# 导入所需包
import sklearn
from sklearn.random_projection import GaussianRandomProjection
import numpy as np
from matplotlib import pyplot as plt

# 随机数据及其变换
X_random = np.random.RandomState(0).rand(100, 10000)
gauss_data = GaussianRandomProjection(random_state=0)
X_transformed = gauss_data.fit_transform(X_random)

# 获取转换后数据的大小
print('Shape of transformed data is: ' + str(X_transformed.shape))

# 设置图形大小
plt.figure(figsize=(7.50, 3.50))
plt.subplots_adjust(bottom=0.05, top=0.9, left=0.05, right=0.95)

# 用直方图可视化变换矩阵的元素
plt.hist(gauss_data.components_.flatten())
plt.title('Histogram of the flattened transformation matrix', size ='18')
plt.show()

Shape of transformed data is: (100, 3947)