tsne = TSNE(n_components=2, random_state=42) tsne_results = tsne.fit_transform(good_features_reshaped) # Perform Gaussian Mixture Modeling on the t-SNE results n_components = 5 # You can adjust this based on your data gmm = GaussianMixture(n_components=n_components, random_state=42) gm...
# 使用TSNE函数进行降维tsne=TSNE(n_components=2,perplexity=30,n_iter=1000,init='random',random_state=0)X_tsne=tsne.fit_transform(X) 1. 2. 3. 降维后的数据X_tsne是一个二维数组,每一行代表一个样本的坐标。 我们可以使用matplotlib库将降维后的数据可视化。 # 可视化降维结果plt.scatter(X_tsne[:,...
# 假设 X 是一个高维数据集,这里我们用随机数据来模拟 np.random.seed(42) # 保证示例的可复现性 X = np.random.rand(100, 20) # 100个样本,每个样本20维 # 使用TSNE进行降维 tsne = TSNE(n_components=2, perplexity=30, learning_rate=200, n_iter=1000, random_state=42, init='pca', verbose=...
2. 使用t-SNE进行降维 fromsklearn.manifoldimportTSNE tsne=TSNE(n_components=2,random_state=42)X_tsne=tsne.fit_transform(X) 1. 2. 3. 4. 3. 可视化数据 importmatplotlib.pyplotasplt plt.figure(figsize=(10,8))foriinrange(10):plt.scatter(X_tsne[y==i,0],X_tsne[y==i,1],label=str(...
from sklearn.manifold import TSNE from sklearn.datasets import load_digits # Random state. RS = 20150101 import matplotlib.pyplot as plt import matplotlib.patheffects as PathEffects import matplotlib # We import seaborn to make nice plots. ...
digits_proj = TSNE(random_state=RS).fit_transform(X)# digits_proj:(1797L, 2L),ndarray 类型 可视化 defscatter(x, colors):# We choose a color palette with seaborn.palette = np.array(sns.color_palette("hls",10))# We create a scatter plot.f = plt.figure(figsize=(8,8)) ...
tsne = manifold.TSNE(n_components=2, init='pca', random_state=0) t0 = time() X_tsne = tsne.fit_transform(X) plot_embedding(X_tsne, 't‐SNE embedding of the digits (time %.2fs)' % (time() ‐ t0)) plt.show() 9应用方面 ...
y = digits.target# 使用t-SNE进行降维tsne = TSNE(n_components=2, random_state=0) X_2d = tsne.fit_transform(X)# 可视化结果plt.figure(figsize=(6,5)) colors ='r','g','b','c','m','y','k','w','orange','purple'fori, cinzip(range(10), colors): ...
# 使用 t-SNE 进行降维tsne = TSNE(n_components=2, random_state=42)X_tsne = tsne.fit_transform(X) # 绘制降维后的数据plt.figure(figsize=(8,6))colors = ['navy','turquoise','darkorange']lw =2 forcolor, i, target_nameinzip(colors, [0,1,2], iris.target_names):plt.scatter(X_tsne...
()data = scaler.fit_transform(fea_data)X_pca = PCA(n_components=2).fit_transform(data) plt.scatter(X_pca:, 0, X_pca:, 1)plt.show()tsne = TSNE(n_components=2, perplexity=20, random_state=42)new_data = tsne.fit_transform(np.array(fea_list))plt.scatter(new_data:, 0, new_...