In this project, you will be implementing linear classifiers beginning with the Perceptron algorithm. You will begin by writing your loss function, a hinge-loss function. For this function you are given the parameters of your model 0 and B. Additionally, you are given a feature matrix in which the rows are feature vectors and the columns are individual features, and a vector of labels representing the actual sentiment of the corresponding feature vector.
Hinge Loss on One Data Sample:
First, implement the basic hinge loss calculation on a single data-point. Instead of the entire feature matrix, you are given one row, representing the feature vector of a single data sample, and its label of +1 or -1 representing the ground truth sentiment of the data sample.
Reminder: You can implement this function locally first, and run python test. py in your sentiment_analysis directory to validate basic functionality before checking against the online grader here.
Available Functions: You have access to the NumPy python library as np; No need to import anything.