minhlab

'''
Created on Nov 17, 2014

@author: Minh Ngoc Le
'''

from pylearn2.models.mlp import Layer
from pylearn2.space import IndexSpace, VectorSpace
from pylearn2.utils import sharedX, wraps

minhlab

from theano import tensor as T 
import theano
import numpy as np

floatX = 'float32'
num_blocks = 2
block_dims = 3
hidden_dims = 5
output_dims = 7

minhlab

Running Torch...
Last cost: 0.01029977761209
Time: 0

Using gpu device 0: GeForce GTX 980
Compiling function...
Running Theano...
Last cost: 3.135782
Time: 6.343242

minhlab

th> i = torch.LongTensor(10)
                                                                      [0.0001s]
th> i:random(10)
  8
  2
  2
  2
  7
  2
  4

minhlab

th> mask=torch.ByteTensor(10, 3):random(2):add(-1)
                                                                      [0.0001s]
th> mask
 1  0  1
 0  1  1
 1  1  1
 1  1  0
 0  1  1
 1  1  0
 1  0  1

minhlab

th> x=torch.Tensor(2,3)
                                                                      [0.0000s]
th> x:uniform()
 0.2714  0.8744  0.5518
 0.3207  0.5189  0.8901
[torch.DoubleTensor of size 2x3]

                                                                      [0.0001s]
th> x[1][2] = -math.huge
                                                                      [0.0000s]

minhlab

local MaskedLogSoftMax, Parent = torch.class('nn.MaskedLogSoftMax', 'nn.Module')

function MaskedLogSoftMax:__init(masks, filler)
    Parent.__init(self)
    self.masks = masks
    self.minvals = torch.Tensor()
    self.mininds = torch.LongTensor() 
    self.temp1 = torch.Tensor()
    self.temp2 = torch.Tensor()

minhlab

import java.util.*;
import java.lang.*;
import java.io.*;

/* Name of the class has to be "Main" only if the class is public. */
class Ideone
{
	public static void main (String[] args) throws java.lang.Exception
	{
		double a = Math.random();

minhlab

import os
import re
count = 0
for root, dir_names, file_names in os.walk('ECB+'):
    for fname in file_names:
        if 'plus' in fname:
            path = os.path.join(root, fname)
            with open(path) as f:
                content = f.read()
                print list(m.group() for m in re.finditer('<token', content))

minhlab

import os
import re
count = 0
for root, dir_names, file_names in os.walk('ECB+'):
    for fname in file_names:
        if 'plus' not in fname:
            path = os.path.join(root, fname)
            with open(path) as f:
                content = f.read()
                print list(m.group() for m in re.finditer('<token', content))