ml-finance-python

principal_component_analysis.py

(1247B)

 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