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% Read a set of vectors stored in the fvec format (int + n * float)
% The function returns a set of output vector (one vector per column)
%
% Syntax: 
%   v = fvecs_read (filename)     -> read all vectors
%   v = fvecs_read (filename, n)  -> read n vectors 
%   v = fvecs_read (filename, [a b]) -> read the vectors from a to b (indices starts from 1)
function v = fvecs_read (filename, bounds)

% open the file and count the number of descriptors

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"""Usage:

python aggregate_tree.py 500000
python aggregate_tree.py -3000

"""

from matplotlib import pyplot as plt
import numpy as np
import pdb

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import numpy as np
import pdb

from sklearn.datasets import make_classification
from sklearn.mixture import GaussianMixture as GMM


def fisher_vector(xx, gmm):
    """Computes the Fisher vector on a set of descriptors.

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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."""