class TestPersistentRefinementOperator(unittest.TestCase):
def setUp(self):
#
# Grid
#
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))
#
# Functor
#
self.functor = PersistentErrorRefinementFunctor(self.alpha, self.grid)
self.functor.setTrainDataset(self.trainData)
self.functor.setClasses(self.classes)
self.functor.setErrors(self.errors)
self.accum = DataVector(self.alpha.__len__())
self.accum.setAll(0.0)
def test_1(self):
storage = self.grid.getStorage()
coord = DataVector(storage.dim())
num_coeff = self.alpha.__len__()
#
# First part
#
values = [
self.functor.__call__(storage, i) for i in xrange(storage.size())
]
expect = []
for j in xrange(num_coeff):
row = DataVector(DIM)
tmp_alpha = DataVector(self.alpha.__len__())
tmp_alpha.setAll(0.0)
tmp_alpha.__setitem__(j, 1.0)
current = 0
for i in xrange(self.trainData.getNrows()):
self.trainData.getRow(i, row)
current += (self.errors.__getitem__(i) *
self.grid.eval(tmp_alpha, row))**2
self.accum.__setitem__(
j,
self.accum.__getitem__(j) * (1 - BETA) +
BETA * current * abs(self.alpha.__getitem__(j)))
expect.append(self.accum.__getitem__(j))
self.assertEqual(values, expect)
#
# Second part
#
values = [
self.functor.__call__(storage, i) for i in xrange(storage.size())
]
expect = []
for j in xrange(num_coeff):
row = DataVector(DIM)
tmp_alpha = DataVector(self.alpha.__len__())
tmp_alpha.setAll(0.0)
tmp_alpha.__setitem__(j, 1.0)
current = 0
for i in xrange(self.trainData.getNrows()):
self.trainData.getRow(i, row)
current += (self.errors.__getitem__(i) *
self.grid.eval(tmp_alpha, row))**2
self.accum.__setitem__(
j,
self.accum.__getitem__(j) * (1 - BETA) +
BETA * current * abs(self.alpha.__getitem__(j)))
expect.append(self.accum.__getitem__(j))
self.assertEqual(values, expect)
class TestWeightedRefinementOperator(unittest.TestCase):
def setUp(self):
#
# Grid
#
DIM = 2
LEVEL = 2
self.grid = Grid.createLinearGrid(DIM)
self.grid_gen = self.grid.getGenerator()
self.grid_gen.regular(LEVEL)
#
# trainData, classes, errors
#
xs = []
DELTA = 0.05
DELTA_RECI = int(1 / DELTA)
for i in range(DELTA_RECI):
for j in range(DELTA_RECI):
xs.append([DELTA * i, DELTA * j])
random.seed(1208813)
ys = [random.randint(-10, 10) for i in range(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)
opEval = createOperationEval(self.grid)
for i in range(self.trainData.getNrows()):
self.trainData.getRow(i, coord)
self.errors.__setitem__(
i, self.classes[i] - opEval.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 test_1(self):
storage = self.grid.getStorage()
coord = DataVector(storage.getDimension())
num_coeff = self.alpha.__len__()
values = [
self.functor.__call__(storage, i) for i in range(storage.getSize())
]
expect = []
opEval = createOperationEval(self.grid)
for i in range(num_coeff):
# print i
val = 0
single = DataVector(num_coeff)
single.__setitem__(i, self.alpha.__getitem__(i))
for j in range(self.trainData.getNrows()):
self.trainData.getRow(j, coord)
val += abs(
opEval.eval(single, coord) *
(self.errors.__getitem__(j)**2))
expect.append(val)
# print values
# print expect
# print [ values[i]/expect[i] for i in xrange(values.__len__())]
self.assertEqual(values, expect)
class TestWeightedRefinementOperator(unittest.TestCase):
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 test_1(self):
storage = self.grid.getStorage()
coord = DataVector(storage.dim())
num_coeff = self.alpha.__len__()
values = [self.functor.__call__(storage,i) for i in xrange(storage.size())]
expect = []
for i in xrange(num_coeff):
# print i
val = 0
single = DataVector(num_coeff)
single.__setitem__(i, self.alpha.__getitem__(i))
for j in xrange(self.trainData.getNrows()):
self.trainData.getRow(j, coord)
val += abs( self.grid.eval(single, coord) * (self.errors.__getitem__(j)**2) )
expect.append(val)
# print values
# print expect
# print [ values[i]/expect[i] for i in xrange(values.__len__())]
self.assertEqual(values, expect)
