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#ifndef FIBONACCI_HEAP_H_
#define FIBONACCI_HEAP_H_

#include <memory>
#include <list>
#include <functional>
#include <algorithm>
#include <limits>
#include <cmath>
#include <vector>

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#ifndef BINOMIAL_HEAP_H_
#define BINOMIAL_HEAP_H_

#include <memory>
#include <list>
#include <functional>
#include <algorithm>
#include <limits>

template <class K, class V, class Compare = std::less<K>> // K - key, V - additional data

persiyanov

if you are using linux, unix, os x:

pip install -U setuptools
pip install -U pip

pip install numpy
pip install scipy
pip install matplotlib
#pip install PySide

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2010 Radim Rehurek <radimrehurek@seznam.cz>
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html


"""Corpus in the Matrix Market format.

This code uses python's struct library to read/write binary data

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----- MODEL "cython-linesentence-word2vec-window-05-workers-01-size-300" RESULTS -----
       * Vocab time: 126.159779072 sec.
       * Total epoch time: 1181.82512498 sec.
       * Processing speed: 144372.118509 words/sec
       * Avg CPU loads: 0.14, 0.35, 5.27, 94.53, 0.09, 0.23, 0.01, 0.02, 0.02, 0.02, 0.02, 0.01, 0.02, 0.02, 0.33, 0.02
       * Sum CPU load: 101.11282
----- MODEL "cython-linesentence-word2vec-window-05-workers-04-size-300" RESULTS -----
       * Vocab time: 126.206352949 sec.
       * Total epoch time: 305.442888975 sec.

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# Optionally, you could now dump the network weights to a file like this:
np.savez('model.npz', *lasagne.layers.get_all_param_values(network))
#
# And load them again later on like this:
with np.load('model.npz') as f:
    param_values = [f['arr_%d' % i] for i in range(len(f.files))]
lasagne.layers.set_all_param_values(network, param_values)

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import gym
import numpy as np

DISCOUNT = 1.0
STEP_REWARD = 0.0
LOSE_REWARD = 0.0
WIN_REWARD = 1.0

def avg_reward(env, s, a):
    avg_reward = 0