import numpy as np
from sklearn.manifold import TSNE
X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
tsne = TSNE(n_components=2)
tsne.fit_transform(X)
print(tsne.embedding_)
'''输出 [[ 3.17274952 -186.43092346] [ 43.70787048 -283.6920166 ] [ 100.43157196 -145.89025879] [ 140.96669006 -243.15138245]]'''

# coding='utf-8'
"""# 一个对S曲线数据集上进行各种降维的说明。"""
from time import time
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.ticker import NullFormatter
from sklearn import manifold, datasets
# # Next line to silence pyflakes. This import is needed.
# Axes3D
n_points = 1000
# X是一个(1000, 3)的2维数据，color是一个(1000,)的1维数据
X, color = datasets.samples_generator.make_s_curve(n_points, random_state=0)
n_neighbors = 10
n_components = 2
fig = plt.figure(figsize=(8, 8))
# 创建了一个figure，标题为"Manifold Learning with 1000 points, 10 neighbors"
plt.suptitle("Manifold Learning with %i points, %i neighbors"
% (1000, n_neighbors), fontsize=14)
'''绘制S曲线的3D图像'''
ax = fig.add_subplot(211, projection='3d')
ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral)
ax.view_init(4, -72)  # 初始化视角
'''t-SNE'''
t0 = time()
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.cm.Spectral)
plt.title("t-SNE (%.2g sec)" % (t1 - t0))
ax.xaxis.set_major_formatter(NullFormatter())  # 设置标签显示格式为空
ax.yaxis.set_major_formatter(NullFormatter())
# plt.axis('tight')
plt.show()

# coding='utf-8'
"""t-SNE对手写数字进行可视化"""
from time import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.manifold import TSNE
def get_data():
digits = datasets.load_digits(n_class=6)
data = digits.data
label = digits.target
n_samples, n_features = data.shape
return data, label, n_samples, n_features
def plot_embedding(data, label, title):
x_min, x_max = np.min(data, 0), np.max(data, 0)
data = (data - x_min) / (x_max - x_min)
fig = plt.figure()
ax = plt.subplot(111)
for i in range(data.shape[0]):
plt.text(data[i, 0], data[i, 1], str(label[i]),
color=plt.cm.Set1(label[i] / 10.),
fontdict={
'weight': 'bold', 'size': 9})
plt.xticks([])
plt.yticks([])
plt.title(title)
return fig
def main():
data, label, n_samples, n_features = get_data()
print('Computing t-SNE embedding')
tsne = TSNE(n_components=2, init='pca', random_state=0)
t0 = time()
result = tsne.fit_transform(data)
fig = plot_embedding(result, label,
't-SNE embedding of the digits (time %.2fs)'
% (time() - t0))
plt.show(fig)
if __name__ == '__main__':
main()