import pysgpp
import math
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from operator import mul
base = 0.1

The first thing we need is a function to evaluate. This function will be evaluated on the domain \([0, 1]^d\). This particular function can be used with any number of dimensions. The input parameter of the function is of type pysgpp.DataVector, so do not treat it like a list. The return type is float.

def f(x):
product = 1.0
for i in range(x.getSize()):
product *= math.exp(-pow(base, i)*x[i])
return product

We have to wrap f in a pysgpp.MultiFunction object.

func = pysgpp.multiFunc(f)

comparison function

def compare():
mydim = 5
operation = pysgpp.CombigridOperation.createLinearLejaQuadrature(mydim, func)
levelManager = operation.getLevelManager()
idx = pysgpp.IndexVector([1 for i in range(mydim)])
result = operation.getResult()
analyticalResult = reduce(mul, [pow(base, -i) * (1.0 - pow(math.e, -pow(base, i))) for i in range(mydim)], 1.0)
print("Full Grid Error: " + str(abs(result - analyticalResult)))
numGridPoints = operation.numGridPoints()
print("Number of grid points: " + str(numGridPoints))


result = operation.evaluate(3)
print("Regular Grid Error: " + str(abs(result - analyticalResult)))
numGridPoints2 = operation.numGridPoints()
print("Number of grid points: " + str(numGridPoints2))


result = operation.getResult()
print("Adaptive Grid Error: " + str(abs(result - analyticalResult)))
numGridPoints3 = operation.numGridPoints()
print("Number of grid points: " + str(numGridPoints3))