importnumpyasnpimportumap# 假设X是高维数据,例如使用随机数据生成X=np.random.randn(100,10)# 100个样本,每个样本10维# 创建UMAP实例umap_model=umap.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...
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....
y = mnist['data'], mnist['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[:,
I am having an issue similar to that referenced below, but I am using Seurat version 4.3.0. I have tried to map the same reference on two separate Seurat query objects, on which I have previously run RunUMAP(return.model= TRUE), but I ge...
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....
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)...
ACM UMAP is the premier international conference for researchers and practitioners working on systems that adapt to individual users or groups of users, and that collect, represent, and model user information. ACM UMAP is sponsored by ACM SIGCHI and SIGWEB. User Modeling Inc., as the core Steer...
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.xlabel('LAP1',fontsize=20)plt.ylabel...
Fixing a bug where the user-defined optimizer was not actually passed toUMAPModel. Making changes to properly add continued training to the._history, in line with other keras model behavior. Adding theParametricUMAPclass to the UMAP API Guide page of the documentation, since it now has enough...