README.md

手写识别

分析流程

1. 数据加载
2. 准备阶段

• 样本的个数
• 图像的形状
• 识别结果
• 可视化

3. 分类阶段

• 训练 -> 分类器
• 测试集 准确率计算

代码

knn

# coding=utf-8
import matplotlib

matplotlib.use('Qt4Agg')
# 手写数字分类
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.metrics import accuracy_score
from sklearn.datasets import load_digits
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import MultinomialNB
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt

# 加载数据
digits = load_digits()
data = digits.data
# 数据探索
print(data.shape)
# 查看第一幅图像
print(digits.images[0])
# 第一幅图像代表的数字含义
print(digits.target[0])
# 将第一幅图像显示出来
plt.gray()
plt.imshow(digits.images[0])
plt.show()

# 分割数据，将25%的数据作为测试集，其余作为训练集
train_x, test_x, train_y, test_y = train_test_split(data, digits.target, test_size=0.25, random_state=33)

# 采用Z-Score规范化
ss = preprocessing.StandardScaler()
train_ss_x = ss.fit_transform(train_x)
test_ss_x = ss.transform(test_x)

# 创建KNN分类器
knn = KNeighborsClassifier()
knn.fit(train_ss_x, train_y) 
predict_y = knn.predict(test_ss_x) 
print("KNN准确率: %.4lf" % accuracy_score(predict_y, test_y))

# 创建SVM分类器
svm = SVC()
svm.fit(train_ss_x, train_y)
predict_y=svm.predict(test_ss_x)
print('SVM准确率: %0.4lf' % accuracy_score(predict_y, test_y))

# 采用Min-Max规范化
mm = preprocessing.MinMaxScaler()
train_mm_x = mm.fit_transform(train_x)
test_mm_x = mm.transform(test_x)

# 创建Naive Bayes分类器
mnb = MultinomialNB()
mnb.fit(train_mm_x, train_y) 
predict_y = mnb.predict(test_mm_x) 
print("多项式朴素贝叶斯准确率: %.4lf" % accuracy_score(predict_y, test_y))

# 创建CART决策树分类器
dtc = DecisionTreeClassifier()
dtc.fit(train_mm_x, train_y) 
predict_y = dtc.predict(test_mm_x) 
print("CART决策树准确率: %.4lf" % accuracy_score(predict_y, test_y))