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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.""" |
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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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"""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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% 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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import numpy as np | |
def fvecs_read(filename, c_contiguous=True): | |
fv = np.fromfile(filename, dtype=np.float32) | |
if fv.size == 0: | |
return np.zeros((0, 0)) | |
dim = fv.view(np.int32)[0] | |
assert dim > 0 | |
fv = fv.reshape(-1, 1 + dim) |
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class Expr(object): | |
def accept(self, visitor): | |
method_name = 'visit_{}'.format(self.__class__.__name__.lower()) | |
visit = getattr(visitor, method_name) | |
return visit(self) | |
class Int(Expr): | |
def __init__(self, value): | |
self.value = value |
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class Expr(object): | |
def accept(self, visitor): | |
method_name = 'visit_{}'.format(self.__class__.__name__.lower()) | |
visit = getattr(visitor, method_name) | |
return visit(self) | |
class Int(Expr): | |
def __init__(self, value): |
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def descriptors_to_sufficient_statistics(xx, gmm, **kwargs): | |
# yael assumes that the data is in C-contiguous format. | |
xx = np.ascontiguousarray(np.atleast_2d(xx)) | |
N = xx.shape[0] | |
K = gmm.k | |
D = gmm.d | |
# Compute posterior probabilities using yael. | |
Q = gmm_predict_proba(xx, gmm) # NxK |
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import logging | |
from gensim.models.doc2vec import ( | |
Doc2Vec, | |
TaggedDocument, | |
) | |
logging.basicConfig( | |
format='%(asctime)s : %(threadName)s : %(levelname)s : %(message)s', | |
level=logging.DEBUG, |
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/** | |
* Created by eurus on 28/02/2017. | |
*/ | |
package components | |
import collection.immutable.Queue | |
import ActionType._ | |
sealed trait SignalValue | |
object `0` extends SignalValue { |
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