Using KNN algorithm to classify

1 ImportMatplotlib.pyplot as Plt2 ImportNumPy as NP3 4  fromSklearn.datasets.samples_generatorImportMake_blobs5 #Generate Data6Centers = [[-2, 2], [2, 2], [0, 4]]7X, y = Make_blobs (n_samples=600, Centers=centers, random_state=0, cluster_std=0.60)8 #Draw the data9Plt.figure (figsize=), dpi=144)Tenc =Np.array (centers) OnePlt.scatter (x[:, 0], x[:, 1], c=y, s=100, cmap='Cool');#Draw a sample APlt.scatter (c[:, 0], c[:, 1], s=100, marker='^', c='Orange');#Draw the center point -  -  fromSklearn.neighborsImportKneighborsclassifier the  fromNumPy as NP - #Model Training -K = 5 -CLF = Kneighborsclassifier (n_neighbors=k) + Clf.fit (X, y); -  + #Make predictions A #x_sample = [[0,2],[1,1],[-1,3]] atX_sample = Np.array ([[0,2],[1,1],[-1,3]],dtype=int) -  -Y_sample =clf.predict (x_sample); -Neighbors = Clf.kneighbors (X_sample, return_distance=False); -  -x_sample_disp_x = Np.array (x_sample[:,0],dtype=int) inx_sample_disp_y = Np.array (x_sample[:,1],dtype=int) - #Draw a toPlt.figure (figsize=), dpi=144) +Plt.scatter (x[:, 0], x[:, 1], c=y, s=100, cmap='Cool');#Sample -Plt.scatter (c[:, 0], c[:, 1], s=100, marker='^', c='k');#Center Point thePlt.scatter (x_sample_disp_x, x_sample_disp_y, marker="x",  *C=y_sample, s=100, cmap='Cool')#points to be predicted $ Panax Notoginseng  -  the  forIinchNeighbors[0]: +Plt.plot ([x[i][0], x_sample[0][0]], [x[i][1], x_sample[0][1]], A              'k--', linewidth=0.8);#The connection between the predicted point and the nearest 5 samples the  forIinchNeighbors[1]: +Plt.plot ([x[i][0], x_sample[1][0]], [x[i][1], x_sample[1][1]], -              'k--', linewidth=0.8); $  forIinchNeighbors[2]: $Plt.plot ([x[i][0], x_sample[2][0]], [x[i][1], x_sample[2][1]], -              'k--', linewidth=0.8);

Using KNN algorithm to classify