KNN

# ==========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([[0, 10, 0, 10]])  # 使用knn对新对象进行预测
print(result)