classsklearn.manifold.TSNE(n_components=2, *, perplexity=30.0, early_exaggeration=12.0, learning_rate='warn', n_iter=1000, n_iter_without_progress=300, min_grad_norm=1e-07, metric='euclidean', init='warn', verbose=0, random_state=None, method='barnes_hut', angle=0.5, n_jobs=None,...
# X是一个(1000, 3)的2维数据,color是一个(1000,)的1维数据 X, color = datasets.samples_generator.make_s_curve(n_points, random_state=0) n_neighbors = 10 n_components = 2 fig = plt.figure(figsize=(8, 8)) # 创建了一个figure,标题为"Manifold Learning with 1000 points, 10 neighbors"...
from sklearn.manifold import TSNE import matplotlib.pyplot as plt # 假设features为高维数据,labels为类别标签 tsne = TSNE(n_components=2, perplexity=30, random_state=42) embeddings = tsne.fit_transform(features) plt.figure(figsize=(10, 6)) scatter = plt.scatter(embeddings[:, 0...
perplexity: 影响结果的平衡,通常在5到50之间. n_iter: 迭代次数,增加次数通常可以提高结果质量. 可以用以下类图表示这些配置项之间的关系: TSNE+int n_components+float perplexity+int n_iter+fit_transform(data) 以下是配置的示例代码高亮: AI检测代码解析 {"tsne_config":{"n_components":2,"perplexity":30...
n_components: int, 可视化的维度, 2 init='pca' perplexity: float,30.影响比较大,建议5-50之间,大型数据集需要使用较大的数值。 early_exaggeration: float,12.0, 不是关键参数,若损失函数在初始优化的时候提高了,则需降低该值 n_iter=5000 learning_rate: float,200.0, 通常取值10 - 1000. 如果损失函数困...
(n_components=2).fit_transform(X)# plt.figure(figsize=(8, 8))# sc = plt.scatter(reduced_x[:,0], reduced_x[:,1],c=y)#,cmap='Spectral')#, lw=0, s=40)# plt.axis('off')# plt.savefig('pca-generated.png', dpi=120)# reduced_x = PCA(n_components=4).fit_transform(X)# ...
from sklearn.manifold import TSNE chip94576_loom.obsm['tsne'] = TSNE(n_components=2).fit_transform(chip94576_loom.X.toarray())
(i) + '类' + '---') for j in range(0,len(cluster_menmbers_list[i])): a = cluster_menmbers_list[i][j] print (corpus[a]) #散点图数据准备,TSNE降维数,weight为tf-idf矩阵weight = tfidf.toarray(), tsne = TSNE(n_components=2) decomposition_data = tsne.fit_transform(weight)...
tsne=TSNE(n_components=2, learning_rate=200) 3. 拟合数据 使用创建的tsne对象对数据进行拟合。 # 将numpy数组转换为torch张量 data_tensor=torch.from_numpy(data) # 拟合数据 embedding=tsne.fit_transform(data_tensor) 4. 获取降维结果 embedding是一个包含降维后的结果的numpy数组。 5. 可视化降维结果 可...
Generally, set NumDimensions to 2 or 3. Example: 3 Data Types: single | double NumPCAComponents— PCA dimension reduction 0 (default) | nonnegative integer PCA dimension reduction, specified as a nonnegative integer. Before tsne embeds the high-dimensional data, it first reduces the ...