Newmu

import sys
sys.path.append('..')

import os
import json
from time import time
import numpy as np
from tqdm import tqdm
from matplotlib import pyplot as plt
from sklearn.externals import joblib

Newmu

import os
import numpy as np
from matplotlib import pyplot as plt
from time import time

from foxhound import activations
from foxhound import updates
from foxhound import inits
from foxhound.theano_utils import floatX, sharedX

Newmu

import json
import numpy as np
from time import time
from matplotlib import pyplot as plt
import random

from sklearn import metrics
from sklearn.linear_model import LogisticRegression as LR
from sklearn.feature_extraction.text import TfidfVectorizer

Newmu

import os
import pandas as pd
import numpy as np
from sklearn import metrics
from sklearn.linear_model import LogisticRegression as LR
from sklearn.feature_extraction.text import TfidfVectorizer

from time import time

def len_filter(text, max_len=1014):

Newmu

import theano
import theano.tensor as T
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
from theano.tensor.signal.downsample import max_pool_2d
from theano.tensor.extra_ops import repeat
from theano.sandbox.cuda.dnn import dnn_conv

from time import time
import numpy as np
from matplotlib import pyplot as plt

Newmu

"""
The MIT License (MIT)

Copyright (c) 2015 Alec Radford

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is

Newmu

import numpy as np
import pandas as pd
from lxml import html
from sklearn import metrics
from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LogisticRegression as LR
from sklearn.feature_extraction.text import TfidfVectorizer

def clean(text):
	return html.fromstring(text).text_content().lower().strip()

Newmu

import numpy as np
from scipy.spatial.distance import cdist

def nearest_neighbor(samples, targets, samples_to_classify, metric='euclidean'):
    return targets[np.argmin(cdist(samples_to_classify, samples, metric=metric), axis=1)]