) HAM = ( "play sports today", "went play sports", "secret sports event", "sports is today", "sports costs money", ) print "=== Naive Bayes CLassifier ===" c = NaiveBayesClassifier(SPAM, HAM) print "Size of vocabulary: %d" % c.different_words result("SPAM", c.spam.p, 0.3750) result("secret|SPAM", c.spam.p_word("secret"), 0.3333) result("secret|HAM", c.ham.p_word("secret"), 0.0667) result("SPAM|sports", c.p_spam_given_word("sports"), 0.1667) result("SPAM|secret is secret)", c.p_spam_given_phrase("secret is secret"), 0.9615) result("SPAM|today is secret)", c.p_spam_given_phrase("today is secret"), 0) print "\n=== Naive Bayes CLassifier with Laplace Smoothing ===" c = NaiveBayesClassifier(SPAM, HAM, 1) result("SPAM", c.spam.p, 0.4) result("HAM", c.ham.p, 0.6) result("today|SPAM", c.spam.p_word("today"), 0.0476) result("today|HAM", c.ham.p_word("today"), 0.1111) result("SPAM|today is secret)", c.p_spam_given_phrase("today is secret"), 0.4858) from linear_regression import linear_regression, gaussian from scipy import matrix print "\n=== Linear Regression ==="
print "\n=== Problem 8 ===" from bayes import NaiveBayesClassifier, result SPAM = ( "Top Gun", "Shy People", "Top Hat", ) HAM = ( "Top Gear", "Gun Shy", ) c = NaiveBayesClassifier(SPAM, HAM, 1) result("OLD", c.spam.p) result("Top|OLD", c.spam.p_word("Top")) result("OLD|Top", c.p_spam_given_word("Top")) print "\n=== Problem 10 ===" from linear_regression import linear_regression, gaussian x = [1.0, 3.0, 4.0, 5.0, 9.0] y = [2.0, 5.2, 6.8, 8.4, 14.8] (w0, w1), err = linear_regression(x, y) print "(w0=%.1f, w1=%.1f) err=%.2f" % (w0, w1, err) print "\n=== Problem 12 ===" from logic import Proposition, implies print Proposition( lambda a: not a, "not a"