def testSerializationLinearBoudaryWithLeaf(self):
"""Uses Linear grid for tests"""
from pysgpp import Grid
srcLeaf = []
factory = Grid.createLinearBoundaryGrid(2)
self.failIfEqual(factory, None)
gen = factory.createGridGenerator()
gen.regular(3)
for i in xrange(factory.getStorage().size()):
srcLeaf.append(factory.getStorage().get(i).isLeaf())
str = factory.serialize()
self.assert_(len(str) > 0)
newfac = Grid.unserialize(str)
self.failIfEqual(newfac, None)
self.assertEqual(factory.getStorage().size(),
newfac.getStorage().size())
for i in xrange(factory.getStorage().size()):
self.failUnlessEqual(newfac.getStorage().get(i).isLeaf(),
srcLeaf[i])
def createGrid(grid, dim, deg=1, addTruncatedBorder=False):
# create new grid
gridType = grid.getType()
if gridType in [Poly, PolyBoundary]:
deg = max(deg, grid.getDegree())
# print gridType, deg
if deg > 1:
if gridType in [LinearBoundary, PolyBoundary]:
return Grid.createPolyBoundaryGrid(dim, deg)
elif gridType == LinearL0Boundary:
raise NotImplementedError("there is no full boundary polynomial grid")
elif gridType in [Linear, Poly]:
return Grid.createPolyGrid(dim, deg)
else:
raise Exception('unknown grid type %s' % gridType)
else:
if gridType == Linear:
return Grid.createLinearGrid(dim)
elif gridType == LinearBoundary:
return Grid.createLinearBoundaryGrid(dim)
elif gridType == LinearL0Boundary:
return Grid.createLinearBoundaryGrid(dim, 0)
else:
raise Exception('unknown grid type %s' % gridType)
def testSerializationLinearBoudaryBoundingBox(self):
"""Uses Linear grid for tests"""
from pysgpp import Grid
factory = Grid.createLinearBoundaryGrid(2)
self.failIfEqual(factory, None)
gen = factory.createGridGenerator()
gen.regular(3)
boundingBox = factory.getBoundingBox()
tempBound = boundingBox.getBoundary(0)
tempBound.leftBoundary = 0.0
tempBound.rightBoundary = 100.0
tempBound.bDirichletLeft = False;
tempBound.bDirichletRight = False;
boundingBox.setBoundary(0, tempBound)
str = factory.serialize()
self.assert_(len(str) > 0)
newfac = Grid.unserialize(str)
self.failIfEqual(newfac, None)
self.assertEqual(factory.getStorage().size(), newfac.getStorage().size())
boundingBox = newfac.getBoundingBox()
tempBound = boundingBox.getBoundary(0)
self.assertEqual(0.0, tempBound.leftBoundary)
self.assertEqual(100.0, tempBound.rightBoundary)
self.assertEqual(False, tempBound.bDirichletLeft)
self.assertEqual(False, tempBound.bDirichletRight)
def testSerializationLinearBoudaryBoundingBox(self):
"""Uses Linear grid for tests"""
from pysgpp import Grid
factory = Grid.createLinearBoundaryGrid(2)
self.failIfEqual(factory, None)
gen = factory.createGridGenerator()
gen.regular(3)
boundingBox = factory.getBoundingBox()
tempBound = boundingBox.getBoundary(0)
tempBound.leftBoundary = 0.0
tempBound.rightBoundary = 100.0
tempBound.bDirichletLeft = False
tempBound.bDirichletRight = False
boundingBox.setBoundary(0, tempBound)
str = factory.serialize()
self.assert_(len(str) > 0)
newfac = Grid.unserialize(str)
self.failIfEqual(newfac, None)
self.assertEqual(factory.getStorage().size(),
newfac.getStorage().size())
boundingBox = newfac.getBoundingBox()
tempBound = boundingBox.getBoundary(0)
self.assertEqual(0.0, tempBound.leftBoundary)
self.assertEqual(100.0, tempBound.rightBoundary)
self.assertEqual(False, tempBound.bDirichletLeft)
self.assertEqual(False, tempBound.bDirichletRight)
def createGrid(grid, dim, deg=1, addTruncatedBorder=False):
# create new grid
gridType = grid.getType()
if gridType in [Poly, PolyBoundary]:
deg = max(deg, grid.getDegree())
# print gridType, deg
if deg > 1:
if gridType in [LinearBoundary, PolyBoundary]:
return Grid.createPolyBoundaryGrid(dim, deg)
elif gridType == LinearL0Boundary:
raise NotImplementedError(
"there is no full boundary polynomial grid")
elif gridType in [Linear, Poly]:
return Grid.createPolyGrid(dim, deg)
else:
raise Exception('unknown grid type %s' % gridType)
else:
if gridType == Linear:
return Grid.createLinearGrid(dim)
elif gridType == LinearBoundary:
return Grid.createLinearBoundaryGrid(dim)
elif gridType == LinearL0Boundary:
return Grid.createLinearBoundaryGrid(dim, 0)
else:
raise Exception('unknown grid type %s' % gridType)
def plot_1d_2d(level=2):
grid1d = Grid.createLinearGrid(1)
grid1d.getGenerator().regular(level)
grid2d2 = extend_grid(grid1d, 1)
plotGrid2d(grid2d2)
grid2d = Grid.createLinearGrid(2)
grid2d.getGenerator().full(level)
plotGrid2d(grid2d)
plt.show()
def run_atan_sg(inputspace, gridType, level, numGridPoints,
boundaryLevel, fullGrid, refinement, out, plot):
testSetting = AtanPeridynamicExample(inputspace)
if refinement is not None:
testSetting.run_adaptive_sparse_grid(Grid.stringToGridType(gridType),
level, numGridPoints, refinement,
boundaryLevel, fullGrid, out,
plot)
else:
testSetting.run_regular_sparse_grid(Grid.stringToGridType(gridType),
level, numGridPoints, boundaryLevel,
fullGrid, out, plot)
def testRefinement2d_two(self):
from pysgpp import Grid, DataVector, SurplusRefinementFunctor
factory = Grid.createLinearBoundaryGrid(2)
storage = factory.getStorage()
gen = factory.createGridGenerator()
gen.regular(0)
alpha = DataVector(4)
for i in xrange(len(alpha)):
alpha[i] = 0.0
alpha[0] = 1.0
func = SurplusRefinementFunctor(alpha)
gen.refine(func)
alpha2 = DataVector(8)
for i in xrange(len(alpha2)):
alpha2[i] = 0.0
alpha2[4] = 1.0
func = SurplusRefinementFunctor(alpha2)
gen.refine(func)
self.failUnlessEqual(storage.size(), 13)
def testRefinement2d_two(self):
from pysgpp import Grid, DataVector, SurplusRefinementFunctor
factory = Grid.createLinearBoundaryGrid(2)
storage = factory.getStorage()
gen = factory.createGridGenerator()
gen.regular(0)
alpha = DataVector(4)
for i in xrange(len(alpha)):
alpha[i] = 0.0
alpha[0] = 1.0
func = SurplusRefinementFunctor(alpha)
gen.refine(func)
alpha2 = DataVector(8)
for i in xrange(len(alpha2)):
alpha2[i] = 0.0
alpha2[4] = 1.0
func = SurplusRefinementFunctor(alpha2)
gen.refine(func)
self.failUnlessEqual(storage.size(), 13)
def testOperationB(self):
from pysgpp import Grid, DataVector, DataMatrix
factory = Grid.createLinearBoundaryGrid(1)
gen = factory.createGridGenerator()
gen.regular(2)
alpha = DataVector(factory.getStorage().size())
p = DataMatrix(1, 1)
beta = DataVector(1)
alpha.setAll(0.0)
p.set(0, 0, 0.25)
beta[0] = 1.0
opb = factory.createOperationB()
opb.mult(beta, p, alpha)
self.failUnlessAlmostEqual(alpha[0], 0.75)
self.failUnlessAlmostEqual(alpha[1], 0.25)
self.failUnlessAlmostEqual(alpha[2], 0.5)
self.failUnlessAlmostEqual(alpha[3], 1.0)
self.failUnlessAlmostEqual(alpha[4], 0.0)
alpha.setAll(0.0)
alpha[2] = 1.0
p.set(0, 0, 0.25)
beta[0] = 0.0
opb.multTranspose(alpha, p, beta)
self.failUnlessAlmostEqual(beta[0], 0.5)
def testOperationTest_test(self):
from pysgpp import Grid, DataVector, DataMatrix
factory = Grid.createLinearBoundaryGrid(1)
gen = factory.createGridGenerator()
gen.regular(1)
alpha = DataVector(factory.getStorage().size())
data = DataMatrix(1, 1)
data.setAll(0.25)
classes = DataVector(1)
classes.setAll(1.0)
testOP = factory.createOperationTest()
alpha[0] = 0.0
alpha[1] = 0.0
alpha[2] = 1.0
c = testOP.test(alpha, data, classes)
self.failUnless(c > 0.0)
alpha[0] = 0.0
alpha[1] = 0.0
alpha[2] = -1.0
c = testOP.test(alpha, data, classes)
self.failUnless(c == 0.0)
def eval_fullGrid(level, dim, border=True):
if border:
grid = Grid.createLinearBoundaryGrid(dim, 1)
else:
grid = Grid.createLinearGrid(dim)
grid.getGenerator().full(level)
gs = grid.getStorage()
ans = np.ndarray((gs.getSize(), dim))
p = DataVector(dim)
for i in range(gs.getSize()):
gs.getCoordinates(gs.getPoint(i), p)
ans[i, :] = p.array()
return ans
def checkPositivity(grid, alpha):
# define a full grid of maxlevel of the grid
gs = grid.getStorage()
fullGrid = Grid.createLinearGrid(gs.dim())
fullGrid.createGridGenerator().full(gs.getMaxLevel())
fullHashGridStorage = fullGrid.getStorage()
A = DataMatrix(fullHashGridStorage.size(), fullHashGridStorage.dim())
p = DataVector(gs.dim())
for i in xrange(fullHashGridStorage.size()):
