Top 10 Examples of "vega-statistics in functional component" in JavaScript
Dive into secure and efficient coding practices with our curated list of the top 10 examples showcasing 'vega-statistics' 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.
let canvas;
document.getElementById("regl").innerText = "";
if ((document.getElementById("debug") as HTMLInputElement).checked) {
canvas = document.createElement("canvas");
document.getElementById("regl").appendChild(canvas);
}
const maxY = (data.data as Float32Array).reduce(
(agg, val) => Math.max(agg, val),
0
);
// compute nice bin boundaries
const binConfigX = bin({ maxbins: binsx, extent: [0, points - 1] });
const binConfigY = bin({ maxbins: binsy, extent: [0, maxY] });
start = Date.now();
compute(data, binConfigX, binConfigY, canvas).then(heatmapData => {
document.getElementById("computetime").innerText = `${(Date.now() - start) /
1000} seconds`;
heatmap(heatmapData, binConfigX, binConfigY);
});
}extent.push(extent[0] === 1 ? extent[0] + 0.1 : extent[0] + 1);
}
// the performance distribution is always [0, 1]
const binIdxes = d3.range(0, this.binCount - 2);
const binFunction = d3.scaleQuantile()
.domain(d3.merge([
this.flatCounts.correct.concat(this.flatCountsRewrite.correct) as number[],
this.flatCounts.incorrect.concat(this.flatCountsRewrite.incorrect) as number[]]))
.range(binIdxes);
this.scale.attrScale_continue.domain(extent);
let bins = d3.merge([
[extent[0]],binFunction.quantiles(),
[extent[1]]]).filter(utils.uniques) as number[];
if ( (bins[bins.length-1] - bins[0] <= 1) || bins.length < this.binCount * 0.6) {
const bins_ = vegaStat.bin({extent: extent, nice: false, maxbins: this.binCount });
this.scale.bins = d3.range(bins_.start, bins_.stop + bins_.step, bins_.step);
} else {
//bins.push(extent[1] === 1 ? extent[1] + 0.1 : extent[1] + 1);
this.scale.bins = bins;
}
} else {
this.scale.bins = this.domain.slice();//this.attr.stats.map(s => s.value);
this.scale.attrScale_discrete.domain(this.scale.bins as string[]);
}
/*
if (this.props.attr.name === "groundtruths_length") {
this.scale.attrScale_continue.domain([1, 20]);
this.scale.bins = [1, 2, 3, 4, 5, 6, 7, 8, 20]
}
if (this.props.attr.name === "prediction_length") {
this.scale.attrScale_continue.domain([1, 50]);lineChart(data);
let canvas;
document.getElementById("regl").innerText = "";
if ((document.getElementById("debug") as HTMLInputElement).checked) {
canvas = document.createElement("canvas");
document.getElementById("regl").appendChild(canvas);
}
const maxY = (data.data as Float32Array).reduce(
(agg, val) => Math.max(agg, val),
0
);
// compute nice bin boundaries
const binConfigX = bin({ maxbins: binsx, extent: [0, points - 1] });
const binConfigY = bin({ maxbins: binsy, extent: [0, maxY] });
start = Date.now();
compute(data, binConfigX, binConfigY, canvas).then(heatmapData => {
document.getElementById("computetime").innerText = `${(Date.now() - start) /
1000} seconds`;
heatmap(heatmapData, binConfigX, binConfigY);
});
}add = p => {
const t = {};
for (let i=0; i add([x, model.predict(x)]));
} else {
// otherwise return trend line sample points
sampleCurve(model.predict, dom, 25, 200).forEach(add);
}
});const source = pulse.materialize(pulse.SOURCE).source,
groups = partition(pulse.source, _.groupby, identity),
smooth = _.smooth || false,
field = _.field,
step = _.step || autostep(source, field),
sort = stableCompare((a, b) => field(a) - field(b)),
as = _.as || Output,
n = groups.length;
// compute dotplot bins per group
let min = Infinity, max = -Infinity, i = 0, j;
for (; i max) max = v;
g[++j][as] = v;
}
}
this.value = {
start: min,
stop: max,
step: step
};
return pulse.reflow(true).modifies(as);
};function update(t) {
var p, idx;
if (res.length < num) {
res.push(t);
} else {
idx = ~~((cnt + 1) * random());
if (idx < res.length && idx >= cap) {
p = res[idx];
if (map[tupleid(p)]) out.rem.push(p); // eviction
res[idx] = t;
}
}
++cnt;
}prototype._bins = function(_) {
if (this.value && !_.modified()) {
return this.value;
}
var field = _.field,
bins = bin(_),
start = bins.start,
stop = bins.stop,
step = bins.step,
a, d;
if ((a = _.anchor) != null) {
d = a - (start + step * Math.floor((a - start) / step));
start += d;
stop += d;
}
var f = function(t) {
var v = field(t);
if (v == null) {
return null;
} else {prototype._bins = function(_) {
if (this.value && !_.modified()) {
return this.value;
}
var field = _.field,
bins = bin(_),
step = bins.step,
start = bins.start,
stop = start + Math.ceil((bins.stop - start) / step) * step,
a, d;
if ((a = _.anchor) != null) {
d = a - (start + step * Math.floor((a - start) / step));
start += d;
stop += d;
}
var f = function(t) {
var v = field(t);
return v == null ? null
: v < start ? -Infinity
: v > stop ? +Infinitygroups.forEach(g => {
const density = randomKDE(g, bandwidth)[method],
scale = _.counts ? g.length : 1,
local = domain || extent(g);
sampleCurve(density, local, minsteps, maxsteps).forEach(v => {
const t = {};
for (let i=0; ivar dist = parseDist(_.distribution, source(pulse)),
minsteps = _.steps || _.minsteps || 25,
maxsteps = _.steps || _.maxsteps || 200,
method = _.method || 'pdf';
if (method !== 'pdf' && method !== 'cdf') {
error('Invalid density method: ' + method);
}
if (!_.extent && !dist.data) {
error('Missing density extent parameter.');
}
method = dist[method];
var as = _.as || ['value', 'density'],
domain = _.extent || extent(dist.data()),
values = sampleCurve(method, domain, minsteps, maxsteps).map(v => {
var tuple = {};
tuple[as[0]] = v[0];
tuple[as[1]] = v[1];
return ingest(tuple);
});
if (this.value) out.rem = this.value;
this.value = out.add = out.source = values;
}
return out;
};