PCA Method¤
stamox.decomposition.princomp(x: ArrayLike, n_components: int) -> PCAState
¤
Performs principal component analysis (PCA) on the given array.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x |
ArrayLike |
The input array of shape (n_samples, n_features). |
required |
n_components |
int |
Number of components to keep. |
required |
Returns:
| Type | Description |
|---|---|
PCAState |
A namedtuple containing the results of the PCA. |
Examples:
>>> import jax.numpy as jnp
>>> from stamox.functions import princomp
>>> X = jnp.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]], dtype=jnp.float32)
>>> pca_state = princomp(X, n_components=2)
>>> pca_state.components
Array([[-0.8384922, 0.5449136],
[-0.5449136, -0.8384922]], dtype=float32)