In the era of big data, we frequently find ourselves manipulating high-dimensional data, whether it be image data (where the number of dimensions will equal the number of pixels per image), or text data (where the number of dimensions is each unique word in a body of text) to name a few. Many times, it is desirable to reduce the dimensionality of this data since it can be represented accurately in lower-dimensional space. This can be useful for:
From GPS navigation to network-layer link-state routing, Dijkstra’s Algorithm powers some of the most taken-for-granted modern services. Utilizing some basic data structures, let’s get an understanding of what it does, how it accomplishes its goal, and how to implement it in Python (first naively, and then with good asymptotic runtime!)
Dijkstra’s Algorithm finds the shortest path between two nodes of a graph. So, if we have a mathematical problem we can model with a graph, we can find the shortest path between our nodes with Dijkstra’s Algorithm.
First things first. A graph is a collection of nodes connected by edges:
A node is just some object, and an edge is a connection between two nodes. …
Or any design pattern, for that matter? In general, design patterns are created and used to promote object composition over inheritance, allow for (black-box) code reuse, better enforce the single responsibility principle, and reduce the size and unwieldiness of your code’s classes in general.
Like any design pattern, Decorators and Strategies have specific scenarios when they are there are beneficial to use. These two patterns can have similar use cases, and it can be easy to confuse when you should use one or the other. According to , both Decorators and Strategies can be employed when your goal is an “alternative to extending functionality by subclassing”. …