# -*- coding: utf-8 -*-
"K邻算法实现"
from sklearn.datasets import load_digits
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV, train_test_split
import matplotlib.pyplot as plt
from tensorflow import keras
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
from sklearn.preprocessing import StandardScaler
def get_data():
data = load_digits()
x, y = data.data, data.target
print(x.shape)
train_x, test_x, train_y, test_y = train_test_split(x, y, test_size= 0.3, random_state= 20)
model = StandardScaler()
train_x = model.fit_transform(train_x)
test_x = model.transform(test_x)
return train_x, test_x, train_y, test_y
def model_fit(x, y):
paras = {'n_neighbors': [5, 6, 7, 8, 9, 10], 'p': [1, 2]}
model = KNeighborsClassifier()
gs = GridSearchCV(model, paras, verbose=2, cv= 5)
gs.fit(x, y)
print('最佳模型:', gs.best_params_, '准确率：',gs.best_score_)
def train(train_x, test_x, train_y, test_y):
model = KNeighborsClassifier(5, p= 1)
model.fit(train_x, train_y)
pre_y = model.predict(test_x)
show_img(test_x, pre_y, test_y)
print(model.score(test_x, test_y))
def show_img(test_x, pre_x, test_y):
num_row = 5
num_col = 3
plt.figure(figsize = (num_row, num_col* 2))
plt.grid(False)
for i in range(num_row * num_col):
plt.subplot(num_row, num_col, i + 1)
show_num_img(test_x[i], pre_x[i], test_y[i])
plt.tight_layout()
plt.show()
def show_num_img(img, pre, y):
plt.xticks([])
plt.yticks([])
plt.imshow(img.reshape(8, 8), cmap=plt.cm.binary)
color = 'green'
if pre != y:
color = 'red'
plt.xlabel("{0}({1})".format(pre, y, color=color))
if __name__ == "__main__":
train_x, test_x, train_y, test_y = get_data()
#model_fit(x, y)
train(train_x, test_x, train_y, test_y)
