def test_split_data():
X = np.ones((12, 3))
y = np.ones(12)
X_learn, X_test = split_data(X, frac=2/3)
assert len(X_learn) == 8 and len(X_test) == 4
X_learn, y_learn, X_test, y_test = split_data(X, y, frac=0.75)
assert (len(X_learn) == 9 and len(X_test) == 3 and
len(y_learn) == 9 and len(y_test) == 3)
y = np.ones(11)
try:
split_data(X, y)
except (TypeError, ValueError):
assert True
else:
assert False, ("'X' and 'y' have different lengths - " +
"an exception should have been raised.")
import matplotlib.pyplot as plt
except ImportError:
plt = None
if __name__ == '__main__':
np.random.seed(0)
# load the data
iris = datasets.load_iris()
print(iris)
X, y = iris
# split into a training and a testing set
X_learn, y_learn, X_test, y_test = cv.split_data(X, y, frac=3/4)
# train the (Gaussian) naive Bayes classifier
naive = ml.NaiveBayesGaussian()
naive.learn(X_learn, y_learn)
print(naive)
# make predictions on the testing set
y_hat = naive.predict(X_test)
print("Incorrect predictions on the testing set: {0:.2f} %"
.format(100. * (y_hat != y_test).sum() / len(y_test)))
if plt is not None:
# plot learning data
colord = {}
for c in naive.classes: