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ipca.py
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ipca.py
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from dataclasses import dataclass
import numpy as np
@dataclass
class IncrementalPCA:
B: int = None
A: np.ndarray = None
mean: np.ndarray = None
eigenvalues: np.ndarray = None
eigenvectors: np.ndarray = None
def __init__(self, initial_sample_size):
self.B = initial_sample_size
def _update_mean(self, x, n):
self.mean = self.mean + 1 / (n + 1) * (x - self.mean)
def _update_eigen(self):
vals, vecs = np.linalg.eig(self.A)
self.eigenvectors = vecs[:,np.argsort(vals)[::-1]]
self.eigenvalues = np.sort(vals)[::-1]
def _get_transformed(self, x):
return np.dot(x, self.eigenvectors)
def fit_transform(self, X):
X_initial = X[:self.B]
self.mean = np.mean(X_initial, axis=0)
X_centered = X_initial - self.mean
self.A = np.cov(X_centered.T)
self._update_eigen()
X_out = self._get_transformed(X_centered)
for n in range(self.B, len(X)):
x = np.reshape(X[n], (1,-1))
self._update_mean(x, n)
x_centered = x - self.mean
self.A = (n - 1) / n * self.A + np.dot(x_centered.T, x_centered) / (n ** 2)
self._update_eigen()
X_out = np.vstack([X_out, self._get_transformed(x_centered)])
return X_out