def main():
data_file = 'ionosphere.data'
data = np.genfromtxt(data_file, delimiter=',', dtype='|S10')
instances = np.array(data[:, :-1], dtype='float')
labels = np.array(data[:, -1] == 'g', dtype='int')
n, d = instances.shape
nlabels = labels.size
if n != nlabels:
raise Exception('Expected same no. of feature vector as no. of labels')
train_data = instances[:200] # first 200 examples
train_labels = labels[:200] # first 200 labels
test_data = instances[200:] # example 201 onwards
test_labels = labels[200:] # label 201 onwards
print 'Running Adaboost...'
adaboost_classifier = run_adaboost(train_data, train_labels, weak_learner)
print 'Done with Adaboost!\n'
confusion_mat = evaluate_classifier(adaboost_classifier, test_data,
test_labels)
print_evaluation_summary(confusion_mat)
def main():
data_file = 'ionosphere.data'
data = np.genfromtxt(data_file, delimiter=',', dtype='|S10')
instances = np.array(data[:, :-1], dtype='float')
labels = np.array(data[:, -1] == 'g', dtype='int')
n, d = instances.shape
nlabels = labels.size
if n != nlabels:
raise Exception('Expected same no. of feature vector as no. of labels')
train_data = instances[:200] # first 200 examples
train_labels = labels[:200] # first 200 labels
test_data = instances[200:] # example 201 onwards
test_labels = labels[200:] # label 201 onwards
print 'Running Adaboost...'
adaboost_classifier = run_adaboost(train_data, train_labels, weak_learner)
print 'Done with Adaboost!\n'
confusion_mat = evaluate_classifier(adaboost_classifier, test_data,
test_labels)
print_evaluation_summary(confusion_mat)