Top 10 Examples of "jstat in functional component" in JavaScript
Dive into secure and efficient coding practices with our curated list of the top 10 examples showcasing 'jstat' in functional components in JavaScript. Our advanced machine learning engine meticulously scans each line of code, cross-referencing millions of open source libraries to ensure your implementation is not just functional, but also robust and secure. Elevate your React applications to new heights by mastering the art of handling side effects, API calls, and asynchronous operations with confidence and precision.
function writeResult(res: Result, dir: string) {
if (!fs.existsSync(dir)) {
fs.mkdirSync(dir);
}
let benchmark = res.benchmark;
let framework = res.framework.name;
let data = res.results;
data = data.slice(0).sort((a: number, b: number) => a - b);
// data = data.slice(0, config.REPEAT_RUN - config.DROP_WORST_RUN);
let s = jStat(data);
console.log(
`result ${fileName(
res.framework,
benchmark
)} min ${s.min()} max ${s.max()} mean ${s.mean()} median ${s.median()} stddev ${s.stdev()}`
);
let result: JSONResult = {
framework: framework,
benchmark: benchmark.id,
type: benchmark.type === BenchmarkType.CPU ? 'cpu' : 'memory',
min: s.min(),
max: s.max(),
mean: s.mean(),
median: s.median(),
geometricMean: s.geomean(),
standardDeviation: s.stdev(),function confidenceInterval95(
{mean, variance}: Distribution, size: number): ConfidenceInterval {
// http://www.stat.yale.edu/Courses/1997-98/101/confint.htm
const t = jstat.studentt.inv(1 - (.05 / 2), size - 1);
const stdDev = Math.sqrt(variance);
const margin = t * stdDev;
return {
low: mean - margin,
high: mean + margin,
};
}mean: _ => _,
prediction: x => (MSE * 1 / (m || 1)) + x,
}[type]).map(Math.sqrt);
let g = values.length;
let statValue = {
// simultaneous region over regression surface
workingHotelling: Math.sqrt(ddof * jStat.centralF.inv(1 - alpha, ddof, n - ddof)),
// simultaneous set
scheffe: Math.sqrt(g * jStat.centralF.inv(1 - alpha, g, n - ddof)),
bonferroni: jStat.studentt.inv(1 - alpha / (2 * g), n - ddof),
// pointwise
t: jStat.studentt.inv(1 - alpha / 2, n - ddof)
}[statistic];
let invLink = {
gaussian: _ => _,
poisson: x => Math.exp(x),
exponential: x => -1 / x,
gamma: x => -1 / x,
binomial: x => 1 / (1 + Math.exp(x))
}[family];
return values
.map((val, i) => [-1, 1].map(sign => invLink(val + sign * statValue * stdErr[i])).sort())
};// MSE is already included in the coefficient variance-covariance matrix
let stdErr = variances.map({
mean: _ => _,
prediction: x => (MSE * 1 / (m || 1)) + x,
}[type]).map(Math.sqrt);
let g = values.length;
let statValue = {
// simultaneous region over regression surface
workingHotelling: Math.sqrt(ddof * jStat.centralF.inv(1 - alpha, ddof, n - ddof)),
// simultaneous set
scheffe: Math.sqrt(g * jStat.centralF.inv(1 - alpha, g, n - ddof)),
bonferroni: jStat.studentt.inv(1 - alpha / (2 * g), n - ddof),
// pointwise
t: jStat.studentt.inv(1 - alpha / 2, n - ddof)
}[statistic];
let invLink = {
gaussian: _ => _,
poisson: x => Math.exp(x),
exponential: x => -1 / x,
gamma: x => -1 / x,
binomial: x => 1 / (1 + Math.exp(x))
}[family];
return values
.map((val, i) => [-1, 1].map(sign => invLink(val + sign * statValue * stdErr[i])).sort())
};let makeIntervals = ({values, variances, statistic, type, family, alpha, n, ddof, MSE, m}) => {
// MSE is already included in the coefficient variance-covariance matrix
let stdErr = variances.map({
mean: _ => _,
prediction: x => (MSE * 1 / (m || 1)) + x,
}[type]).map(Math.sqrt);
let g = values.length;
let statValue = {
// simultaneous region over regression surface
workingHotelling: Math.sqrt(ddof * jStat.centralF.inv(1 - alpha, ddof, n - ddof)),
// simultaneous set
scheffe: Math.sqrt(g * jStat.centralF.inv(1 - alpha, g, n - ddof)),
bonferroni: jStat.studentt.inv(1 - alpha / (2 * g), n - ddof),
// pointwise
t: jStat.studentt.inv(1 - alpha / 2, n - ddof)
}[statistic];
let invLink = {
gaussian: _ => _,
poisson: x => Math.exp(x),
