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1. Data Collection and Preprocessing In this section, we describe the process of gathering and preparing the dataset for our machine learning project. 1.1 Data Reading and Loading We begin by reading and loading the preprocessed EEG data from the dat...
MAP DESCRIPTION: A container that represents an associative array of key-value pairs. Allows efficient retrieval of values based on their associated keys. Ensures that each key is unique within the map. Suitable for scenarios where a collection o...
SET DESCRIPTION: A container that represents a sorted set of unique elements. Ensures that the elements are stored in sorted order according to the provided comparison criterion. Suitable for scenarios where a collection of unique, sorted elements...
DESCRIPTION A container that represents a priority queue, implementing a binary heap to maintain elements in a specific order (max heap or min heap). Follows the priority queue principle, where the element with the highest or lowest priority is ser...
DESCRIPTION A container that follows the First-In-First-Out (FIFO) principle, where the first element added is the first one to be removed. Provides a simple and efficient interface for basic queue operations. Suitable for scenarios where elements...
DESCRIPTION A container that follows the Last-In-First-Out (LIFO) principle, where the last element added is the first one to be removed. Provides a simple and efficient interface for basic stack operations. Suitable for scenarios where elements n...
DESCRIPTION: Doubly linked list that supports efficient insertion and deletion at any position. Resembles a vector but with constant time complexity for insertions and deletions at any position. Suitable for scenarios requiring frequent insertions...
Description: Double-ended queue that supports efficient insertion and deletion at both ends. Resembles a vector but provides constant time complexity for insertion and deletion at both the beginning and the end. Suitable for scenarios requiring fa...
Vector Container: