"use strict";
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/**
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* @license
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* Copyright 2018 Google LLC. 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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function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
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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) : adopt(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 (g && (g = 0, op[0] && (_ = 0)), _) 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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Object.defineProperty(exports, "__esModule", { value: true });
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var tf = require("@tensorflow/tfjs");
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var callbacks_1 = require("./callbacks");
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describe('progbarLogger', function () {
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// Fake progbar class written for testing.
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var FakeProgbar = /** @class */ (function () {
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function FakeProgbar(specs, config) {
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this.specs = specs;
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this.config = config;
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this.tickConfigs = [];
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}
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FakeProgbar.prototype.tick = function (tickConfig) {
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this.tickConfigs.push(tickConfig);
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};
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return FakeProgbar;
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}());
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var originalStderrColumns;
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beforeEach(function () {
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// In some CI environments, process.stderr.columns has a null value.
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originalStderrColumns = process.stderr.columns;
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process.stderr.columns = 100;
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});
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afterEach(function () {
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process.stderr.columns = originalStderrColumns;
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});
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it('Model.fit with loss, no metric, no validation, verobse = 1', function () { return __awaiter(void 0, void 0, void 0, function () {
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var fakeProgbars, consoleMessages, model, numSamples, epochs, batchSize, xs, ys, _i, fakeProgbars_1, fakeProgbar, tickConfigs, i;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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fakeProgbars = [];
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spyOn(callbacks_1.progressBarHelper, 'ProgressBar')
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.and.callFake(function (specs, config) {
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var fakeProgbar = new FakeProgbar(specs, config);
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fakeProgbars.push(fakeProgbar);
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return fakeProgbar;
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});
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consoleMessages = [];
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spyOn(callbacks_1.progressBarHelper, 'log').and.callFake(function (message) {
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consoleMessages.push(message);
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});
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model = tf.sequential();
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model.add(tf.layers.dense({ units: 10, inputShape: [8], activation: 'relu' }));
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model.add(tf.layers.dense({ units: 1 }));
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model.compile({ loss: 'meanSquaredError', optimizer: 'sgd' });
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numSamples = 14;
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epochs = 3;
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batchSize = 8;
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xs = tf.randomNormal([numSamples, 8]);
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ys = tf.randomNormal([numSamples, 1]);
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return [4 /*yield*/, model.fit(xs, ys, { epochs: epochs, batchSize: batchSize, verbose: 1 })];
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case 1:
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_a.sent();
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// A progbar object is created for each epoch.
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expect(fakeProgbars.length).toEqual(3);
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for (_i = 0, fakeProgbars_1 = fakeProgbars; _i < fakeProgbars_1.length; _i++) {
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fakeProgbar = fakeProgbars_1[_i];
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tickConfigs = fakeProgbar.tickConfigs;
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// There are ceil(14 / 8) = 2 batchs per epoch. There should be 1 tick
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// for epoch batch, plus a tick at the end of the epoch.
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expect(tickConfigs.length).toEqual(3);
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for (i = 0; i < 2; ++i) {
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expect(Object.keys(tickConfigs[i])).toEqual([
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'placeholderForLossesAndMetrics'
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]);
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expect(tickConfigs[i]['placeholderForLossesAndMetrics'])
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.toMatch(/^loss=.*/);
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}
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expect(tickConfigs[2]).toEqual({ placeholderForLossesAndMetrics: '' });
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}
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expect(consoleMessages.length).toEqual(6);
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expect(consoleMessages[0]).toEqual('Epoch 1 / 3');
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expect(consoleMessages[1]).toMatch(/.*ms .*us\/step - loss=.*/);
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expect(consoleMessages[2]).toEqual('Epoch 2 / 3');
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expect(consoleMessages[3]).toMatch(/.*ms .*us\/step - loss=.*/);
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expect(consoleMessages[4]).toEqual('Epoch 3 / 3');
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expect(consoleMessages[5]).toMatch(/.*ms .*us\/step - loss=.*/);
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return [2 /*return*/];
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}
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});
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}); });
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it('Model.fit with loss, metric and validation, verbose = 2', function () { return __awaiter(void 0, void 0, void 0, function () {
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var fakeProgbars, consoleMessages, model, numSamples, epochs, batchSize, validationSplit, xs, ys, _i, fakeProgbars_2, fakeProgbar, tickConfigs, i;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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fakeProgbars = [];
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spyOn(callbacks_1.progressBarHelper, 'ProgressBar')
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.and.callFake(function (specs, config) {
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var fakeProgbar = new FakeProgbar(specs, config);
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fakeProgbars.push(fakeProgbar);
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return fakeProgbar;
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});
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consoleMessages = [];
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spyOn(callbacks_1.progressBarHelper, 'log').and.callFake(function (message) {
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consoleMessages.push(message);
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});
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model = tf.sequential();
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model.add(tf.layers.dense({ units: 10, inputShape: [8], activation: 'relu' }));
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model.add(tf.layers.dense({ units: 1 }));
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model.compile({ loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['acc'] });
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numSamples = 40;
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epochs = 2;
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batchSize = 8;
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validationSplit = 0.15;
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xs = tf.randomNormal([numSamples, 8]);
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ys = tf.randomNormal([numSamples, 1]);
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return [4 /*yield*/, model.fit(xs, ys, { epochs: epochs, batchSize: batchSize, validationSplit: validationSplit, verbose: 2 })];
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case 1:
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_a.sent();
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// A progbar object is created for each epoch.
