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Stanislaw Jastrzebski kudkudak

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import theano
import theano.tensor as T
import keras.backend as K
import numpy as np
x = T.fmatrix()
mask = T.ones_like(x[:, 0])
mask = mask.dimshuffle(0, "x")
mask = K.dropout(mask, 0.5)
fnc = theano.function([x], mask)
print fnc(np.array([[1,2,3.], [4,5,6], [7,8,9]]).astype("float32"))
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
This file that defines model factories.
It is quite messy
"""
import logging
import sys
import numpy as np
from web import embedding
import web
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.decomposition import RandomizedPCA
import logging
Embedding(self.vocabulary_size + 1, self.word_dim,
trainable=True, mask_zero=True,
dropout=self.dropout,
input_length=self.max_len)(sequence)
forward = LSTM(self.hidden_size, return_sequences=True)(emb)
backward = LSTM(self.hidden_size, go_backwards=True, return_sequences=True)(emb)
merged = merge([forward, backward], mode='concat')#, concat_axis=-1) # for theano uncomment
output = TimeDistributed(Dense(len(self.tag2ind), activation="softmax"))(merged)
#!/usr/bin/env python
"""
Probability model
Posterior: (2-dimensional) Normal
Variational model
Likelihood: Mean-field Normal
"""
import edward as ed
import tensorflow as tf
from scipy.stats.stats import spearmanr
import numpy as np
import cPickle
# Same score for the second included embedding
E = cPickle.load(open("Trans_embds/D_RNN_500k_144h.pkl"))
data = pd.read_csv("SimLex-999/SimLex-999.txt", sep="\t")
scores, golden_ratings = [], []
for _, row in data.iterrows():
@kudkudak
kudkudak / krakrobot2016_ranking.py
Created April 9, 2016 17:29
Script used to calculate final ranking
import pandas as pd
import json
import copy
def krakrobot2016_calculate_ranking(data):
data = copy.deepcopy(data)
def get_wins(player_a, player_b):
only_ab = A_pairs[(A_pairs["id_x"] == player_a) & (A_pairs["id_y"] == player_b)]
wins = only_ab[(only_ab['points_x'] > only_ab['points_y']) | \
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Simple script running experiments from experiment_baselines only on MACCS
"""
import experiment_baselines_refactor
import optparse
from drgmum.utils import config_log_to_file
from drgmum.base import LOG_DIR
4166 5-HT1a_actives.smi
1155 5-HT1a_inactives.smi
628 5-HT1b_actives.smi
297 5-HT1b_inactives.smi
1870 5-HT2a_actives.smi
976 5-HT2a_inactives.smi
407 5-HT2b_actives.smi
333 5-HT2b_inactives.smi
1490 5-HT6_actives.smi
341 5-HT6_inactives.smi
@kudkudak
kudkudak / conditional_entropy.ipynb
Last active February 3, 2016 13:33
Conditional entropy calculation in Python, Numba and Cython (ugly! sorry)
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