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线性回归算法

import numpy as npimport matplotlib.pyplot as pltfrom sklearn.linear_model import LinearRegressionx = np.linspace(0, 10, 50)noise = np.random.uniform(-2,2,size=50)y = 5 * x + 6 + noiseliner = LinearRegression()liner.fit(np.reshape(x,(-1,1)),np.reshape(y,(-1,1)))print(liner)y_pred = liner.predict(np.reshape(x,(-1,1)))plt.figure(figsize=(5,5))plt.scatter(x,y)plt.plot(x,y_pred, color="r")plt.show()print(liner.coef_)print(liner.intercept_)

聚类算法

其中代码聚类代码如下（

from sklearn.datasets.samples_generator import make_blobsX, y = make_blobs(n_samples=300, centers=4,                  random_state=0, cluster_std=0.60)plt.scatter(X[:, 0], X[:, 1], s=50)plt.show()from sklearn.cluster import KMeansest = KMeans(4)  # 4 clustersest.fit(X)y_kmeans = est.predict(X)plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='rainbow');plt.show()

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