def rank(self, grid, gp, alphas, params, *args, **kws):
# get grid point associated to ix
gs = grid.getStorage()
p = [gp.getCoord(j) for j in xrange(gs.dim())]
# get joint distribution
ap = params.activeParams()
U = ap.getIndependentJointDistribution()
T = ap.getJointTransformation()
q = T.unitToProbabilistic(p)
# scale surplus by probability density
ix = gs.seq(gp)
# get area of basis function
A = 1.0
for d in xrange(gs.dim()):
A *= getIntegral(grid, gp.getLevel(d), gp.getIndex(d))
return abs(alphas[ix]) * A * U.pdf(q)
def rank(self, grid, gp, alphas, params, *args, **kws):
# get grid point associated to ix
gs = grid.getStorage()
p = [gs.getCoordinates(gp, j) for j in range(gs.getDimension())]
# get joint distribution
ap = params.activeParams()
U = ap.getIndependentJointDistribution()
T = ap.getJointTransformation()
q = T.unitToProbabilistic(p)
# estimate the surplus
alpha = estimateSurplus(grid, gp, alphas)
# get area of basis function
A = 1.0
for d in range(gs.getDimension()):
A *= getIntegral(grid, gp.getLevel(d), gp.getIndex(d))
return abs(alpha) * U.pdf(q) * A
def rank(self, grid, gp, alphas, params, *args, **kws):
# get grid point associated to ix
gs = grid.getStorage()
p = [gp.getCoord(j) for j in xrange(gs.dim())]
# get joint distribution
ap = params.activeParams()
U = ap.getIndependentJointDistribution()
T = ap.getJointTransformation()
q = T.unitToProbabilistic(p)
# scale surplus by probability density
ix = gs.seq(gp)
# get area of basis function
A = 1.0
for d in xrange(gs.dim()):
A *= getIntegral(grid, gp.getLevel(d), gp.getIndex(d))
return abs(alphas[ix]) * A * U.pdf(q)
def rank(self, grid, gp, alphas, params, *args, **kws):
gs = grid.getStorage()
# estimate the convergence of ancestors of gp
ratio = estimateConvergence(grid, gp, alphas)
# get grid point associated to ix
p = [gp.getCoord(j) for j in xrange(gs.dim())]
# get joint distribution
ap = params.activeParams()
U = ap.getIndependentJointDistribution()
T = ap.getJointTransformation()
q = T.unitToProbabilistic(p)
# get area of basis function
A = 1.0
for d in xrange(gs.dim()):
A *= getIntegral(grid, gp.getLevel(d), gp.getIndex(d))
return abs(ratio) * U.pdf(q) * A
def rank(self, grid, gp, alphas, params, *args, **kws):
gs = grid.getStorage()
# estimate the convergence of ancestors of gp
ratio = estimateConvergence(grid, gp, alphas)
# get grid point associated to ix
p = [gp.getCoord(j) for j in xrange(gs.dim())]
# get joint distribution
ap = params.activeParams()
U = ap.getIndependentJointDistribution()
T = ap.getJointTransformation()
q = T.unitToProbabilistic(p)
# get area of basis function
A = 1.0
for d in xrange(gs.dim()):
A *= getIntegral(grid, gp.getLevel(d), gp.getIndex(d))
return abs(ratio) * U.pdf(q) * A
def __doMarginalize(grid, alpha, dd, measure=None):
gs = grid.getStorage()
dim = gs.dim()
if dim < 2:
raise AttributeError("The grid has to be at least of dimension 2")
if dd >= dim:
raise AttributeError("The grid has only %i dimensions, so I can't \
integrate over %i" % (dim, dd))
# create new grid
n_dim = dim - 1
n_grid = createGrid(grid, n_dim)
n_gs = n_grid.getStorage()
# insert grid points
n_gp = HashGridIndex(n_dim)
for i in xrange(gs.size()):
gp = gs.get(i)
for d in range(dim):
if d == dd:
# omit marginalization direction
continue
elif d < dd:
n_gp.set(d, gp.getLevel(d), gp.getIndex(d))
else:
n_gp.set(d - 1, gp.getLevel(d), gp.getIndex(d))
# insert grid point
if not n_gs.has_key(n_gp):
n_gs.insert(n_gp)
n_gs.recalcLeafProperty()
# create coefficient vector
n_alpha = DataVector(n_gs.size())
n_alpha.setAll(0.0)
# set function values for n_alpha
for i in xrange(gs.size()):
gp = gs.get(i)
for d in range(dim):
if d == dd:
dd_level = gp.getLevel(d)
dd_index = gp.getIndex(d)
elif d < dd:
n_gp.set(d, gp.getLevel(d), gp.getIndex(d))
else:
n_gp.set(d - 1, gp.getLevel(d), gp.getIndex(d))
if not n_gs.has_key(n_gp):
raise Exception("This should not happen!")
# compute the integral of the given basis
if measure is None:
q, err = getIntegral(grid, dd_level, dd_index), 0.
else:
dist, trans = measure[0][dd], measure[1][dd]
lf = LinearGaussQuadratureStrategy([dist], [trans])
basis = getBasis(grid)
gpdd = HashGridIndex(1)
gpdd.set(0, dd_level, dd_index)
q, err = lf.computeLinearFormByList([gpdd], basis)
q = q[0]
# search for the corresponding index
j = n_gs.seq(n_gp)
n_alpha[j] += alpha[i] * q
return n_grid, n_alpha, err
def __doMarginalize(grid, alpha, linearForm, dd, measure=None):
gs = grid.getStorage()
dim = gs.getDimension()
if dim < 2:
raise AttributeError("The grid has to be at least of dimension 2")
if dd >= dim:
raise AttributeError("The grid has only %i dimensions, so I can't \
integrate over %i" % (dim, dd))
# create new grid
n_dim = dim - 1
n_grid = createGrid(grid, n_dim)
n_gs = n_grid.getStorage()
# insert grid points
n_gp = HashGridPoint(n_dim)
for i in range(gs.getSize()):
gp = gs.getPoint(i)
for d in range(dim):
if d == dd:
# omit marginalization direction
continue
elif d < dd:
n_gp.set(d, gp.getLevel(d), gp.getIndex(d))
else:
n_gp.set(d - 1, gp.getLevel(d), gp.getIndex(d))
# insert grid point
if not n_gs.isContaining(n_gp):
n_gs.insert(n_gp)
n_gs.recalcLeafProperty()
# create coefficient vector
n_alpha = np.zeros(n_gs.getSize())
basis = getBasis(grid)
# set function values for n_alpha
for i in range(gs.getSize()):
gp = gs.getPoint(i)
for d in range(dim):
if d == dd:
dd_level = gp.getLevel(d)
dd_index = gp.getIndex(d)
elif d < dd:
n_gp.set(d, gp.getLevel(d), gp.getIndex(d))
else:
n_gp.set(d - 1, gp.getLevel(d), gp.getIndex(d))
if not n_gs.isContaining(n_gp):
raise Exception("This should not happen!")
# compute the integral of the given basis
if measure is None:
q, err = getIntegral(grid, dd_level, dd_index), 0.
else:
dist, trans = measure[0][dd], measure[1][dd]
linearForm.setDistributionAndTransformation([dist], [trans])
gpdd = HashGridPoint(1)
gpdd.set(0, dd_level, dd_index)
q, err = linearForm.computeLinearFormByList(gs, [gpdd], basis)
q = q[0] * trans.vol()
err *= trans.vol()
# search for the corresponding index
j = n_gs.getSequenceNumber(n_gp)
n_alpha[j] += alpha[i] * q
return n_grid, n_alpha, err
