/**
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* @license
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* Copyright 2017 Google Inc. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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import {env} from './environment';
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import {DataType, DataTypeMap, FlatVector, NumericDataType, RecursiveArray, TensorLike, TypedArray} from './types';
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/**
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* Shuffles the array in-place using Fisher-Yates algorithm.
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*
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* ```js
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* const a = [1, 2, 3, 4, 5];
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* tf.util.shuffle(a);
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* console.log(a);
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* ```
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*
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* @param array The array to shuffle in-place.
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*/
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/** @doc {heading: 'Util', namespace: 'util'} */
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// tslint:disable-next-line:no-any
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export function shuffle(array: any[]|Uint32Array|Int32Array|
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Float32Array): void {
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let counter = array.length;
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let temp = 0;
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let index = 0;
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// While there are elements in the array
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while (counter > 0) {
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// Pick a random index
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index = (Math.random() * counter) | 0;
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// Decrease counter by 1
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counter--;
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// And swap the last element with it
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temp = array[counter];
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array[counter] = array[index];
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array[index] = temp;
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}
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}
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/** Clamps a value to a specified range. */
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export function clamp(min: number, x: number, max: number): number {
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return Math.max(min, Math.min(x, max));
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}
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export function nearestLargerEven(val: number): number {
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return val % 2 === 0 ? val : val + 1;
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}
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export function sum(arr: number[]): number {
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let sum = 0;
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for (let i = 0; i < arr.length; i++) {
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sum += arr[i];
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}
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return sum;
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}
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/**
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* Returns a sample from a uniform [a, b) distribution.
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*
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* @param a The minimum support (inclusive).
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* @param b The maximum support (exclusive).
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* @return A pseudorandom number on the half-open interval [a,b).
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*/
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export function randUniform(a: number, b: number) {
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const r = Math.random();
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return (b * r) + (1 - r) * a;
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}
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/** Returns the squared Euclidean distance between two vectors. */
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export function distSquared(a: FlatVector, b: FlatVector): number {
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let result = 0;
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for (let i = 0; i < a.length; i++) {
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const diff = Number(a[i]) - Number(b[i]);
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result += diff * diff;
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}
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return result;
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}
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/**
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* Asserts that the expression is true. Otherwise throws an error with the
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* provided message.
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*
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* ```js
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* const x = 2;
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* tf.util.assert(x === 2, 'x is not 2');
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* ```
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*
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* @param expr The expression to assert (as a boolean).
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* @param msg A function that returns the message to report when throwing an
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* error. We use a function for performance reasons.
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*/
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/** @doc {heading: 'Util', namespace: 'util'} */
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export function assert(expr: boolean, msg: () => string) {
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if (!expr) {
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throw new Error(typeof msg === 'string' ? msg : msg());
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}
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}
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export function assertShapesMatch(
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shapeA: number[], shapeB: number[], errorMessagePrefix = ''): void {
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assert(
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arraysEqual(shapeA, shapeB),
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() => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
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}
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export function assertNonNull(a: TensorLike): void {
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assert(
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a != null,
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() => `The input to the tensor constructor must be a non-null value.`);
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}
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// NOTE: We explicitly type out what T extends instead of any so that
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// util.flatten on a nested array of number doesn't try to infer T as a
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// number[][], causing us to explicitly type util.flatten<number>().
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/**
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* Flattens an arbitrarily nested array.
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*
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* ```js
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* const a = [[1, 2], [3, 4], [5, [6, [7]]]];
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* const flat = tf.util.flatten(a);
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* console.log(flat);
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* ```
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*
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* @param arr The nested array to flatten.
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* @param result The destination array which holds the elements.
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* @param skipTypedArray If true, avoids flattening the typed arrays. Defaults
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* to false.
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*/
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/** @doc {heading: 'Util', namespace: 'util'} */
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export function
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flatten<T extends number|boolean|string|Promise<number>|TypedArray>(
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arr: T|RecursiveArray<T>, result: T[] = [], skipTypedArray = false): T[] {
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if (result == null) {
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result = [];
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}
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if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {
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for (let i = 0; i < arr.length; ++i) {
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flatten(arr[i], result, skipTypedArray);
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}
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} else {
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result.push(arr as T);
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}
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return result;
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}
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/**
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* Returns the size (number of elements) of the tensor given its shape.
