tsterker’s gists

tsterker / gist_clone_from_hash.sh

Created November 6, 2011 23:15

[sh] checkout private gist by hash

	# Function and alias for ~/.bashrc to checkout a private gist by hash

	# TRY IT OUT
	# checkout this very gist after you copy/pasted this file into your ~/.bashrc
	# with this terminal command:
	# gist 1343759

	# temp dir for checkouts
	GIST_DIR=~/gist_tmp

tsterker / traverse.c

Created November 11, 2011 00:02

[c] btree traversal

	/* btree traversal - most left to most right */

	pnode traverse(pnode root, void (*func)(pnode))
	{
	pnode ret, tmp = root;
	if(!root) return root;
	while((tmp = traverse(tmp->left, func))){; }
	ret = traverse(root->right, fun

tsterker / circle.py

Created November 27, 2011 02:00

[py] circle in pyglet

tsterker / file_sizeof.c

Created December 20, 2011 22:47

[c] Get filesize


	/* Returns file size in BYTES */
	int file_sizeof(FILE *fp)
	{
	int filesize;
	fseek(fp, 0L, SEEK_END); /* jump to end of file */
	filesize = ftell(fp); /* current byte of file == f

tsterker / gist:1503993

Created December 21, 2011 00:46 — forked from chrisgilmerproj/gist:1503913

[py] Password Generator

p = ''.join(random.sample('ABCDEFGHJKLMNPQRSTUVWXYZ23456789', 6))

tsterker / Evolution of a Python programmer.py

Created December 21, 2011 00:58

[py] programmer levels

	#Newbie programmer
	def factorial(x):
	if x == 0:
	return 1
	else:
	return x * factorial(x - 1)
	print factorial(6)


	#First year programmer, studied Pascal

tsterker / linregr.py

Created December 21, 2011 01:12 — forked from marcelcaraciolo/linregr.py

[py] linear regression

	#Evaluate the linear regression
	def compute_cost(X, y, theta):
	'''
	Comput cost for linear regression
	'''
	#Number of training samples
	m = y.size

	predictions = X.dot(theta).flatten()

tsterker / multlin.py

Created December 21, 2011 01:14 — forked from marcelcaraciolo/multlin.py

[py] multivariate linear regression

	def feature_normalize(X):
	'''
	Returns a normalized version of X where
	the mean value of each feature is 0 and the standard deviation
	is 1. This is often a good preprocessing step to do when
	working with learning algorithms.
	'''
	mean_r = []
	std_r = []

tsterker / ex1.py

Created December 21, 2011 01:15 — forked from gorlum0/ex1.py

[py] ml-class - ex1

	#!/usr/bin/env python
	import numpy as np
	import matplotlib.pyplot as plt
	from numpy import newaxis, r_, c_, mat
	from numpy.linalg import *

	def plotData(X, y):
	plt.plot(X, y, 'rx', markersize=7)
	plt.ylabel('Profit in $10,000s')
	plt.xlabel('Population of City in 10,000s')

tsterker / gist:2408805

Created April 17, 2012 20:31

[js] Remove all children DOM elements

	// Remove all children DOM elements

	while (node.hasChildNodes()) {
	node.removeChild(node.lastChild);
	}

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