AjayTalati

M[16],X=16,W,k;main(){T(system("stty cbreak")
);puts(W&1?"WIN":"LOSE");}K[]={2,3,1};s(f,d,i
,j,l,P){for(i=4;i--;)for(j=k=l=0;k<4;)j<4?P=M
[w(d,i,j++)],W|=P>>11,l*P&&(f?M[w(d,i,k)]=l<<
(l==P):0,k++),l=l?P?l-P?P:0:l:P:(f?M[w(d,i,k)
]=l:0,++k,W|=2*!l,l=0);}w(d,i,j){return d?w(d
-1,j,3-i):4*i+j;}T(i){for(i=X+rand()%X;M[i%X]
*i;i--);i?M[i%X]=2<<rand()%2:0;for(W=i=0;i<4;
)s(0,i++);for(i=X,puts("\e[2J\e[H");i--;i%4||
puts(""))printf(M[i]?"%4d|":"    |",M[i]);W-2

AjayTalati

#include<iostream>
#include<ctime>
#include<unistd.h>
#include<cstdlib>
#include<cstdio>
#include<cmath>

using namespace std;

int press_enter;

AjayTalati

"""
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

AjayTalati

"""
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

AjayTalati

# Load the MNIST digit recognition dataset into R
# http://yann.lecun.com/exdb/mnist/
# assume you have all 4 files and gunzip'd them
# creates train$n, train$x, train$y  and test$n, test$x, test$y
# e.g. train$x is a 60000 x 784 matrix, each row is one digit (28x28)
# call:  show_digit(train$x[5,])   to see a digit.
# brendan o'connor - gist.github.com/39760 - anyall.org

load_mnist <- function() {
  load_image_file <- function(filename) {

AjayTalati

--[[ 
Efficient LSTM in Torch using nngraph library. This code was optimized 
by Justin Johnson (@jcjohnson) based on the trick of batching up the 
LSTM GEMMs, as also seen in my efficient Python LSTM gist.
--]]

function LSTM.fast_lstm(input_size, rnn_size)
  local x = nn.Identity()()
  local prev_c = nn.Identity()()
  local prev_h = nn.Identity()()

AjayTalati

--[[
LSTM cell. Modified from 
https://github.com/oxford-cs-ml-2015/practical6/blob/master/LSTM.lua

--]]
local LSTM = {}

-- Creates one timestep of one LSTM
function LSTM.lstm(opt)
    local x = nn.Identity()()

AjayTalati

# 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.compat.python2x import OrderedDict

AjayTalati

import re

bvh_file = "Example1.bvh"

def identifier(scanner, token):  return "IDENT", token
def operator(scanner, token):    return "OPERATOR", token
def digit(scanner, token):       return "DIGIT", token
def open_brace(scanner, token):  return "OPEN_BRACE", token
def close_brace(scanner, token): return "CLOSE_BRACE", token

AjayTalati

import bpy

# get the material
mat = bpy.data.materials['Material']
# get the nodes
nodes = mat.node_tree.nodes

# clear all nodes to start clean
for node in nodes: