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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

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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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Goal

Want to move computation on machine with much power. We will set up Anaconda 4.0.0 and XGBoost 0.4 (it is tricky installable).

Preliminaries

• Amazon AWS Educate gives 100$ for MIPT students.
• GitHub Students Pack additionaly gives 15$.

Let's start

AWS Console and launching EC2 Instance.

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import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
import logging


class BagOfWordsModel(object):
    OUT_FOLDER = 'out'

    def __init__(self, id_document_dict, max_features=None, max_df=1.0):
        """Builds bow model.

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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

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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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