fullHashGridStorage.get(i).getCoords(p)
A.setRow(i, p)
res = evalSGFunctionMulti(grid, alpha, A)
ymin, ymax, cnt = 0, -1e10, 0
for i, yi in enumerate(res.array()):
if yi < 0. and abs(yi) > 1e-13:
cnt += 1
ymin = min(ymin, yi)
ymax = max(ymax, yi)
A.getRow(i, p)
print " %s = %g" % (p, yi)
if cnt > 0:
print "warning: function is not positive"
print "%i/%i: [%g, %g]" % (cnt, fullHashGridStorage.size(), ymin, ymax)
return cnt == 0
def checkPositivity(grid, alpha):
# define a full grid of maxlevel of the grid
gs = grid.getStorage()
fullGrid = Grid.createLinearGrid(gs.getDimension())
fullGrid.getGenerator().full(gs.getMaxLevel())
fullHashGridStorage = fullGrid.getStorage()
A = np.ndarray(
(fullHashGridStorage.getSize(), fullHashGridStorage.getDimension()))
p = DataVector(gs.getDimension())
for i in range(fullHashGridStorage.getSize()):
fullHashGridStorage.getCoordinates(fullHashGridStorage.getPoint(i), p)
A[i, :] = p.array()
negativeGridPoints = {}
res = evalSGFunctionMulti(grid, alpha, A)
ymin, ymax, cnt = 0, -1e10, 0
for i, yi in enumerate(res):
# print( A[i, :], yi )
if yi < -1e-11:
cnt += 1
negativeGridPoints[i] = yi, HashGridPoint(
fullHashGridStorage.getPoint(i))
ymin = min(ymin, yi)
ymax = max(ymax, yi)
# print( " %s = %g" % (A[i, :], yi) )
if cnt > 0:
print("warning: function is not positive")
print("%i/%i: [%g, %g]" %
(cnt, fullHashGridStorage.getSize(), ymin, ymax))
return negativeGridPoints
def eval_fullGrid(level, dim, border=True):
if border:
grid = Grid.createLinearBoundaryGrid(dim)
else:
grid = Grid.createLinearGrid(dim)
grid.createGridGenerator().full(level)
gs = grid.getStorage()
ans = DataMatrix(gs.size(), dim)
p = DataVector(dim)
for i in xrange(gs.size()):
gs.get(i).getCoords(p)
ans.setRow(i, p)
return ans
def testOperationB(self):
from pysgpp import Grid, DataVector, DataMatrix
factory = Grid.createLinearBoundaryGrid(1)
gen = factory.createGridGenerator()
gen.regular(2)
alpha = DataVector(factory.getStorage().size())
p = DataMatrix(1,1)
beta = DataVector(1)
alpha.setAll(0.0)
p.set(0,0,0.25)
beta[0] = 1.0
opb = factory.createOperationB()
opb.mult(beta, p, alpha)
self.failUnlessAlmostEqual(alpha[0], 0.75)
self.failUnlessAlmostEqual(alpha[1], 0.25)
self.failUnlessAlmostEqual(alpha[2], 0.5)
self.failUnlessAlmostEqual(alpha[3], 1.0)
self.failUnlessAlmostEqual(alpha[4], 0.0)
alpha.setAll(0.0)
alpha[2] = 1.0
p.set(0,0, 0.25)
beta[0] = 0.0
opb.multTranspose(alpha, p, beta)
self.failUnlessAlmostEqual(beta[0], 0.5)
def run_sobol_g_function_sg(fullModel, gridType, level, numGridPoints,
fullGrid, refinement, out):
testSetting = SobolGFunctionSudret2008(fullModel)
sobol_indices, N = testSetting.run_sparse_grids(
Grid.stringToGridType(gridType), level, numGridPoints, fullGrid,
refinement, out)
return testSetting.sobol_indices, sobol_indices, N
def testOperationTest_test(self):
from pysgpp import Grid, DataVector, DataMatrix
factory = Grid.createLinearBoundaryGrid(1)
gen = factory.createGridGenerator()
gen.regular(1)
alpha = DataVector(factory.getStorage().size())
data = DataMatrix(1,1)
data.setAll(0.25)
classes = DataVector(1)
classes.setAll(1.0)
testOP = factory.createOperationTest()
alpha[0] = 0.0
alpha[1] = 0.0
alpha[2] = 1.0
c = testOP.test(alpha, data, classes)
self.failUnless(c > 0.0)
alpha[0] = 0.0
alpha[1] = 0.0
alpha[2] = -1.0
c = testOP.test(alpha, data, classes)
self.failUnless(c == 0.0)
def checkPositivity(grid, alpha):
# define a full grid of maxlevel of the grid
gs = grid.getStorage()
fullGrid = Grid.createLinearGrid(gs.dim())
fullGrid.createGridGenerator().full(gs.getMaxLevel())
fullHashGridStorage = fullGrid.getStorage()
A = DataMatrix(fullHashGridStorage.size(), fullHashGridStorage.dim())
p = DataVector(gs.dim())
for i in xrange(fullHashGridStorage.size()):
fullHashGridStorage.get(i).getCoords(p)
A.setRow(i, p)
res = evalSGFunctionMulti(grid, alpha, A)
ymin, ymax, cnt = 0, -1e10, 0
for i, yi in enumerate(res.array()):
if yi < 0. and abs(yi) > 1e-13:
cnt += 1
ymin = min(ymin, yi)
