【机器学习】手写数字识别 - ToB企服应用市场:ToB评测及商务社交产业平台

## logistis回归，是一个分类算法，可以处理二元分类，多元分类。
## 首先逻辑回归构造冠以的线性回归函数，然后使用sigmoid函数将回归值映射到散列类别
## sklearn 分类算法与手写数字识别
## 数据介绍
## MNIST数据集来自美国国家标准与技术研究所，训练集由250个不同人手写数字构成，50%高中学生，50%来自人口普查局
## 导包
import struct,os
import numpy as np
from array import array as pyarray
from numpy import append,array,int8,uint8,zeros
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score, classification_report
from sklearn.linear_model import LogisticRegression
## 加载数据集
def load_mnist(image_file,label_file,path="mnist"):
digits=np.arange(10)
fname_image = os.path.join(path,image_file)
fname_label = os.path.join(path, label_file)
flbl = open(fname_label,'rb')
magic_nr, size = struct.unpack(">II", flbl.read(8))
lbl = pyarray("b",flbl.read())
flbl.close()
fimg = open(fname_image,'rb')
magic_nr, size, rows, cols = struct.unpack(">IIII", fimg.read(16))
img = pyarray("B",fimg.read())
fimg.close()
ind = [ k for k in range(size) if lbl[k] in digits ]
N = len(ind)
images = zeros((N, rows*cols),dtype = uint8)
labels = zeros((N,1), dtype = int8)
for i in range(len(ind)):
images[i] = array(img[ind[i]*rows*cols : (ind[i]+1)*rows*cols]).reshape((1,rows*cols))
labels[i] = lbl[ind[i]]
return images,labels
train_image, train_label = load_mnist('train-images.idx3-ubyte', 'train-labels.idx1-ubyte')
test_image, test_label = load_mnist('t10k-images.idx3-ubyte','t10k-labels.idx1-ubyte')
## 数据展示
## 28*28
def show_image(imgdata, imgtarget, show_column, show_row,titlename):
for index, (im, it) in enumerate(list(zip(imgdata, imgtarget))):
xx = im.reshape(28,28)
plt.subplots_adjust(left=1, bottom=None, right=3,top=2, wspace=None, hspace=None)
plt.subplot(show_row,show_column,index+1)
plt.axis('off')
plt.imshow(xx, cmap='gray', interpolation='nearest')
plt.title(titlename+':%i' % it)
# plt.savefig(titlename+'.png')
# 这个地方可能会有一个警告，可能因为图太大了，不过没关系，代码正常运行
plt.show()
show_image(train_image[:50], train_label[:50],10,5,'label')
## sklearn 分类模型
## 数据归一化
train_image = [im/255.0 for im in train_image]
test_image = [im/255.0 for im in test_image]
print(len(train_image))
print(len(test_image))
print(len(train_label))
print(len(test_label))
## 模型分类
## 模型实例化
lr = LogisticRegression(max_iter=1000)
## 模型训练
lr.fit(train_image,train_label.ravel())
## 模型验证
predict = lr.predict(test_image)
print("accuracy score: %.4lf"% accuracy_score(predict,test_label))
print("classfication report for %s:\n%s\n"%(lr, classification_report(test_label, predict)))
show_image(test_image[:100],predict,10,10,'predict')

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