plt.figure(figsize=(8, 8)) sns.scatterplot(data=data_pca, hue='class', x='1st_component', y='2nd_component') plt.show()第5步-t-SNE降维与可视化 （1）导入所需的库 from sklearn.manifold import TSNE （2）t-SNE降维 tsne = TSNE(n_components=2)tsne.fit(X_std)（3）可视化t-SNE降维...
sns.scatterplot(x='Component 1', y='Component 2', data=tsne_df) plt.title(f't-SNE Visualization with perplexity={perplexity}') plt.show() 七、处理大规模数据集 t-SNE对大规模数据集的处理能力有限,通常建议在大数据集上使用先进行降采样或其他降维方法(如PCA)进行预处理。 from sklearn.decompositi...
result = tsne.fit_transform(data) plt.scatter(result[:, 0], result[:, 1]) plt.show() 通过以上步骤,即可在Python中实现t-SNE可视化。
plt.plot(x,y)x0=np.pi y0=0# 画出标注点plt.scatter(x0,y0,s=50)plt.annotate('sin(np.pi)=%s'%y0,xy=(np.pi,0),xycoords='data',xytext=(+30,-30),textcoords='offset points',fontsize=16,arrowprops=dict(arrowstyle='->',connectionstyle="arc3,rad=.2"))plt.text(0.5,-0.25,"sin...
plt.scatter(x0, y0, s=50, color='b') plt.plot([x0, x0], [0, y0], 'k--', linewidth=2) plt.annotate(r"$2x+1=%s$" % y0, xy=(x0, y0), xycoords='data', xytext=(+30, -30), textcoords='offset points',fontsize=16, arrowprops=dict(arrowstyle='->', connectionstyle=...
['red', 'green', 'blue'] for i in range(len(colors)): plt.scatter(X_tsne[y == i, 0], X_tsne[y == i, 1], c=colors[i], label=iris.target_names[i]) plt.legend() plt.title('t-SNE Scatter Plot of Iris Dataset') plt.xlabel('t-SNE Component 1') plt.ylabel('t-SNE ...
Z=Z.rename(columns={0:"dim1",1:"dim2"})Z['state']=df['state'].astype('str').astype('category')sns.scatterplot(x="dim1",y="dim2",hue="state",data=Z,edgecolors='none',alpha=0.2,palette='husl')sns.despine()plt.tight_layout()plt.xlabel('Dimension 1')plt.ylabel('Dimension ...
class_distr = []# Plot the different class distributionsfor i, l in enumerate(np.unique(y)):_x1 = x1[y == l]_x2 = x2[y == l]_y = y[y == l]class_distr.append(plt.scatter(_x1, _x2, color=colors[i])) # Add a legendplt.l...
pyplot.scatter(xxx1,xxx2,c=color[i]) pyplot.xlim(numpy.min(Y)-5,numpy.max(Y)+5) pyplot.xlim(numpy.min(Y)-5,numpy.max(Y)+5) pyplot.title('CUSTOM: %ss'%str(round(t,2))) pyplot.subplot(1,2,2) t1=time.time() Y1=manifold.TSNE(2).fit_transform(data.data) ...
[idx]] f, ax = plt.subplots(1, 2, figsize=(14, 6)) ax[0].scatter(pca[idx, 0], pca[idx, 1], c=label_subset) ax[0].set_title('PCA Cluster Plot') ax[1].scatter(tsne[idx, 0], tsne[idx, 1], c=label_subset) ax[1].set_title('TSNE Cluster Plot') plot_tsne_pca(text...