def spambase(T=100): train, target = load_spambase() target = np.array(map(lambda v: -1.0 if v == 0 else 1.0, target)) train, test, train_target, test_target = train_test_shuffle_split(train, target, len(train) / 10) boost = AdaBoost() start = timeit.default_timer() boost.boost(train, train_target, test, test_target, T) stop = timeit.default_timer() print "Total Run Time: %s secs" % (stop - start)
def spambase(T=100): train, target = load_spambase() target = np.array(map(lambda v: -1.0 if v == 0 else 1.0, target)) train, test, train_target, test_target = train_test_shuffle_split( train, target, len(train) / 10) boost = AdaBoost() start = timeit.default_timer() boost.boost(train, train_target, test, test_target, T) stop = timeit.default_timer() print "Total Run Time: %s secs" % (stop - start)
def spam(step, loop, converge): train, target = load_spambase() train, test, train_target, test_target = cross_validation.train_test_shuffle_split(train, target, len(train) / 10) scaler = normalize(train) train = append_new_column(train, 1.0, 0) scaler.scale_test(test) test = append_new_column(test, 1.0, 0) print '\n============== Logistic Regression - Stochastic Gradient Descending===============' spam_logistic(train, test, train_target, test_target, step, loop, converge) print '\n============== Linear Regression - Stochastic Gradient Descending ===============' spam_linear(train, test, train_target, test_target, step, loop, converge) print '\n============== Linear Regression - Normal Equation===============' spam_normal_equation(train, test, train_target, test_target) print '\n============== Decision Tree ====================================' spam_decision_tree(train, test, train_target, test_target)