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pca_transform.py
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pca_transform.py
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import numpy as np
class PCA:
def __init__(self, n_components):
self.n_components = n_components
self.components = None
self.mean = None
def fit(self, X):
#mean centering
self.mean = np.mean(X, axis=0)
X = X - self.mean
#covariance (needs samples as columns)
cov = np.cov(X.T)
# eigenvalues, eigenvectors
eigenvalues, eigenvectors = np.linalg.eig(cov)
# -> eigenvector v = [:,i]
# transpore for easier calculations
eigenvectors = eigenvectors.T
# sort eigenvectors
idxs = np.argsort(eigenvalues)[::-1]
eigenvalues = eigenvalues[idxs]
eigenvectors = eigenvectors[idxs]
#store first n eigenvectors
self.components = eigenvectors[0 : self.n_components]
def transform(self, X)
#project data
X= X - self.mean
return np.dot(X, self.components.T)
# project the data onto the 2 primary principal components
pca = PCA(2)
pca.fit(X)
X_projected = pca.transform(X)