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@ebetica
ebetica / parity.py
Created February 16, 2018 20:36
LSTMs suck
import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Variable
import sys
nlen = 5
model_type = nn.LSTM
running_loss = 1
@ebetica
ebetica / PKGBUILD
Created June 3, 2017 15:04
Singularity Container pkgbuild
pkgname='singularity-container'
pkgver='2.3'
pkgrel='0'
pkgdesc='Container platform focused on supporting "Mobility of Compute".'
arch=('i686' 'x86_64')
url='http://singularity.lbl.gov'
license=('BSD')
depends=('bash' 'python')
source=("https://github.com/singularityware/singularity/releases/download/${pkgver}/singularity-${pkgver}.tar.gz")
md5sums=('dbc02b17f15680c378c1ec9e4d80956d')
#include <ctime>
#include <iostream>
#include "replayer.h"
using namespace std;
using namespace torchcraft::replayer;
int main() {
std::clock_t start;
double duration;
@ebetica
ebetica / rep_info.py
Created February 16, 2017 18:29
Example script to go through some starcraft replays and grab infomation about it, dumping into a CSV
# This script tries as best as possible to filter out bad replays
# Pass it a subdir, and it will read all '.rep' files, and spit out a list
# of the corrupt files in stdout
from __future__ import print_function
from pyreplib import replay
from itertools import repeat
from multiprocessing import Pool, Process, Pipe
from multiprocessing.pool import ThreadPool
from Queue import Queue
import os
@ebetica
ebetica / check_rep.py
Created February 9, 2017 22:59
Runs through a directory of starcraft replays and outputs all the corrupt ones
# This script tries as best as possible to filter out bad replays
# Pass it a subdir, and it will read all '.rep' files, and spit out a list
# of the corrupt files in stdout
from __future__ import print_function
from pyreplib import replay # https://github.com/HearthSim/pyreplib/
from itertools import repeat
from multiprocessing import Pool, Process, Pipe
from multiprocessing.pool import ThreadPool
import os
import sys
@ebetica
ebetica / snippet.py
Last active January 23, 2017 19:42
Pytoch reinforce function
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class Policy(nn.Module):
def __init__(self):
super(Policy, self).__init__()
self.affine1 = nn.Linear(4, 128)
import argparse
import gym
import numpy as np
from itertools import count
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.autograd as autograd