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expect(fakeProgbars.length).toEqual(2);
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for (_i = 0, fakeProgbars_2 = fakeProgbars; _i < fakeProgbars_2.length; _i++) {
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fakeProgbar = fakeProgbars_2[_i];
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tickConfigs = fakeProgbar.tickConfigs;
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// There are 5 batchs per epoch. There should be 1 tick for epoch batch,
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// plus a tick at the end of the epoch.
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expect(tickConfigs.length).toEqual(6);
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for (i = 0; i < 5; ++i) {
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expect(Object.keys(tickConfigs[i])).toEqual([
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'placeholderForLossesAndMetrics'
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]);
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expect(tickConfigs[i]['placeholderForLossesAndMetrics'])
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.toMatch(/^acc=.* loss=.*/);
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}
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expect(tickConfigs[5]).toEqual({ placeholderForLossesAndMetrics: '' });
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}
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expect(consoleMessages.length).toEqual(4);
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expect(consoleMessages[0]).toEqual('Epoch 1 / 2');
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expect(consoleMessages[1])
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.toMatch(/.*ms .*us\/step - acc=.* loss=.* val_acc=.* val_loss=.*/);
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expect(consoleMessages[2]).toEqual('Epoch 2 / 2');
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expect(consoleMessages[3])
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.toMatch(/.*ms .*us\/step - acc=.* loss=.* val_acc=.* val_loss=.*/);
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return [2 /*return*/];
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}
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});
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}); });
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it('Model.fit does not create ProgbarLogger if verbose is 0', function () { return __awaiter(void 0, void 0, void 0, function () {
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var fakeProgbars, consoleMessages, model, numSamples, epochs, batchSize, validationSplit, xs, ys;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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fakeProgbars = [];
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spyOn(callbacks_1.progressBarHelper, 'ProgressBar')
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.and.callFake(function (specs, config) {
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var fakeProgbar = new FakeProgbar(specs, config);
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fakeProgbars.push(fakeProgbar);
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return fakeProgbar;
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});
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consoleMessages = [];
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spyOn(callbacks_1.progressBarHelper, 'log').and.callFake(function (message) {
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consoleMessages.push(message);
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});
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model = tf.sequential();
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model.add(tf.layers.dense({ units: 10, inputShape: [8], activation: 'relu' }));
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model.add(tf.layers.dense({ units: 1 }));
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model.compile({ loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['acc'] });
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numSamples = 40;
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epochs = 2;
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batchSize = 8;
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validationSplit = 0.15;
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xs = tf.randomNormal([numSamples, 8]);
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ys = tf.randomNormal([numSamples, 1]);
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return [4 /*yield*/, model.fit(xs, ys, { epochs: epochs, batchSize: batchSize, validationSplit: validationSplit, verbose: 0 })];
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case 1:
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_a.sent();
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expect(fakeProgbars.length).toEqual(0);
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return [2 /*return*/];
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}
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});
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}); });
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it('Model.fitDataset: batchesPerEpoch specified, verbose = 1', function () { return __awaiter(void 0, void 0, void 0, function () {
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var fakeProgbars, consoleMessages, epochs, xDataset, yDataset, dataset, model;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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fakeProgbars = [];
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spyOn(callbacks_1.progressBarHelper, 'ProgressBar')
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.and.callFake(function (specs, config) {
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var fakeProgbar = new FakeProgbar(specs, config);
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fakeProgbars.push(fakeProgbar);
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return fakeProgbar;
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});
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consoleMessages = [];
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spyOn(callbacks_1.progressBarHelper, 'log').and.callFake(function (message) {
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consoleMessages.push(message);
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});
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epochs = 2;
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xDataset = tf.data.array([[1, 2], [3, 4], [5, 6], [7, 8]])
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.map(function (x) { return tf.tensor2d(x, [1, 2]); });
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yDataset = tf.data.array([[1], [2], [3], [4]]).map(function (y) { return tf.tensor2d(y, [1, 1]); });
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dataset = tf.data.zip({ xs: xDataset, ys: yDataset }).repeat(epochs);
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model = tf.sequential();
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model.add(tf.layers.dense({ units: 1, inputShape: [2] }));
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model.compile({ loss: 'meanSquaredError', optimizer: 'sgd' });
