demo basic stdlib.random functions

random-demo.juttle

// 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