vaclavcadek’s gists

vaclavcadek / FooBar.java

Created May 24, 2013 13:05

Annotation example

	package com.example;

	public FooBar {

	@WiredMessage(mapping="BUTTONS-FOO")
	private String foo = "foo";

	// clear selected button label
	@WiredMessage(mapping="BUTTONS-BAR")
	private String bar = "bar";

vaclavcadek / remove_punctuation.py

Created April 28, 2014 12:44

Remove punctuation from string

	import string

	'!Hello,'.strip(string.punctuation)

vaclavcadek / pillow.py

Created July 10, 2014 15:36

Download image from url, make polyline and convert it to base64

	from base64 import b64encode
	from PIL import Image, ImageDraw
	import requests
	from StringIO import StringIO

	url = 'http://jssgallery.org/other_artists/andy_warhol/campbells.jpg'

	response = requests.get(url)
	img = Image.open(StringIO(response.content))
	draw = ImageDraw.Draw(img)

vaclavcadek / arst-orari-scraper.py

Created August 5, 2014 13:17

Simple web scraper for collecting all ARST schedules (for offline use :))

	import urllib2
	from bs4 import BeautifulSoup
	import os

	html_doc_example = """
	<html><head><title>ARST - Orari</title></head>

	<div onclick="openClose('a1')" class="mainExpand"><h2><a style="cursor:pointer; src="pdfmini.jpg"/>ABBASANTA</h2></div>
	<div id="a1" class="texter">
	<a href="402.pdf" target="_blank"><img src="pdfmini.jpg" />402 - ABBASANTA-GHILARZA-AIDOMAGGIORE-NORBELLO-ABBASANTA</a></br>

vaclavcadek / ajax.js

Created January 20, 2015 14:27

Simple XMLHttpRequest

	var xhr = new XMLHttpRequest();
	xhr.open('GET', '/media/resources/gamedev/weapon.json', true);
	xhr.onload = function() {
	JSON.parse(this.responseText);
	};
	xhr.send();

vaclavcadek / comparing_runtimes.py

Last active August 29, 2015 14:25

Comparison of runtime behavior of three approaches: pure Python, naive NumPy and good NumPy

	import timeit

	normal_py_sec = timeit.timeit('sum(x*x for x in xrange(1000))', number=10000)
	naive_np_sec = timeit.timeit('sum(na*na)', setup="import numpy as np; na=np.arange(1000)", number=10000)
	good_np_sec = timeit.timeit('na.dot(na)', setup="import numpy as np; na=np.arange(1000)", number=10000)

	print("Normal Python: %f sec" % normal_py_sec)
	print("Naive NumPy: %f sec" % naive_np_sec)
	print("Good NumPy: %f sec" % good_np_sec)

vaclavcadek / df_correlation.py

Created August 4, 2015 13:11

Plot the correlation matrix (Spearman's) of values within the dataframe

	import numpy as np

	def plot_correlation(dataframe, title='', corr_type=''):
	lang_names = dataframe.columns.tolist()
	tick_indices = np.arange(0.5, len(lang_names) + 0.5)
	plt.figure(figsize=(12,7))
	plt.pcolor(dataframe.values, cmap='RdBu', vmin=-1, vmax=1)
	colorbar = plt.colorbar()
	colorbar.set_label(corr_type)
	plt.title(title)

vaclavcadek / vizualize_tree.py

Last active August 29, 2015 14:26

Vizualize sklearn decision tree using graphviz

	from sklearn.externals.six import StringIO
	from sklearn import tree
	from graphviz import Source
	from IPython.display import SVG

	def vizualize_tree(model, figsize=(14,8)):
	out = StringIO()
	tree.export_graphviz(model, out_file=out)
	graph = Source(out.getvalue())
	graph.format = 'svg'

vaclavcadek / feature_selection_cv.py

Created August 7, 2015 08:03

Feature selection with cross-validation

	import matplotlib.pyplot as plt
	from sklearn.svm import SVC
	from sklearn.cross_validation import StratifiedKFold
	from sklearn.feature_selection import RFECV
	from sklearn.datasets import make_classification

	# Create the RFE object and compute a cross-validated score.
	svc = SVC(kernel='linear')
	# The "accuracy" scoring is proportional to the number of correct
	# classifications

vaclavcadek / generate_bit_strings.py

Last active August 29, 2015 14:27

Generate all bit string permatation

	for bits in product([0, 1], repeat=4):
	print("".join(str(b) for b in bits)

Václav Čadek vaclavcadek