Matthew Mayo mmmayo13

## portilla-ann-14.py
len(mlp.coefs_[0])

## hould-binary-2.py
import numpy as np

class Perceptron(object):
    """Perceptron classifier.

    Parameters
    ------------
    eta : float
        Learning rate (between 0.0 and 1.0)
    n_iter : int

## hould-binary-3.py
# Partial portion of the "fit" function
for xi, target in zip(X, y):
    update = self.eta * (target - self.predict(xi))
    self.w_[1:] += update * xi
    self.w_[0] += update
    errors += int(update != 0.0)

## indico-faq-bot-2.py
def run():
    data = faqs.keys()
    print "FAQ data received. Finding features."

feats = make_feats(data)
    with open('faq_feats.pkl', 'wb') as f:
        pickle.dump(feats, f)
    print "FAQ features found!"

## dask-searchcv-11.sh
$ dask-ec2 up --keyname mykey --keypair ~/.ssh/mykey.pem --nprocs 8 --type m4.2xlarge

## stock-analysis-13.py
stocks_df.plot(kind='line', grid=True, title='GOOG, FB, AMZN Adjusted Closes, May 2012').vlines(x='May 18, 2012', ymin=0, ymax=350)

## text_data_preprocessing_6.py
def stem_and_lemmatize(words):
    stems = stem_words(words)
    lemmas = lemmatize_verbs(words)
    return stems, lemmas

stems, lemmas = stem_and_lemmatize(words)
print('Stemmed:\n', stems)
print('\nLemmatized:\n', lemmas)

## sample_text_2.py
sample = """Title Goes Here
Bolded Text
Italicized Text

But this will still be here!

I run. He ran. She is running. Will they stop running?

I talked. She was talking. They talked to them about running. Who ran to the talking runner?

## sample_text.py
sample = """<h1>Title Goes Here</h1>

<b>Bolded Text</b>
<i>Italicized Text</i>

<img src="this should all be gone"/>

<a href="this will be gone, too">But this will still be here!</a>

I run. He ran. She is running. Will they stop running?

## text_data_preprocessing_4.py
words = nltk.word_tokenize(sample)
print(words)
	import numpy as np

	class Perceptron(object):
	"""Perceptron classifier.

	Parameters
	------------
	eta : float
	Learning rate (between 0.0 and 1.0)
	n_iter : int
	# Partial portion of the "fit" function
	for xi, target in zip(X, y):
	update = self.eta * (target - self.predict(xi))
	self.w_[1:] += update * xi
	self.w_[0] += update
	errors += int(update != 0.0)
	def run():
	data = faqs.keys()
	print "FAQ data received. Finding features."

	feats = make_feats(data)
	with open('faq_feats.pkl', 'wb') as f:
	pickle.dump(feats, f)
	print "FAQ features found!"
	def stem_and_lemmatize(words):
	stems = stem_words(words)
	lemmas = lemmatize_verbs(words)
	return stems, lemmas

	stems, lemmas = stem_and_lemmatize(words)
	print('Stemmed:\n', stems)
	print('\nLemmatized:\n', lemmas)
	sample = """Title Goes Here
	Bolded Text
	Italicized Text

	But this will still be here!

	I run. He ran. She is running. Will they stop running?

	I talked. She was talking. They talked to them about running. Who ran to the talking runner?
	sample = """<h1>Title Goes Here</h1>

	<b>Bolded Text</b>
	<i>Italicized Text</i>

	<img src="this should all be gone"/>

	<a href="this will be gone, too">But this will still be here!</a>

	I run. He ran. She is running. Will they stop running?