ymax = max(ymax, yi)
A.getRow(i, p)
print " %s = %g" % (p, yi)
if cnt > 0:
print "warning: function is not positive"
print "%i/%i: [%g, %g]" % (cnt, fullHashGridStorage.size(), ymin, ymax)
return cnt == 0
def testEval(self):
grid = Grid.createLinearGrid(1)
grid.getGenerator().regular(3)
gs = grid.getStorage()
# prepare surplus vector
nodalValues = DataVector(gs.getSize())
nodalValues.setAll(0.0)
# interpolation on nodal basis
p = DataVector(gs.getDimension())
for i in range(gs.getSize()):
gs.getCoordinates(gs.getPoint(i), p)
nodalValues[i] = f(p[0])
# nodalValues[i] = f(p[0], p[1])
# hierarchization
alpha = hierarchize(grid, nodalValues)
# eval the sparse grid function
x = np.linspace(0, 1, 1000)
y = [f(xi) for xi in x]
y1 = [evalSGFunction(grid, alpha, np.array([xi])) for xi in x]
y2 = evalSGFunctionMulti(grid, alpha, np.array([x]).T)
assert np.all(y1 - y2 <= 1e-13)
def eval_fullGrid(level, dim, border=True):
if border:
grid = Grid.createLinearBoundaryGrid(dim)
else:
grid = Grid.createLinearGrid(dim)
grid.createGridGenerator().full(level)
gs = grid.getStorage()
ans = DataMatrix(gs.size(), dim)
p = DataVector(dim)
for i in xrange(gs.size()):
gs.get(i).getCoords(p)
ans.setRow(i, p)
return ans
def test44(self):
from pysgpp import Grid, DataVector, FullGrid, FullGridSet
dim = 2
level = 8
function = buildParableBoundary(dim)
grid = Grid.createSquareRootGrid(dim)
testFG(self, grid, level, function)
def test34(self):
from pysgpp import Grid, DataVector, FullGrid, FullGridSet
dim = 2
level = 9
function = buildParableBoundary(dim)
grid = Grid.createLinearBoundaryGrid(dim)
testFG(self, grid, level, function)
def plot_2d_3d(level=2):
grid2d = Grid.createLinearGrid(2)
grid2d.getGenerator().regular(level)
plotGrid2d(grid2d)
grid3d = extend_grid(grid2d, 1)
plotGrid3dSlices(grid3d)
plt.show()
def testSerializationLinearBoudary(self):
"""Uses Linear grid for tests"""
from pysgpp import Grid
factory = Grid.createLinearBoundaryGrid(2)
self.failIfEqual(factory, None)
gen = factory.createGridGenerator()
gen.regular(3)
str = factory.serialize()
self.assert_(len(str) > 0)
newfac = Grid.unserialize(str)
self.failIfEqual(newfac, None)
self.assertEqual(factory.getStorage().size(), newfac.getStorage().size())
def testHierarchisationDBoundary(self):
from pysgpp import Grid
dim = 3
level = 5
function = buildParableBoundary(dim)
grid = Grid.createLinearBoundaryGrid(dim)
testHierarchisationDehierarchisation(self, grid, level, function)
def testHierarchisationDModLinear(self):
from pysgpp import Grid
dim = 3
level = 5
function = buildParable(dim)
grid = Grid.createModLinearGrid(dim)
testHierarchisationDehierarchisation(self, grid, level, function)
def testHierarchisationDBoundary(self):
from pysgpp import Grid
dim = 3
level = 5
function = buildParableBoundary(dim)
grid = Grid.createLinearBoundaryGrid(dim)
testHierarchisationDehierarchisation(self, grid, level, function)
def testHierarchisationDModLinear(self):
from pysgpp import Grid
dim = 3
level = 5
function = buildParable(dim)
grid = Grid.createModLinearGrid(dim)
testHierarchisationDehierarchisation(self, grid, level, function)
def test43(self):
from pysgpp import Grid, DataVector, FullGrid, FullGridSet
dim = 4
level = 4
function = buildParableBoundary(dim)
grid = Grid.createSquareRootGrid(dim)
testFG(self, grid, level, function)
def test13(self):
from pysgpp import Grid, DataVector, FullGrid, FullGridSet
dim = 4
level = 6
function = buildParableBoundary(dim)
grid = Grid.createLinearBoundaryGrid(dim, 0)
testFG(self, grid, level, function)
def test44(self):
from pysgpp import Grid, DataVector, FullGrid, FullGridSet
dim = 2
level = 8
function = buildParableBoundary(dim)
grid = Grid.createLinearTruncatedBoundaryGrid(dim)
testFG(self, grid, level, function)
def createGrid(grid, dim, deg=1, addTruncatedBorder=False):
# create new grid
gridType = grid.getType()
deg = max(deg, grid.getDegree())
# print( gridType, deg )
if deg > 1 and gridType in [GridType_Linear]:
return Grid.createPolyGrid(dim, deg)
