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-.13015382 .033825178 -.02594826 -.288166 .04162671 -.06678655 -.22145432 .1749719 .030906504 -.12878266 .08305055 .11668899 -.06319859 -.0777372 .040560413 -.0305699 .17826276 -.10443129 -.00669569 .014138044 -.064837754 -.24928348 .060342744 -.08906554 .080405414 .0045637907 .007919261 -.14719364 .07927967 .011611855 -.0018610754 .08388769 .09033888 .0517999 .020472864 .19127382 .022346925 .06461436 .035737775 .049969383 .006792533 .11398394 -.03975431 -.112482205 -.03677935 .023018813 .044070702 .09208278 -.021259554 .13439591 -.048604295 .058010057 -.051273342 .03115164 .100593045 -.12984459 .10762949 .18540311 -.009627994 .0026174872 -.05229366 -.23992816 -.076821424 .12318988 -.1506341 -.13093792 .0035753904 -.09583123 .067648284 -.09169266 -.09788873 -.1272856 -.1690156 .017811107 -.027340638 .07608332 .10959153 -.027422326 .08745462 -.18536375 .013429028 -.05132068 .04882859 .23418726 -.026919987 .034001417 -.010213713 .035071775 -.038683213 -.092883974 -.056313466 .111097194 -.074547 -.035446037 -.00 |
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import cgt | |
import cgt.nn as nn | |
import numpy as np | |
from scipy.stats import norm | |
def gaussian_density(x, mu, sigma): | |
return cgt.exp(-cgt.square(x - mu) / 2 / cgt.square(sigma)) \ | |
/ cgt.sqrt(2 * np.pi) / sigma | |
var_mu = nn.parameter(np.array(0.5)) |
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import cgt | |
import cgt.nn as nn | |
import numpy as np | |
from scipy.stats import norm | |
def gaussian_density(x, mu, sigma): | |
return cgt.exp(-cgt.square(x - mu) / 2 / cgt.square(sigma)) \ | |
/ cgt.sqrt(2 * np.pi) / sigma | |
var_mu = nn.parameter(np.array(0.5)) |
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import cgt | |
import cgt.nn as nn | |
import numpy as np | |
batch = 5000 | |
mean = 0. | |
for i in xrange(batch): | |
mean += (cgt.randn() - mean) / (i+1) |
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import theano as th | |
import theano.tensor as T | |
import lasagne | |
import numpy as np | |
from theano.tensor.shared_randomstreams import RandomStreams | |
from lasagne.nonlinearities import tanh | |
import matplotlib.pyplot as plt | |
import sys | |
# change to True and see what happens |
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==> Using Homebrew-provided fortran compiler. | |
This may be changed by setting the FC environment variable. | |
==> Cloning https://github.com/JuliaLang/julia.git | |
git --git-dir /Library/Caches/Homebrew/julia--git/.git status -s | |
Updating /Library/Caches/Homebrew/julia--git | |
git config remote.origin.url https://github.com/JuliaLang/julia.git | |
git config remote.origin.fetch +refs/heads/master:refs/remotes/origin/master | |
git fetch origin | |
git checkout -f master | |
Already on 'master' |
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function write_word2vec(path::AbstractString, vm::VectorModel, dict::Dictionary) | |
fout = open(path, "w") | |
sense_prob = zeros(T(vm)) | |
write(fout, "$(V(vm)) $(T(vm)) $(M(vm))\n") | |
for v in 1:V(vm) | |
write(fout, "$(dict.id2word[v])\n") | |
expected_pi!(sense_prob, vm, v) | |
for k in 1:T(vm) | |
if sense_prob[k] < 1e-3 continue end | |
write(fout, "$k $(sense_prob[k]) ") |
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module HMM | |
using Distributions | |
import Distributions.rand | |
import Distributions.fit | |
immutable HiddenMarkovModel{TP, K} | |
theta::Vector{TP} | |
A::Matrix{Float64} |
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Снова учитель что-то | |
Пытается нам объяснять | |
Мне случать его неохота | |
Мне хочется погулять | |
Я после звонка наплевала | |
На следующий урок | |
Сломала окно и сьежала | |
Слушать тюменский панк-рок |
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