def generateBBTMatrix(factory, training, verbose=False):
from pysgpp import DataVector
storage = factory.getStorage()
b = factory.createOperationB()
alpha = DataVector(storage.getSize())
temp = DataVector(training.getSize())
erg = DataVector(alpha.getSize())
col = 0
# create B matrix
m = np.zeros((storage.getSize(), storage.getSize()))
for i in range(storage.getSize()):
# apply unit vectors
temp.setAll(0.0)
erg.setAll(0.0)
alpha.setAll(0.0)
alpha[i] = 1.0
b.multTranspose(alpha, training, temp)
b.mult(temp, training, erg)
#Sets the column in m
for j in range(storage.getSize()):
m[j, col] = erg[j]
col = col + 1
return m
def addConst(grid, alpha, c, y):
alpha_vec = DataVector(alpha)
opHier = createOperationHierarchisation(grid)
opHier.doDehierarchisation(alpha_vec)
for i in range(alpha_vec.getSize()):
alpha_vec[i] = c * alpha_vec[i] + y
opHier.doHierarchisation(alpha_vec)
return alpha_vec.array()
def generateBBTMatrixPython(factory, training, verbose=False):
storage = factory.getStorage()
b = factory.createOperationB()
alpha = DataVector(storage.getSize())
erg = DataVector(alpha.getSize())
temp = DataVector(training.getSize())
# create B matrix
m = DataVector(storage.getSize(), storage.getSize())
for i in range(storage.getSize()):
# apply unit vectors
temp.setAll(0.0)
erg.setAll(0.0)
alpha.setAll(0.0)
alpha[i] = 1.0
b.multTranspose(alpha, training, temp)
b.mult(temp, training, erg)
#Sets the column in m
m.setColumn(i, erg)
return m
dim = 2
grid = Grid.createLinearGrid(dim)
gridStorage = grid.getStorage()
print "dimensionality: {}".format(dim)
# create regular grid, level 3
level = 3
gridGen = grid.createGridGenerator()
gridGen.regular(level)
print "number of initial grid points: {}".format(gridStorage.size())
# definition of function to interpolate - nonsymmetric(!)
f = lambda x0, x1: 16.0 * (x0 - 1) * x0 * (x1 - 1) * x1 * x1
# create coefficient vector
alpha = DataVector(gridStorage.size())
print "length of alpha vector: {}".format(alpha.getSize())
# now refine adaptively 5 times
for refnum in range(5):
# set function values in alpha
for i in xrange(gridStorage.size()):
gp = gridStorage.get(i)
alpha[i] = f(gp.getCoord(0), gp.getCoord(1))
# hierarchize
createOperationHierarchisation(grid).doHierarchisation(alpha)
# refine a single grid point each time
gridGen.refine(SurplusRefinementFunctor(alpha, 1))
print "refinement step {}, new grid size: {}".format(
refnum + 1, gridStorage.size())
dim = 2
grid = Grid.createLinearGrid(dim)
gridStorage = grid.getStorage()
print "dimensionality: {}".format(dim)
# create regular grid, level 3
level = 3
gridGen = grid.createGridGenerator()
gridGen.regular(level)
print "number of initial grid points: {}".format(gridStorage.size())
# definition of function to interpolate - nonsymmetric(!)
f = lambda x0, x1: 16.0 * (x0-1)*x0 * (x1-1)*x1*x1
# create coefficient vector
alpha = DataVector(gridStorage.size())
print "length of alpha vector: {}".format(alpha.getSize())
# now refine adaptively 5 times
for refnum in range(5):
# set function values in alpha
for i in xrange(gridStorage.size()):
gp = gridStorage.get(i)
alpha[i] = f(gp.getCoord(0), gp.getCoord(1))
# hierarchize
createOperationHierarchisation(grid).doHierarchisation(alpha)
# refine a single grid point each time
gridGen.refine(SurplusRefinementFunctor(alpha, 1))
print "refinement step {}, new grid size: {}".format(refnum+1, gridStorage.size())
