pwc2 / perceptron

Implementation of perceptron algorithm for binary classification using numpy. Online, average, and polynomial kernel models available.

perceptron

perceptron contains implementations of the perceptron with online learning, the average perceptron, and a polynomial kernel perceptron.

Requirements:

• numpy 1.17.2

• pandas 0.25.1

• progressbar2 3.37.1

Usage:

from models.perceptron import Perceptron

model = Perceptron(train='pa2_train_clean.csv',
                   validation='pa2_valid_clean.csv',
                   test='pa2_test_no_label_clean.csv',
                   label='label', # Specify target name
                   mod_type='online', # Choose model type
                   max_iter=15, # Set maximum iterations for training
                   p=None) # If using polynomial kernel, set degree

learned_model = model.train_model()

Data:

The data/ folder contains .csv files with training, validation, and test sets.

To run models:

• run_part0.py runs pre-processing.
• run_part1.py runs online perceptron.
• run_part2.py runs average perceptron.
• run_part3.py runs perceptron using a polynomial kernel.

python main.py will run all four parts in order, output will be saved in model_output folder.