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return [4 /*yield*/, model.fitDataset(dataset, { batchesPerEpoch: 4, epochs: epochs, verbose: 1 })];
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case 1:
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_a.sent();
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expect(consoleMessages.length).toEqual(4);
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expect(consoleMessages[0]).toEqual('Epoch 1 / 2');
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expect(consoleMessages[1]).toMatch(/.*ms .*us\/step - loss=.*/);
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expect(consoleMessages[2]).toEqual('Epoch 2 / 2');
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expect(consoleMessages[3]).toMatch(/.*ms .*us\/step - loss=.*/);
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return [2 /*return*/];
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}
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});
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}); });
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it('Model.fitDataset: batchesPerEpoch unavailable, verbose = 1', function () { return __awaiter(void 0, void 0, void 0, function () {
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var fakeProgbars, consoleMessages, epochs, xDataset, yDataset, dataset, model;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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fakeProgbars = [];
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spyOn(callbacks_1.progressBarHelper, 'ProgressBar')
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.and.callFake(function (specs, config) {
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var fakeProgbar = new FakeProgbar(specs, config);
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fakeProgbars.push(fakeProgbar);
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return fakeProgbar;
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});
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consoleMessages = [];
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spyOn(callbacks_1.progressBarHelper, 'log').and.callFake(function (message) {
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consoleMessages.push(message);
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});
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epochs = 2;
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xDataset = tf.data.array([[1, 2], [3, 4], [5, 6], [7, 8]])
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.map(function (x) { return tf.tensor2d(x, [1, 2]); });
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yDataset = tf.data.array([[1], [2], [3], [4]]).map(function (y) { return tf.tensor2d(y, [1, 1]); });
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dataset = tf.data.zip({ xs: xDataset, ys: yDataset }).repeat(epochs);
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model = tf.sequential();
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model.add(tf.layers.dense({ units: 1, inputShape: [2] }));
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model.compile({ loss: 'meanSquaredError', optimizer: 'sgd' });
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// `batchesPerEpoch` is not specified. Instead, `fitDataset()` relies on
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// the `done` field being `true` to terminate the epoch(s).
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return [4 /*yield*/, model.fitDataset(dataset, { epochs: epochs, verbose: 1 })];
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case 1:
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// `batchesPerEpoch` is not specified. Instead, `fitDataset()` relies on
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// the `done` field being `true` to terminate the epoch(s).
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_a.sent();
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expect(consoleMessages.length).toEqual(4);
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expect(consoleMessages[0]).toEqual('Epoch 1 / 2');
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expect(consoleMessages[1]).toMatch(/.*ms .*us\/step - loss=.*/);
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expect(consoleMessages[2]).toEqual('Epoch 2 / 2');
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expect(consoleMessages[3]).toMatch(/.*ms .*us\/step - loss=.*/);
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return [2 /*return*/];
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}
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});
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}); });
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it('Model.fitDataset: verbose = 0 leads to no logging', function () { return __awaiter(void 0, void 0, void 0, function () {
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var fakeProgbars, consoleMessages, xDataset, yDataset, dataset, model, history;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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fakeProgbars = [];
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spyOn(callbacks_1.progressBarHelper, 'ProgressBar')
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.and.callFake(function (specs, config) {
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var fakeProgbar = new FakeProgbar(specs, config);
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fakeProgbars.push(fakeProgbar);
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return fakeProgbar;
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});
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consoleMessages = [];
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spyOn(callbacks_1.progressBarHelper, 'log').and.callFake(function (message) {
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consoleMessages.push(message);
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});
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xDataset = tf.data.array([[1, 2], [3, 4], [5, 6], [7, 8]])
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.map(function (x) { return tf.tensor2d(x, [1, 2]); });
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yDataset = tf.data.array([[1], [2], [3], [4]]).map(function (y) { return tf.tensor2d(y, [1, 1]); });
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dataset = tf.data.zip({ xs: xDataset, ys: yDataset });
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model = tf.sequential();
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model.add(tf.layers.dense({ units: 1, inputShape: [2] }));
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model.compile({ loss: 'meanSquaredError', optimizer: 'sgd' });
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return [4 /*yield*/, model.fitDataset(dataset, { epochs: 1, verbose: 0 })];
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case 1:
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history = _a.sent();
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expect(history.history.loss.length).toEqual(1);
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expect(consoleMessages.length)
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.toEqual(0); // No logging should have happened.
