UMAP(n_neighbors=15, min_dist=0.1) # 拟合数据 umap_model.fit(X) # 将数据降到2维 embedding = umap_model.transform(X) # 现在embedding变量包含了数据在2维空间中的表示,可以用于可视化 # 结合sklearn 示例 import umap from sklearn.datasets import load_digits digits = load_digits() # embedding...
import umap import numpy as np # 假设data是一个高维数据集 data = np.random.rand(100, 10) # 示例数据,100个样本,每个样本10个特征 # UMAP参数设置 umap_model = umap.UMAP(n_neighbors=15, min_dist=0.1, n_components=2, metric='euclidean', random_state=42) # 应用UMAP进行降维 reduced_data...
model = SpectralEmbedding(n_components = 2, n_neighbors = 50)se = model.fit_transform(np.log(X_train + 1))plt.figure(figsize=(20,15))plt.scatter(se[:, 0], se[:, 1], c = y_train.astype(int), cmap = 'tab10', s = 50)plt.title('Laplacian Eigenmap', fontsize = 20)plt....
data_on_host=True# Whether the data is stored on the host (CPU) # UMAP model configuration reducer=UMAP( n_neighbors=10, min_dist=0.01, build_algo="nn_descent", build_kwds={"nnd_n_clusters": num_clusters}, ) # Fit and transform the data embeddings=reducer.fit_transform(X, data_on...
['target']# 初始化UMAP模型umap_model = UMAP(n_neighbors=15, min_dist=0.1, n_components=2)# 进行降维X_umap = umap_model.fit_transform(X)# 可视化结果plt.scatter(X_umap[:, 0], X_umap[:, 1], c=y, cmap='Spectral', s=5)plt.set_title('UMAP projection of the MNIST dataset')plt...
importumap# 初始化UMAPumap_model=umap.UMAP(n_neighbors=15,n_components=2,metric='euclidean')# 拟合并转换数据X_umap=umap_model.fit_transform(X)# 输出降维后的数据形状print(f"降维后的数据形状:{X_umap.shape}") 1. 2. 3. 4. 5.
model=umap.UMAP(n_components=2) reduced_x=model.fit_transform(reduced_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('umap-generated.png', dpi=120)...
I'd like to be able to save the reducer, so that I can load it up again elsewhere, and then access the sampleIds, from the fit model - this is the bit that I can't figure out how to do. import anndata import scanpy as sc import umap from joblib import dump, load import umap....
The 33rd International Conference on User Modeling, Adaptation, and Personalization (ACM UMAP 2025) is the premier international conference bringing together research in AI and HCI to support effective human-AI collaboration via interactive systems that can model, adapt and personalize to their users....
From these assumptions it is possible to model the manifold with a fuzzy topological structure. The embedding is found by searching for a low dimensional projection of the data that has the closest possible equivalent fuzzy topological structure....