In [2]:

    

import pygp
import numpy as np


    

In [63]:

    

data = np.load('xy.npz')
X = data['X']
y = data['y']
print X
print y


    

    




[[ 2.08397043]
 [-0.82101807]
 [-0.6178707 ]
 [-1.18382261]
 [ 0.27408744]
 [ 0.59944173]
 [ 1.76889792]
 [-0.46564555]
 [ 0.58885278]
 [-0.83298221]
 [-0.51210653]
 [ 0.27788314]
 [-0.06587043]
 [-0.82141236]
 [ 0.18539944]
 [-0.85829617]
 [ 0.37078663]
 [-1.40986916]
 [-0.14466841]
 [-0.55329962]]
[ 2.50721853 -0.21750539  0.02024332 -0.42130159  1.11842953 -0.28980512
  1.86022698 -0.34258445  0.02840726 -0.3052106  -0.47671761  1.06117071
  0.2335452  -0.14932212  1.53588071 -0.0670771   0.757132    0.28406853
 -0.25679137 -0.34599644]

In [17]:

    

gp = pygp.BasicGP(sn=.1, sf=1, ell=.1, mu=0)
gp.add_data(X, y)
#gp.add_data(X, y)


    

In [58]:

    

print gp.data[0].shape
print gp.ndata


    

    




(20, 1)
20

In [20]:

    

from scipy.optimize import minimize, rosen, rosen_der


    

In [26]:

    

x0 = [1.3, 0.7, 0.8, 1.9, 1.2]


    

In [27]:

    

res = minimize(rosen, x0, method='Nelder-Mead')
res.x


    

    
Out[27]:





array([ 0.99910115,  0.99820923,  0.99646346,  0.99297555,  0.98600385])

In [28]:

    

res = minimize(rosen, x0, method='BFGS', jac=rosen_der, options={'gtol': 1e-6, 'disp': True})
res.x


    

    




Optimization terminated successfully.
         Current function value: 0.000000
         Iterations: 52
         Function evaluations: 64
         Gradient evaluations: 64






    
Out[28]:





array([ 1.,  1.,  1.,  1.,  1.])

In [30]:

    

X_y = ([], [])
def callbackF(Xi):
    global X_y
    X_y[0].append(Xi)
    X_y[1].append(rosen(Xi))

res = minimize(rosen, x0, method='BFGS',
               jac=rosen_der,
               options={'gtol': 1e-6, 'disp': True}, callback=callbackF)
res.x


    

    




Optimization terminated successfully.
         Current function value: 0.000000
         Iterations: 52
         Function evaluations: 64
         Gradient evaluations: 64






    
Out[30]:





array([ 1.,  1.,  1.,  1.,  1.])

In [32]:

    

X, y = X_y


    

In [36]:

    

from scipy.spatial import ConvexHull


    

In [46]:

    

import matplotlib.pyplot as plt
%matplotlib inline


    

In [49]:

    

X = np.array(X)
y = np.array(y)
print X.shape, y.shape


    

    




(52, 5) (52,)

In [50]:

    

hull = ConvexHull(X)


    

In [52]:

    

hull.vertices.shape


    

    
Out[52]:





(51,)

In [55]:

    

points = np.random.rand(30, 2)  
hull = ConvexHull(points)
hull.vertices


    

    
Out[55]:





array([19, 16,  3,  4, 28, 15, 22,  6, 10], dtype=int32)

In [56]:

    

plt.plot(points[:,0], points[:,1], 'o')
plt.plot(points[hull.vertices,0], points[hull.vertices,1], 'ro')


    

    
Out[56]:





[<matplotlib.lines.Line2D at 0x7fcec6dd0d10>]






    

In [ ]:

    




    

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