jovianlin

from functools import lru_cache

@lru_cache(maxsize=100)
def fibonacci(n):
  # Check that the input is a positive integer
  if type(n) != int:
    raise TypeError("n must be a positive int")
  if n < 1:
    raise ValueError("n must be a positive int")
    

jovianlin

def foo(s):
	if len(s) <= 0:
		return None
	else:
		output, curr_char, curr_count = '', '', 0
		for idx in range(0, len(s)):
			if s[idx] == curr_char:
				curr_count += 1
			else:
				output += curr_char + str(curr_count) if curr_count > 0 else curr_char

jovianlin

from sklearn.linear_model import SGDRegressor
 
# https://adventuresindatascience.wordpress.com/2014/12/30/minibatch-learning-for-large-scale-data-using-scikit-learn/
  
def iter_minibatches(chunksize, numtrainingpoints):
    # Provide chunks one by one
    chunkstartmarker = 0
    while chunkstartmarker < numtrainingpoints:
        chunkrows = range(chunkstartmarker,chunkstartmarker+chunksize)
        X_chunk, y_chunk = getrows(chunkrows)

jovianlin

# -*- coding: utf-8 -*-

from afinn import Afinn
import spacy
import re


class TargetedSentimentAnalysis(object):
	def __init__(self):
		self.afinn = Afinn(emoticons=True)

jovianlin

import argparse
import sys

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--x', type=float, default=1.0,
                        help='What is the first number?')
    parser.add_argument('--y', type=float, default=1.0,
                        help='What is the second number?')
    parser.add_argument('--operation', type=str, default='add',

jovianlin

import re

# Returns all the emoji in this string. 'text' is a unicode string.
def get_emoji(text):
    text = unicode(text, 'ignore')
    try:
        ranges = re.compile(u'([\U00002600-\U000027BF])|([\U0001f300-\U0001f64F])|([\U0001f680-\U0001f6FF])')
    except re.error:
        ranges = re.compile(u'([\u2600-\u27BF])|([\uD83C][\uDF00-\uDFFF])|([\uD83D][\uDC00-\uDE4F])|([\uD83D][\uDE80-\uDEFF])')
    emojis = (ranges.findall(text))

jovianlin

import networkx as nx
from networkx.algorithms import bipartite

B = nx.Graph()
B.add_nodes_from([1,2,3,4], bipartite=0) # Add the node attribute "bipartite"
B.add_nodes_from(['a', 'b', 'c', 'd'], bipartite=1)
B.add_edges_from([(1,'a'), (1,'b'), (2,'b'), (2,'c'), (3,'c'), (4,'a')])

print 'Is connected? %s' % nx.is_connected(B)

jovianlin

from multiprocessing import Pool

def job(x):
    return x ** 2
# End of job(...).

if __name__ == "__main__":
    p = Pool(processes=50)
    data_1 = p.map(job, range(10))
    data_2 = p.map(job, [99, 111, 7236])

jovianlin

du -h <dir> | grep '[0-9\.]\+G'
# e.g.:
# du -h /home/peterparker | grep '[0-9\.]\+G'

jovianlin

# Snippet for identifying the best classifier from svm, per, KNN based on their respective (accuracy) scores.
idx = np.argmax([svm_accuracy, per_accuracy, knn_accuracy])
classifiers = {0: 'SVM', 1: 'Perceptron', 2: 'KNN'}
print('Best classifier is {}'.format(classifiers[idx]))