def krigagemMatrix(self, colY, colX, colZ):
self.X = list(self.matrix[colY, colX, colZ])
testfun = testfunctions().squared
self.y = testfun(self.X)
self.k = kriging(self.X, self.y, testfunction=testfun, testPoints=300)
self.k.train()
self.k.snapshot()
def getTrainData():
"""获取训练数据"""
X = io.getData(dataFilePath)
Y = io.getData(labelsFilePath)
return X, Y
# The Kriging model starts by defining a sampling plan, we use an optimal Latin Hypercube here
sp = samplingplan(3)
X = sp.optimallhc(30)
print("[DEBUG] X: {}".format(X))
# Next, we define the problem we would like to solve
testfun = testfunctions().squared
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, testPoints=300)
# The model is then trained
k.train()
k.snapshot()
# It's typically beneficial to add additional points based on the results of the initial training
# The infill method can be used for this
# In this example, we will add nine points in three batches. The model gets trained after each stage
for i in range(10):
print(k.history['rsquared'][-1])
print('Infill iteration {0}'.format(i + 1))
import pyKriging
from pyKriging.krige import kriging
from pyKriging.samplingplan import samplingplan
from pyKriging.testfunctions import testfunctions
# The Kriging model starts by defining a sampling plan, we use an optimal Latin Hypercube here
sp = samplingplan(3)
X = sp.optimallhc(30)
# Next, we define the problem we would like to solve
testfun = testfunctions().squared
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, testPoints=300)
# The model is then trained
k.train()
k.snapshot()
# It's typically beneficial to add additional points based on the results of the initial training
# The infill method can be used for this
# In this example, we will add nine points in three batches. The model gets trained after each stage
for i in range(10):
print k.history['rsquared'][-1]
print 'Infill iteration {0}'.format(i + 1)
infillPoints = k.infill(10)
# Evaluate the infill points and add them back to the Kriging model
for point in infillPoints: