测试算法
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
y = iris.target
X.shape
(150, 4)
y.shape
(150,)
train_test_split
y.reshape(1,-1)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]])
# 使用vstack将x和y合并
tatol_data = np.hstack([X,y.reshape(-1,1)])
tatol_data
array([[5.1, 3.5, 1.4, 0.2, 0. ],
[4.9, 3. , 1.4, 0.2, 0. ],
[4.7, 3.2, 1.3, 0.2, 0. ],
[4.6, 3.1, 1.5, 0.2, 0. ],
[5. , 3.6, 1.4, 0.2, 0. ],
[5.4, 3.9, 1.7, 0.4, 0. ],
[4.6, 3.4, 1.4, 0.3, 0. ],
[5. , 3.4, 1.5, 0.2, 0. ],
[4.4, 2.9, 1.4, 0.2, 0. ],
[4.9, 3.1, 1.5, 0.1, 0. ],
[5.4, 3.7, 1.5, 0.2, 0. ],
[4.8, 3.4, 1.6, 0.2, 0. ],
[4.8, 3. , 1.4, 0.1, 0. ],
[4.3, 3. , 1.1, 0.1, 0. ],
[5.8, 4. , 1.2, 0.2, 0. ],
[5.7, 4.4, 1.5, 0.4, 0. ],
[5.4, 3.9, 1.3, 0.4, 0. ],
[5.1, 3.5, 1.4, 0.3, 0. ],
[5.7, 3.8, 1.7, 0.3, 0. ],
[5.1, 3.8, 1.5, 0.3, 0. ],
[5.4, 3.4, 1.7, 0.2, 0. ],
[5.1, 3.7, 1.5, 0.4, 0. ],
[4.6, 3.6, 1. , 0.2, 0. ],
[5.1, 3.3, 1.7, 0.5, 0. ],
[4.8, 3.4, 1.9, 0.2, 0. ],
[5. , 3. , 1.6, 0.2, 0. ],
[5. , 3.4, 1.6, 0.4, 0. ],
[5.2, 3.5, 1.5, 0.2, 0. ],
[5.2, 3.4, 1.4, 0.2, 0. ],
[4.7, 3.2, 1.6, 0.2, 0. ],
[4.8, 3.1, 1.6, 0.2, 0. ],
[5.4, 3.4, 1.5, 0.4, 0. ],
[5.2, 4.1, 1.5, 0.1, 0. ],
[5.5, 4.2, 1.4, 0.2, 0. ],
[4.9, 3.1, 1.5, 0.1, 0. ],
[5. , 3.2, 1.2, 0.2, 0. ],
[5.5, 3.5, 1.3, 0.2, 0. ],
[4.9, 3.1, 1.5, 0.1, 0. ],
[4.4, 3. , 1.3, 0.2, 0. ],
[5.1, 3.4, 1.5, 0.2, 0. ],
[5. , 3.5, 1.3, 0.3, 0. ],
[4.5, 2.3, 1.3, 0.3, 0. ],
[4.4, 3.2, 1.3, 0.2, 0. ],
[5. , 3.5, 1.6, 0.6, 0. ],
[5.1, 3.8, 1.9, 0.4, 0. ],
[4.8, 3. , 1.4, 0.3, 0. ],
[5.1, 3.8, 1.6, 0.2, 0. ],
[4.6, 3.2, 1.4, 0.2, 0. ],
[5.3, 3.7, 1.5, 0.2, 0. ],
[5. , 3.3, 1.4, 0.2, 0. ],
[7. , 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. , 1.3, 1. ],
[6.5, 2.8, 4.6, 1.5, 1. ],
[5.7, 2.8, 4.5, 1.3, 1. ],
[6.3, 3.3, 4.7, 1.6, 1. ],
[4.9, 2.4, 3.3, 1. , 1. ],
[6.6, 2.9, 4.6, 1.3, 1. ],
[5.2, 2.7, 3.9, 1.4, 1. ],
[5. , 2. , 3.5, 1. , 1. ],
[5.9, 3. , 4.2, 1.5, 1. ],
[6. , 2.2, 4. , 1. , 1. ],
[6.1, 2.9, 4.7, 1.4, 1. ],
[5.6, 2.9, 3.6, 1.3, 1. ],
[6.7, 3.1, 4.4, 1.4, 1. ],
[5.6, 3. , 4.5, 1.5, 1. ],
[5.8, 2.7, 4.1, 1. , 1. ],
[6.2, 2.2, 4.5, 1.5, 1. ],
[5.6, 2.5, 3.9, 1.1, 1. ],
[5.9, 3.2, 4.8, 1.8, 1. ],
[6.1, 2.8, 4. , 1.3, 1. ],
[6.3, 2.5, 4.9, 1.5, 1. ],
[6.1, 2.8, 4.7, 1.2, 1. ],
[6.4, 2.9, 4.3, 1.3, 1. ],
[6.6, 3. , 4.4, 1.4, 1. ],
[6.8, 2.8, 4.8, 1.4, 1. ],
[6.7, 3. , 5. , 1.7, 1. ],
[6. , 2.9, 4.5, 1.5, 1. ],
[5.7, 2.6, 3.5, 1. , 1. ],
[5.5, 2.4, 3.8, 1.1, 1. ],