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*
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* ```js
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* const shape = [3, 4, 2];
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* const size = tf.util.sizeFromShape(shape);
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* console.log(size);
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* ```
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*/
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/** @doc {heading: 'Util', namespace: 'util'} */
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export function sizeFromShape(shape: number[]): number {
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if (shape.length === 0) {
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// Scalar.
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return 1;
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}
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let size = shape[0];
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for (let i = 1; i < shape.length; i++) {
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size *= shape[i];
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}
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return size;
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}
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export function isScalarShape(shape: number[]): boolean {
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return shape.length === 0;
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}
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export function arraysEqual(n1: FlatVector, n2: FlatVector) {
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if (n1 === n2) {
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return true;
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}
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if (n1 == null || n2 == null) {
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return false;
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}
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if (n1.length !== n2.length) {
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return false;
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}
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for (let i = 0; i < n1.length; i++) {
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if (n1[i] !== n2[i]) {
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return false;
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}
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}
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return true;
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}
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export function isInt(a: number): boolean {
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return a % 1 === 0;
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}
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export function tanh(x: number): number {
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// tslint:disable-next-line:no-any
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if ((Math as any).tanh != null) {
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// tslint:disable-next-line:no-any
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return (Math as any).tanh(x);
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}
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if (x === Infinity) {
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return 1;
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} else if (x === -Infinity) {
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return -1;
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} else {
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const e2x = Math.exp(2 * x);
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return (e2x - 1) / (e2x + 1);
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}
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}
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export function sizeToSquarishShape(size: number): [number, number] {
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const width = Math.ceil(Math.sqrt(size));
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return [width, Math.ceil(size / width)];
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}
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/**
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* Creates a new array with randomized indicies to a given quantity.
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*
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* ```js
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* const randomTen = tf.util.createShuffledIndices(10);
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* console.log(randomTen);
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* ```
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*
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* @param number Quantity of how many shuffled indicies to create.
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*/
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/** @doc {heading: 'Util', namespace: 'util'} */
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export function createShuffledIndices(n: number): Uint32Array {
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const shuffledIndices = new Uint32Array(n);
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for (let i = 0; i < n; ++i) {
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shuffledIndices[i] = i;
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}
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shuffle(shuffledIndices);
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return shuffledIndices;
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}
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export function rightPad(a: string, size: number): string {
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if (size <= a.length) {
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return a;
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}
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return a + ' '.repeat(size - a.length);
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}
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export function repeatedTry(
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checkFn: () => boolean, delayFn = (counter: number) => 0,
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maxCounter?: number): Promise<void> {
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return new Promise<void>((resolve, reject) => {
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let tryCount = 0;
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const tryFn = () => {
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if (checkFn()) {
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resolve();
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return;
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}
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tryCount++;
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const nextBackoff = delayFn(tryCount);
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if (maxCounter != null && tryCount >= maxCounter) {
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reject();
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return;
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}
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setTimeout(tryFn, nextBackoff);
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};
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tryFn();
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});
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}
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/**
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* Given the full size of the array and a shape that may contain -1 as the
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* implicit dimension, returns the inferred shape where -1 is replaced.
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* E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].
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*
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* @param shape The shape, which may contain -1 in some dimension.
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* @param size The full size (number of elements) of the array.
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* @return The inferred shape where -1 is replaced with the inferred size.
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*/
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export function inferFromImplicitShape(
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shape: number[], size: number): number[] {
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let shapeProd = 1;
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let implicitIdx = -1;
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for (let i = 0; i < shape.length; ++i) {
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if (shape[i] >= 0) {
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shapeProd *= shape[i];
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} else if (shape[i] === -1) {
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if (implicitIdx !== -1) {
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throw Error(
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`Shapes can only have 1 implicit size. ` +
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`Found -1 at dim ${implicitIdx} and dim ${i}`);
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}
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implicitIdx = i;
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} else if (shape[i] < 0) {
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throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);
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}
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}
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if (implicitIdx === -1) {
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if (size > 0 && size !== shapeProd) {
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throw Error(`Size(${size}) must match the product of shape ${shape}`);
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}
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return shape;
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}
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if (shapeProd === 0) {
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throw Error(
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`Cannot infer the missing size in [${shape}] when ` +
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`there are 0 elements`);
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}
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if (size % shapeProd !== 0) {
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throw Error(
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`The implicit shape can't be a fractional number. ` +
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`Got ${size} / ${shapeProd}`);
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}
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const newShape = shape.slice();
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newShape[implicitIdx] = size / shapeProd;
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return newShape;
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}
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export function parseAxisParam(
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axis: number|number[], shape: number[]): number[] {
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const rank = shape.length;
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// Normalize input
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axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);
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// Check for valid range
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assert(
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axis.every(ax => ax >= -rank && ax < rank),
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() =>
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`All values in axis param must be in range [-${rank}, ${rank}) but ` +
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`got axis ${axis}`);
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// Check for only integers
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assert(
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axis.every(ax => isInt(ax)),
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() => `All values in axis param must be integers but ` +
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`got axis ${axis}`);
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// Handle negative axis.