exponential: x => -1 / x,
gamma: x => -1 / x,
binomial: x => 1 / (1 + Math.exp(x))
}[family];let makeIntervals = ({values, variances, statistic, type, family, alpha, n, ddof, MSE, m}) => {
// MSE is already included in the coefficient variance-covariance matrix
let stdErr = variances.map({
mean: _ => _,
prediction: x => (MSE * 1 / (m || 1)) + x,
}[type]).map(Math.sqrt);
let g = values.length;
let statValue = {
// simultaneous region over regression surface
workingHotelling: Math.sqrt(ddof * jStat.centralF.inv(1 - alpha, ddof, n - ddof)),
// simultaneous set
scheffe: Math.sqrt(g * jStat.centralF.inv(1 - alpha, g, n - ddof)),
bonferroni: jStat.studentt.inv(1 - alpha / (2 * g), n - ddof),
// pointwise
t: jStat.studentt.inv(1 - alpha / 2, n - ddof)
}[statistic];
let invLink = {
gaussian: _ => _,
poisson: x => Math.exp(x),
exponential: x => -1 / x,
gamma: x => -1 / x,
binomial: x => 1 / (1 + Math.exp(x))
}[family];
return values
.map((val, i) => [-1, 1].map(sign => invLink(val + sign * statValue * stdErr[i])).sort())exports.NORM.DIST = function(x, mean, sd, cumulative) {
cumulative = parseBool(cumulative)
x = parseNumber(x);
mean = parseNumber(mean);
sd = parseNumber(sd);
if (anyIsError(x, mean, sd)) {
return Error(ERROR_VALUE);
}
if (sd <= 0) {
return Error(ERROR_NUM);
}
// Return normal distribution computed by jStat [http://jstat.org]
return (cumulative) ? jStat.normal.cdf(x, mean, sd) : jStat.normal.pdf(x, mean, sd);
};return max - (max - mode)*Math.sqrt(2*(1-u))
}
}
// Source:
// https://en.wikipedia.org/wiki/Beta_distribution#Transformations
function PERT(min, max, mode = (min + max)/2, lambda = 4) {
const width = max - min
const a = 1 + lambda * ((mode - min)/width)
const b = 1 + lambda * ((max - mode)/width)
const p = jStat.beta.sample(a, b)
return min + p*width
}
export const Distributions = {
beta: jStat.beta.sample,
centralF: jStat.centralF.sample,
cauchy: jStat.cauchy.sample,
chisquare: jStat.chisquare.sample,
exponential: jStat.exponential.sample,
invgamma: jStat.invgamma.sample,
lognormal: jStat.lognormal.sample,
normal: jStat.normal.sample,
studentt: jStat.studentt.sample,
weibull: jStat.weibull.sample,
uniform: jStat.uniform.sample,
gamma: jStat.gamma.sample,
triangular,
doubleTriangular,
PERT,
bernoulli: bernoulli,
if: bernoulli,// Source:
// https://en.wikipedia.org/wiki/Beta_distribution#Transformations
function PERT(min, max, mode = (min + max)/2, lambda = 4) {
const width = max - min
const a = 1 + lambda * ((mode - min)/width)
const b = 1 + lambda * ((max - mode)/width)
const p = jStat.beta.sample(a, b)
return min + p*width
}
export const Distributions = {
beta: jStat.beta.sample,
centralF: jStat.centralF.sample,
cauchy: jStat.cauchy.sample,
chisquare: jStat.chisquare.sample,
exponential: jStat.exponential.sample,
invgamma: jStat.invgamma.sample,
lognormal: jStat.lognormal.sample,
normal: jStat.normal.sample,
studentt: jStat.studentt.sample,
weibull: jStat.weibull.sample,
uniform: jStat.uniform.sample,
gamma: jStat.gamma.sample,
triangular,
doubleTriangular,
PERT,
bernoulli: bernoulli,
if: bernoulli,
test: bernoulli,
binomial: binomial,
poisson: poisson,
negBinomial: negBinomial}
// Source:
// https://en.wikipedia.org/wiki/Beta_distribution#Transformations
function PERT(min, max, mode = (min + max)/2, lambda = 4) {
const width = max - min
const a = 1 + lambda * ((mode - min)/width)
const b = 1 + lambda * ((max - mode)/width)
const p = jStat.beta.sample(a, b)
return min + p*width
}
export const Distributions = {
beta: jStat.beta.sample,
centralF: jStat.centralF.sample,
cauchy: jStat.cauchy.sample,
chisquare: jStat.chisquare.sample,
exponential: jStat.exponential.sample,
invgamma: jStat.invgamma.sample,
lognormal: jStat.lognormal.sample,
normal: jStat.normal.sample,
studentt: jStat.studentt.sample,
weibull: jStat.weibull.sample,
uniform: jStat.uniform.sample,
gamma: jStat.gamma.sample,
triangular,
doubleTriangular,
PERT,
bernoulli: bernoulli,
if: bernoulli,
test: bernoulli,
binomial: binomial,