# Copyright (C) 2008-today The SG++ project
# This file is part of the SG++ project. For conditions of distribution and
# use, please see the copyright notice provided with SG++ or at
# sgpp.sparsegrids.org
import numpy as np
import matplotlib.pyplot as plt
from pysgpp import DataVector, Grid, createOperationHierarchisation, createOperationEval
from pysgpp.extensions.datadriven.uq.operations import hierarchize
from pysgpp.extensions.datadriven.uq.plot import plotFunction3d, plotSG3d
from pysgpp.extensions.datadriven.uq.dists import Normal, J
from pysgpp.extensions.datadriven.uq.operations.sparse_grid import evalSGFunction
U = J([Normal.by_alpha(0.5, 0.05, 0.001),
Normal.by_alpha(0.5, 0.05, 0.001)])
grid = Grid.createPolyGrid(2, 2)
grid.getGenerator().regular(3)
gs = grid.getStorage()
nodalValues = np.ndarray(gs.getSize())
p = DataVector(2)
for i in range(gs.getSize()):
gs.getCoordinates(gs.getPoint(i), p)
nodalValues[i] = U.pdf(p.array())
alpha = hierarchize(grid, nodalValues)
fig, _, _ = plotFunction3d(U.pdf)
def withNormalDistribution(self, mu, sigma, alpha):
self._dist = Normal.by_alpha(mu, sigma, alpha)
return self
def withNormalDistribution(self, mu, sigma, alpha):
self._dist = Normal.by_alpha(mu, sigma, alpha)
return self