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name: "Encoder" | |
input: "data" | |
input_dim: None | |
input_dim: 3 | |
input_dim: None | |
input_dim: None | |
layer { | |
name: "centered" |
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from multiprocessing import Process, Pool, Queue | |
def f(q0): | |
for i in range(100): | |
q0.put(i) | |
def g(q0, q1): | |
while True: | |
q1.put(q0.get()) |
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import theano as th | |
import theano.tensor as tt | |
def gaussian_kernel(x, y, sigma=1.): | |
return tt.exp(-tt.sum(tt.square(x - y)) / sigma**2) | |
def gram_matrix(X, Y, kernel): | |
M = X.shape[0] | |
N = Y.shape[0] | |
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#!/usr/bin/env python | |
from distutils.core import setup, Extension | |
modules = [ | |
Extension('_test', | |
language='c++', | |
sources=[ | |
'test.cpp'])] |
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import sys | |
from numpy import * | |
from numpy.random import * | |
from matplotlib.pyplot import * | |
from scipy.stats import * | |
p17 = .05 | |
p18 = .03 |
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__author__ = 'Lucas Theis <lucas@theis.io>' | |
__license__ = 'MIT License <http://www.opensource.org/licenses/mit-license.php>' | |
import sys | |
from numpy import * | |
from numpy.random import * | |
from matplotlib.pyplot import * | |
from scipy.special import gammaln | |
from scipy.stats import norm, invgamma |
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#!/usr/bin/env python | |
""" | |
Manage and display experimental results. | |
""" | |
__license__ = 'MIT License <http://www.opensource.org/licenses/mit-license.php>' | |
__author__ = 'Lucas Theis <lucas@theis.io>' | |
__docformat__ = 'epytext' | |
__version__ = '0.4.3' |
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