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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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describe('getSuccinctNumberDisplay', function () {
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it('Not finite', function () {
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expect((0, callbacks_1.getSuccinctNumberDisplay)(Infinity)).toEqual('Infinity');
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expect((0, callbacks_1.getSuccinctNumberDisplay)(-Infinity)).toEqual('-Infinity');
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expect((0, callbacks_1.getSuccinctNumberDisplay)(NaN)).toEqual('NaN');
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});
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it('zero', function () {
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expect((0, callbacks_1.getSuccinctNumberDisplay)(0)).toEqual('0.00');
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});
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it('Finite and positive', function () {
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expect((0, callbacks_1.getSuccinctNumberDisplay)(300)).toEqual('300.00');
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expect((0, callbacks_1.getSuccinctNumberDisplay)(30)).toEqual('30.00');
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expect((0, callbacks_1.getSuccinctNumberDisplay)(1)).toEqual('1.00');
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expect((0, callbacks_1.getSuccinctNumberDisplay)(1e-2)).toEqual('0.0100');
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expect((0, callbacks_1.getSuccinctNumberDisplay)(1e-3)).toEqual('1.00e-3');
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expect((0, callbacks_1.getSuccinctNumberDisplay)(4e-3)).toEqual('4.00e-3');
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expect((0, callbacks_1.getSuccinctNumberDisplay)(1e-6)).toEqual('1.00e-6');
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});
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it('Finite and negative', function () {
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expect((0, callbacks_1.getSuccinctNumberDisplay)(-300)).toEqual('-300.00');
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expect((0, callbacks_1.getSuccinctNumberDisplay)(-30)).toEqual('-30.00');
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expect((0, callbacks_1.getSuccinctNumberDisplay)(-1)).toEqual('-1.00');
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expect((0, callbacks_1.getSuccinctNumberDisplay)(-1e-2)).toEqual('-0.0100');
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expect((0, callbacks_1.getSuccinctNumberDisplay)(-1e-3)).toEqual('-1.00e-3');
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expect((0, callbacks_1.getSuccinctNumberDisplay)(-4e-3)).toEqual('-4.00e-3');
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expect((0, callbacks_1.getSuccinctNumberDisplay)(-1e-6)).toEqual('-1.00e-6');
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});
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});
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describe('getDisplayDecimalPlaces', function () {
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it('Not finite', function () {
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expect((0, callbacks_1.getDisplayDecimalPlaces)(Infinity)).toEqual(2);
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expect((0, callbacks_1.getDisplayDecimalPlaces)(-Infinity)).toEqual(2);
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expect((0, callbacks_1.getDisplayDecimalPlaces)(NaN)).toEqual(2);
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});
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it('zero', function () {
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expect((0, callbacks_1.getDisplayDecimalPlaces)(0)).toEqual(2);
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});
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it('Finite and positive', function () {
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expect((0, callbacks_1.getDisplayDecimalPlaces)(300)).toEqual(2);
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expect((0, callbacks_1.getDisplayDecimalPlaces)(30)).toEqual(2);
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expect((0, callbacks_1.getDisplayDecimalPlaces)(1)).toEqual(2);
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expect((0, callbacks_1.getDisplayDecimalPlaces)(1e-2)).toEqual(4);
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expect((0, callbacks_1.getDisplayDecimalPlaces)(1e-3)).toEqual(5);
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expect((0, callbacks_1.getDisplayDecimalPlaces)(4e-3)).toEqual(5);
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expect((0, callbacks_1.getDisplayDecimalPlaces)(1e-6)).toEqual(8);
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});
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it('Finite and negative', function () {
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expect((0, callbacks_1.getDisplayDecimalPlaces)(-300)).toEqual(2);
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expect((0, callbacks_1.getDisplayDecimalPlaces)(-30)).toEqual(2);
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expect((0, callbacks_1.getDisplayDecimalPlaces)(-1)).toEqual(2);
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expect((0, callbacks_1.getDisplayDecimalPlaces)(-1e-2)).toEqual(4);
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expect((0, callbacks_1.getDisplayDecimalPlaces)(-1e-3)).toEqual(5);
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expect((0, callbacks_1.getDisplayDecimalPlaces)(-4e-3)).toEqual(5);
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expect((0, callbacks_1.getDisplayDecimalPlaces)(-1e-6)).toEqual(8);
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});
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});
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