[5.5, 2.4, 3.7, 1. , 1. ],
[5.8, 2.7, 3.9, 1.2, 1. ],
[6. , 2.7, 5.1, 1.6, 1. ],
[5.4, 3. , 4.5, 1.5, 1. ],
[6. , 3.4, 4.5, 1.6, 1. ],
[6.7, 3.1, 4.7, 1.5, 1. ],
[6.3, 2.3, 4.4, 1.3, 1. ],
[5.6, 3. , 4.1, 1.3, 1. ],
[5.5, 2.5, 4. , 1.3, 1. ],
[5.5, 2.6, 4.4, 1.2, 1. ],
[6.1, 3. , 4.6, 1.4, 1. ],
[5.8, 2.6, 4. , 1.2, 1. ],
[5. , 2.3, 3.3, 1. , 1. ],
[5.6, 2.7, 4.2, 1.3, 1. ],
[5.7, 3. , 4.2, 1.2, 1. ],
[5.7, 2.9, 4.2, 1.3, 1. ],
[6.2, 2.9, 4.3, 1.3, 1. ],
[5.1, 2.5, 3. , 1.1, 1. ],
[5.7, 2.8, 4.1, 1.3, 1. ],
[6.3, 3.3, 6. , 2.5, 2. ],
[5.8, 2.7, 5.1, 1.9, 2. ],
[7.1, 3. , 5.9, 2.1, 2. ],
[6.3, 2.9, 5.6, 1.8, 2. ],
[6.5, 3. , 5.8, 2.2, 2. ],
[7.6, 3. , 6.6, 2.1, 2. ],
[4.9, 2.5, 4.5, 1.7, 2. ],
[7.3, 2.9, 6.3, 1.8, 2. ],
[6.7, 2.5, 5.8, 1.8, 2. ],
[7.2, 3.6, 6.1, 2.5, 2. ],
[6.5, 3.2, 5.1, 2. , 2. ],
[6.4, 2.7, 5.3, 1.9, 2. ],
[6.8, 3. , 5.5, 2.1, 2. ],
[5.7, 2.5, 5. , 2. , 2. ],
[5.8, 2.8, 5.1, 2.4, 2. ],
[6.4, 3.2, 5.3, 2.3, 2. ],
[6.5, 3. , 5.5, 1.8, 2. ],
[7.7, 3.8, 6.7, 2.2, 2. ],
[7.7, 2.6, 6.9, 2.3, 2. ],
[6. , 2.2, 5. , 1.5, 2. ],
[6.9, 3.2, 5.7, 2.3, 2. ],
[5.6, 2.8, 4.9, 2. , 2. ],
[7.7, 2.8, 6.7, 2. , 2. ],
[6.3, 2.7, 4.9, 1.8, 2. ],
[6.7, 3.3, 5.7, 2.1, 2. ],
[7.2, 3.2, 6. , 1.8, 2. ],
[6.2, 2.8, 4.8, 1.8, 2. ],
[6.1, 3. , 4.9, 1.8, 2. ],
[6.4, 2.8, 5.6, 2.1, 2. ],
[7.2, 3. , 5.8, 1.6, 2. ],
[7.4, 2.8, 6.1, 1.9, 2. ],
[7.9, 3.8, 6.4, 2. , 2. ],
[6.4, 2.8, 5.6, 2.2, 2. ],
[6.3, 2.8, 5.1, 1.5, 2. ],
[6.1, 2.6, 5.6, 1.4, 2. ],
[7.7, 3. , 6.1, 2.3, 2. ],
[6.3, 3.4, 5.6, 2.4, 2. ],
[6.4, 3.1, 5.5, 1.8, 2. ],
[6. , 3. , 4.8, 1.8, 2. ],
[6.9, 3.1, 5.4, 2.1, 2. ],
[6.7, 3.1, 5.6, 2.4, 2. ],
[6.9, 3.1, 5.1, 2.3, 2. ],
[5.8, 2.7, 5.1, 1.9, 2. ],
[6.8, 3.2, 5.9, 2.3, 2. ],
[6.7, 3.3, 5.7, 2.5, 2. ],
[6.7, 3. , 5.2, 2.3, 2. ],
[6.3, 2.5, 5. , 1.9, 2. ],
[6.5, 3. , 5.2, 2. , 2. ],
[6.2, 3.4, 5.4, 2.3, 2. ],
[5.9, 3. , 5.1, 1.8, 2. ]])
import random
random.shuffle(tatol_data)
tatol_data
array([[5.1, 3.5, 1.4, 0.2, 0. ],
[5.1, 3.5, 1.4, 0.2, 0. ],
[4.9, 3. , 1.4, 0.2, 0. ],
[4.9, 3. , 1.4, 0.2, 0. ],
[5.1, 3.5, 1.4, 0.2, 0. ],
[4.6, 3.1, 1.5, 0.2, 0. ],
[4.6, 3.1, 1.5, 0.2, 0. ],
[5. , 3.4, 1.5, 0.2, 0. ],
[4.6, 3.4, 1.4, 0.3, 0. ],
[5.1, 3.5, 1.4, 0.2, 0. ],
[5.4, 3.7, 1.5, 0.2, 0. ],
[5.4, 3.9, 1.7, 0.4, 0. ],
[5. , 3.4, 1.5, 0.2, 0. ],
[5.4, 3.9, 1.7, 0.4, 0. ],
[4.8, 3. , 1.4, 0.1, 0. ],
[4.8, 3.4, 1.6, 0.2, 0. ],
[5.7, 4.4, 1.5, 0.4, 0. ],
[4.8, 3. , 1.4, 0.1, 0. ],
[5.4, 3.9, 1.7, 0.4, 0. ],
[5.1, 3.5, 1.4, 0.2, 0. ],
[5.4, 3.7, 1.5, 0.2, 0. ],
[4.6, 3.4, 1.4, 0.3, 0. ],
[5.4, 3.4, 1.7, 0.2, 0. ],
[5.7, 4.4, 1.5, 0.4, 0. ],
[4.6, 3.4, 1.4, 0.3, 0. ],
[4.4, 2.9, 1.4, 0.2, 0. ],
[5.1, 3.3, 1.7, 0.5, 0. ],
[4.8, 3.4, 1.6, 0.2, 0. ],
[4.6, 3.4, 1.4, 0.3, 0. ],
[5.1, 3.3, 1.7, 0.5, 0. ],
[5.2, 3.5, 1.5, 0.2, 0. ],
[5. , 3. , 1.6, 0.2, 0. ],
[4.8, 3.1, 1.6, 0.2, 0. ],
[5.1, 3.8, 1.5, 0.3, 0. ],
[4.8, 3. , 1.4, 0.1, 0. ],
[5. , 3.6, 1.4, 0.2, 0. ],
[4.6, 3.6, 1. , 0.2, 0. ],
[4.8, 3. , 1.4, 0.1, 0. ],
[5.1, 3.5, 1.4, 0.2, 0. ],
[5.7, 4.4, 1.5, 0.4, 0. ],
[5. , 3.4, 1.5, 0.2, 0. ],
[4.4, 2.9, 1.4, 0.2, 0. ],
[5.1, 3.8, 1.5, 0.3, 0. ],
[5.1, 3.5, 1.4, 0.2, 0. ],
[5.1, 3.3, 1.7, 0.5, 0. ],
[4.8, 3.4, 1.9, 0.2, 0. ],
[5.4, 3.9, 1.7, 0.4, 0. ],
[5.2, 3.5, 1.5, 0.2, 0. ],
[4.7, 3.2, 1.6, 0.2, 0. ],
[5.5, 3.5, 1.3, 0.2, 0. ],
[4.6, 3.2, 1.4, 0.2, 0. ],
[4.7, 3.2, 1.6, 0.2, 0. ],
[5. , 3.4, 1.5, 0.2, 0. ],
[4.6, 3.4, 1.4, 0.3, 0. ],
[5.4, 3.4, 1.5, 0.4, 0. ],
[5.5, 4.2, 1.4, 0.2, 0. ],
[5. , 3.5, 1.3, 0.3, 0. ],
[6.5, 2.8, 4.6, 1.5, 1. ],
[4.6, 3.6, 1. , 0.2, 0. ],
[5. , 3.5, 1.3, 0.3, 0. ],
[6.4, 3.2, 4.5, 1.5, 1. ],
[5.3, 3.7, 1.5, 0.2, 0. ],
[6. , 2.2, 4. , 1. , 1. ],
[4.8, 3.1, 1.6, 0.2, 0. ],
[6. , 2.2, 4. , 1. , 1. ],
[5.7, 2.8, 4.5, 1.3, 1. ],
[4.9, 3.1, 1.5, 0.1, 0. ],
[4.7, 3.2, 1.6, 0.2, 0. ],
[5. , 3.3, 1.4, 0.2, 0. ],
[4.3, 3. , 1.1, 0.1, 0. ],
[4.9, 3. , 1.4, 0.2, 0. ],
[5.4, 3.4, 1.7, 0.2, 0. ],
[5.5, 4.2, 1.4, 0.2, 0. ],
[6.5, 2.8, 4.6, 1.5, 1. ],
[4.8, 3.1, 1.6, 0.2, 0. ],
[5.9, 3.2, 4.8, 1.8, 1. ],
[7. , 3.2, 4.7, 1.4, 1. ],
[6.7, 3.1, 4.4, 1.4, 1. ],
[6.8, 2.8, 4.8, 1.4, 1. ],
[4.8, 3.4, 1.9, 0.2, 0. ],
[6.2, 2.2, 4.5, 1.5, 1. ],
[5.7, 4.4, 1.5, 0.4, 0. ],
[5.6, 2.9, 3.6, 1.3, 1. ],
[5.8, 2.7, 3.9, 1.2, 1. ],
[4.4, 2.9, 1.4, 0.2, 0. ],
[6.6, 3. , 4.4, 1.4, 1. ],
[5.1, 3.7, 1.5, 0.4, 0. ],
[4.9, 3. , 1.4, 0.2, 0. ],
[4.6, 3.6, 1. , 0.2, 0. ],
[5. , 3.2, 1.2, 0.2, 0. ],
[4.4, 2.9, 1.4, 0.2, 0. ],
[5.1, 3.8, 1.5, 0.3, 0. ],
[5.1, 3.4, 1.5, 0.2, 0. ],
[5.8, 2.7, 3.9, 1.2, 1. ],
[5. , 2. , 3.5, 1. , 1. ],
[7. , 3.2, 4.7, 1.4, 1. ],
[4.7, 3.2, 1.6, 0.2, 0. ],
[5.8, 2.7, 3.9, 1.2, 1. ],
[4.6, 3.2, 1.4, 0.2, 0. ],
[5.6, 2.9, 3.6, 1.3, 1. ],
[4.7, 3.2, 1.6, 0.2, 0. ],
[5.8, 2.6, 4. , 1.2, 1. ],
[6.2, 2.2, 4.5, 1.5, 1. ],
[5.7, 2.6, 3.5, 1. , 1. ],
[5.1, 3.4, 1.5, 0.2, 0. ],
[6.8, 2.8, 4.8, 1.4, 1. ],
[6.1, 2.9, 4.7, 1.4, 1. ],
[5.9, 3. , 4.2, 1.5, 1. ],
[5.5, 2.3, 4. , 1.3, 1. ],
[5. , 2.3, 3.3, 1. , 1. ],
[6.6, 2.9, 4.6, 1.3, 1. ],
[6.3, 3.3, 4.7, 1.6, 1. ],
[5.5, 2.5, 4. , 1.3, 1. ],
[5.6, 2.9, 3.6, 1.3, 1. ],
[5.4, 3.9, 1.7, 0.4, 0. ],
[5.7, 2.9, 4.2, 1.3, 1. ],
[5.7, 2.8, 4.5, 1.3, 1. ],
[6.3, 2.9, 5.6, 1.8, 2. ],
[5.7, 2.9, 4.2, 1.3, 1. ],
[6.3, 3.3, 4.7, 1.6, 1. ],
[6. , 2.2, 5. , 1.5, 2. ],
[5.5, 2.4, 3.8, 1.1, 1. ],
[6.5, 3. , 5.5, 1.8, 2. ],
[6.2, 2.9, 4.3, 1.3, 1. ],
[5.2, 3.5, 1.5, 0.2, 0. ],
[7.3, 2.9, 6.3, 1.8, 2. ],
[4.6, 3.6, 1. , 0.2, 0. ],
[6.3, 3.3, 6. , 2.5, 2. ],
[6.7, 3.3, 5.7, 2.1, 2. ],
[5.6, 3. , 4.5, 1.5, 1. ],
[6.2, 2.9, 4.3, 1.3, 1. ],
[6.3, 2.5, 4.9, 1.5, 1. ],
[4.8, 3. , 1.4, 0.3, 0. ],
[6. , 3.4, 4.5, 1.6, 1. ],
[5. , 3.4, 1.5, 0.2, 0. ],
[4.8, 3. , 1.4, 0.1, 0. ],
[5.1, 3.5, 1.4, 0.2, 0. ],
[5.7, 3. , 4.2, 1.2, 1. ],
[5.2, 2.7, 3.9, 1.4, 1. ],
[6.3, 3.3, 4.7, 1.6, 1. ],
[4.6, 3.1, 1.5, 0.2, 0. ],
[6.9, 3.1, 4.9, 1.5, 1. ],
[6.7, 3.3, 5.7, 2.1, 2. ],
[5.8, 2.7, 5.1, 1.9, 2. ],
[7.9, 3.8, 6.4, 2. , 2. ],
[6.2, 2.8, 4.8, 1.8, 2. ],
[6.5, 3. , 5.8, 2.2, 2. ],
[6.3, 3.3, 6. , 2.5, 2. ],
[5.7, 3. , 4.2, 1.2, 1. ],
[5.4, 3.4, 1.7, 0.2, 0. ]])
X_train,y_train = np.hsplit(tatol_data,[-1])
X_train
array([[5.1, 3.5, 1.4, 0.2],
[5.1, 3.5, 1.4, 0.2],
[4.9, 3. , 1.4, 0.2],
[4.9, 3. , 1.4, 0.2],
[5.1, 3.5, 1.4, 0.2],
[4.6, 3.1, 1.5, 0.2],
[4.6, 3.1, 1.5, 0.2],
[5. , 3.4, 1.5, 0.2],
[4.6, 3.4, 1.4, 0.3],
[5.1, 3.5, 1.4, 0.2],
[5.4, 3.7, 1.5, 0.2],
[5.4, 3.9, 1.7, 0.4],
[5. , 3.4, 1.5, 0.2],
[5.4, 3.9, 1.7, 0.4],
[4.8, 3. , 1.4, 0.1],
[4.8, 3.4, 1.6, 0.2],
[5.7, 4.4, 1.5, 0.4],
[4.8, 3. , 1.4, 0.1],
[5.4, 3.9, 1.7, 0.4],
[5.1, 3.5, 1.4, 0.2],
[5.4, 3.7, 1.5, 0.2],
[4.6, 3.4, 1.4, 0.3],
[5.4, 3.4, 1.7, 0.2],
[5.7, 4.4, 1.5, 0.4],
[4.6, 3.4, 1.4, 0.3],
[4.4, 2.9, 1.4, 0.2],
[5.1, 3.3, 1.7, 0.5],
[4.8, 3.4, 1.6, 0.2],
[4.6, 3.4, 1.4, 0.3],
[5.1, 3.3, 1.7, 0.5],
[5.2, 3.5, 1.5, 0.2],
[5. , 3. , 1.6, 0.2],
[4.8, 3.1, 1.6, 0.2],
[5.1, 3.8, 1.5, 0.3],
[4.8, 3. , 1.4, 0.1],
[5. , 3.6, 1.4, 0.2],
[4.6, 3.6, 1. , 0.2],
[4.8, 3. , 1.4, 0.1],
[5.1, 3.5, 1.4, 0.2],
[5.7, 4.4, 1.5, 0.4],
[5. , 3.4, 1.5, 0.2],
[4.4, 2.9, 1.4, 0.2],
[5.1, 3.8, 1.5, 0.3],
[5.1, 3.5, 1.4, 0.2],
[5.1, 3.3, 1.7, 0.5],
[4.8, 3.4, 1.9, 0.2],
[5.4, 3.9, 1.7, 0.4],
[5.2, 3.5, 1.5, 0.2],
[4.7, 3.2, 1.6, 0.2],
[5.5, 3.5, 1.3, 0.2],
[4.6, 3.2, 1.4, 0.2],
[4.7, 3.2, 1.6, 0.2],
[5. , 3.4, 1.5, 0.2],
[4.6, 3.4, 1.4, 0.3],
[5.4, 3.4, 1.5, 0.4],
[5.5, 4.2, 1.4, 0.2],
[5. , 3.5, 1.3, 0.3],
[6.5, 2.8, 4.6, 1.5],
[4.6, 3.6, 1. , 0.2],
[5. , 3.5, 1.3, 0.3],
[6.4, 3.2, 4.5, 1.5],
[5.3, 3.7, 1.5, 0.2],
[6. , 2.2, 4. , 1. ],
[4.8, 3.1, 1.6, 0.2],
[6. , 2.2, 4. , 1. ],
[5.7, 2.8, 4.5, 1.3],
[4.9, 3.1, 1.5, 0.1],
[4.7, 3.2, 1.6, 0.2],
[5. , 3.3, 1.4, 0.2],
[4.3, 3. , 1.1, 0.1],
[4.9, 3. , 1.4, 0.2],
[5.4, 3.4, 1.7, 0.2],
[5.5, 4.2, 1.4, 0.2],
[6.5, 2.8, 4.6, 1.5],
[4.8, 3.1, 1.6, 0.2],
[5.9, 3.2, 4.8, 1.8],
[7. , 3.2, 4.7, 1.4],
[6.7, 3.1, 4.4, 1.4],
[6.8, 2.8, 4.8, 1.4],
[4.8, 3.4, 1.9, 0.2],
[6.2, 2.2, 4.5, 1.5],
[5.7, 4.4, 1.5, 0.4],
[5.6, 2.9, 3.6, 1.3],
[5.8, 2.7, 3.9, 1.2],
[4.4, 2.9, 1.4, 0.2],
[6.6, 3. , 4.4, 1.4],
[5.1, 3.7, 1.5, 0.4],
[4.9, 3. , 1.4, 0.2],
[4.6, 3.6, 1. , 0.2],
[5. , 3.2, 1.2, 0.2],
[4.4, 2.9, 1.4, 0.2],
[5.1, 3.8, 1.5, 0.3],
[5.1, 3.4, 1.5, 0.2],
[5.8, 2.7, 3.9, 1.2],
[5. , 2. , 3.5, 1. ],
[7. , 3.2, 4.7, 1.4],
[4.7, 3.2, 1.6, 0.2],
[5.8, 2.7, 3.9, 1.2],
[4.6, 3.2, 1.4, 0.2],
[5.6, 2.9, 3.6, 1.3],
[4.7, 3.2, 1.6, 0.2],
[5.8, 2.6, 4. , 1.2],
[6.2, 2.2, 4.5, 1.5],
[5.7, 2.6, 3.5, 1. ],
[5.1, 3.4, 1.5, 0.2],
[6.8, 2.8, 4.8, 1.4],
[6.1, 2.9, 4.7, 1.4],
[5.9, 3. , 4.2, 1.5],
[5.5, 2.3, 4. , 1.3],
[5. , 2.3, 3.3, 1. ],
[6.6, 2.9, 4.6, 1.3],
[6.3, 3.3, 4.7, 1.6],
[5.5, 2.5, 4. , 1.3],
[5.6, 2.9, 3.6, 1.3],
[5.4, 3.9, 1.7, 0.4],
[5.7, 2.9, 4.2, 1.3],
[5.7, 2.8, 4.5, 1.3],
[6.3, 2.9, 5.6, 1.8],
[5.7, 2.9, 4.2, 1.3],
[6.3, 3.3, 4.7, 1.6],
[6. , 2.2, 5. , 1.5],
[5.5, 2.4, 3.8, 1.1],
[6.5, 3. , 5.5, 1.8],
[6.2, 2.9, 4.3, 1.3],
[5.2, 3.5, 1.5, 0.2],
[7.3, 2.9, 6.3, 1.8],
[4.6, 3.6, 1. , 0.2],
[6.3, 3.3, 6. , 2.5],
[6.7, 3.3, 5.7, 2.1],
[5.6, 3. , 4.5, 1.5],
[6.2, 2.9, 4.3, 1.3],
[6.3, 2.5, 4.9, 1.5],
[4.8, 3. , 1.4, 0.3],
[6. , 3.4, 4.5, 1.6],
[5. , 3.4, 1.5, 0.2],
[4.8, 3. , 1.4, 0.1],
[5.1, 3.5, 1.4, 0.2],
[5.7, 3. , 4.2, 1.2],
[5.2, 2.7, 3.9, 1.4],
[6.3, 3.3, 4.7, 1.6],
[4.6, 3.1, 1.5, 0.2],
[6.9, 3.1, 4.9, 1.5],
[6.7, 3.3, 5.7, 2.1],
[5.8, 2.7, 5.1, 1.9],
[7.9, 3.8, 6.4, 2. ],
[6.2, 2.8, 4.8, 1.8],
[6.5, 3. , 5.8, 2.2],
[6.3, 3.3, 6. , 2.5],
[5.7, 3. , 4.2, 1.2],
[5.4, 3.4, 1.7, 0.2]])
y_train = np.transpose(y_train)
# 获取随机索引
shuffle_indexs = np.random.permutation(len(X))
shuffle_indexs
array([126, 60, 115, 72, 12, 107, 51, 36, 52, 18, 32, 87, 135,
137, 105, 24, 37, 89, 64, 31, 131, 43, 0, 17, 23, 106,
97, 45, 92, 70, 65, 4, 68, 95, 40, 121, 81, 139, 69,
133, 49, 20, 79, 74, 46, 48, 6, 9, 63, 120, 103, 91,
