shurain

'''
Non-parametric computation of entropy and mutual-information

Adapted by G Varoquaux for code created by R Brette, itself
from several papers (see in the code).

These computations rely on nearest-neighbor statistics
'''
import numpy as np

shurain

"""
A deep neural network with or w/o dropout in one file.

License: Do What The Fuck You Want to Public License http://www.wtfpl.net/
"""

import numpy, theano, sys, math
from theano import tensor as T
from theano import shared
from theano.tensor.shared_randomstreams import RandomStreams

shurain

# Alec Radford, Indico, Kyle Kastner
# License: MIT
"""
Convolutional VAE in a single file.
Bringing in code from IndicoDataSolutions and Alec Radford (NewMu)
Additionally converted to use default conv2d interface instead of explicit cuDNN
"""
import theano
import theano.tensor as T
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams

shurain

on replace_chars(this_text, search_string, replacement_string)
  set AppleScript's text item delimiters to the search_string
  set the item_list to every text item of this_text
  set AppleScript's text item delimiters to the replacement_string
  set this_text to the item_list as string
  set AppleScript's text item delimiters to ""
  return this_text 
end replace_chars

tell application "Google Chrome"

shurain

# Authors: Kyle Kastner
# License: BSD 3-clause
import theano.tensor as T
import numpy as np
import theano


class rmsprop(object):
    """
    RMSProp with nesterov momentum and gradient rescaling

shurain

"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code

class LSTM:
  
  @staticmethod
  def init(input_size, hidden_size, fancy_forget_bias_init = 3):

shurain

import numpy as np
from numpy.random import choice

def stirling(N, m):
    if N < 0 or m < 0:
        raise Exception("Bad input to stirling.")
    if m == 0 and N > 0:
        return 0
    elif (N, m) == (0, 0):
        return 1

shurain

""" Poisson-loss Factorization Machines with Numba"""
# Author: Vlad Niculae <[email protected]>
# License: Simplified BSD

from __future__ import print_function
import numpy as np
from numba import jit
from scipy import sparse as sp

from sklearn.utils import check_random_state, check_array, check_X_y

shurain

# to generate your dhparam.pem file, run in the terminal
openssl dhparam -out /etc/nginx/ssl/dhparam.pem 2048

shurain