In [2]:

    

%matplotlib inline
import os
print os.getcwd()
try:
    from andrew_exercises.mlex1 import ex1 as ex
except:
    import ex1 as ex
from matplotlib.pyplot import *
from mpl_toolkits.mplot3d import axes3d, Axes3D
from matplotlib import cm
import itertools

X, y, theta = ex.load_data()
ex.plot_data(X, y)
theta_opt, J_history, theta_hist = ex.run_gradient_descent(X, y, theta)
ex.plot_cost_convergence(J_history)
ex.plot_hipothesys_fit(X, theta_opt)
ex.plot_3d_cost(X, y, theta_hist, J_history)


    

    




/home/tony/work/solo/octaveplay
loaded 97 training samples
The cost for theta=[0,0] is 32.0727338775
Gradient descent got us this optimum theta: 
[[-3.63029144]
 [ 1.16636235]]






    













    













    

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