if deg > 1 and gridType in [
GridType_LinearBoundary, GridType_LinearL0Boundary
]:
return Grid.createPolyBoundaryGrid(dim, deg)
elif deg > 1 and gridType in [GridType_LinearClenshawCurtis]:
return Grid.createPolyClenshawCurtisGrid(dim, deg)
elif deg > 1 and gridType in [GridType_LinearClenshawCurtisBoundary]:
return Grid.createPolyClenshawCurtisBoundaryGrid(dim, deg)
elif deg > 1 and gridType in [GridType_ModLinear]:
return Grid.createModPolyGrid(dim, deg)
elif deg > 1 and gridType in [GridType_ModLinearClenshawCurtis]:
return Grid.createModPolyClenshawCurtisGrid(dim, deg)
else:
gridConfig = RegularGridConfiguration()
gridConfig.type_ = gridType
gridConfig.dim_ = dim
gridConfig.maxDegree_ = deg
return Grid.createGrid(gridConfig)
def general_test(self, d, l, bb, xs):
test_desc = "dim=%d, level=%d, len(x)=%s" % (d, l, len(xs))
print(test_desc)
self.grid = Grid.createLinearGrid(d)
self.grid_gen = self.grid.getGenerator()
self.grid_gen.regular(l)
alpha = DataVector(
[self.get_random_alpha() for i in range(self.grid.getSize())])
bb_ = BoundingBox(d)
for d_k in range(d):
dimbb = BoundingBox1D()
dimbb.leftBoundary = bb[d_k][0]
dimbb.rightBoundary = bb[d_k][1]
bb_.setBoundary(d_k, dimbb)
# Calculate the expected value without the bounding box
expected_normal = [
self.calc_exp_value_normal(x, d, bb, alpha) for x in xs
]
#expected_transposed = [self.calc_exp_value_transposed(x, d, bb, alpha) for x in xs]
# Now set the bounding box
self.grid.getStorage().setBoundingBox(bb_)
dm = DataMatrix(len(xs), d)
for k, x in enumerate(xs):
dv = DataVector(x)
dm.setRow(k, dv)
multEval = createOperationMultipleEval(self.grid, dm)
actual_normal = DataVector(len(xs))
#actual_transposed = DataVector(len(xs))
multEval.mult(alpha, actual_normal)
#multEval.mult(alpha, actual_transposed)
actual_normal_list = []
for k in range(len(xs)):
actual_normal_list.append(actual_normal.__getitem__(k))
#actual_transposed_list = []
#for k in xrange(len(xs)):
# actual_transposed_list.append(actual_transposed.__getitem__(k))
self.assertAlmostEqual(actual_normal_list, expected_normal)
#self.assertAlmostEqual(actual_tranposed_list, expected_tranposed)
del self.grid
def testSerializationLinearBoudary(self):
"""Uses Linear grid for tests"""
from pysgpp import Grid
factory = Grid.createLinearBoundaryGrid(2)
self.failIfEqual(factory, None)
gen = factory.createGridGenerator()
gen.regular(3)
str = factory.serialize()
self.assert_(len(str) > 0)
newfac = Grid.unserialize(str)
self.failIfEqual(newfac, None)
self.assertEqual(factory.getStorage().size(),
newfac.getStorage().size())
def __init__(self, grid):
super(FullGridCandidates, self).__init__()
# genreate new full grid
gs = grid.getStorage()
maxLevel = gs.getMaxLevel()
self.numDims = gs.getDimension()
self.fullGrid = Grid.createLinearGrid(self.numDims)
self.fullGrid.getGenerator().full(maxLevel)
def testCreation(self):
"""Uses Linear grid for tests"""
from pysgpp import Grid
factory = Grid.createLinearGrid(2)
self.failIfEqual(factory, None)
storage = factory.getStorage()
self.failIfEqual(storage, None)
def testCreation(self):
"""Uses Linear grid for tests"""
from pysgpp import Grid
factory = Grid.createLinearGrid(2)
self.failIfEqual(factory, None)
storage = factory.getStorage()
self.failIfEqual(storage, None)
def setUp(self):
self.grid = Grid.createLinearGrid(2) # a simple 2D grid
self.grid.createGridGenerator().regular(3) # max level 3 => 17 points
self.HashGridStorage = self.grid.getStorage()
alpha = DataVector(self.grid.getSize())
alpha.setAll(1.0)
for i in [9, 10, 11, 12]:
alpha[i] = 0.0
coarseningFunctor = SurplusCoarseningFunctor(alpha, 4, 0.5)
self.grid.createGridGenerator().coarsen(coarseningFunctor, alpha)
def testHatRegular1D(self):
from pysgpp import Grid
factory = Grid.createModLinearGrid(1)
m = generateLaplaceMatrix(factory, 5)
m_ref = readReferenceMatrix(self, factory.getStorage(), 'data/C_laplace_phi_li_ausgeklappt_dim_1_nopsgrid_31_float.dat.gz')
# compare
compareStiffnessMatrices(self, m, m_ref)
