--- a
+++ b/visualization/tSNE.py
@@ -0,0 +1,26 @@
+# use t-SNE to show the feature distribution
+
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import manifold, datasets
+from einops import reduce
+import scipy.io
+import torch
+
+def plt_tsne(data, label, per):
+    data = data.cpu().detach().numpy()
+    data = reduce(data, 'b n e -> b e', reduction='mean')
+    label = label.cpu().detach().numpy()
+
+    tsne = manifold.TSNE(n_components=2, perplexity=per, init='pca', random_state=166)
+    X_tsne = tsne.fit_transform(data)
+
+    x_min, x_max = X_tsne.min(0), X_tsne.max(0)
+    X_norm = (X_tsne - x_min) / (x_max - x_min)
+    plt.figure()
+    for i in range(X_norm.shape[0]):
+        plt.scatter(X_norm[i, 0], X_norm[i, 1], color=plt.Set1(label[i]))
+        plt.xticks([])
+        plt.yticks([])
+    # plt.show()
+    plt.savefig('./test.png', dpi=600)