"use strict";
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/**
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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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var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
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return new (P || (P = Promise))(function (resolve, reject) {
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function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
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function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
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function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
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step((generator = generator.apply(thisArg, _arguments || [])).next());
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});
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};
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var __generator = (this && this.__generator) || function (thisArg, body) {
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var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
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return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
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function verb(n) { return function (v) { return step([n, v]); }; }
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function step(op) {
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if (f) throw new TypeError("Generator is already executing.");
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while (_) try {
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if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
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if (y = 0, t) op = [op[0] & 2, t.value];
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switch (op[0]) {
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case 0: case 1: t = op; break;
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case 4: _.label++; return { value: op[1], done: false };
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case 5: _.label++; y = op[1]; op = [0]; continue;
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case 7: op = _.ops.pop(); _.trys.pop(); continue;
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default:
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if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
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if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
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if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
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if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
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if (t[2]) _.ops.pop();
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_.trys.pop(); continue;
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}
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op = body.call(thisArg, _);
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} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
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if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
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}
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};
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var _this = this;
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Object.defineProperty(exports, "__esModule", { value: true });
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var tf = require("../index");
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var jasmine_util_1 = require("../jasmine_util");
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var test_util_1 = require("../test_util");
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jasmine_util_1.describeWithFlags('multinomial', jasmine_util_1.ALL_ENVS, function () {
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var NUM_SAMPLES = 1000;
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// Allowed Variance in probability (in %).
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var EPSILON = 0.05;
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var SEED = 3.14;
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it('Flip a fair coin and check bounds', function () { return __awaiter(_this, void 0, void 0, function () {
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var probs, result, outcomeProbs, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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probs = tf.tensor1d([1, 1]);
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result = tf.multinomial(probs, NUM_SAMPLES, SEED);
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expect(result.dtype).toBe('int32');
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expect(result.shape).toEqual([NUM_SAMPLES]);
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_a = computeProbs;
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return [4 /*yield*/, result.data()];
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case 1:
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outcomeProbs = _a.apply(void 0, [_b.sent(), 2]);
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test_util_1.expectArraysClose(outcomeProbs, [0.5, 0.5], EPSILON);
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return [2 /*return*/];
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}
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});
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}); });
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it('Flip a two-sided coin with 100% of heads', function () { return __awaiter(_this, void 0, void 0, function () {
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var logits, result, outcomeProbs, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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logits = tf.tensor1d([1, -100]);
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result = tf.multinomial(logits, NUM_SAMPLES, SEED);
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expect(result.dtype).toBe('int32');
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expect(result.shape).toEqual([NUM_SAMPLES]);
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_a = computeProbs;
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return [4 /*yield*/, result.data()];
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case 1:
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outcomeProbs = _a.apply(void 0, [_b.sent(), 2]);
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test_util_1.expectArraysClose(outcomeProbs, [1, 0], EPSILON);
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return [2 /*return*/];
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}
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});
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}); });
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it('Flip a two-sided coin with 100% of tails', function () { return __awaiter(_this, void 0, void 0, function () {
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var logits, result, outcomeProbs, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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logits = tf.tensor1d([-100, 1]);
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result = tf.multinomial(logits, NUM_SAMPLES, SEED);
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expect(result.dtype).toBe('int32');
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expect(result.shape).toEqual([NUM_SAMPLES]);
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_a = computeProbs;
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return [4 /*yield*/, result.data()];
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case 1:
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outcomeProbs = _a.apply(void 0, [_b.sent(), 2]);
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test_util_1.expectArraysClose(outcomeProbs, [0, 1], EPSILON);
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return [2 /*return*/];
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}
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});
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}); });
