"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('movingAverage', jasmine_util_1.ALL_ENVS, function () {
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// Use the following tensorflow to generate reference values for
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// `zeroDebias` = `true`;
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//
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// ```python
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// import tensorflow as tf
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// from tensorflow.python.training.moving_averages import
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// assign_moving_average
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//
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// with tf.Session() as sess:
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// v = tf.get_variable("v1", shape=[2, 2], dtype=tf.float32,
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// initializer=tf.zeros_initializer)
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// x = tf.Variable([[1.0, 2.0], [3.0, 4.0]])
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// inc_x = x.assign_add([[10.0, 10.0], [10.0, 10.0]])
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// update = assign_moving_average(v, x, 0.6)
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//
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// sess.run(tf.global_variables_initializer())
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//
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// sess.run(update)
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// print(sess.run(v))
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//
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// sess.run(inc_x)
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// sess.run(update)
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// print(sess.run(v))
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// ```
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it('zeroDebias=true, decay and step are numbers', function () { return __awaiter(_this, void 0, void 0, function () {
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var v0, x, decay, v1, _a, y, v2, _b;
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return __generator(this, function (_c) {
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switch (_c.label) {
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case 0:
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v0 = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
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x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
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decay = 0.6;
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v1 = tf.movingAverage(v0, x, decay, 1);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, v1.array()];
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case 1:
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_a.apply(void 0, [_c.sent(), [[1, 2], [3, 4]]]);
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y = tf.tensor2d([[11, 12], [13, 14]], [2, 2]);
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v2 = tf.movingAverage(v1, y, decay, 2);
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_b = test_util_1.expectArraysClose;
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return [4 /*yield*/, v2.array()];
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case 2:
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_b.apply(void 0, [_c.sent(), [[7.25, 8.25], [9.25, 10.25]]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('zeroDebias=true, decay and step are scalars', function () { return __awaiter(_this, void 0, void 0, function () {
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var v0, x, decay, v1, _a, y, v2, _b;
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return __generator(this, function (_c) {
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switch (_c.label) {
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case 0:
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v0 = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
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x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
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decay = tf.scalar(0.6);
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v1 = tf.movingAverage(v0, x, decay, tf.scalar(1));
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, v1.array()];
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case 1:
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_a.apply(void 0, [_c.sent(), [[1, 2], [3, 4]]]);
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y = tf.tensor2d([[11, 12], [13, 14]], [2, 2]);
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v2 = tf.movingAverage(v1, y, decay, tf.scalar(2));
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_b = test_util_1.expectArraysClose;
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return [4 /*yield*/, v2.array()];
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case 2:
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_b.apply(void 0, [_c.sent(), [[7.25, 8.25], [9.25, 10.25]]]);
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return [2 /*return*/];
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}
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});
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}); });
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// Use the following tensorflow to generate reference values for
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// `zeroDebias` = `false`;
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//
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// ```python
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// import tensorflow as tf
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// from tensorflow.python.training.moving_averages import
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// assign_moving_average
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//
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// with tf.Session() as sess:
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// v = tf.get_variable("v1", shape=[2, 2], dtype=tf.float32,
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// initializer=tf.zeros_initializer)
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// x = tf.Variable([[1.0, 2.0], [3.0, 4.0]])
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// inc_x = x.assign_add([[10.0, 10.0], [10.0, 10.0]])
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// update = assign_moving_average(v, x, 0.6, zero_debias=False)
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//
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// sess.run(tf.global_variables_initializer())
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//
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// sess.run(update)
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// print(sess.run(v))
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//
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// sess.run(inc_x)
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// sess.run(update)
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// print(sess.run(v))
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// ```
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it('zeroDebias=false, decay and step are numbers', function () { return __awaiter(_this, void 0, void 0, function () {
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var v0, x, decay, v1, _a, y, v2, _b;
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return __generator(this, function (_c) {
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switch (_c.label) {
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case 0:
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v0 = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
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x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
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decay = 0.6;
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v1 = tf.movingAverage(v0, x, decay, null, false);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, v1.array()];
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case 1:
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_a.apply(void 0, [_c.sent(), [[0.4, 0.8], [1.2, 1.6]]]);
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y = tf.tensor2d([[11, 12], [13, 14]], [2, 2]);
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v2 = tf.movingAverage(v1, y, decay, null, false);
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_b = test_util_1.expectArraysClose;
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return [4 /*yield*/, v2.array()];
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case 2:
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_b.apply(void 0, [_c.sent(), [[4.64, 5.28], [5.92, 6.56]]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('zeroDebias=false, decay is scalar', function () { return __awaiter(_this, void 0, void 0, function () {
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var v0, x, decay, v1, _a, y, v2, _b;
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return __generator(this, function (_c) {
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switch (_c.label) {
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case 0:
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v0 = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
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x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
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decay = tf.scalar(0.6);
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v1 = tf.movingAverage(v0, x, decay, null, false);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, v1.array()];
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case 1:
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_a.apply(void 0, [_c.sent(), [[0.4, 0.8], [1.2, 1.6]]]);
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y = tf.tensor2d([[11, 12], [13, 14]], [2, 2]);
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v2 = tf.movingAverage(v1, y, decay, null, false);
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_b = test_util_1.expectArraysClose;
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return [4 /*yield*/, v2.array()];
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case 2:
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_b.apply(void 0, [_c.sent(), [[4.64, 5.28], [5.92, 6.56]]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('zeroDebias=true, no step throws error', function () {
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var v0 = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
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var x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
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var decay = tf.scalar(0.6);
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expect(function () { return tf.movingAverage(v0, x, decay, null); }).toThrowError();
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});
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it('shape mismatch in v and x throws error', function () {
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var v0 = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
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var x = tf.tensor2d([[1, 2]], [1, 2]);
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var decay = tf.scalar(0.6);
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expect(function () { return tf.movingAverage(v0, x, decay, null); }).toThrowError();
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});
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it('throws when passed v as a non-tensor', function () {
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var x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
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expect(function () { return tf.movingAverage({}, x, 1); })
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.toThrowError(/Argument 'v' passed to 'movingAverage' must be a Tensor/);
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});
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it('throws when passed v as a non-tensor', function () {
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var v = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
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expect(function () { return tf.movingAverage(v, {}, 1); })
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.toThrowError(/Argument 'x' passed to 'movingAverage' must be a Tensor/);
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});
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it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
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var v0, x, decay, v1, _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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v0 = [[0, 0], [0, 0]];
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x = [[1, 2], [3, 4]];
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decay = 0.6;
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v1 = tf.movingAverage(v0, x, decay, 1);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, v1.array()];
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case 1:
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_a.apply(void 0, [_b.sent(), [[1, 2], [3, 4]]]);
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return [2 /*return*/];
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}
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});
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}); });
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});
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//# sourceMappingURL=moving_average_test.js.map
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