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)