tsne = manifold.TSNE(n_components=n_components, init='pca', random_state=0) Y = tsne.fit_transform(X) # 转换后的输出 t1 = time() print("t-SNE: %.2g sec" % (t1 - t0)) # 算法用时 ax = fig.add_subplot(2, 1, 2) plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt....
def TSNE(data): start = time.time() tsne = manifold.TSNE(n_components=2, init='pca', random_state=501) datat_tsne = tsne.fit_transform(data) # 归一化 x_min, x_max = datat_tsne.min(0), datat_tsne.max(0) X_norm = (datat_tsne - x_min) / (x_max - x_min) # 归一化...
tsne = TSNE(n_components=2, init='pca', random_state=0) X_tsne = tsne.fit_transform(X) plt.scatter(X_tsne[:,0],X_tsne[:,1],c=y) plt.colorbar() def plot_embedding(data, label, title): x_min, x_max = np.min(data, 0), np.max(data, 0) data = (data - x_min) / ...
tsne = TSNE(n_components=2, init='pca', random_state=0) X_tsne = tsne.fit_transform(X) plt.scatter(X_tsne[:,0],X_tsne[:,1],c=y) plt.colorbar() def plot_embedding(data, label, title): x_min, x_max = np.min(data, 0), np.max(data, 0) data = (data - x_min) / ...
defmain():data,label,n_samples,n_features=get_data()# 调用函数,获取数据集信息print(Starting compute t-SNEEmbedding...)ts=TSNE(n_components=2,init=pca,random_state=0)# t-SNE降维 reslut=ts.fit_transform(data)# 调用函数,绘制图像
Y_data = Y_data[0:21000,:] Y_data = Y_data.reshape(21000,).astype(int) """ n_components:嵌入式空间的维度(默认为2) init:初始化方式 random_state:随机种子,None默认 """ tsne = TSNE(n_components=2,init='pca',random_state=500) ...
perplexity =30tsne = TSNE(perplexity=perplexity, n_components=2, init='pca', n_iter=3500, random_state=32) embeddings_en_2d_gif = np.array(tsne.fit_transform(embed_cluster.reshape(n * m, k))).reshape(n, m,2) tsne_plot_similar_words_png('Vizualizing similar words from Google News ...
= TSNE(n_components=2, random_state=42) X_tsne = tsne.fit_transform(X) # 可视化 plt.figure(figsize=(8,...加载经典的 iris 数据集,然后使用 TSNE 类将 4 维特征降到 2 维。...使用 OpenTSNE 进行降维和可视化的代码如下: from openTSNE import TSNE from sklearn.datasets import load_digits ...
init : string or numpy array, optional (default: "random") Initialization of embedding. Possible options are 'random', 'pca', and a numpy array of shape (n_samples, n_components). PCA initialization cannot be used with precomputed distances and ...
n_components : int, optional (default: 2) Dimension of the embedded space. perplexity : float, optional (default: 30) The perplexity is related to the number of nearest neighbors that is used in other manifold learning algorithms. Larger datasets ...