Laurens很好地利用上图中的“瑞士卷”数据集很好地说明了PCA和t-SNE方法（实线为t-SNE，虚线为PCA）。你可以看到，由于这个“瑞士卷”数据集（流形）的非线性并保持了大距离，PCA会错误地保留数据的结构。t—SNE算法原理 现在我们知道为什么有时候我们不用pca而用t-SNE，让我们来看看t-SNE是如何工作的，其背后有...
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...
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 ...
# 加载必要的库importmatplotlib.pyplotasplt# 创建一个绘图窗口plt.figure(figsize=(10,10))# 绘制t-SNE图plt.scatter(X_tsne[:,0],X_tsne[:,1])plt.title('t-SNE Visualization')plt.show() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 类图 Data+load_data()TSNE+fit_transform()Matplotlib+plot_...
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) ...
= plt.cm.Set1(label / 13) # 定义颜色的种类 # plt.title('TSNE') plt.scatter(X...
flg5=Plot.scatter(Y[idx_5,0], Y[idx_5,1], 20, color=colors[4],label='5'); # flg=Plot.scatter(Y[:,0], Y[:,1], 20,labels); Plot.legend() Plot.savefig('figure4000.pdf') Plot.show() 标签: python , tsne , 降维 好文要顶 关注我 收藏该文 微信分享 华东博客 粉丝- 71...
我正在尝试使用seaborn散点图从t-SNE输出中绘制聚类图。下面的代码tsne_cluster =TSNE(perplexity=40, n_components=2, init='pca', n_iter=2500, random_state=23)sns.scatterplot(new_values_cluster[:,0], n 浏览14提问于2020-07-10得票数0
Hover functionality available on the tSNE scatter plot to get more information of each image Save, zoom, etc each of the Bokeh canvases Python >= 3.6 Install Python from here:https://www.python.org/downloads/ Open a command prompt (or the Terminal application): ...