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return axis.map(a => a < 0 ? rank + a : a);
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}
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/** Reduces the shape by removing all dimensions of shape 1. */
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export function squeezeShape(shape: number[], axis?: number[]):
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{newShape: number[], keptDims: number[]} {
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const newShape: number[] = [];
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const keptDims: number[] = [];
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const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;
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const axes = (axis == null || isEmptyArray) ?
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null :
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parseAxisParam(axis, shape).sort();
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let j = 0;
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for (let i = 0; i < shape.length; ++i) {
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if (axes != null) {
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if (axes[j] === i && shape[i] !== 1) {
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throw new Error(
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`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);
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}
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if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {
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newShape.push(shape[i]);
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keptDims.push(i);
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}
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if (axes[j] <= i) {
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j++;
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}
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}
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if (shape[i] !== 1) {
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newShape.push(shape[i]);
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keptDims.push(i);
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}
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}
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return {newShape, keptDims};
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}
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export function getTypedArrayFromDType<D extends NumericDataType>(
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dtype: D, size: number): DataTypeMap[D] {
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let values = null;
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if (dtype == null || dtype === 'float32') {
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values = new Float32Array(size);
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} else if (dtype === 'int32') {
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values = new Int32Array(size);
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} else if (dtype === 'bool') {
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values = new Uint8Array(size);
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} else {
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throw new Error(`Unknown data type ${dtype}`);
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}
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return values as DataTypeMap[D];
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}
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export function getArrayFromDType<D extends DataType>(
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dtype: D, size: number): DataTypeMap[D] {
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let values = null;
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if (dtype == null || dtype === 'float32') {
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values = new Float32Array(size);
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} else if (dtype === 'int32') {
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values = new Int32Array(size);
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} else if (dtype === 'bool') {
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values = new Uint8Array(size);
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} else if (dtype === 'string') {
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values = new Array<'string'>(size);
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} else {
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throw new Error(`Unknown data type ${dtype}`);
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}
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return values as DataTypeMap[D];
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}
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export function checkConversionForErrors<D extends DataType>(
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vals: DataTypeMap[D]|number[], dtype: D): void {
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for (let i = 0; i < vals.length; i++) {
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const num = vals[i] as number;
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if (isNaN(num) || !isFinite(num)) {
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throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);
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}
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}
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}
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/** Returns true if the dtype is valid. */
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export function isValidDtype(dtype: DataType): boolean {
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return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' ||
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dtype === 'int32' || dtype === 'string';
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}
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/**
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* Returns true if the new type can't encode the old type without loss of
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* precision.
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*/
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export function hasEncodingLoss(oldType: DataType, newType: DataType): boolean {
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if (newType === 'complex64') {
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return false;
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}
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if (newType === 'float32' && oldType !== 'complex64') {
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return false;
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}
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if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {
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return false;
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}
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if (newType === 'bool' && oldType === 'bool') {
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return false;
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}
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return true;
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}
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export function isTypedArray(a: {}): a is Float32Array|Int32Array|Uint8Array {
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return a instanceof Float32Array || a instanceof Int32Array ||
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a instanceof Uint8Array;
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}
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export function bytesPerElement(dtype: DataType): number {
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if (dtype === 'float32' || dtype === 'int32') {
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return 4;
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} else if (dtype === 'complex64') {
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return 8;
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} else if (dtype === 'bool') {
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return 1;
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} else {
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throw new Error(`Unknown dtype ${dtype}`);
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}
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}
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/**
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* Returns the approximate number of bytes allocated in the string array - 2
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* bytes per character. Computing the exact bytes for a native string in JS is
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* not possible since it depends on the encoding of the html page that serves
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* the website.
