catniff
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A small Torch-like deep learning framework for Javascript
51 lines (50 loc) • 1.58 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
exports.erf = erf;
exports.erfc = erfc;
exports.erfinv = erfinv;
exports.randUniform = randUniform;
exports.randNormal = randNormal;
exports.randInt = randInt;
// Error function using Abramowitz and Stegun approximation
function erf(x) {
const a1 = 0.254829592;
const a2 = -0.284496736;
const a3 = 1.421413741;
const a4 = -1.453152027;
const a5 = 1.061405429;
const p = 0.3275911;
const sign = x >= 0 ? 1 : -1;
x = Math.abs(x);
// Formula 7.1.26
const t = 1.0 / (1.0 + p * x);
const y = 1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * Math.exp(-x * x);
return sign * y;
}
// Complementary error function
function erfc(x) {
return 1 - erf(x);
}
// Inverse error function using Winitzki approximation
function erfinv(x) {
if (Math.abs(x) >= 1)
throw new Error("Input must be in range (-1, 1)");
const a = 0.147;
const ln = Math.log(1 - x * x);
const part1 = 2 / (Math.PI * a) + ln / 2;
const part2 = ln / a;
const sign = x >= 0 ? 1 : -1;
return sign * Math.sqrt(-part1 + Math.sqrt(part1 * part1 - part2));
}
function randUniform(low = 0, high = 1) {
return Math.random() * (high - low) + low;
}
function randNormal(mean = 0, stdDev = 1) {
const u = 1 - Math.random();
const v = 1 - Math.random();
const z = Math.sqrt(-2.0 * Math.log(u)) * Math.cos(2.0 * Math.PI * v);
return z * stdDev + mean;
}
function randInt(low, high) {
return Math.floor(Math.random() * (high - low) + low);
}