CSC 1061 Data Structure: Bag Static Array

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Questions and Answers

What is a key characteristic of static arrays?

  • They are stored on the stack part of memory. (correct)
  • They can dynamically change size during runtime.
  • They can use negative indexing.
  • They store elements of different data types.

Which of the following is true regarding the indexing of arrays?

  • Array indexing starts from 1.
  • Arrays can have flexible indexing depending on the data type.
  • Array indexing begins at 0. (correct)
  • The index of an array can be negative.

What does Big-O notation primarily evaluate?

  • The efficiency of an algorithm in terms of time complexity. (correct)
  • The memory usage of an algorithm directly.
  • The ease of understanding the algorithm.
  • The exact number of steps an algorithm takes.

Which of the following data structures is NOT included in the covered list this semester?

<p>Dictionary (D)</p> Signup and view all the answers

What is one of the rules for arrays mentioned?

<p>Arrays must store elements of the same data type. (D)</p> Signup and view all the answers

In algorithm analysis, what is the focus of the Big-O notation?

<p>The performance of an algorithm relative to input size. (D)</p> Signup and view all the answers

What is a common feature of data structures?

<p>Specialized means of organizing and storing data. (B)</p> Signup and view all the answers

What must be checked to prevent index-out-of-bounds errors when using a fixed size array?

<p>The index must be less than the size of the data member (A)</p> Signup and view all the answers

How is the size data member initialized in the ImportantDates class?

<p>size is initialized to 0 to indicate an empty data structure (A)</p> Signup and view all the answers

Which function accurately tracks the capacity of the data structure?

<p>getSize() (A)</p> Signup and view all the answers

What would be the result of calling clearAll() method in the Bag ADT?

<p>It sets the size member to the initial value (B)</p> Signup and view all the answers

What does the operator[] function generally do in the context of arrays?

<p>It retrieves elements or allows modification of array elements (D)</p> Signup and view all the answers

What best describes a Bag Data Structure?

<p>An unordered collection of values that may have duplicates. (A)</p> Signup and view all the answers

What is the primary characteristic of the size of a Bag Data Structure?

<p>It has a fixed maximum capacity. (B)</p> Signup and view all the answers

Which function has the best Big-O efficiency for finding an item in the Bag?

<p>BigO(1) (C)</p> Signup and view all the answers

How does one check if a Bag is empty?

<p>By using a specific function to check emptiness. (B)</p> Signup and view all the answers

What would be the time complexity to clear all items from a Bag?

<p>BigO(n) (B)</p> Signup and view all the answers

Which operation is likely to be the least efficient in terms of time complexity when manipulating a Bag?

<p>Removing an item by index. (A)</p> Signup and view all the answers

In terms of efficiency, how is determining the MAX_SIZE of a Bag typically evaluated?

<p>BigO(1) (D)</p> Signup and view all the answers

Which of the following statements about Bag Data Structures is false?

<p>Bags cannot hold duplicate elements. (B)</p> Signup and view all the answers

What describes the overall Big-O efficiency for adding an item to an unordered Bag?

<p>BigO(1) (A)</p> Signup and view all the answers

Flashcards

Bag ADT

A data structure that stores a collection of elements, where the order of elements is not important and duplicates are allowed.

append(item)

A function that adds an element to a bag.

getSize()

A function that returns the number of elements currently in a bag.

isEmpty()

A function that checks if a bag is empty.

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at(index)

A function that allows you to access an element in a bag at a specific index. It's a safer way to access elements than using the [] operator.

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Static Array

A memory management technique that allocates a fixed amount of memory at compile time. The array cannot grow or shrink during runtime.

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Big-O Notation

The use of mathematical notation to analyze the efficiency of an algorithm. It describes the algorithm's execution time based on the input size, without considering specific implementations or hardware.

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Dynamic Array

A data structure that stores a collection of elements of the same data type, with the ability to add or remove elements dynamically. They can grow or shrink as needed.

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Linked List

A data structure that consists of a linear sequence of nodes, where each node contains data and a reference to the subsequent node in the sequence.

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Stack

A specialized data structure that follows the Last-In-First-Out (LIFO) principle. Items are added and removed from the top of the structure.

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Queue

A data structure that follows the First-In-First-Out (FIFO) principle. Items are added to the rear and removed from the front of the structure.

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Binary Tree

A specialized data structure that is a tree where each node has at most two children (left and right).

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Bag Data Structure

A data structure that stores a collection of values without any specific order, allowing duplicates. Think of it like a grocery bag where items don't have a specific place and you can have multiple of the same item.

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Algorithm Efficiency

A measure of how the execution time of an algorithm grows as the input size increases. Expressed in terms of 'Big O' notation, it classifies algorithms based on their efficiency.

