| X = 0, | ||
| Y = 0, | ||
| XY = 0, | ||
| X2 = 0; | ||
| const denom = 1 / Math.abs(xv[edge] - dx || 1); // avoid singularity! | ||
@@ -1118,3 +1185,2 @@ | ||
| xkw = xk * w; | ||
| W += w; | ||
@@ -1125,9 +1191,8 @@ X += xkw; | ||
| X2 += xk * xkw; | ||
| } | ||
| } // linear regression fit | ||
| // linear regression fit | ||
| const [a, b] = ols(X / W, Y / W, XY / W, X2 / W); | ||
| yhat[i] = a + b * dx; | ||
| residuals[i] = Math.abs(yv[i] - yhat[i]); | ||
| updateInterval(xv, i + 1, interval); | ||
@@ -1143,7 +1208,7 @@ } | ||
| for (let i = 0, arg, w; i < n; ++i){ | ||
| arg = residuals[i] / (6 * medianResidual); | ||
| // default to epsilon (rather than zero) for large deviations | ||
| for (let i = 0, arg, w; i < n; ++i) { | ||
| arg = residuals[i] / (6 * medianResidual); // default to epsilon (rather than zero) for large deviations | ||
| // keeping weights tiny but non-zero prevents singularites | ||
| robustWeights[i] = (arg >= 1) ? epsilon : ((w = 1 - arg * arg) * w); | ||
| robustWeights[i] = arg >= 1 ? epsilon : (w = 1 - arg * arg) * w; | ||
| } | ||
@@ -1153,10 +1218,9 @@ } | ||
| return output(xv, yhat, ux, uy); | ||
| } | ||
| } // weighting kernel for local regression | ||
| // weighting kernel for local regression | ||
| function tricube(x) { | ||
| return (x = 1 - x * x * x) * x * x; | ||
| } | ||
| } // advance sliding window interval of nearest neighbors | ||
| // advance sliding window interval of nearest neighbors | ||
| function updateInterval(xv, i, interval) { | ||
@@ -1166,8 +1230,6 @@ const val = xv[i]; | ||
| right = interval[1] + 1; | ||
| if (right >= xv.length) return; // step right if distance to new right edge is <= distance to old left edge | ||
| // step when distance is equal to ensure movement over duplicate x values | ||
| if (right >= xv.length) return; | ||
| // step right if distance to new right edge is <= distance to old left edge | ||
| // step when distance is equal to ensure movement over duplicate x values | ||
| while (i > left && (xv[right] - val) <= (val - xv[left])) { | ||
| while (i > left && xv[right] - val <= val - xv[left]) { | ||
| interval[0] = ++left; | ||
@@ -1177,15 +1239,20 @@ interval[1] = right; | ||
| } | ||
| } | ||
| } // generate smoothed output points | ||
| // average points with repeated x values | ||
| // generate smoothed output points | ||
| // average points with repeated x values | ||
| function output(xv, yhat, ux, uy) { | ||
| const n = xv.length, out = []; | ||
| let i = 0, cnt = 0, prev = [], v; | ||
| const n = xv.length, | ||
| out = []; | ||
| let i = 0, | ||
| cnt = 0, | ||
| prev = [], | ||
| v; | ||
| for (; i<n; ++i) { | ||
| for (; i < n; ++i) { | ||
| v = xv[i] + ux; | ||
| if (prev[0] === v) { | ||
| // average output values via online update | ||
| prev[1] += (yhat[i] - prev[1]) / (++cnt); | ||
| prev[1] += (yhat[i] - prev[1]) / ++cnt; | ||
| } else { | ||
@@ -1199,4 +1266,4 @@ // add new output point | ||
| } | ||
| prev[1] += uy; | ||
| return out; | ||
@@ -1206,6 +1273,5 @@ } | ||
| // subdivide up to accuracy of 0.1 degrees | ||
| const MIN_RADIANS = 0.1 * Math.PI / 180; | ||
| const MIN_RADIANS = 0.1 * Math.PI / 180; // Adaptively sample an interpolated function over a domain extent | ||
| // Adaptively sample an interpolated function over a domain extent | ||
| function sampleCurve(f, extent, minSteps, maxSteps) { | ||
| function sampleCurve (f, extent, minSteps, maxSteps) { | ||
| minSteps = minSteps || 25; | ||
@@ -1225,4 +1291,5 @@ maxSteps = Math.max(minSteps, maxSteps || 200); | ||
| for (let i = 1; i < maxSteps; ++i) { | ||
| prev.push(point(minX + (i / minSteps) * span)); | ||
| prev.push(point(minX + i / minSteps * span)); | ||
| } | ||
| prev.push(point(maxX)); | ||
@@ -1234,4 +1301,5 @@ return prev; | ||
| next.push(point(maxX)); | ||
| for (let i = minSteps; --i > 0;) { | ||
| next.push(point(minX + (i / minSteps) * span)); | ||
| next.push(point(minX + i / minSteps * span)); | ||
| } | ||
@@ -1259,2 +1327,3 @@ } | ||
| } | ||
| p1 = next[next.length - 1]; | ||
@@ -1261,0 +1330,0 @@ } |
@@ -1,1 +0,2 @@ | ||
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t=r;--t>0;)s.push(o(l+t/r*f));let c=i[0],h=s[s.length-1];for(;h;){const t=o((c[0]+h[0])/2);t[0]-c[0]>=a&&Q(c,t,h)>K?s.push(t):(c=h,i.push(h),s.pop()),h=s[s.length-1]}return i},t.sampleLogNormal=v,t.sampleNormal=m,t.sampleUniform=q,t.setRandom=function(n){t.random=n},t}({}); | ||
| //# sourceMappingURL=vega-statistics.min.js.map |
| { | ||
| "name": "vega-statistics", | ||
| "version": "1.7.8", | ||
| "version": "1.7.9", | ||
| "description": "Statistical routines and probability distributions.", | ||
@@ -14,18 +14,16 @@ "keywords": [ | ||
| "main": "build/vega-statistics.js", | ||
| "module": "index", | ||
| "module": "build/vega-statistics.module.js", | ||
| "unpkg": "build/vega-statistics.min.js", | ||
| "repository": "vega/vega", | ||
| "scripts": { | ||
| "rollup": "rollup -g d3-array:d3 -f umd -n vega -o build/vega-statistics.js -- index.js", | ||
| "prebuild": "rimraf build && mkdir build", | ||
| "build": "yarn rollup", | ||
| "postbuild": "terser build/vega-statistics.js -c -m -o build/vega-statistics.min.js", | ||
| "pretest": "yarn prebuild && yarn rollup", | ||
| "prebuild": "rimraf build", | ||
| "build": "rollup -c", | ||
| "pretest": "yarn build --config-test", | ||
| "test": "tape 'test/**/*-test.js'", | ||
| "prepublishOnly": "yarn test && yarn build", | ||
| "postpublish": "git push && git push --tags" | ||
| "prepublishOnly": "yarn test && yarn build" | ||
| }, | ||
| "dependencies": { | ||
| "d3-array": "^2.7.0" | ||
| "d3-array": "^2.7.1" | ||
| }, | ||
| "gitHead": "8d6793f4ca7eaaf2d22186764e9ce2dae687cf52" | ||
| "gitHead": "4affcbedb9d14815dbb6d3b250ed231b54fc95c0" | ||
| } |
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Dependency changes
Updatedd3-array@^2.7.1