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import gym
import time
from gym import envs

print(envs.registry.all())

env = gym.make('CartPole-v0')
env.reset()
t = 0

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import numpy as np
import time
import sys

print("## Checking Keras\n\n")
import keras.backend as K
backend = K.backend()

vlen = 10 * 30 * 768
iters = 1000

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from gensim.models import Doc2Vec
import pickle

with open('./docs_proc.pkl', 'rb') as file:
    docs = pickle.load(file)

model = Doc2Vec(docs, vector_size = 500, window = 9, min_count = 20, workers=8, dm=0)
with open('./d2v_model.pkl', 'wb') as file:
    pickle.dump(model, file)
    

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word_list = [doc.words for doc in docs]

bigram = gensim.models.Phrases(word_list)
bigram_phr = gensim.models.phrases.Phraser(bigram)

bi_docs = []
for doc in docs:
    words = bigram_phr[doc.words]
    tags = doc.tags

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def series_to_supervised(df, n_in=1, n_out=1, targets=[], dropnan=True):
    """
    Converts a time series Pandas DataFrame into a supervised learning problem
    returns: X(t-n_in+1,...,t), y(t+1,....,t+n_out)
    """
    assert n_in > 0 and n_out > 0
    assert all([t in df.columns for t in targets])
    
    n_vars = len(df.columns)
    

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using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.IO;
using System.Linq;
using System.Reflection;
using System.Text;
using System.Threading.Tasks;

namespace CSV

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# http://blog.jessitron.com/2013/08/finding-and-removing-large-files-in-git.html
git rev-list master | while read rev; do git ls-tree -lr $rev | cut -c54- | grep -v '^ '; done | sort -u | perl -e '
  while (<>) {
    chomp;
    @stuff=split("\t");
    $sums{$stuff[1]} += $stuff[0];
  }
  print "$sums{$_} $_\n" for (keys %sums);
' | sort -rn >> /tmp/large_files
gedit /tmp/large_files

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# START_0f56bb1d
# To Install permanently:
# curl -s https://gist.githubusercontent.com/HeshamMeneisi/0f56bb1dfd3ca3d09d304d3a02107205/raw/Productivity >> ~/.bashrc && source ~/.bashrc
# To Uninstall:
# cp ~/.bashrc bashrc.bak && perl -0777 -i -pe "s/# START_0f56bb1d.+# END_0f56bb1d//igs" ~/.bashrc
# To use temporarily:
# source <(curl -s https://gist.githubusercontent.com/HeshamMeneisi/0f56bb1dfd3ca3d09d304d3a02107205/raw/Productivity)
# Useful paths
# !! MAKE SURE TO UPDATE VERSION NUMBERS !!
export PATH=/home/$USER/.dotnet:/usr/local/cuda-9.0/bin:~/browser-drivers:~/.composer/vendor/bin${PATH:+:${PATH}}

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#!/usr/bin/env python
# coding: utf-8

# Requirements
# sudo apt install mdbtools

import os
import subprocess

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#!/bin/bash
usage() {
    cat << EOF
Usage: $0 [OPTION]
Build frontend.

Arguments:
-d    Dev-mode (This will skip any production-specific operations)
-s    Push stable to docker.io, ignored in dev-mode. (auto-set by "feat:*" commit)
-b    Build image (auto-set after a new commit)