def predict(self, data, x_label, y_class):
"""
Gaussian Naive Bayes Model to predict the
label given the class values.
PARAMETERS
==========
x_label: ndarray(dtype=int,ndim=1,axis=1)
Array of labels.
y_class: ndarray(dtype=int,ndim=1,axis=1)
Array of classes.
RETURNS
=======
Predicts the label, for given class values
by user.
"""
mean, var = get_mean_var(x_label, y_class)
argmax = 0
for (k1, v1), (k2, v2) in zip(mean.items(), var.items()):
pre_prob = Counter(x_label)[k1] / len(x_label)
pro = 1
for i in range(len(v1)):
pro *= p_y_given_x(data[i], v1[i], v2[i])
pxy = pro * pre_prob
if (pxy > argmax):
prediction = k1
return prediction
def predict(self, data, x_label, y_class):
mean, var = get_mean_var(x_label, y_class)
argmax = 0
for (k1, v1), (k2, v2) in zip(mean.items(), var.items()):
pre_prob = Counter(x_label)[k1] / len(x_label)
pro = 1
for i in range(len(v1)):
pro *= p_y_given_x(data[i], v1[i], v2[i])
pxy = pro * pre_prob
if (pxy > argmax):
prediction = k1
return prediction
