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Sergey Bartunov sbos

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sbos / Н.О.Ж. - Одноклассник
Created July 1, 2018 14:28
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
@sbos
sbos / adagram_export.jl
Last active October 22, 2016 18:04
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]) ")
@sbos
sbos / baseline.ipynb
Created December 15, 2015 04:03
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@sbos
sbos / debug_th.py
Created November 26, 2015 13:30
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
@sbos
sbos / random_bug.py
Created October 24, 2015 05:01
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)
@sbos
sbos / cgt_memory.py
Last active October 21, 2015 21:27
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))
@sbos
sbos / cgt_simplification_bug.py
Created October 20, 2015 19:36
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))
@sbos
sbos / SVM.py
Last active October 19, 2022 04:06
Simple linear SVM using quadratic programming
import numpy as np
from cvxopt import matrix, spmatrix
from cvxopt.solvers import qp
from cvxopt import solvers
class LinearSVM():
def __init__(self, C):
self.C = C
def fit(self, data, labels):
@sbos
sbos / gist:018955227319ead18529
Created November 10, 2014 06:31
-.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
@sbos
sbos / HMM.jl
Created November 1, 2013 11:25
Hidden Markov Model in Julia
module HMM
using Distributions
import Distributions.rand
import Distributions.fit
immutable HiddenMarkovModel{TP, K}
theta::Vector{TP}
A::Matrix{Float64}