@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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text/typescript
/**
* @license
* Copyright 2017 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import * as seedrandom from 'seedrandom';
export interface RandomBase {
nextValue(): number;
}
export interface RandomGamma {
nextValue(): number;
}
export interface RandNormalDataTypes {
float32: Float32Array;
int32: Int32Array;
}
export interface RandGammaDataTypes {
float32: Float32Array;
int32: Int32Array;
}
// https://en.wikipedia.org/wiki/Marsaglia_polar_method
export class MPRandGauss implements RandomBase {
private mean: number;
private stdDev: number;
private nextVal: number;
private dtype?: keyof RandNormalDataTypes;
private truncated?: boolean;
private upper?: number;
private lower?: number;
private random: seedrandom.prng;
constructor(
mean: number, stdDeviation: number, dtype?: keyof RandNormalDataTypes,
truncated?: boolean, seed?: number) {
this.mean = mean;
this.stdDev = stdDeviation;
this.dtype = dtype;
this.nextVal = NaN;
this.truncated = truncated;
if (this.truncated) {
this.upper = this.mean + this.stdDev * 2;
this.lower = this.mean - this.stdDev * 2;
}
const seedValue = seed ? seed : Math.random();
this.random = seedrandom.alea(seedValue.toString());
}
/** Returns next sample from a Gaussian distribution. */
public nextValue(): number {
if (!isNaN(this.nextVal)) {
const value = this.nextVal;
this.nextVal = NaN;
return value;
}
let resultX: number, resultY: number;
let isValid = false;
while (!isValid) {
let v1: number, v2: number, s: number;
do {
v1 = 2 * this.random() - 1;
v2 = 2 * this.random() - 1;
s = v1 * v1 + v2 * v2;
} while (s >= 1 || s === 0);
const mul = Math.sqrt(-2.0 * Math.log(s) / s);
resultX = this.mean + this.stdDev * v1 * mul;
resultY = this.mean + this.stdDev * v2 * mul;
if (!this.truncated || this.isValidTruncated(resultX)) {
isValid = true;
}
}
if (!this.truncated || this.isValidTruncated(resultY)) {
this.nextVal = this.convertValue(resultY);
}
return this.convertValue(resultX);
}
/** Handles proper rounding for non-floating-point numbers. */
private convertValue(value: number): number {
if (this.dtype == null || this.dtype === 'float32') {
return value;
}
return Math.round(value);
}
/** Returns true if less than 2-standard-deviations from the mean. */
private isValidTruncated(value: number): boolean {
return value <= this.upper && value >= this.lower;
}
}
// Marsaglia, George, and Wai Wan Tsang. 2000. "A Simple Method for Generating
// Gamma Variables."
export class RandGamma implements RandomGamma {
private alpha: number;
private beta: number;
private d: number;
private c: number;
private dtype?: keyof RandGammaDataTypes;
private randu: seedrandom.prng;
private randn: MPRandGauss;
constructor(
alpha: number, beta: number, dtype: keyof RandGammaDataTypes,
seed?: number) {
this.alpha = alpha;
this.beta = 1 / beta; // convert rate to scale parameter
this.dtype = dtype;
const seedValue = seed ? seed : Math.random();
this.randu = seedrandom.alea(seedValue.toString());
this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());
if (alpha < 1) {
this.d = alpha + (2 / 3);
} else {
this.d = alpha - (1 / 3);
}
this.c = 1 / Math.sqrt(9 * this.d);
}
/** Returns next sample from a gamma distribution. */
public nextValue(): number {
let x2: number, v0: number, v1: number, x: number, u: number, v: number;
while (true) {
do {
x = this.randn.nextValue();
v = 1 + (this.c * x);
} while (v <= 0);
v *= v * v;
x2 = x * x;
v0 = 1 - (0.331 * x2 * x2);
v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v)));
u = this.randu();
if (u < v0 || Math.log(u) < v1) {
break;
}
}
v = (1 / this.beta) * this.d * v;
if (this.alpha < 1) {
v *= Math.pow(this.randu(), 1 / this.alpha);
}
return this.convertValue(v);
}
/** Handles proper rounding for non-floating-point numbers. */
private convertValue(value: number): number {
if (this.dtype === 'float32') {
return value;
}
return Math.round(value);
}
}
export class UniformRandom implements RandomBase {
private min: number;
private range: number;
private random: seedrandom.prng;
private dtype?: keyof RandNormalDataTypes;
constructor(
min = 0, max = 1, dtype?: keyof RandNormalDataTypes,
seed?: string|number) {
this.min = min;
this.range = max - min;
this.dtype = dtype;
if (seed == null) {
seed = Math.random();
}
if (typeof seed === 'number') {
seed = seed.toString();
}
if (!this.canReturnFloat() && this.range <= 1) {
throw new Error(
`The difference between ${min} - ${max} <= 1 and dtype is not float`);
}
this.random = seedrandom.alea(seed);
}
/** Handles proper rounding for non floating point numbers. */
private canReturnFloat = () =>
(this.dtype == null || this.dtype === 'float32');
private convertValue(value: number): number {
if (this.canReturnFloat()) {
return value;
}
return Math.round(value);
}
nextValue() {
return this.convertValue(this.min + this.range * this.random());
}
}