bebraw

import pyeliza

class Eliza:
    aliases = 'eliza'
    description = 'Virtual therapist'
    _therapist = pyeliza.eliza()

    def execute(self, expression, context):
        '''
        >>> from mock import Mock

jimbojsb

Step 0:

Get Homebrew installed on your mac if you don't already have it

Step 1:

Install highlight. "brew install highlight". (This brings down Lua and Boost as well)

Step 2:

sbos

module HMM

using Distributions

import Distributions.rand
import Distributions.fit

immutable HiddenMarkovModel{TP, K}
    theta::Vector{TP}
    A::Matrix{Float64}

panisson

#  Copyright (C) 2013 Istituto per l'Interscambio Scientifico I.S.I.
#  You can contact us by email (isi@isi.it) or write to:
#  ISI Foundation, Via Alassio 11/c, 10126 Torino, Italy.
#
#  This work is licensed under a Creative Commons 4.0
#  Attribution-NonCommercial-ShareAlike License
#  You may obtain a copy of the License at
#  http://creativecommons.org/licenses/by-nc-sa/4.0/
#
#  This program was written by Andre Panisson <panisson@gmail.com> at

JayKickliter

# Complex transforms
# Sign argument
#   -1  = Complex to Complex reverse FFT
#   1   = Complex to Complex forward FFT

# ffts_plan_t *ffts_init_1d(size_t N, int sign); 
# Create a plan for a forward 1D FFT of size 16 
N         = 2^16
Direction = 1 
ffts_plan = ccall((:ffts_init_1d, "libffts"), Ptr{Void}, (Csize_t, Cint), N, Direction)

syhw

"""
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

tmramalho

import theano
import theano.tensor as T
import numpy as np
import cPickle
import random
import matplotlib.pyplot as plt

class RNN(object):

	def __init__(self, nin, n_hidden, nout):

lrytz

Shortcut for Syntax Highlighting in Keynote

The idea is based on a gist by @jimbojsb.

You can use Pygments or Highlight.

Install Pygments

brew install python

kastnerkyle

# 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

kastnerkyle

# Stacked LSTMs
# Author: Kyle Kastner
# Based on script from /u/siblbombs
# License: BSD 3-Clause
import tensorflow as tf
from tensorflow.models.rnn import rnn
from tensorflow.models.rnn.rnn_cell import LSTMCell
import numpy as np
import time