38, 42, 35, 109, 41, 101, 30, 86, 47, 94, 111, 116, 129,
29, 88, 55, 127, 117, 26, 113, 78, 13, 25, 57, 44, 34,
149, 67, 28, 22, 123, 132, 142, 90, 102, 110, 15, 21, 16,
54, 98, 39, 58, 75, 1, 96, 82, 3, 143, 144, 71, 59,
125, 19, 56, 114, 141, 83, 61, 50, 148, 118, 84, 11, 10,
8, 33, 147, 146, 85, 80, 76, 100, 99, 108, 128, 7, 145,
93, 27, 138, 2, 134, 77, 53, 112, 119, 66, 124, 140, 62,
136, 130, 5, 73, 14, 104, 122])
test_ratio = 0.2
test_size = int(len(X)*test_ratio)
test_size
30
test_indexes = shuffle_indexs[:test_size]
train_indexs = shuffle_indexs[test_size:]
print(test_indexes)
X_train = X[train_indexs]
y_train = y[train_indexs]
X_test = X[test_indexes]
y_test = y[test_indexes]
X_train
[126 60 115 72 12 107 51 36 52 18 32 87 135 137 105 24 37 89
64 31 131 43 0 17 23 106 97 45 92 70]
array([[6.7, 3.1, 4.4, 1.4],
[5. , 3.6, 1.4, 0.2],
[6.2, 2.2, 4.5, 1.5],
[5.7, 3. , 4.2, 1.2],
[5. , 3.5, 1.3, 0.3],
[5.6, 2.8, 4.9, 2. ],
[5.5, 2.4, 3.7, 1. ],
[6.9, 3.1, 5.4, 2.1],
[5.6, 2.5, 3.9, 1.1],
[6.3, 2.8, 5.1, 1.5],
[5. , 3.3, 1.4, 0.2],
[5.4, 3.4, 1.7, 0.2],
[5.7, 2.6, 3.5, 1. ],
[6.4, 2.9, 4.3, 1.3],
[5.1, 3.8, 1.6, 0.2],
[5.3, 3.7, 1.5, 0.2],
[4.6, 3.4, 1.4, 0.3],
[4.9, 3.1, 1.5, 0.1],
[6.1, 2.9, 4.7, 1.4],
[6.9, 3.2, 5.7, 2.3],
[6.3, 2.9, 5.6, 1.8],
[6.1, 3. , 4.6, 1.4],
[4.4, 3. , 1.3, 0.2],
[4.4, 3.2, 1.3, 0.2],
[5. , 3.2, 1.2, 0.2],
[7.2, 3.6, 6.1, 2.5],
[4.5, 2.3, 1.3, 0.3],
[5.8, 2.7, 5.1, 1.9],
[4.8, 3.1, 1.6, 0.2],
[6.7, 3.1, 4.7, 1.5],
[4.6, 3.2, 1.4, 0.2],
[5.6, 2.7, 4.2, 1.3],
[6.4, 2.7, 5.3, 1.9],
[6.5, 3. , 5.5, 1.8],
[7.2, 3. , 5.8, 1.6],
[4.7, 3.2, 1.6, 0.2],
[5.6, 3. , 4.1, 1.3],
[5.7, 2.8, 4.5, 1.3],
[6.1, 3. , 4.9, 1.8],
[7.7, 3.8, 6.7, 2.2],
[5. , 3.4, 1.6, 0.4],
[5.7, 2.5, 5. , 2. ],
[6. , 2.9, 4.5, 1.5],
[4.3, 3. , 1.1, 0.1],
[5. , 3. , 1.6, 0.2],
[4.9, 2.4, 3.3, 1. ],
[5.1, 3.8, 1.9, 0.4],
[4.9, 3.1, 1.5, 0.1],
[5.9, 3. , 5.1, 1.8],
[5.8, 2.7, 4.1, 1. ],
[5.2, 3.4, 1.4, 0.2],
[4.6, 3.6, 1. , 0.2],
[6.3, 2.7, 4.9, 1.8],
[6.4, 2.8, 5.6, 2.2],
[5.8, 2.7, 5.1, 1.9],
[5.5, 2.6, 4.4, 1.2],
[7.1, 3. , 5.9, 2.1],
[6.5, 3.2, 5.1, 2. ],
[5.7, 4.4, 1.5, 0.4],
[5.1, 3.7, 1.5, 0.4],
[5.4, 3.9, 1.3, 0.4],
[6.5, 2.8, 4.6, 1.5],
[5.1, 2.5, 3. , 1.1],
[5.1, 3.4, 1.5, 0.2],
[6.6, 2.9, 4.6, 1.3],
[6.6, 3. , 4.4, 1.4],
[4.9, 3. , 1.4, 0.2],
[5.7, 2.9, 4.2, 1.3],
[5.8, 2.7, 3.9, 1.2],
[4.6, 3.1, 1.5, 0.2],
[6.8, 3.2, 5.9, 2.3],
[6.7, 3.3, 5.7, 2.5],
[6.1, 2.8, 4. , 1.3],
[5.2, 2.7, 3.9, 1.4],