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*/
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export function bytesFromStringArray(arr: Uint8Array[]): number {
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if (arr == null) {
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return 0;
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}
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let bytes = 0;
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arr.forEach(x => bytes += x.length);
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return bytes;
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}
|
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/** Returns true if the value is a string. */
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export function isString(value: {}): value is string {
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return typeof value === 'string' || value instanceof String;
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}
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export function isBoolean(value: {}): boolean {
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return typeof value === 'boolean';
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}
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export function isNumber(value: {}): boolean {
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return typeof value === 'number';
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}
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export function inferDtype(values: TensorLike): DataType {
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if (Array.isArray(values)) {
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return inferDtype(values[0]);
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}
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if (values instanceof Float32Array) {
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return 'float32';
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} else if (values instanceof Int32Array || values instanceof Uint8Array) {
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return 'int32';
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} else if (isNumber(values)) {
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return 'float32';
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} else if (isString(values)) {
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return 'string';
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} else if (isBoolean(values)) {
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return 'bool';
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}
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return 'float32';
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}
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export function isFunction(f: Function) {
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return !!(f && f.constructor && f.call && f.apply);
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}
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export function nearestDivisor(size: number, start: number): number {
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for (let i = start; i < size; ++i) {
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if (size % i === 0) {
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return i;
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}
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}
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return size;
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}
|
|
export function computeStrides(shape: number[]): number[] {
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const rank = shape.length;
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if (rank < 2) {
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return [];
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}
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|
// Last dimension has implicit stride of 1, thus having D-1 (instead of D)
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// strides.
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const strides = new Array(rank - 1);
|
strides[rank - 2] = shape[rank - 1];
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for (let i = rank - 3; i >= 0; --i) {
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strides[i] = strides[i + 1] * shape[i + 1];
|
}
|
return strides;
|
}
|
|
export function toTypedArray(
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a: TensorLike, dtype: DataType, debugMode: boolean): TypedArray {
|
if (dtype === 'string') {
|
throw new Error('Cannot convert a string[] to a TypedArray');
|
}
|
if (Array.isArray(a)) {
|
a = flatten(a);
|
}
|
if (debugMode) {
|
checkConversionForErrors(a as number[], dtype);
|
}
|
if (noConversionNeeded(a, dtype)) {
|
return a as TypedArray;
|
}
|
if (dtype == null || dtype === 'float32' || dtype === 'complex64') {
|
return new Float32Array(a as number[]);
|
} else if (dtype === 'int32') {
|
return new Int32Array(a as number[]);
|
} else if (dtype === 'bool') {
|
const bool = new Uint8Array((a as number[]).length);
|
for (let i = 0; i < bool.length; ++i) {
|
if (Math.round((a as number[])[i]) !== 0) {
|
bool[i] = 1;
|
}
|
}
|
return bool;
|
} else {
|
throw new Error(`Unknown data type ${dtype}`);
|
}
|
}
|
|
function createNestedArray(offset: number, shape: number[], a: TypedArray) {
|
const ret = new Array();
|
if (shape.length === 1) {
|
const d = shape[0];
|
for (let i = 0; i < d; i++) {
|
ret[i] = a[offset + i];
|
}
|
} else {
|
const d = shape[0];
|
const rest = shape.slice(1);
|
const len = rest.reduce((acc, c) => acc * c);
|
for (let i = 0; i < d; i++) {
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ret[i] = createNestedArray(offset + i * len, rest, a);
|
}
|
}
|
return ret;
|
}
|
|
// Provide a nested array of TypedArray in given shape.
|
export function toNestedArray(shape: number[], a: TypedArray) {
|
if (shape.length === 0) {
|
// Scalar type should return a single number.
|
return a[0];
|
}
|
const size = shape.reduce((acc, c) => acc * c);
|
if (size === 0) {
|
// A tensor with shape zero should be turned into empty list.