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Constant (O(1))

Constant time complexity - the time taken for an operation remains constant regardless of the input size. Represented as O(1).

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Logarithmic (O(log(n)))

Logarithmic time complexity - the time taken for an operation increases proportionally to the logarithm of the input size. Represented as O(log(n)).

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Linear (O(n))

Linear time complexity - the time taken for an operation increases proportionally to the input size. Represented as O(n).

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Quadratic (O(n^2))

Quadratic time complexity - the time taken for an operation increases proportionally to the square of the input size. Represented as O(n^2).

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Algorithm Analysis

A way to analyze and compare the efficiency of algorithms based on their time or space complexity. It considers the worst-case scenario for algorithm execution.

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Big-O Analysis

A way to compare and analyze different algorithms by examining how their performance scales as the input size increases. It helps choose the best algorithm for a specific problem based on efficiency.

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Study Notes

CSC 1061 Data Structure: Bag Static Array

  • The course covers bag data structures implemented using static arrays.
  • Objectives include designing and implementing collection classes using partially filled arrays to store collections of elements
  • Students need to write and maintain accurate invariants for each class they implement.
  • Algorithm analysis, including Big O notation, is crucial for understanding algorithm efficiency.

Static Arrays

  • Static arrays are managed by the compiler.
  • The "static" keyword in C++ is not relevant to defining static arrays.
  • Static arrays are stored on the stack.
  • Array elements must all be of the same data type.
  • Static arrays have a fixed size determined at compile time and cannot change during the program's execution.
  • Array indexes are 0-based when referencing array elements.
  • Array declarations use a counting number for size, not 0.

Data Structures

  • Data structures are specialized tools for organizing and storing data, often to allow for more efficient operations.
  • The course covers several data structures this semester including Static Arrays, Dynamic Arrays, Linked Lists, Stacks, Queues, Graphs, Binary Trees, Heaps, and B-Trees,

Array Challenge

  • Students should complete questions 1 and 2.
  • Skip question 3 as it is not relevant to the subject matter.

Algorithm Analysis

  • Big-O notation is used to analyze the performance of algorithms.
  • Big-O notation provides an approximation of the number of steps in an algorithm's computation.
  • The efficiency of an algorithm is characterized by Big-O in terms of execution time, independent of any particular program or computer.
    • Includes Constant, Logarithmic, Linear and Quadratic run-times.
  • Students need to complete a tutorial and activities on algorithm analysis, excluding parts related to mathematical notations.

Bag Data Structure

  • A bag is an unordered collection of values that can contain duplicates.
  • Bags are like bags of groceries; they don't care about order of items.
  • A bag is a container that holds other objects.
  • The container manages storage space and provides functions to access and modify elements.

Bag ADT Data Structure

  • Bags are limited-sized, unordered data structures.
  • Bags start empty.
  • Bags keep a count of elements.
  • The "size" data member tracks the number of elements.
  • The video covers the MAX_SIZE as capacity and the number of elements in use.

Algorithm Efficiency

  • Students must complete an exercise to determine the Big-O efficiency of several tasks.

Invariant: Rules of Data Members

  • Students should read the tutorial and complete the multiple-choice question activities related to data member invariants.

Invariant: Rules of Data Members (specific details)

  • Fixed-size arrays use a constant MAX_SIZE.
  • All array elements must be the same data type.
  • Index checking is essential to prevent out-of-bounds errors.
  • The size data member tracks the number of elements.
  • The size data member is initialized to 0 for an empty container.
  • The size data member is incremented when an element is added, and decremented when one is removed.

Example Bag of Date Objects

  • Code example showcasing a bag of dates (likely using a static array).
  • The static variable MAX (and likely other data members) are constants in the example code.
  • The code emphasizes that a static variable is only created once for the class.
  • The ImportantDates class has a constructor and append method to add items to the Bag container.

Bag ADT: Modifier Member Functions

  • Discusses member functions like append (adds an element, checking for capacity), removeByIndex (removes element at a certain index), removeItem (removes an item), and clearAll (empties the bag).

Bag ADT: Capacity Member Functions

  • Functions that manage storage capacity (e.g., getSize(), getMaxSize(), isEmpty()).

Bag ADT: Access Member Functions

  • Access member functions (e.g., contains(), at(), operator[]), which allow for retrieving or checking elements in the bag, and their behaviour.

Operator[]

  • The subscript operator [] is used for retrieval and potentially modification of elements within an array.
  • The at() function is similar, it is both used to check if valid index and return element.

At Member Function

  • The at() member function behaves similarly to [] in retrieving array elements.

Bag ADT: Other Member Functions

  • Details on specific functions readFile() and writeFile() for handling data I/O from/to files. These functions involve file operations relevant to data storage and retrieval.

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