python 机器学习 ML LinearRegression KNN KMeans

机器学习模型：

线性回归LinearRegression：给定xy样本集，y与x的关系属于线性y=ax+b，测试集给定x，看输出是否属于y=ax+b

KNN：监督学习，给定样本集和target训练，测试集看输出target是否正确

Kmean：对样本进行聚类，无label

复制代码

from sklearn.cluster import KMeans
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import matplotlib

matplotlib.use('TkAgg')
from sklearn.datasets import make_blobs
import joblib

x = [[i] for i in range(5)]
y = [i * 2 + 7 for i in range(5)]

regression = LinearRegression()
regression.fit(x, y)

joblib.dump(regression, 'regression.pkl')

regression1 = joblib.load('regression.pkl')

print(regression1.predict([[6], [8]]))

print(f'coef = {regression.coef_},inter = {regression.intercept_}')

x1 = [[-10, -11], [-1, -11], [-11, -22], [22, 32], [33, 31], [44, 100]]
y1 = [0, 0, 0, 1, 1, 1]

knn = KNeighborsClassifier()
knn.fit(x1, y1)
print(knn.predict([[-2, -111], [2222, 333]]))

x3, y3 = make_blobs(n_samples=1000, n_features=2, centers=[[0, 0], [1, 1], [2, 2]], cluster_std=[0.4, 0.3, 0.3])
kmeans = KMeans(n_clusters=3)
kmeans.fit(x3)
print(x3)
print(kmeans.labels_)

plt.figure()
plt.scatter(x3[:, 0], x3[:, 1], c=kmeans.labels_, cmap='viridis')
plt.show()

F:\Users\anaconda3\python.exe E:\PycharmProjects\pythonProject\2.py
$19. 23.$
coef = $2.$ ,inter = 7.0
$0 1$ $\[1.18237797 1.10239862$ $2.52009639 2.24120624$ $1.51328496 0.74354331$
...
$0.40767352 0.11376598$ $1.57515938 1.95613922$ $0.71643209 0.77786856\]$
[0 1 0 0 2 0 1 1 1 0 1 0 0 1 2 2 2 0 1 2 2 0 1 1 0 0 0 2 0 1 1 1 1 2 1 0 0

1 0 2 0 2 0 2 1 2 1 2 1 1 1 1 1 1 2 2 2 0 2 1 2 0 0 1 2 2 1 0 0 2 0 2 0 2

1 0 1 2 2 2 2 0 0 2 0 0 0 1 1 2 1 1 1 0 1 2 0 2 0 1 1 1 1 0 2 0 1 1 1 2 2

1 0 0 1 2 2 2 2 2 2 1 0 1 2 0 1 2 2 2 2 0 2 1 0 2 1 2 1 1 2 0 1 2 1 1 0 2

2 1 1 2 2 2 2 0 1 0 0 1 0 0 2 2 1 0 2 1 1 0 2 0 0 1 1 2 0 2 2 1 2 1 2 2 0

2 0 0 1 0 1 1 1 0 0 0 1 1 0 0 0 2 2 2 0 1 0 2 0 1 0 2 2 2 2 2 1 0 2 0 0 1

2 0 1 0 1 0 2 1 0 2 1 2 0 0 2 0 2 0 0 1 0 1 1 0 1 2 2 1 1 1 2 1 0 0 0 2 2

2 0 2 0 0 1 0 2 1 2 1 2 1 0 2 0 2 0 0 1 0 0 1 0 0 0 0 2 1 1 2 2 2 0 2 0 1

2 0 2 1 0 0 2 2 2 1 2 0 0 0 1 1 1 0 1 0 0 0 1 1 2 2 1 0 0 0 2 0 0 2 1 2 0

1 1 2 1 2 1 0 2 1 0 2 0 1 0 2 1 0 1 0 0 1 2 2 2 2 2 1 0 2 1 2 0 0 2 1 1 2

1 0 0 1 1 0 1 1 0 2 1 2 1 2 0 0 0 0 0 2 2 2 1 2 0 2 2 1 2 0 0 1 1 2 2 1 1

1 2 1 2 1 1 2 0 0 1 1 1 2 1 2 0 0 0 0 1 2 1 1 0 0 2 0 2 0 1 2 2 1 1 0 2 0

0 1 2 1 2 1 1 0 0 0 1 2 0 0 2 2 0 2 2 0 1 0 0 2 2 0 0 1 0 1 0 2 0 2 2 0 2

0 1 1 1 2 0 2 1 1 1 2 0 2 0 1 2 2 2 0 2 1 1 1 0 1 0 1 1 2 2 2 1 0 1 2 2 0

1 2 2 1 1 1 0 0 0 2 2 0 1 0 2 2 0 2 0 1 1 0 1 2 2 1 0 1 0 0 0 2 2 1 1 2 2

2 1 0 1 1 0 1 0 1 2 2 0 1 1 0 0 2 0 0 1 2 2 1 0 0 2 2 0 1 2 2 0 0 1 1 0 0

2 2 2 0 1 0 1 2 0 0 1 0 1 0 2 1 1 0 0 1 2 0 0 2 1 2 0 2 2 0 0 0 2 0 2 2 1

1 0 2 2 1 2 2 2 1 0 1 0 1 1 2 0 0 2 1 2 2 0 0 0 0 0 2 0 0 2 2 0 1 1 1 0 2

2 1 2 1 1 2 0 1 1 1 2 0 1 2 0 2 0 2 0 1 0 0 1 1 1 1 1 0 0 1 1 2 2 1 2 1 2

2 1 1 2 0 1 2 2 2 2 2 1 1 0 2 1 0 2 0 1 0 2 2 0 2 2 2 0 1 0 0 0 2 0 0 1 1

2 2 1 1 2 2 0 1 0 1 1 1 1 0 2 2 2 2 0 0 1 1 1 1 0 1 1 2 2 0 1 0 0 1 2 0 1

2 0 1 1 1 1 1 2 1 2 1 2 2 1 0 2 2 0 1 2 1 1 2 2 2 0 1 0 1 0 1 2 1 0 0 0 0

0 0 1 1 0 1 0 0 0 0 1 1 1 0 0 0 2 0 2 1 1 1 2 2 2 1 1 0 1 0 0 0 2 0 0 1 1

2 1 0 2 2 1 0 2 2 2 0 0 2 0 0 1 0 2 1 0 2 0 1 1 0 1 0 2 0 0 1 1 2 0 1 2 0

1 1 0 0 1 0 1 2 2 0 0 0 2 1 1 0 2 1 2 2 2 2 0 0 2 0 2 1 1 1 2 0 0 2 1 2 1

2 0 0 0 1 0 2 1 1 1 1 2 0 0 0 1 2 2 1 1 0 0 1 2 0 2 1 1 0 1 2 1 2 1 1 1 2

1 1 2 0 2 2 0 0 0 1 0 2 1 1 0 0 0 2 0 0 2 2 0 0 2 0 0 1 1 0 0 0 0 1 2 2 1

0]

Process finished with exit code 0