# 使用t-SNE进行降维tsne=TSNE(n_components=2,random_state=42)X_embedded=tsne.fit_transform(X)# 可视化t-SNE结果plt.scatter(X_embedded[y==0][:,0],X_embedded[y==0][:,1],color='red',label='Class 0')plt.scatter(X_embedded[y==1][:,0],X_embedded[y==1][:,1],color='blue',la...
labels2 = [f'n{i}' for i in range(1, len(roberta_data2) + 1)] labels3 = [f'n{i}' for i in range(1, len(roberta_data3) + 1)] combined_labels = labels1 + labels2 + labels3 2.3 绘制tSNE图 # 应用t-SNE tsne = TSNE(n_components=2, # 将数据降至2维 random_state=42,...
tsne=TSNE(n_components=2,random_state=42)X_embedded=tsne.fit_transform(X)# 输出降维后的数据print(X_embedded) 1. 2. 3. 4. 5. n_components=2表示我们将数据降维到二维;random_state=42是为了确保每次运行结果的一致性。 5. 绘图并添加边框 接下来,我们会绘制降维后的数据,并添加边框: plt.figure(...
= TSNE(n_components=2, random_state=42) X_tsne = tsne.fit_transform(X) # 可视化 plt.figure(figsize=(8,...加载经典的 iris 数据集,然后使用 TSNE 类将 4 维特征降到 2 维。...使用 OpenTSNE 进行降维和可视化的代码如下: from openTSNE import TSNE from sklearn.datasets import load_digits ...
fromsklearnimportmanifoldfromsklearn.decompositionimportPCA# LLEdata_1 = lle(X, n_neighbors=30)# sklearn LLEdata_2 = manifold.LocallyLinearEmbedding(n_components=2, n_neighbors=30).fit_transform(X)# PCApca_data = PCA(n_components=2).fit_transform(X)# 画图plt.figure(figsize=(8,4)) ...
data_tsne = TSNE(n_components=2, perplexity=k).fit_transform(X) plt.scatter(data_tsne[:, 0], data_tsne[:, 1], c=color, cmap=plt.cm.Spectral) 對於在手寫數字識別上使用TSNE得到的結果,能夠更加明顯地看出TSNE的優點: 可以看出,TSNE解決了資料在降維之後的擁擠問題。
Issue Running pynndescent multiple times with the same random seed would return different results. Description of changes Fix. Cosmetic fixes. Includes Code changes Tests Documentation
import numpy as np from sklearn.manifold import TSNE from sklearn.datasets import load_digits from matplotlib import pyplot as plt # 加载数据集 digits = load_digits() X = digits.data y = digits.target # 应用t-SNE降维 tsne = TSNE(n_components=2, random_state=0) X_tsne = tsne.fit_tr...
load_iris() x, y = iris["data"], iris["target"] tsne = TSNE( n_components=2, perplexity=30, learning_rate=200, n_jobs=4, angle=0.5, initialization="pca", metric="euclidean", early_exaggeration_iter=250, early_exaggeration=12, n_iter=750, neighbors="exact", negative_gradient_...
(n_components=2,random_state=42)reduced_data=tsne.fit_transform(data)# 可视化结果plt.figure(figsize=(10,8))scatter=plt.scatter(reduced_data[:,0],reduced_data[:,1],c=labels,cmap='tab10',alpha=0.5)plt.title('t-SNE visualization of MNIST digits')plt.xlabel('Dimension 1')plt.ylabel('...