问题描述
我试图用matplotlib.mlab.PCA做一个简单的主成分分析,但是由于有了类的属性,我无法得到一个解决问题的干净方法.这是一个示例:
I trying to do a simple principal component analysis with matplotlib.mlab.PCA but with the attributes of the class I can't get a clean solution to my problem. Here's an example:
以2D方式获取一些虚拟数据并启动PCA:
Get some dummy data in 2D and start PCA:
from matplotlib.mlab import PCA
import numpy as np
N = 1000
xTrue = np.linspace(0,1000,N)
yTrue = 3*xTrue
xData = xTrue + np.random.normal(0, 100, N)
yData = yTrue + np.random.normal(0, 100, N)
xData = np.reshape(xData, (N, 1))
yData = np.reshape(yData, (N, 1))
data = np.hstack((xData, yData))
test2PCA = PCA(data)
现在,我只想在原始坐标中将主成分作为矢量获取,并以箭头的形式将其绘制到我的数据上.
Now, I just want to get the principal components as vectors in my original coordinates and plot them as arrows onto my data.
什么是到达目的地的快速而干净的方法?
What is a quick and clean way to get there?
谢谢,泰拉克斯
推荐答案
我认为mlab.PCA
类不适合您要执行的操作.特别地,PCA
类在查找特征向量之前会重新缩放数据:
I don't think the mlab.PCA
class is appropriate for what you want to do. In particular, the PCA
class rescales the data before finding the eigenvectors:
a = self.center(a)
U, s, Vh = np.linalg.svd(a, full_matrices=False)
center
方法除以sigma
:
def center(self, x):
'center the data using the mean and sigma from training set a'
return (x - self.mu)/self.sigma
这将生成特征向量pca.Wt
,如下所示:
This results in eigenvectors, pca.Wt
, like this:
[[-0.70710678 -0.70710678]
[-0.70710678 0.70710678]]
它们是垂直的,但与原始数据的主轴不直接相关.它们是相对于按摩数据的主轴.
They are perpendicular, but not directly relevant to the principal axes of your original data. They are principal axes with respect to massaged data.
也许直接编写想要的代码(不使用mlab.PCA
类)可能会更容易:
Perhaps it might be easier to code what you want directly (without the use of the mlab.PCA
class):
import numpy as np
import matplotlib.pyplot as plt
N = 1000
xTrue = np.linspace(0, 1000, N)
yTrue = 3 * xTrue
xData = xTrue + np.random.normal(0, 100, N)
yData = yTrue + np.random.normal(0, 100, N)
xData = np.reshape(xData, (N, 1))
yData = np.reshape(yData, (N, 1))
data = np.hstack((xData, yData))
mu = data.mean(axis=0)
data = data - mu
# data = (data - mu)/data.std(axis=0) # Uncommenting this reproduces mlab.PCA results
eigenvectors, eigenvalues, V = np.linalg.svd(data.T, full_matrices=False)
projected_data = np.dot(data, eigenvectors)
sigma = projected_data.std(axis=0).mean()
print(eigenvectors)
fig, ax = plt.subplots()
ax.scatter(xData, yData)
for axis in eigenvectors:
start, end = mu, mu + sigma * axis
ax.annotate(
'', xy=end, xycoords='data',
xytext=start, textcoords='data',
arrowprops=dict(facecolor='red', width=2.0))
ax.set_aspect('equal')
plt.show()
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