(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 = umap_model.fit_transform(data) # reduced_data现在是一个100x2的数组,表示降维后...
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[:,
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 = umap.UMAP().fit_transform(digits.data) embedding = umap....
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("LAP2", fontsize ...
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=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...
# 创建一个新的UMAP实例,调整参数umap_model=umap.UMAP(n_neighbors=25,min_dist=0.01,n_components=2,random_state=42)X_umap=umap_model.fit_transform(X)# 可视化调整后的数据plt.scatter(X_umap[:,0],X_umap[:,1],c=y,cmap='Spectral',s=5)plt.title('UMAP Projection with Adjusted Parameters'...
# 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_host=data_on_host) ...
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)...
#angular.rp.forest, verbose = True ) dat=np.loadtxt("backup/dims.data.txt",delimiter=" ", skiprows=1, usecols=[x for x in range(1,11)]) print(dat.shape) #(2638, 10) result=model.fit_transform(dat) print(result.shape) #(2638, 2) # output np.savetxt("backup/umap_output.py....