def train(train_x, train_y):
col = 0
# mean_variance_3d_array[0]: corresponds to a partucular feature i.e all row values of a col
# mean_variance_3d_array[0][0]: all feature values belonging to label 0, for a particular col
mean_variance_3d_array = []
while (col < Constants.tot_features):
feature_data = Database.get_feature_data(train_x, col)
label_sorted_2d_array = Database.partition_by_class_label(
feature_data, train_y)
mean_variance_2d_array = Statistician.get_mean_variance_of_label_sorted_2d_array(
label_sorted_2d_array)
mean_variance_2d_array = Statistician.handle_zero_variance_data(
mean_variance_2d_array)
mean_variance_3d_array.append(mean_variance_2d_array)
col = col + 1
Database.dump_data(
Constants.dataset_base + Constants.mean_variance_3d_array,
mean_variance_3d_array)
Naive_bayes.mean_variance_3d_array = mean_variance_3d_array
Naive_bayes.calculate_class_probabilities(train_y)
