georgepar

# Create a new bgpvpn
neutron bgpvpn-create --type l3 --name mybgpvpn -- --import-targets 55:55 88:88 --export-targets 55:55 88:88 --route-distinguishers 55:55

# Associate network to bgpvpn
neutron bgpvpn-net-assoc-create --network my_net mybgpvpn

# Associate router to bgpvpn
neutron bgpvpn-router-assoc-create --router my_router mybgpvpn2

# Update bgpvpn route targets

georgepar

##############################################################################
# Copyright (c) 2015 Ericsson AB and others.
# [email protected]
# All rights reserved. This program and the accompanying materials
# are made available under the terms of the Apache License, Version 2.0
# which accompanies this distribution, and is available at
# http://www.apache.org/licenses/LICENSE-2.0
##############################################################################

TOP := $(shell pwd)

georgepar

#!/usr/bin/env python3
"""
Credits https://github.com/ivanychev/learning/blob/master/Python/ipynb2pdf/ipynb2pdf.py

Current version of Jupyter doesn't support pdf exporting when it comes to 
greek language in the document. To fix this, current script has born.
It requires nbconvert as long as jupyter to be installed.


Author:     Sergey Ivanychev

georgepar

from sklearn.base import BaseEstimator, ClassifierMixin

class EuclideanClassifier(BaseEstimator, ClassifierMixin):  
    """Classify samples based on the distance from the mean feature value"""

    def __init__(self):
        self.X_mean_ = None


    def fit(self, X, y):

georgepar

#!/usr/bin/env bash

SPELL_CHECKER_COMPILED=${1}

python test.py | 
    fstcompile --isymbols=letter_syms.txt --osymbols=letter_syms.txt | 
    fstcompose - ${SPELL_CHECKER_COMPILED} | 
    fstshortestpath |
    fstrmepsilon |
    fsttopsort |

georgepar

#!/usr/bin/env bash

python create_fst.py | fstcompile --isymbols=chars.syms --osymbols=chars.syms - rosebud.bin.fst

georgepar

# Initialize word2vec. Context is taken as the 2 previous and 2 next words
model = Word2Vec(sentences, window=5, size=100, workers=4)
model.train(sentences, total_examples=len(sentences), epochs=1000)

# get ordered vocabulary list
voc = model.wv.index2word

# get vector size
dim = model.vector_size

georgepar

import os

data_dir = './aclImdb/'
train_dir = os.path.join(data_dir, 'train')
test_dir = os.path.join(data_dir, 'test')
pos_train_dir = os.path.join(train_dir, 'pos')
neg_train_dir = os.path.join(train_dir, 'neg')
pos_test_dir = os.path.join(test_dir, 'pos')
neg_test_dir = os.path.join(test_dir, 'neg')

georgepar

from torch.utils.data import Dataset
from sklearn.preprocessing import LabelEncoder


class LabelTransformer(LabelEncoder):
    def inverse(self, y):
        try:
            return super(LabelTransformer, self).inverse_transform(y)
        except:
            return super(LabelTransformer, self).inverse_transform([y])

georgepar

import numpy as np
import gzip
import copy
from sklearn.preprocessing import LabelEncoder
from torch.utils.data import Dataset
from torch.utils.data import SubsetRandomSampler, DataLoader


# Combine similar classes and remove underrepresented classes
class_mapping = {