SVMアルゴリズムは、手書き識別体(元のピクチャをどのように処理するかを含むコード)を処理する.

github   ，     。
https://github.com/ranran4082391/ran_11
# coding=gbk
from PIL import Image
import numpy as np
import os
from sklearn.datasets import load_digits  #           
from sklearn.model_selection import train_test_split  #         
from sklearn.preprocessing import StandardScaler  #      
from sklearn.svm import LinearSVC
from sklearn.metrics import classification_report  #         


def ImageToMatrix(filename):
    #     
    im = Image.open(filename)
    img = im.resize((32, 32), Image.ANTIALIAS)
    img = img.convert("1")
    data = img.getdata()
    data = np.matrix(data, dtype='float')/255.0
    return np.array(data)

mat = []
for filename in os.walk('train'):
    for list_f in filename[2]:
        data = ImageToMatrix('train/'+list_f)
        print(data)
        mat.append(data.reshape(1, -1).tolist()[0])

X = np.array(mat)
y = np.array([x for x in range(0, 10)])


test_filename = r'train\9.jpg'

test_data = ImageToMatrix(test_filename)
print(test_data[0])
svm = LinearSVC()
svm.fit(X, y)
print(svm.predict(test_data))

'''
            ，                 ，      。
'''
digits = load_digits()
#     ，1797  ，8*8
print(digits.data.shape)

#     
X_train, X_test, Y_train, Y_test = train_test_split(digits.data, digits.target, test_size=0.25, random_state=33)

ss = StandardScaler()
# fit     ，       
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)

lsvc = LinearSVC()
lsvc.fit(X_train, Y_train)

Y_predict = lsvc.predict(X_test)

print(classification_report(Y_test, Y_predict, target_names=digits.target_names.astype(str)))