python - Sklearn，高斯过程 : XA and XB must have the same number of columns

Question

我对 python 很陌生，对做高斯回归很感兴趣。我在 py3.6 和 SKlearn 0.19 下。

我有简单的代码，但我得到了一个关于预测调用的 cdist 中向量维度的错误。我知道我的输入有问题。但我不明白为什么...

我找了高斯过程回归器的例子，但它似乎不是最常用的工具。

预先感谢您的帮助。

干杯。

这是我的代码示例:

import pandas as pd
import numpy as np
import numpy as np
from sklearn.gaussian_process import GaussianProcessRegressor as gpr
from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C

....

#X_train are the training samples
X_train= np.column_stack((xc,yc,zc))
print('X_train')
print(X_train.shape)
print(X_train)

这是 X_train 的输出:

  X_train  (4576, 3) 
      [[ 0.71958336 -1.12719598  0.47889958]
       [ 0.71958336 -1.12719598  0.47889958] 
      [ 0.71958336 -1.12719598  0.34285071]
       ...   
      [ 0.55255508 -1.18817547 -1.63666023] 
      [ 0.55255508 -1.18817547 -1.70468466]


     [ 0.55255508 -1.18817547 -1.77270909]]

这是训练的目标特征:

print('v1')
print(v1.shape)
print(v1)

它的打印

v1
(4576,)

0       10.0
1       14.0
2       13.0
3       19.0
....
4573    39.0
4574    16.0
4575    12.0

这里是要预测的样本:

x = np.column_stack((xp,
                     yp,
                     zp))


print('x')
print(x.shape)
print(x)

这是打印品:

x
(75, 3)
[[-1.41421356 -1.41421356 -1.22474487]
 [-0.70710678 -1.41421356 -1.22474487]
 [ 0.         -1.41421356 -1.22474487]
 [ 0.70710678 -1.41421356 -1.22474487]
.....
 [ 0.70710678 -0.70710678 -1.22474487]
 [ 1.41421356 -0.70710678 -1.22474487]
 [-1.41421356  0.         -1.22474487]
 [-0.70710678  0.         -1.22474487]
 [ 0.          0.         -1.22474487]

这里是拟合和预测

v1 = v1.ravel()
#default kernel
kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))

X_train, v1 = make_regression()
model = gpr(kernel=kernel, n_restarts_optimizer=9)
model.fit(X_train,v1)

#Predict v1 
v1_pred = model.predict(x)

运行时出现如下错误:

File "test.py", line 189, in test v1_pred = model.predict(x) File "/usr/local/lib/python3.6/site-packages/sklearn/gaussian_process/gpr.py", line 315, in predict K_trans = self.kernel_(X, self.X_train_) File "/usr/local/lib/python3.6/site-packages/sklearn/gaussian_process/kernels.py", line 758, in call return self.k1(X, Y) * self.k2(X, Y) File "/usr/local/lib/python3.6/site-packages/sklearn/gaussian_process/kernels.py", line 1215, in call metric='sqeuclidean') File "/usr/local/lib/python3.6/site-packages/scipy/spatial/distance.py", line 2373, in cdist raise ValueError('XA and XB must have the same number of columns ' ValueError: XA and XB must have the same number of columns (i.e. feature dimension.)

Answer 1

我只是简单地复制粘贴了一段代码，然后做了一些愚蠢的事情:

X_train, v1 = make_regression()

只好删除它。