[7.2, 3.2, 6. , 1.8],
[5.1, 3.8, 1.5, 0.3],
[6.3, 3.3, 4.7, 1.6],
[5.8, 2.8, 5.1, 2.4],
[6.9, 3.1, 5.1, 2.3],
[6. , 2.7, 5.1, 1.6],
[5.9, 3. , 4.2, 1.5],
[7. , 3.2, 4.7, 1.4],
[6.2, 3.4, 5.4, 2.3],
[7.7, 2.6, 6.9, 2.3],
[5.4, 3. , 4.5, 1.5],
[4.8, 3.4, 1.6, 0.2],
[5.4, 3.7, 1.5, 0.2],
[4.4, 2.9, 1.4, 0.2],
[5.5, 4.2, 1.4, 0.2],
[6.5, 3. , 5.2, 2. ],
[6.3, 2.5, 5. , 1.9],
[6. , 3.4, 4.5, 1.6],
[5.5, 2.4, 3.8, 1.1],
[6.8, 2.8, 4.8, 1.4],
[6.3, 3.3, 6. , 2.5],
[5.7, 2.8, 4.1, 1.3],
[6.7, 2.5, 5.8, 1.8],
[6.4, 2.8, 5.6, 2.1],
[5. , 3.4, 1.5, 0.2],
[6.7, 3. , 5.2, 2.3],
[5. , 2.3, 3.3, 1. ],
[5.2, 3.5, 1.5, 0.2],
[6. , 3. , 4.8, 1.8],
[4.7, 3.2, 1.3, 0.2],
[6.1, 2.6, 5.6, 1.4],
[6.7, 3. , 5. , 1.7],
[5.5, 2.3, 4. , 1.3],
[6.8, 3. , 5.5, 2.1],
[6. , 2.2, 5. , 1.5],
[5.6, 3. , 4.5, 1.5],
[6.7, 3.3, 5.7, 2.1],
[6.7, 3.1, 5.6, 2.4],
[6. , 2.2, 4. , 1. ],
[6.3, 3.4, 5.6, 2.4],
[7.4, 2.8, 6.1, 1.9],
[5.4, 3.9, 1.7, 0.4],
[6.1, 2.8, 4.7, 1.2],
[5.8, 4. , 1.2, 0.2],
[6.5, 3. , 5.8, 2.2],
[7.7, 2.8, 6.7, 2. ]])
使用算法
from script.kNN_function.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y)
X_train.shape
(120, 4)
X
array([[5.1, 3.5, 1.4, 0.2],
[4.9, 3. , 1.4, 0.2],
[4.7, 3.2, 1.3, 0.2],
[4.6, 3.1, 1.5, 0.2],
[5. , 3.6, 1.4, 0.2],
[5.4, 3.9, 1.7, 0.4],
[4.6, 3.4, 1.4, 0.3],
[5. , 3.4, 1.5, 0.2],
[4.4, 2.9, 1.4, 0.2],
[4.9, 3.1, 1.5, 0.1],
[5.4, 3.7, 1.5, 0.2],
[4.8, 3.4, 1.6, 0.2],
[4.8, 3. , 1.4, 0.1],
[4.3, 3. , 1.1, 0.1],
[5.8, 4. , 1.2, 0.2],
[5.7, 4.4, 1.5, 0.4],
[5.4, 3.9, 1.3, 0.4],
[5.1, 3.5, 1.4, 0.3],
[5.7, 3.8, 1.7, 0.3],
[5.1, 3.8, 1.5, 0.3],
[5.4, 3.4, 1.7, 0.2],
[5.1, 3.7, 1.5, 0.4],
[4.6, 3.6, 1. , 0.2],
[5.1, 3.3, 1.7, 0.5],
[4.8, 3.4, 1.9, 0.2],
[5. , 3. , 1.6, 0.2],
[5. , 3.4, 1.6, 0.4],
[5.2, 3.5, 1.5, 0.2],
[5.2, 3.4, 1.4, 0.2],
[4.7, 3.2, 1.6, 0.2],
[4.8, 3.1, 1.6, 0.2],
[5.4, 3.4, 1.5, 0.4],
[5.2, 4.1, 1.5, 0.1],
[5.5, 4.2, 1.4, 0.2],
[4.9, 3.1, 1.5, 0.1],
[5. , 3.2, 1.2, 0.2],
[5.5, 3.5, 1.3, 0.2],
[4.9, 3.1, 1.5, 0.1],
[4.4, 3. , 1.3, 0.2],
[5.1, 3.4, 1.5, 0.2],
[5. , 3.5, 1.3, 0.3],
[4.5, 2.3, 1.3, 0.3],
[4.4, 3.2, 1.3, 0.2],
[5. , 3.5, 1.6, 0.6],
[5.1, 3.8, 1.9, 0.4],
[4.8, 3. , 1.4, 0.3],
[5.1, 3.8, 1.6, 0.2],
[4.6, 3.2, 1.4, 0.2],
[5.3, 3.7, 1.5, 0.2],
[5. , 3.3, 1.4, 0.2],
[7. , 3.2, 4.7, 1.4],
[6.4, 3.2, 4.5, 1.5],
[6.9, 3.1, 4.9, 1.5],
[5.5, 2.3, 4. , 1.3],
[6.5, 2.8, 4.6, 1.5],
[5.7, 2.8, 4.5, 1.3],
[6.3, 3.3, 4.7, 1.6],
[4.9, 2.4, 3.3, 1. ],
