def plot_toy_results(algo_name, thetas): try: utils.plot_toy_data(algo_name, toy_features, toy_labels, thetas) except Exception, e: print 'Error in utils.plot_toy_data function' type, value, tb = sys.exc_info() traceback.print_exc() pdb.post_mortem(tb)
def plot_toy_results(algo_name, thetas): utils.plot_toy_data(algo_name, toy_features, toy_labels, thetas)
def plot_toy_results(algo_name, thetas): print('theta for', algo_name, 'is', ', '.join(map(str, list(thetas[0])))) print('theta_0 for', algo_name, 'is', str(thetas[1])) utils.plot_toy_data(algo_name, toy_features, toy_labels, thetas)
import project1 as p1 from project1 import perceptron, average_perceptron, pegasos import utils import numpy as np import numpy.testing as npt import re toy_features, toy_labels = toy_data = utils.load_toy_data('toy_data.tsv') T = 10 L = 0.2 thetas = perceptron(toy_features, toy_labels, T) print(thetas) utils.plot_toy_data("Perceptron", toy_features, toy_labels, thetas) thetas = average_perceptron(toy_features, toy_labels, T) print(thetas) utils.plot_toy_data("Average Perceptron", toy_features, toy_labels, thetas) thetas = pegasos(toy_features, toy_labels, T, L) print(thetas) utils.plot_toy_data("Pegasos", toy_features, toy_labels, thetas)
""" Comparison between convergence of perceptron, average perceptron, and pegasos linear classifiers on a toy dataset Created on Mon Sep 7 17:43:05 2020 @author: mohamad jalalimanesh """ import matplotlib.pyplot as plt import matplotlib.animation as animation import numpy as np from utils import create_toy_data, plot_toy_data from perceptron import perceptron from averaged_perceptron import average_perceptron from pegasos import pegasos features, labels = create_toy_data(350) fig, ax = plot_toy_data(features, labels) xmin, xmax = plt.axis()[:2] x = np.linspace(xmin, xmax) lines = [] plotlabels = ['perceptron', 'average perceptron', 'pegasos'] plotcols = ["black", "m", "g"] for index in range(3): lobj, = ax.plot(x, np.zeros(x.shape), lw=3, color=plotcols[index], \ label=plotlabels[index]) lines.append(lobj) ax.set_title('epoch = {}'.format(str(0))) ax.legend(loc='upper right')