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it('Flip a single-sided coin throws error', function () {
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var probs = tf.tensor1d([1]);
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expect(function () { return tf.multinomial(probs, NUM_SAMPLES, SEED); }).toThrowError();
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});
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it('Flip a ten-sided coin and check bounds', function () { return __awaiter(_this, void 0, void 0, function () {
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var numOutcomes, logits, result, outcomeProbs, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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numOutcomes = 10;
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logits = tf.fill([numOutcomes], 1).as1D();
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result = tf.multinomial(logits, NUM_SAMPLES, SEED);
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expect(result.dtype).toBe('int32');
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expect(result.shape).toEqual([NUM_SAMPLES]);
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_a = computeProbs;
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return [4 /*yield*/, result.data()];
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case 1:
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outcomeProbs = _a.apply(void 0, [_b.sent(), numOutcomes]);
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expect(outcomeProbs.length).toBeLessThanOrEqual(numOutcomes);
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return [2 /*return*/];
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}
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});
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}); });
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it('Flip 3 three-sided coins, each coin is 100% biases', function () { return __awaiter(_this, void 0, void 0, function () {
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var numOutcomes, logits, result, outcomeProbs, _a, _b, _c;
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return __generator(this, function (_d) {
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switch (_d.label) {
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case 0:
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numOutcomes = 3;
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logits = tf.tensor2d([[-100, -100, 1], [-100, 1, -100], [1, -100, -100]], [3, numOutcomes]);
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result = tf.multinomial(logits, NUM_SAMPLES, SEED);
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expect(result.dtype).toBe('int32');
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expect(result.shape).toEqual([3, NUM_SAMPLES]);
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_a = computeProbs;
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return [4 /*yield*/, result.data()];
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case 1:
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outcomeProbs = _a.apply(void 0, [(_d.sent()).slice(0, NUM_SAMPLES), numOutcomes]);
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test_util_1.expectArraysClose(outcomeProbs, [0, 0, 1], EPSILON);
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_b = computeProbs;
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return [4 /*yield*/, result.data()];
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case 2:
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// Second coin always gets middle event.
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outcomeProbs = _b.apply(void 0, [(_d.sent()).slice(NUM_SAMPLES, 2 * NUM_SAMPLES), numOutcomes]);
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test_util_1.expectArraysClose(outcomeProbs, [0, 1, 0], EPSILON);
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_c = computeProbs;
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return [4 /*yield*/, result.data()];
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case 3:
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// Third coin always gets first event
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outcomeProbs =
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_c.apply(void 0, [(_d.sent()).slice(2 * NUM_SAMPLES), numOutcomes]);
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test_util_1.expectArraysClose(outcomeProbs, [1, 0, 0], EPSILON);
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return [2 /*return*/];
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}
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});
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}); });
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it('passing Tensor3D throws error', function () {
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var probs = tf.zeros([3, 2, 2]);
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var normalized = true;
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expect(function () { return tf.multinomial(probs, 3, SEED, normalized); })
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.toThrowError();
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});
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it('throws when passed a non-tensor', function () {
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// tslint:disable-next-line:no-any
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expect(function () { return tf.multinomial({}, NUM_SAMPLES, SEED); })
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.toThrowError(/Argument 'logits' passed to 'multinomial' must be a Tensor/);
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});
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it('accepts a tensor-like object for logits (biased coin)', function () { return __awaiter(_this, void 0, void 0, function () {
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var res, outcomeProbs, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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res = tf.multinomial([-100, 1], NUM_SAMPLES, SEED);
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expect(res.dtype).toBe('int32');
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expect(res.shape).toEqual([NUM_SAMPLES]);
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_a = computeProbs;
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return [4 /*yield*/, res.data()];
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case 1:
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outcomeProbs = _a.apply(void 0, [_b.sent(), 2]);
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test_util_1.expectArraysClose(outcomeProbs, [0, 1], EPSILON);
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return [2 /*return*/];
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}
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});
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}); });
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function computeProbs(events, numOutcomes) {
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var counts = [];
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for (var i = 0; i < numOutcomes; ++i) {
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counts[i] = 0;
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}
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var numSamples = events.length;
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for (var i = 0; i < events.length; ++i) {
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counts[events[i]]++;
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}
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// Normalize counts to be probabilities between [0, 1].
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for (var i = 0; i < counts.length; i++) {
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counts[i] /= numSamples;
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}
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return counts;
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}
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});
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//# sourceMappingURL=multinomial_test.js.map
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