davrot/pytutorial
1
0
Fork 0
mirror of https://github.com/davrot/pytutorial.git synced 2025-06-06 22:00:01 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Update README.md

Browse source 
Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
This commit is contained in:
David Rotermund 2023-12-19 15:56:20 +01:00 • committed by GitHub
parent 58bf5f9c2d
commit de9ed36a56
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
1 changed files with 57 additions and 0 deletions
Download patch file Download diff file Expand all files Collapse all files

57
scikit-learn/fast_ica/README.md
View file

@ -164,6 +164,63 @@ plt.show()
![image2](image2.png) ![image2](image2.png)
## Inspect the extracted coordinate system​
> **components_** : ndarray of shape (n_components, n_features)
>
> The linear operator to apply to the data to get the independent sources. This is equal to the unmixing matrix when whiten is False, and equal to np.dot(unmixing_matrix, self.whitening_) when whiten is True.
**Be aware that the sign of any axis can switch !!!​** Like it happend in this example:
```python
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import FastICA
rng = np.random.default_rng(1)
a_x = rng.normal(0.0, 1.0, size=(5000))[:, np.newaxis]
a_y = rng.normal(0.0, 1.0, size=(5000))[:, np.newaxis] ** 3
data_a = np.concatenate((a_x, a_y), axis=1)
b_x = rng.normal(0.0, 1.0, size=(5000))[:, np.newaxis] ** 3
b_y = rng.normal(0.0, 1.0, size=(5000))[:, np.newaxis]
data_b = np.concatenate((b_x, b_y), axis=1)
data = np.concatenate((data_a, data_b), axis=0)
angle_x = -0.3
angle_y = 0.3
roation_matrix = np.array(
[[np.cos(angle_x), -np.sin(angle_x)], [np.sin(angle_y), np.cos(angle_y)]]
)
data_r = data @ roation_matrix
# Train
ica = FastICA(n_components=2)
ica.fit(data_r)
plt.plot([-ica.components_.max(), ica.components_.max()], [0, 0], "k")
plt.plot([0, 0], [-ica.components_.max(), ica.components_.max()], "k")
plt.plot(
[-ica.components_[0, 0], ica.components_[0, 0]],
[-ica.components_[0, 1], ica.components_[0, 1]],
"m",
)
plt.plot(
[-ica.components_[1, 0], ica.components_[1, 0]],
[-ica.components_[1, 1], ica.components_[1, 1]],
"c",
)
plt.show()
```
![image3](image3.png)
## Fast ICA Methods ## Fast ICA Methods
||| |||