如何使用Scikit制作和使用朴素贝叶斯分类器

import numpy as np
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB 
from sklearn import cross_validation

from utilities import visualize_classifier

# Input file containing data
input_file = 'data_multivar_nb.txt'

# Load data from input file
data = np.loadtxt(input_file, delimiter=',')
X, y = data[:, :-1], data[:, -1] 

# Create Naive Bayes classifier 
classifier = GaussianNB()

# Train the classifier
classifier.fit(X, y)

# Predict the values for training data
y_pred = classifier.predict(X)

# Compute accuracy
accuracy = 100.0 * (y == y_pred).sum() / X.shape[0]
print("Accuracy of Naive Bayes classifier =", round(accuracy, 2), "%")

我只有几个问题:

输入文件的形式为:

2.18,0.57,0
4.13,5.12,1
9.87,1.95,2
4.02,-0.8,3
1.18,1.03,0
4.59,5.74,1

计算精度部分是如何工作的? 最后,如何使用分类器预测新值的y?