[6.6, 2.9, 4.6, 1.3],
[5.2, 2.7, 3.9, 1.4],
[5. , 2. , 3.5, 1. ],
[5.9, 3. , 4.2, 1.5],
[6. , 2.2, 4. , 1. ],
[6.1, 2.9, 4.7, 1.4],
[5.6, 2.9, 3.6, 1.3],
[6.7, 3.1, 4.4, 1.4],
[5.6, 3. , 4.5, 1.5],
[5.8, 2.7, 4.1, 1. ],
[6.2, 2.2, 4.5, 1.5],
[5.6, 2.5, 3.9, 1.1],
[5.9, 3.2, 4.8, 1.8],
[6.1, 2.8, 4. , 1.3],
[6.3, 2.5, 4.9, 1.5],
[6.1, 2.8, 4.7, 1.2],
[6.4, 2.9, 4.3, 1.3],
[6.6, 3. , 4.4, 1.4],
[6.8, 2.8, 4.8, 1.4],
[6.7, 3. , 5. , 1.7],
[6. , 2.9, 4.5, 1.5],
[5.7, 2.6, 3.5, 1. ],
[5.5, 2.4, 3.8, 1.1],
[5.5, 2.4, 3.7, 1. ],
[5.8, 2.7, 3.9, 1.2],
[6. , 2.7, 5.1, 1.6],
[5.4, 3. , 4.5, 1.5],
[6. , 3.4, 4.5, 1.6],
[6.7, 3.1, 4.7, 1.5],
[6.3, 2.3, 4.4, 1.3],
[5.6, 3. , 4.1, 1.3],
[5.5, 2.5, 4. , 1.3],
[5.5, 2.6, 4.4, 1.2],
[6.1, 3. , 4.6, 1.4],
[5.8, 2.6, 4. , 1.2],
[5. , 2.3, 3.3, 1. ],
[5.6, 2.7, 4.2, 1.3],
[5.7, 3. , 4.2, 1.2],
[5.7, 2.9, 4.2, 1.3],
[6.2, 2.9, 4.3, 1.3],
[5.1, 2.5, 3. , 1.1],
[5.7, 2.8, 4.1, 1.3],
[6.3, 3.3, 6. , 2.5],
[5.8, 2.7, 5.1, 1.9],
[7.1, 3. , 5.9, 2.1],
[6.3, 2.9, 5.6, 1.8],
[6.5, 3. , 5.8, 2.2],
[7.6, 3. , 6.6, 2.1],
[4.9, 2.5, 4.5, 1.7],
[7.3, 2.9, 6.3, 1.8],
[6.7, 2.5, 5.8, 1.8],
[7.2, 3.6, 6.1, 2.5],
[6.5, 3.2, 5.1, 2. ],
[6.4, 2.7, 5.3, 1.9],
[6.8, 3. , 5.5, 2.1],
[5.7, 2.5, 5. , 2. ],
[5.8, 2.8, 5.1, 2.4],
[6.4, 3.2, 5.3, 2.3],
[6.5, 3. , 5.5, 1.8],
[7.7, 3.8, 6.7, 2.2],
[7.7, 2.6, 6.9, 2.3],
[6. , 2.2, 5. , 1.5],
[6.9, 3.2, 5.7, 2.3],
[5.6, 2.8, 4.9, 2. ],
[7.7, 2.8, 6.7, 2. ],
[6.3, 2.7, 4.9, 1.8],
[6.7, 3.3, 5.7, 2.1],
[7.2, 3.2, 6. , 1.8],
[6.2, 2.8, 4.8, 1.8],
[6.1, 3. , 4.9, 1.8],
[6.4, 2.8, 5.6, 2.1],
[7.2, 3. , 5.8, 1.6],
[7.4, 2.8, 6.1, 1.9],
[7.9, 3.8, 6.4, 2. ],
[6.4, 2.8, 5.6, 2.2],
[6.3, 2.8, 5.1, 1.5],
[6.1, 2.6, 5.6, 1.4],
[7.7, 3. , 6.1, 2.3],
[6.3, 3.4, 5.6, 2.4],
[6.4, 3.1, 5.5, 1.8],
[6. , 3. , 4.8, 1.8],
[6.9, 3.1, 5.4, 2.1],
[6.7, 3.1, 5.6, 2.4],
[6.9, 3.1, 5.1, 2.3],
[5.8, 2.7, 5.1, 1.9],
[6.8, 3.2, 5.9, 2.3],
[6.7, 3.3, 5.7, 2.5],
[6.7, 3. , 5.2, 2.3],
[6.3, 2.5, 5. , 1.9],
[6.5, 3. , 5.2, 2. ],
[6.2, 3.4, 5.4, 2.3],
[5.9, 3. , 5.1, 1.8]])
from script.kNN_function.kNN import kNNClassifier
my_knn_clf = kNNClassifier(3)
my_knn_clf.fit(X_train,y_train)
y_predict = my_knn_clf.predict(X_test)
y_predict
array([2, 0, 1, 1, 2, 1, 2, 0, 2, 0, 1, 1, 2, 0, 0, 1, 2, 2, 0, 0, 0, 0,
0, 1, 2, 2, 2, 2, 0, 0])
sum(y_predict ==y_test)/len(y_test)
0.9333333333333333
sklean 中的train_test_split
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y)
