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
[Machine Learning From Scratch]-unsupervised_learning-principal_component_analysis
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