"use strict";
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/**
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* @license
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* Copyright 2018 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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/**
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* Unit tests for confusionMatrix().
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*/
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jasmine_util_1.describeWithFlags('confusionMatrix', jasmine_util_1.ALL_ENVS, function () {
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// Reference (Python) TensorFlow code:
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//
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// ```py
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// import tensorflow as tf
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//
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// tf.enable_eager_execution()
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//
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// labels = tf.constant([0, 1, 2, 1, 0])
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// predictions = tf.constant([0, 2, 2, 1, 0])
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// out = tf.confusion_matrix(labels, predictions, 3)
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//
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// print(out)
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// ```
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it('3x3 all cases present in both labels and predictions', function () { return __awaiter(_this, void 0, void 0, function () {
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var labels, predictions, numClasses, out, _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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labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');
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predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');
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numClasses = 3;
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out = tf.math.confusionMatrix(labels, predictions, numClasses);
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_a = test_util_1.expectArraysEqual;
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return [4 /*yield*/, out.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [2, 0, 0, 0, 1, 1, 0, 0, 1]]);
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expect(out.dtype).toBe('int32');
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expect(out.shape).toEqual([3, 3]);
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return [2 /*return*/];
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}
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});
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}); });
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it('float32 arguments are accepted', function () { return __awaiter(_this, void 0, void 0, function () {
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var labels, predictions, numClasses, out, _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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labels = tf.tensor1d([0, 1, 2, 1, 0], 'float32');
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predictions = tf.tensor1d([0, 2, 2, 1, 0], 'float32');
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numClasses = 3;
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out = tf.math.confusionMatrix(labels, predictions, numClasses);
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_a = test_util_1.expectArraysEqual;
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return [4 /*yield*/, out.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [2, 0, 0, 0, 1, 1, 0, 0, 1]]);
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expect(out.dtype).toBe('int32');
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expect(out.shape).toEqual([3, 3]);
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return [2 /*return*/];
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}
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});
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}); });
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// Reference (Python) TensorFlow code:
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//
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// ```py
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// import tensorflow as tf
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//
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// tf.enable_eager_execution()
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//
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// labels = tf.constant([3, 3, 2, 2, 1, 1, 0, 0])
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// predictions = tf.constant([2, 2, 2, 2, 0, 0, 0, 0])
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// out = tf.confusion_matrix(labels, predictions, 4)
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//
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// print(out)
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// ```
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it('4x4 all cases present in labels, but not predictions', function () { return __awaiter(_this, void 0, void 0, function () {
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var labels, predictions, numClasses, out, _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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labels = tf.tensor1d([3, 3, 2, 2, 1, 1, 0, 0], 'int32');
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predictions = tf.tensor1d([2, 2, 2, 2, 0, 0, 0, 0], 'int32');
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numClasses = 4;
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out = tf.math.confusionMatrix(labels, predictions, numClasses);
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_a = test_util_1.expectArraysEqual;
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return [4 /*yield*/, out.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0]]);
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expect(out.dtype).toBe('int32');
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expect(out.shape).toEqual([4, 4]);
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return [2 /*return*/];
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}
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});
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}); });
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it('4x4 all cases present in predictions, but not labels', function () { return __awaiter(_this, void 0, void 0, function () {
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var labels, predictions, numClasses, out, _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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labels = tf.tensor1d([2, 2, 2, 2, 0, 0, 0, 0], 'int32');
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predictions = tf.tensor1d([3, 3, 2, 2, 1, 1, 0, 0], 'int32');
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numClasses = 4;
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out = tf.math.confusionMatrix(labels, predictions, numClasses);
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_a = test_util_1.expectArraysEqual;
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return [4 /*yield*/, out.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0]]);
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expect(out.dtype).toBe('int32');
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expect(out.shape).toEqual([4, 4]);
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return [2 /*return*/];
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}
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});
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}); });
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it('Plain arrays as inputs', function () { return __awaiter(_this, void 0, void 0, function () {
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var labels, predictions, numClasses, out, _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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labels = [3, 3, 2, 2, 1, 1, 0, 0];
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predictions = [2, 2, 2, 2, 0, 0, 0, 0];
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numClasses = 4;
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out = tf.math.confusionMatrix(labels, predictions, numClasses);
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_a = test_util_1.expectArraysEqual;
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return [4 /*yield*/, out.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0]]);
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expect(out.dtype).toBe('int32');
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expect(out.shape).toEqual([4, 4]);
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return [2 /*return*/];
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}
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});
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}); });
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it('Int32Arrays as inputs', function () { return __awaiter(_this, void 0, void 0, function () {
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var labels, predictions, numClasses, out, _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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labels = new Int32Array([3, 3, 2, 2, 1, 1, 0, 0]);
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predictions = new Int32Array([2, 2, 2, 2, 0, 0, 0, 0]);
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numClasses = 4;
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out = tf.math.confusionMatrix(labels, predictions, numClasses);
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_a = test_util_1.expectArraysEqual;
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return [4 /*yield*/, out.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0]]);
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expect(out.dtype).toBe('int32');
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expect(out.shape).toEqual([4, 4]);
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return [2 /*return*/];
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}
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});
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}); });
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// Reference (Python) TensorFlow code:
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//
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// ```py
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// import tensorflow as tf
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//
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// tf.enable_eager_execution()
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//
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// labels = tf.constant([0, 4])
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// predictions = tf.constant([4, 0])
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// out = tf.confusion_matrix(labels, predictions, 5)
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//
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// print(out)
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// ```
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it('5x5 predictions and labels both missing some cases', function () { return __awaiter(_this, void 0, void 0, function () {
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var labels, predictions, numClasses, out, _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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labels = tf.tensor1d([0, 4], 'int32');
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predictions = tf.tensor1d([4, 0], 'int32');
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numClasses = 5;
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out = tf.math.confusionMatrix(labels, predictions, numClasses);
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_a = test_util_1.expectArraysEqual;
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return [4 /*yield*/, out.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [
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0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0
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]]);
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expect(out.dtype).toBe('int32');
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expect(out.shape).toEqual([5, 5]);
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return [2 /*return*/];
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}
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});
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}); });
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it('Invalid numClasses leads to Error', function () {
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expect(function () { return tf.math.confusionMatrix(tf.tensor1d([0, 1]), tf.tensor1d([1, 0]), 2.5); })
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.toThrowError(/numClasses .* positive integer.* got 2\.5/);
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});
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it('Incorrect tensor rank leads to Error', function () {
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expect(function () { return tf.math.confusionMatrix(
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// tslint:disable-next-line:no-any
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tf.scalar(0), tf.scalar(0), 1); })
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.toThrowError(/rank .* 1.*got 0/);
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expect(function () {
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// tslint:disable-next-line:no-any
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return tf.math.confusionMatrix(tf.zeros([3, 3]), tf.zeros([9]), 2);
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})
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.toThrowError(/rank .* 1.*got 2/);
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expect(function () {
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// tslint:disable-next-line:no-any
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return tf.math.confusionMatrix(tf.zeros([9]), tf.zeros([3, 3]), 2);
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})
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.toThrowError(/rank .* 1.*got 2/);
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});
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it('Mismatch in lengths leads to Error', function () {
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expect(
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// tslint:disable-next-line:no-any
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function () { return tf.math.confusionMatrix(tf.zeros([3]), tf.zeros([9]), 2); })
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.toThrowError(/Mismatch .* 3 vs.* 9/);
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});
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});
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//# sourceMappingURL=confusion_matrix_test.js.map
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