|
return [];
|
}
|
if (size !== a.length) {
|
throw new Error(`[${shape}] does not match the input size.`);
|
}
|
|
return createNestedArray(0, shape, a);
|
}
|
|
function noConversionNeeded(a: TensorLike, dtype: DataType): boolean {
|
return (a instanceof Float32Array && dtype === 'float32') ||
|
(a instanceof Int32Array && dtype === 'int32') ||
|
(a instanceof Uint8Array && dtype === 'bool');
|
}
|
|
export function makeOnesTypedArray<D extends DataType>(
|
size: number, dtype: D): DataTypeMap[D] {
|
const array = makeZerosTypedArray(size, dtype);
|
for (let i = 0; i < array.length; i++) {
|
array[i] = 1;
|
}
|
return array;
|
}
|
|
export function makeZerosTypedArray<D extends DataType>(
|
size: number, dtype: D): DataTypeMap[D] {
|
if (dtype == null || dtype === 'float32' || dtype === 'complex64') {
|
return new Float32Array(size) as DataTypeMap[D];
|
} else if (dtype === 'int32') {
|
return new Int32Array(size) as DataTypeMap[D];
|
} else if (dtype === 'bool') {
|
return new Uint8Array(size) as DataTypeMap[D];
|
} else {
|
throw new Error(`Unknown data type ${dtype}`);
|
}
|
}
|
|
/**
|
* Returns the current high-resolution time in milliseconds relative to an
|
* arbitrary time in the past. It works across different platforms (node.js,
|
* browsers).
|
*
|
* ```js
|
* console.log(tf.util.now());
|
* ```
|
*/
|
/** @doc {heading: 'Util', namespace: 'util'} */
|
export function now(): number {
|
return env().platform.now();
|
}
|
|
export function assertNonNegativeIntegerDimensions(shape: number[]) {
|
shape.forEach(dimSize => {
|
assert(
|
Number.isInteger(dimSize) && dimSize >= 0,
|
() =>
|
`Tensor must have a shape comprised of positive integers but got ` +
|
`shape [${shape}].`);
|
});
|
}
|
|
/**
|
* Returns a platform-specific implementation of
|
* [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).
|
*
|
* If `fetch` is defined on the global object (`window`, `process`, etc.),
|
* `tf.util.fetch` returns that function.
|
*
|
* If not, `tf.util.fetch` returns a platform-specific solution.
|
*
|
* ```js
|
* const resource = await tf.util.fetch('https://unpkg.com/@tensorflow/tfjs');
|
* // handle response
|
* ```
|
*/
|
/** @doc {heading: 'Util'} */
|
export function fetch(
|
path: string, requestInits?: RequestInit): Promise<Response> {
|
return env().platform.fetch(path, requestInits);
|
}
|
|
/**
|
* Encodes the provided string into bytes using the provided encoding scheme.
|
*
|
* @param s The string to encode.
|
* @param encoding The encoding scheme. Defaults to utf-8.
|
*
|
*/
|
/** @doc {heading: 'Util'} */
|
export function encodeString(s: string, encoding = 'utf-8'): Uint8Array {
|
encoding = encoding || 'utf-8';
|
return env().platform.encode(s, encoding);
|
}
|
|
/**
|
* Decodes the provided bytes into a string using the provided encoding scheme.
|
* @param bytes The bytes to decode.
|
*
|
* @param encoding The encoding scheme. Defaults to utf-8.
|
*/
|
/** @doc {heading: 'Util'} */
|
export function decodeString(bytes: Uint8Array, encoding = 'utf-8'): string {
|
encoding = encoding || 'utf-8';
|
return env().platform.decode(bytes, encoding);
|
}
|
|
/**
|
* Computes flat index for a given location (multidimentionsal index) in a
|
* Tensor/multidimensional array.
|
*
|
* @param locs Location in the tensor.
|
* @param rank Rank of the tensor.
|
* @param strides Tensor strides.
|
*/
|
export function locToIndex(
|
locs: number[], rank: number, strides: number[]): number {
|
if (rank === 0) {
|
return 0;
|
} else if (rank === 1) {
|
return locs[0];
|
}
|
let index = locs[locs.length - 1];
|
for (let i = 0; i < locs.length - 1; ++i) {
|
index += strides[i] * locs[i];
|
}
|
return index;
|
}
|
|
/**
|
* Computes the location (multidimensional index) in a tensor/multidimentional
|
* array for a given flat index.
|
*
|
* @param index Index in flat array.
|
* @param rank Rank of tensor.
|
* @param strides Strides of tensor.
|
*/
|
export function indexToLoc(
|
index: number, rank: number, strides: number[]): number[] {
|
if (rank === 0) {
|
return [];
|
} else if (rank === 1) {
|
return [index];
|
}
|
const locs: number[] = new Array(rank);
|
for (let i = 0; i < locs.length - 1; ++i) {
|
locs[i] = Math.floor(index / strides[i]);
|
index -= locs[i] * strides[i];
|
}
|
locs[locs.length - 1] = index;
|
return locs;
|
}
|
