sklearn PCA rotates a single vector

### Describe the bug

The issue we recently discovered is that sklearn PCA rotates the input when only a single variable is fed into the model.  

I am aware there are infinite rotations when there is a single vector fed into the model, however, the output from PCA should intuitively make sense. So I suggest hard coding this scenario (which I guess already done in R)


### Steps/Code to Reproduce
```python
import numpy as np
from sklearn.decomposition import PCA

x = np.array([1,2,3,4,5,6,7,8,9,10.]).reshape(-1,1).astype('float64')

p = PCA().fit_transform(x)
p
```
### output:
```
array([[ 4.5],
       [ 3.5],
       [ 2.5],
       [ 1.5],
       [ 0.5],
       [-0.5],
       [-1.5],
       [-2.5],
       [-3.5],
       [-4.5]])
```

as we see here the value 4.5 is corresponding to the smallest value in the set (1) and the value -4.5 is corresponding to the highest value in the input set (10).

### Expected Results:
```
array([[-4.5],
       [-3.5],
       [-2.5],
       [-1.5],
       [-0.5],
       [ 0.5],
       [ 1.5],
       [ 2.5],
       [ 3.5],
       [ 4.5]])
```



### Steps/Code to Reproduce

```
from sklearn.decomposition import PCA

x= np.array([1,2,3,4,5,6,7,8,9,10.]).reshape(-1,1).astype('float64')

p = PCA().fit_transform(x)
p
```

### Expected Results

```
array([[-4.5],
       [-3.5],
       [-2.5],
       [-1.5],
       [-0.5],
       [ 0.5],
       [ 1.5],
       [ 2.5],
       [ 3.5],
       [ 4.5]])
```

### Actual Results

```
array([[ 4.5],
       [ 3.5],
       [ 2.5],
       [ 1.5],
       [ 0.5],
       [-0.5],
       [-1.5],
       [-2.5],
       [-3.5],
       [-4.5]])
```

### Versions

```shell
System:
    python: 3.11.4 | packaged by Anaconda, Inc. | (main, Jul  5 2023, 13:47:18) [MSC v.1916 64 bit (AMD64)]
executable: e:\Users\***\miniconda3\python.exe
   machine: Windows-10-10.0.22621-SP0

Python dependencies:
      sklearn: 1.3.0
          pip: 23.2.1
   setuptools: 68.0.0
        numpy: 1.24.3
        scipy: 1.11.1
       Cython: None
       pandas: 2.0.3
   matplotlib: 3.7.1
       joblib: 1.2.0
threadpoolctl: 2.2.0

Built with OpenMP: True

threadpoolctl info:
       filepath: E:\Users\***\miniconda3\Library\bin\mkl_rt.2.dll
         prefix: mkl_rt
       user_api: blas
   internal_api: mkl
        version: 2023.1-Product
    num_threads: 8
threading_layer: intel

       filepath: E:\Users\***\miniconda3\vcomp140.dll
         prefix: vcomp
       user_api: openmp
   internal_api: openmp
        version: None
    num_threads: 16

       filepath: E:\Users\***\miniconda3\Library\bin\libiomp5md.dll
         prefix: libiomp
       user_api: openmp
   internal_api: openmp
        version: None
    num_threads: 16
```

COMMENT:
Thanks for reporting this.

For me your code produces your expected output (1 corresponds to -4.5).

Output of `show_versions` for me:
```
System:
    python: 3.10.9 | packaged by conda-forge | (main, Feb  2 2023, 20:26:08) [Clang 14.0.6 ]
executable: /Users/***/bin/python3.10
   machine: macOS-13.5.2-arm64-arm-64bit

Python dependencies:
      sklearn: 1.4.dev0
          pip: 23.1.2
   setuptools: 67.1.0
        numpy: 1.25.2
        scipy: 1.11.2
       Cython: 3.0.2
       pandas: 2.0.3
   matplotlib: 3.7.2
       joblib: 1.2.0
threadpoolctl: 3.1.0

Built with OpenMP: True

threadpoolctl info:
       user_api: openmp
   internal_api: openmp
         prefix: libomp
       filepath: /Users/thead/miniconda/envs/sklearn-py3.10-v2/lib/libomp.dylib
        version: None
    num_threads: 10
```

There are two differences I can spot: newer Numpy version and that I'm using the latest `main`. I doubt the change comes from Numpy. Could you try out [the nightly build](https://scikit-learn.org/stable/developers/advanced_installation.html#installing-nightly-builds) and see if this fixes the problem?

Labels: Bug