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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 |
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#include<iostream> | |
#include<ctime> | |
#include<unistd.h> | |
#include<cstdlib> | |
#include<cstdio> | |
#include<cmath> | |
using namespace std; | |
int press_enter; |
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""" | |
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 |
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""" | |
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 |
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# 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) { |
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--[[ | |
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()() |
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--[[ | |
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()() |
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# 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 |
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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 |
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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: |
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