def createGrid(dim, level, borderType, isFull=False):
from pysgpp.extensions.datadriven.learner.Types import BorderTypes
if borderType == BorderTypes.NONE:
grid = Grid.createLinearGrid(dim)
elif borderType == BorderTypes.TRAPEZOIDBOUNDARY:
grid = Grid.createLinearTrapezoidBoundaryGrid(dim)
elif borderType == BorderTypes.COMPLETEBOUNDARY:
grid = Grid.createLinearBoundaryGrid(dim, 0)
else:
raise Exception('Unknown border type')
# create regular grid of level accLevel
gridGen = grid.createGridGenerator()
if isFull:
gridGen.full(level)
else:
gridGen.regular(level)
return grid
def testHatRegular1D_two(self):
from pysgpp import Grid
factory = Grid.createLinearTrapezoidBoundaryGrid(1)
m = generateLaplaceMatrix(factory, 5)
m_ref = readReferenceMatrix(self, factory.getStorage(), 'data/C_laplace_phi_li_hut_trapezrand_dim_1_nopsgrid_33_float.dat.gz')
# compare
compareStiffnessMatrices(self, m, m_ref)
def createGrid(dim, level, borderType, isFull=False):
from pysgpp.extensions.datadriven.learner.Types import BorderTypes
if borderType == BorderTypes.NONE:
grid = Grid.createLinearGrid(dim)
elif borderType == BorderTypes.TRAPEZOIDBOUNDARY:
grid = Grid.createLinearTrapezoidBoundaryGrid(dim)
elif borderType == BorderTypes.COMPLETEBOUNDARY:
grid = Grid.createLinearBoundaryGrid(dim, 0)
else:
raise Exception('Unknown border type')
# create regular grid of level accLevel
gridGen = grid.getGenerator()
if isFull:
gridGen.full(level)
else:
gridGen.regular(level)
return grid
def testHatRegulardD_two(self):
from pysgpp import Grid
factory = Grid.createLinearBoundaryGrid(3)
m = generateLaplaceMatrix(factory, 4)
m_ref = readReferenceMatrix(self, factory.getStorage(), 'data/C_laplace_phi_li_hut_l0_rand_dim_3_nopsgrid_297_float.dat.gz')
# compare
compareStiffnessMatrices(self, m, m_ref)
def general_test(self, d, l, bb, xs):
test_desc = "dim=%d, level=%d, len(x)=%s" % (d, l, len(xs))
print test_desc
self.grid = Grid.createLinearGrid(d)
self.grid_gen = self.grid.createGridGenerator()
self.grid_gen.regular(l)
alpha = DataVector([self.get_random_alpha() for i in xrange(self.grid.getSize())])
bb_ = BoundingBox(d)
for d_k in xrange(d):
dimbb = DimensionBoundary()
dimbb.leftBoundary = bb[d_k][0]
dimbb.rightBoundary = bb[d_k][1]
bb_.setBoundary(d_k, dimbb)
# Calculate the expected value without the bounding box
expected_normal = [self.calc_exp_value_normal(x, d, bb, alpha) for x in xs]
#expected_transposed = [self.calc_exp_value_transposed(x, d, bb, alpha) for x in xs]
# Now set the bounding box
self.grid.getStorage().setBoundingBox(bb_)
dm = DataMatrix(len(xs), d)
for k, x in enumerate(xs):
dv = DataVector(x)
dm.setRow(k, dv)
multEval = createOperationMultipleEval(self.grid, dm)
actual_normal = DataVector(len(xs))
#actual_transposed = DataVector(len(xs))
multEval.mult(alpha, actual_normal)
#multEval.mult(alpha, actual_transposed)
actual_normal_list = []
for k in xrange(len(xs)):
actual_normal_list.append(actual_normal.__getitem__(k))
#actual_transposed_list = []
#for k in xrange(len(xs)):
# actual_transposed_list.append(actual_transposed.__getitem__(k))
self.assertAlmostEqual(actual_normal_list, expected_normal)
#self.assertAlmostEqual(actual_tranposed_list, expected_tranposed)
del self.grid
def setUp(self):
#
# Grid
#
DIM = 2
LEVEL = 2
self.grid = Grid.createLinearGrid(DIM)
self.grid_gen = self.grid.createGridGenerator()
self.grid_gen.regular(LEVEL)
#
# trainData, classes, errors
#
xs = []
DELTA = 0.05
DELTA_RECI = int(1/DELTA)
for i in xrange(DELTA_RECI):
for j in xrange(DELTA_RECI):
xs.append([DELTA*i, DELTA*j])
random.seed(1208813)
ys = [ random.randint(-10, 10) for i in xrange(DELTA_RECI**2)]
# print xs
# print ys
self.trainData = DataMatrix(xs)
self.classes = DataVector(ys)
self.alpha = DataVector([3, 6, 7, 9, -1])
self.errors = DataVector(DELTA_RECI**2)
coord = DataVector(DIM)
for i in xrange(self.trainData.getNrows()):
self.trainData.getRow(i, coord)
self.errors.__setitem__ (i, self.classes[i] - self.grid.eval(self.alpha, coord))
#print "Errors:"
#print self.errors
#
# Functor
#
self.functor = WeightedErrorRefinementFunctor(self.alpha, self.grid)
self.functor.setTrainDataset(self.trainData)
self.functor.setClasses(self.classes)
self.functor.setErrors(self.errors)
def setUp(self,):
self.__gridFormatter = None
self.filename = pathlocal + "/datasets/grid.gz"
self.savefile = pathlocal + "/datasets/savetest.grid.gz"
self.correct_str = ""
self.grid = None
self.__gridFormatter = GridFormatter()
dim = 3
self.grid = Grid.createLinearGrid(dim)
self.grid.createGridGenerator().regular(3)
self.correct_str = self.grid.serialize()
def makePositive(grid, alpha):
"""
insert full grid points if they are negative and the father
node is part of the sparse grid
@param grid:
@param alpha:
"""
# copy old sg function
jgrid = copyGrid(grid)
# evaluate the sparse grid function at all full grid points
level = grid.getStorage().getMaxLevel()
fg = Grid.createLinearGrid(grid.getStorage().dim())
fg.createGridGenerator().full(level)
# copy the old grid and use it as reference
jgs = jgrid.getStorage()
fgs = fg.getStorage()
# run over all results and check where the function value
# is lower than zero
cnt = 1
while True:
print "run %i: full grid size = %i" % (cnt, fgs.size())
gps = []
# insert those fg points, which are not yet positive
values = computeNodalValues(fg, grid, alpha)
for i in xrange(len(values)):
gp = fgs.get(i)
if values[i] < 0 and not jgs.has_key(gp):
gps += insertPoint(jgrid, gp)
gps += insertHierarchicalAncestors(jgrid, gp)
jgrid.getStorage().recalcLeafProperty()
# 1. compute nodal values for new grid points
jnodalValues = computeNodalValues(jgrid, grid, alpha)
# 2. set the new ones to zero
jgs = jgrid.getStorage()
for gp in gps:
jnodalValues[jgs.seq(gp)] = 0.
# 3. hierarchize
jalpha = hierarchize(jgrid, jnodalValues)
# stop loop if no points have been added
if len(gps) == 0:
break
# 4. reset values for next loop
grid = copyGrid(jgrid)
alpha = DataVector(jalpha)
cnt += 1
return jgrid, jalpha
def fromJson(cls, jsonObject):
"""
Restores the ASGC object from the json object with its
attributes.
@param jsonObject: json object
@return: the restored ASGC object
"""
knowledge = ASGCKnowledge()
# restore iteration
key = '_ASGCKnowledge__iteration'
if key in jsonObject:
knowledge.setIteration(int(jsonObject[key]))
# restore surpluses: {iteration: {qoi: {dtype: {t: <Grid>}}}}
key = '_ASGCKnowledge__grids'
if key in jsonObject:
grids = {}
for iteration, v1 in jsonObject[key].items():
d1 = {}
for qoi, gridString in v1.items():
# undo the hack that made it json compatible
gridString = gridString.replace('__', '\n')\
.encode('utf8')
# compatibility with older poly basis type
gridString = gridString.replace('myPoly', 'poly')
# deserialize ...
grid = Grid.unserialize(gridString)
# ... and store it
d1[qoi] = grid
grids[int(iteration)] = d1
knowledge.setGrids(grids)
# restore surpluses: {iteration: {qoi: {dtype: {t: <list float>}}}}
key = '_ASGCKnowledge__alphas'
if key in jsonObject:
alphas = {}
for iteration, v1 in jsonObject[key].items():
d1 = {}
for qoi, v2 in v1.items():
d2 = {}
for dtype, v3 in v2.items():
d3 = {}
for t, alpha in v3.items():
d3[float(t)] = DataVector(alpha)
d2[int(dtype)] = d3
d1[qoi] = d2
alphas[int(iteration)] = d1
knowledge.setAlphas(alphas)
return knowledge
def general_test(self, d, l, bb, x):
test_desc = "dim=%d, level=%d, bb=%s, x=%s" % (d, l, bb, x)
print test_desc
self.grid = Grid.createLinearGrid(d)
self.grid_gen = self.grid.createGridGenerator()
self.grid_gen.regular(l)
alpha = DataVector([1] * self.grid.getSize())
bb_ = BoundingBox(d)
for d_k in xrange(d):
dimbb = DimensionBoundary()
dimbb.leftBoundary = bb[d_k][0]
dimbb.rightBoundary = bb[d_k][1]
bb_.setBoundary(d_k, dimbb)
# Calculate the expected value without the bounding box
expected = 0.0
inside = True
x_trans = DataVector(d)
for d_k in xrange(d):
if not (bb[d_k][0] <= x[d_k] and x[d_k] <= bb[d_k][1]):
inside = False
break
else:
x_trans[d_k] = (x[d_k] - bb[d_k][0]) / (bb[d_k][1] - bb[d_k][0])
