tsterker

#!/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

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

#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

#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

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