7.3 試程式設計實現拉普拉斯修正的樸素貝葉斯分類器，並以西瓜資料集3.0為訓練集，對p151“測1”樣本進行判別。

程式設計得到結果為:

[0.024223607117715082, 4.4242506192749345e-05]

因此測試例應預測為好瓜。

# -*- coding: utf-8 -*-
# exercise 7.1: apply LL method to estimate the likelihood based on the first 3 attributes with Laplacian correction
import numpy as np

D = np.array([
    [1, 1, 1, 1, 1, 1, 0.697, 0.460, 1],
    [2, 1, 2, 1, 1, 1, 0.774, 0.376, 1],
    [2, 1, 1, 1, 1, 1, 0.634, 0.264, 1],
    [1, 1, 2, 1, 1, 1, 0.608, 0.318, 1],
    [3, 1, 1, 1, 1, 1, 0.556, 0.215, 1],
    [1, 2, 1, 1, 2, 2, 0.403, 0.237, 1],
    [2, 2, 1, 2, 2, 2, 0.481, 0.149, 1],
    [2, 2, 1, 1, 2, 1, 0.437, 0.211, 1],
    [2, 2, 2, 2, 2, 1, 0.666, 0.091, 0],
    [1, 3, 3, 1, 3, 2, 0.243, 0.267, 0],
    [3, 3, 3, 3, 3, 1, 0.245, 0.057, 0],
    [3, 1, 1, 3, 3, 2, 0.343, 0.099, 0],
    [1, 2, 1, 2, 1, 1, 0.639, 0.161, 0],
    [3, 2, 2, 2, 1, 1, 0.657, 0.198, 0],
    [2, 2, 1, 1, 2, 2, 0.360, 0.370, 0],
    [3, 1, 1, 3, 3, 1, 0.593, 0.042, 0],
    [1, 1, 2, 2, 2, 1, 0.719, 0.103, 0]])
m, n = D.shape[0], D.shape[1]-1  # number of instances,attributes
label = np.unique(D[:,-1])
class_dict = {int(l): 0 for l in label}
for i in range(m):
    class_dict[D[i,-1]] += 1
p_class = {l: (class_dict[l]+1)/(m+2) for l in class_dict}
DICT0 = [{} for item in range(n)]  # list of dicts that contain their own samples with class 0
DICT1 = [{} for item in range(n)]  # list of dicts that contain their own samples with class 1

for i, d in enumerate(DICT0[:-2]):
    DICT0[i] = {int(a): 0 for a in np.unique(D[:, i])}
    d = DICT0[i]
    k = len(np.unique(D[:, i]))  # number of attributes in column i
    for j in range(8):
        d[D[j,i]] += 1
    DICT0[i] = {l: (d[l]+1)/(8+k) for l in d}

for i, d in enumerate(DICT1[:-2]):
    DICT1[i] = {int(a): 0 for a in np.unique(D[:, i])}
    d = DICT1[i]
    k = len(np.unique(D[:, i]))  # number of attributes in column i
    for j in range(8,m):
        d[D[j, i]] += 1
    DICT1[i] = {l: (d[l] + 1) / (9 + k) for l in d}

def prob_continuous(x,data_n):  # probability of continuous variables
    mean = np.mean(data_n)
    var = np.var(data_n)
    p = np.exp(-(x-mean)**2*0.5/var)/(np.sqrt(2*np.pi*var))
    return p

test = [1,1,1,1,1,1,0.697,0.46]  # the predict sample
result = [p_class[0], p_class[1]]
DICT0[6], DICT0[7] = prob_continuous(test[-2], D[:8, 6]), prob_continuous(test[-1], D[:8, 7])
DICT1[6], DICT1[7] = prob_continuous(test[-2], D[8:, 6]), prob_continuous(test[-1], D[8:, 7])

for i, t in enumerate(test[:-2]):
    result[0] *= DICT0[i][t]
    result[1] *= DICT1[i][t]
result[0] *= DICT0[6] * DICT0[7]
result[1] *= DICT1[6] * DICT1[7]

print(result)