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import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets
from torchvision import transforms
from torch.utils.data import DataLoader


if torch.cuda.is_available():

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#!/usr/bin/env python
"""
simple example script for running and testing notebooks.

Usage: `ipnbdoctest.py foo.ipynb [bar.ipynb [...]]`

Each cell is submitted to the kernel, and the outputs are compared with those stored in the notebook.
"""
# License: Public Domain, but credit is nice (Min RK).

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from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
from sklearn.linear_model import LogisticRegression

def plot_decision_regions(X, y, classifier, resolution=0.1):

    # setup marker generator and color map
    markers = ('s', 'x', 'o', '^', 'v')

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import nltk

def eng_ratio(text):
    ''' Returns the ratio of non-English to English words from a text '''

    english_vocab = set(w.lower() for w in nltk.corpus.words.words()) 
    text_vocab = set(w.lower() for w in text.split() if w.lower().isalpha()) 
    unusual = text_vocab.difference(english_vocab)
    diff = len(unusual)/len(text_vocab)
    return diff

rasbt

" Sebastian Raschka
" 09/11/2013
"

syntax on
set nonumber
set ruler

set tabstop=4
set shiftwidth=4 " controls the depth of autoindentation