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sk-knn.py
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# # ==============================无监督查找最近邻（常在聚类中使用，例如变色龙聚类算法）========================
#
# from sklearn.neighbors import NearestNeighbors
# import numpy as np # 快速操作结构数组的工具
#
# X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) # 样本数据
# test_x = np.array([[-3.2, -2.1], [-2.6, -1.3], [1.4, 1.0], [3.1, 2.6], [2.5, 1.0], [-1.2, -1.3]]) # 设置测试数据
# # test_x=X # 测试数据等于样本数据。这样就相当于在样本数据内部查找每个样本的邻节点了。
# nbrs = NearestNeighbors(n_neighbors=2, algorithm='ball_tree').fit(X) # 为X生成knn模型
# distances, indices = nbrs.kneighbors(test_x) # 为test_x中的数据寻找模型中的邻节点
# print('邻节点：',indices)
# print('邻节点距离：',distances)
#
# # ==============================使用kd树和Ball树实现无监督查找最近邻========================
#
# from sklearn.neighbors import KDTree,BallTree
# import numpy as np # 快速操作结构数组的工具
#
# X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
# # test_x = np.array([[-3.2, -2.1], [-2.6, -1.3], [1.4, 1.0], [3.1, 2.6], [2.5, 1.0], [-1.2, -1.3]]) # 设置测试数据
# test_x=X # 测试数据等于样本数据。这样就相当于在样本数据内部查找每个样本的邻节点了。
# kdt = KDTree(X, leaf_size=30, metric='euclidean')
# distances,indices = kdt.query(test_x, k=2, return_distance=True)
# print('邻节点：',indices)
# print('邻节点距离：',distances)
# # ==============================k最近邻分类========================
# import numpy as np # 快速操作结构数组的工具
# from sklearn.neighbors import KNeighborsClassifier,KDTree # 导入knn分类器
#
#
# # 数据集。4种属性，3种类别
# data=[
# [ 5.1, 3.5, 1.4, 0.2, 0],
# [ 4.9, 3.0, 1.4, 0.2, 0],
# [ 4.7, 3.2, 1.3, 0.2, 0],
# [ 4.6, 3.1, 1.5, 0.2, 0],
# [ 5.0, 3.6, 1.4, 0.2, 0],
# [ 7.0, 3.2, 4.7, 1.4, 1],
# [ 6.4, 3.2, 4.5, 1.5, 1],
# [ 6.9, 3.1, 4.9, 1.5, 1],
# [ 5.5, 2.3, 4.0, 1.3, 1],
# [ 6.5, 2.8, 4.6, 1.5, 1],
# [ 6.3, 3.3, 6.0, 2.5, 2],
# [ 5.8, 2.7, 5.1, 1.9, 2],
# [ 7.1, 3.0, 5.9, 2.1, 2],
# [ 6.3, 2.9, 5.6, 1.8, 2],
# [ 6.5, 3.0, 5.8, 2.2, 2],
# ]
#
# # 构造数据集
# dataMat = np.array(data)
# X = dataMat[:,0:4]
# y = dataMat[:,4]
#
# knn = KNeighborsClassifier(n_neighbors=2,weights='distance') # 初始化一个knn模型，设置k=2。weights='distance'样本权重等于距离的倒数。'uniform'为统一权重
# knn.fit(X, y) #根据样本集、结果集，对knn进行建模
# result = knn.predict([[3, 2, 2, 5]]) #使用knn对新对象进行预测
# print(result)
# ==============================k最近邻回归========================
import numpy as np
import matplotlib.pyplot as plt
from sklearn import neighbors
np.random.seed(0)
X = np.sort(5 * np.random.rand(40, 1), axis=0)
T = np.linspace(0, 5, 500)[:, np.newaxis]
y = np.sin(X).ravel()
# 为输出值添加噪声
y[::5] += 1 * (0.5 - np.random.rand(8))
# 训练回归模型
n_neighbors = 5
for i, weights in enumerate(['uniform', 'distance']):
knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)
y_ = knn.fit(X, y).predict(T)
plt.subplot(2, 1, i + 1)
plt.scatter(X, y, c='k', label='data')
plt.plot(T, y_, c='g', label='prediction')
plt.axis('tight')
plt.legend()
plt.title("KNeighborsRegressor (k = %i, weights = '%s')" % (n_neighbors,weights))
plt.show()