Fisher vector normalization

fisher_normalize.py

import numpy as np


def power_normalize(xx, alpha=0.5):
    """Computes a alpha-power normalization for the matrix xx."""
    return np.sign(xx) * np.abs(xx) ** alpha


def L2_normalize(xx):
    """L2-normalizes each row of the data xx."""
    Zx = np.sum(xx * xx, 1)
    xx_norm = xx / np.sqrt(Zx[:, np.newaxis])
    xx_norm[np.isnan(xx_norm)] = 0
    return xx_norm