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demo basic stdlib.random functions
// stdlib.random demos:
//
import "random.juttle" as random;
export sub poissonHisto() {
// render draws from 3 poisson distributions as a scatter chart.
// see https://en.wikipedia.org/wiki/Poisson_distribution
emit -limit 10000 -from 0
| put lambda_1=random.poisson(1), lambda_4=random.poisson(4), lambda_10=random.poisson(10)
| split lambda_1, lambda_4, lambda_10
| reduce n = count() by name,value
| @scatterchart -controlField 'value' -valueField 'n' -keyField 'name' -title 'poisson distributions'
}
reducer xdp(y, P) {
// given a list of data values and corresponding percentiles, return the
// derivative. This numerically differentiates the EDF to get an approximate
// PDF. It can be a bumpy ride.
//
var Y = [];
function update() {
Y = *y; //P = *pct;
}
function iter(X, dPdX, n) {
if (X == null || X[n+1] == null) {
return dPdX;
} else if (n > 0) {
dPdX[n] = [(X[n+1]+X[n])/2, (P[n+1] - P[n]) / (X[n+1] - X[n])];
}
return iter(X, dPdX, n+1);
}
function result() {
return iter(Y, [0], 0);
}
}
export sub pdfChart(x, by, title) {
// compute percentiles for a metric stream (an approximate
// empirical distribution function), differentiate them to get an
// approximate probability distribution, then make a scatter chart.
//
const PCT = [0, .01, .02, .03, .04,.05,.06,.07, .1, .15, .2, .25, .3, .35, .4, .45, .5,
.55, .6, .65, .7, .75, .8, .85, .9, .92,.93,.94,.95,.96, .97, .98, .99, 1];
reduce px = percentile(x, PCT) by by
| put dpx = xdp(px, PCT) by by
| split dpx
| put x=value[0], dp=value[1]
| @scatterchart -controlField 'x' -valueField 'dp' -keyField by -title title
}
export sub normalHisto() {
// render draws from some normal distributions as a scatter chart.
// see https://en.wikipedia.org/wiki/Normal_distribution
//
emit -limit 10000 -from 0
| put x = random.normal(0,Math.sqrt(0.2)), y = random.normal(0, 1), z = random.normal(-2, Math.sqrt(0.5))
| split x, y, z
| put series = name
| pdfChart -x 'value' -by 'series' -title 'normal distributions'
}
export sub expHisto() {
// render draws from some exponential distributions as a scatter chart.
// see https://en.wikipedia.org/wiki/Exponential_distribution
//
emit -limit 10000 -from 0
| put lambda_05=random.exponential(2), lambda_1=random.exponential(1), lambda_15=random.exponential(1/1.5)
| split lambda_05, lambda_1, lambda_15
| put series = name
| filter value < 8
| pdfChart -x 'value' -by 'series' -title 'exponential distributions'
}
export sub demo() {
poissonHisto;
normalHisto;
expHisto;
}
demo
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