wehub-resource-sync
30 lines
1.2 KiB
Python
30 lines
1.2 KiB
Python
from __future__ import print_function, division
|
|
import numpy as np
|
|
from mlfromscratch.utils import calculate_covariance_matrix
|
|
|
|
|
|
class PCA():
|
|
"""A method for doing dimensionality reduction by transforming the feature
|
|
space to a lower dimensionality, removing correlation between features and
|
|
maximizing the variance along each feature axis. This class is also used throughout
|
|
the project to plot data.
|
|
"""
|
|
def transform(self, X, n_components):
|
|
""" Fit the dataset to the number of principal components specified in the
|
|
constructor and return the transformed dataset """
|
|
covariance_matrix = calculate_covariance_matrix(X)
|
|
|
|
# Where (eigenvector[:,0] corresponds to eigenvalue[0])
|
|
eigenvalues, eigenvectors = np.linalg.eig(covariance_matrix)
|
|
|
|
# Sort the eigenvalues and corresponding eigenvectors from largest
|
|
# to smallest eigenvalue and select the first n_components
|
|
idx = eigenvalues.argsort()[::-1]
|
|
eigenvalues = eigenvalues[idx][:n_components]
|
|
eigenvectors = np.atleast_1d(eigenvectors[:, idx])[:, :n_components]
|
|
|
|
# Project the data onto principal components
|
|
X_transformed = X.dot(eigenvectors)
|
|
|
|
return X_transformed
|