if inside:
p = DataVector(x_trans)
expected = self.grid.eval(alpha, p)
else:
expected = 0.0
# Now set the bounding box
self.grid.getStorage().setBoundingBox(bb_)
p = DataVector(x)
actual = self.grid.eval(alpha, p)
self.assertAlmostEqual(actual, expected)
del self.grid
def testGeneration(self):
from pysgpp import Grid, DataVector
factory = Grid.createLinearBoundaryGrid(2)
storage = factory.getStorage()
gen = factory.createGridGenerator()
self.failIfEqual(gen, None)
self.failUnlessEqual(storage.size(), 0)
gen.regular(3)
self.failUnlessEqual(storage.size(), 37)
#This should fail
self.failUnlessRaises(Exception, gen.regular, 3)
def testHatRegulardD_two(self):
from pysgpp import Grid
factory = Grid.createLinearBoundaryGrid(3)
training = buildTrainingVector(readDataVector('data/data_dim_3_nops_512_float.arff.gz'))
level = 3
gen = factory.createGridGenerator()
gen.regular(level)
m = generateBBTMatrix(factory, training)
m_ref = readReferenceMatrix(self, factory.getStorage(), 'data/BBT_phi_li_hut_trapezrand_dim_3_nopsgrid_225_float.dat.gz')
# compare
compareBBTMatrices(self, m, m_ref)
def testPrewaveletdD_two(self):
from pysgpp import Grid
factory = Grid.createPrewaveletGrid(3)
training = buildTrainingVector(readDataVector('data/data_dim_3_nops_512_float.arff.gz'))
level = 4
gen = factory.createGridGenerator()
gen.regular(level)
m = generateBBTMatrix(factory, training)
m_ref = readReferenceMatrix(self, factory.getStorage(), 'data/BBT_prewavelet_dim_3_nopsgrid_111_float.dat.gz')
# compare
compareBBTMatrices(self, m, m_ref)
def testHatRegular1D_one(self):
from pysgpp import Grid
factory = Grid.createLinearGrid(1)
training = buildTrainingVector(readDataVector('data/data_dim_1_nops_8_float.arff.gz'))
level = 3
gen = factory.createGridGenerator()
gen.regular(level)
m = generateBBTMatrix(factory, training)
m_ref = readReferenceMatrix(self, factory.getStorage(), 'data/BBT_phi_li_hut_dim_1_nopsgrid_7_float.dat.gz')
# compare
compareBBTMatrices(self, m, m_ref)
def setUp(self):
#
# Grid
#
DIM = 2
LEVEL = 2
self.grid = Grid.createLinearGrid(DIM)
self.grid_gen = self.grid.createGridGenerator()
self.grid_gen.regular(LEVEL)
#
# trainData, classes, errors
#
xs = []
DELTA = 0.05
DELTA_RECI = int(1/DELTA)
for i in xrange(DELTA_RECI):
for j in xrange(DELTA_RECI):
xs.append([DELTA*i, DELTA*j])
random.seed(1208813)
ys = [ random.randint(-10, 10) for i in xrange(DELTA_RECI**2)]
self.trainData = DataMatrix(xs)
self.classes = DataVector(ys)
self.alpha = DataVector([3, 6, 7, 9, -1])
self.multEval = createOperationMultipleEval(self.grid, self.trainData)
self.errors = DataVector(DELTA_RECI**2)
coord = DataVector(DIM)
for i in xrange(self.trainData.getNrows()):
self.trainData.getRow(i, coord)
self.errors.__setitem__ (i, abs(self.classes[i] - self.grid.eval(self.alpha, coord)))
#
# OnlinePredictiveRefinementDimension
#
hash_refinement = HashRefinement();
self.strategy = OnlinePredictiveRefinementDimension(hash_refinement)
self.strategy.setTrainDataset(self.trainData)
self.strategy.setClasses(self.classes)
self.strategy.setErrors(self.errors)
def discretizeFunction(f, bounds, level=2, hasBorder=False, *args, **kws):
# define linear transformation to the unit hyper cube
T = JointTransformation()
for xlim in bounds:
T.add(LinearTransformation(xlim[0], xlim[1]))
# create grid
dim = len(bounds)
# create adequate grid
if hasBorder:
grid = Grid.createLinearBoundaryGrid(dim)
else:
grid = Grid.createLinearGrid(dim)
# init storage
grid.createGridGenerator().regular(level)
gs = grid.getStorage()
# discretize on given level
p = DataVector(dim)
nodalValues = DataVector(gs.size())
for i in xrange(gs.size()):
gs.get(i).getCoords(p)
# transform to the right space
q = T.unitToProbabilistic(p.array())
# apply the given function
nodalValues[i] = float(f(q))
# hierarchize
alpha = hierarchize(grid, nodalValues)
# estimate the l2 error
err = estimateDiscreteL2Error(grid, alpha, f)
# TODO: adaptive refinement
return grid, alpha, err