samKriging.py

import pyKriging  
from pyKriging.krige import kriging  
from pyKriging.samplingplan import samplingplan

# The Kriging model starts by defining a sampling plan, we use an optimal Latin Hypercube here
sp = samplingplan(6)  
X = sp.optimallhc(20)

# Next, we define the problem we would like to solve
testfun = pyKriging.testfunctions().branin  
y = testfun(X)

# Now that we have our initial data, we can create an instance of a Kriging model
k = kriging(X, y, testfunction=testfun, name='simple')  
k.train()

# # Now, five infill points are added. Note that the model is re-trained after each point is added
# numiter = 5  
# for i in range(numiter):  
#     print ('Infill iteration {0} of {1}....'.format(i + 1, numiter))
#     newpoints = k.infill(1)
#     for point in newpoints:
#         k.addPoint(point, testfun(point)[0])
#     k.train()

# And plot the results
k.plot()