b’4c45c90c475e243d1bd461fe64c6e5d428e9de’ blob 2014�package programmingexample4; import java.util.Iterator; /** * Interface for a bag. A bag is similar to a set but allows for * duplicates. */ public interface Bag extends Iterable { /** * Adds a single copy of an element to the bag * @param e The element to add * @postcondition count(e) = old […]
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b’ca6ea0111f106e50d1c3288c4006a985c56394′ blob 21648�COMP2511 Practice Questions ==================================== The following questions are practice questions. They may not be representative of the style or difficulty of the questions in the exam. There may be questions in the Final Exam of a different style and structure to these examples. To get a better idea of the structure/style of questions
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1a: Neuroanatomy and Perceptrons Week 1: Overview In this �rst week, we will look at the historical background of arti�cial intelligence and deep learning, biological and arti�cial neurons, the perceptron learning algorithm, and the training of multi-layer neural networks by gradient descent. Weekly learning outcomes By the end of this module, you will be able
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b’8818666b435cc4852b2eeea3eb1724c0434953′ blob 718�package programmingexample5; import java.util.ArrayList; public class ShapeGroup implements ShapeVisitable { private ArrayList elems; public ShapeGroup() { elems = new ArrayList(); } public void add(ShapeVisitable shape) { elems.add(shape); } public void remove(ShapeVisitable shape) { elems.remove(shape); } @Override public void accept(ShapeVisitor v) { /* * TODO You need to implement this method, to answer the
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b’28b28903c26817da4306cb9c9dbf3a74067663′ blob 835�package programmingexample5; import java.awt.Color; public class ShapeColourVisitor implements ShapeVisitor { @Override public void visit(Circle circle) { // If a circle set colour to red circle.setColour(Color.RED); } @Override public void visit(Rectangle rectangle) { // If a rectangle set colour to green rectangle.setColour(Color.GREEN); } @Override public void visit(Triangle triangle) { // If a triangle set
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package comp1110.ass1; import org.junit.jupiter.api.*; import static java.util.concurrent.TimeUnit.MILLISECONDS; import static org.junit.jupiter.api.Assertions.assertEquals; @Timeout(value = 1000, unit = MILLISECONDS) @TestMethodOrder(MethodOrderer.OrderAnnotation.class) public class LocationTask2Test { private void offBoardTest(Location input, boolean expected) { boolean output = input.offBoard(); assertEquals(expected, output, “Expected offBoard called on Location (” + input.getX() + “,” + input.getY() + “) to return ” + expected + “,
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package comp1110.ass1.gui; import comp1110.ass1.*; import javafx.application.Application; import javafx.application.Platform; import javafx.event.ActionEvent; import javafx.event.EventHandler; import javafx.scene.Group; import javafx.scene.Scene; import javafx.scene.control.Button; import javafx.scene.control.Label; import javafx.scene.control.Slider; import javafx.scene.effect.DropShadow; import javafx.scene.image.Image; import javafx.scene.image.ImageView; import javafx.scene.input.KeyCode; import javafx.scene.paint.Color; import javafx.scene.shape.Line; import javafx.scene.text.Font; import javafx.scene.text.FontWeight; import javafx.scene.text.Text; import javafx.scene.text.TextAlignment; import javafx.stage.Stage; /** * This class implements a GUI for the Walk the
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package comp1110.ass1; import org.junit.jupiter.api.*; import static java.util.concurrent.TimeUnit.MILLISECONDS; import static org.junit.jupiter.api.Assertions.assertEquals; @Timeout(value = 1000, unit = MILLISECONDS) @TestMethodOrder(MethodOrderer.OrderAnnotation.class) public class IsPlacementWellFormedTest { private void test(String input, boolean expected) { boolean output = WalkTheDog.isPlacementWellFormed(input); assertEquals(expected, output, “Expected isPlacementWellFormed called on ‘” + input + “‘ to be ” + expected + “, but got ” + output
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7b: Language Processing Discussion: Translation, Transformers and ChatBots Attention Mechanism and Neural Machine Translation These articles explain how an attention mechanism can be used for sequence-to-sequence prediction, and how stacked LSTMs, combined with attention and word vectors, can be used for multi-lingual neural machine translation: Cho, K., 2015. Introduction to Neural Machine Translation with GPUs
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