TSNE - 3D图形

Question

把data ncoll和wcoll想象成4000左右的随机数。

我想通过TSNE运行它们，然后创建一个3维图。

如果我绘制这个图，我基本上会得到一个2D图，所以有些地方出了问题，但我不完全确定是什么。

最终，我想在同一个3D图上绘制红色的前半部分和蓝色的后半部分。

print(__doc__)

from time import time

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.ticker import NullFormatter

from sklearn import manifold
from sklearn.utils import check_random_state

c = 1000
 # Open File
ncoll_fn = "C:/Users/xxlassi/Downloads/trajectory_demo/trajectory_270252769939974_run__uid_-1808183947_tag_collision_0.0.txt"
wcoll_fn = "C:/Users/xxlassi/Downloads/trajectory_demo/trajectory_271551342150600_run__uid_-918721219_tag_collision_0.01.txt"

ncoll = []
wcoll = []

with open( ncoll_fn ) as f:
    ncoll = [ np.array([ float(el) for el in line.strip().split(',') ]) for line in f.readlines() ]
    ncoll = np.array( ncoll )

with open( wcoll_fn ) as f:
    wcoll = [ np.array([ float(el) for el in line.strip().split(',') ]) for line in f.readlines() ]
    wcoll = np.array( wcoll )

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

arr = np.concatenate((wcoll, ncoll), axis=0)
mid = int(len(arr)/2)
print (mid)

tsne = manifold.TSNE(n_components=3, init='pca',random_state=0, perplexity= 30, n_iter=5000)
trans_data = tsne.fit_transform(arr)

ax.scatter(arr[:,0][0:mid], arr[:,1][0:mid], c= 'r')
ax.scatter(arr[:,0][mid:], arr[:,1][mid:], c= 'b')

ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
plt.title("t-SNE")
plt.axis('tight')
plt.show()

Answer 1

您需要使用3d散点绘制trans_data，以绘制t-SNE转换后的数据：

trans_data = tsne.fit_transform(arr)

ax.scatter(trans_data[:,0][0:mid], trans_data[:,1][0:mid], trans_data[:,2][0:mid], c= 'r', s = 100, marker='+')
ax.scatter(trans_data[:,0][mid:], trans_data[:,1][mid:], trans_data[:,2][mid:], c= 'b', s = 100, marker='.')

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