import matplotlib.pyplot as plt

from matplotlib.colors import ListedColormap

import numpy as np

def plot_decision_regions(model, X, y, resolution=0.02):

"""

拟合效果可视化

:param X:training sets

:param y:training labels

:param resolution:分辨率

:return:None

"""

# initialization colors map

colors = ['red', 'blue']

markers = ['o', 'x']

cmap = ListedColormap(colors[:len(np.unique(y))])

# plot the decision regions

x1_max, x1_min = max(X[:, 0]) + 1, min(X[:, 0]) - 1

x2_max, x2_min = max(X[:, 1]) + 1, min(X[:, 1]) - 1

xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),

np.arange(x2_min, x2_max, resolution))

Z = model.predict(np.array([xx1.ravel(), xx2.ravel()]).T)

Z = Z.reshape(xx1.shape)

plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)

plt.xlim(xx1.min(), xx1.max())

plt.ylim(xx2.min(), xx2.max())

# plot class samples

for idx, cl in enumerate(np.unique(y)):

plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],

alpha=0.8, c=cmap(idx),

marker=markers[idx], label=cl)

plt.show()

def plot_knn_predict(model, dataset, label, x):

dataset = np.array(dataset)

plt.scatter(x[0], x[1], c="r", marker='*', s = 40) # 测试点

near, predict_label = model.predict(x) # 设置临近点的个数

plt.scatter(dataset[:,0], dataset[:,1], c=label, s = 50) # 画所有的数据点

for n in near:

print(n)

plt.scatter(n.data[0], n.data[1], c="r", marker='+', s = 40) # k个最近邻点

plt.show()