def calc_indicator_value(self, index):
numData = self.trainData.getNrows()
numCoeff = self.grid.getSize()
seq = self.grid.getStorage().seq(index)
num = 0
denom = 0
tmp = DataVector(numCoeff)
self.multEval.multTranspose(self.errors, tmp)
num = tmp.__getitem__(seq)
num **= 2
alpha = DataVector(numCoeff)
col = DataVector(numData)
alpha.__setitem__(seq, 1.0)
self.multEval.mult(alpha, col)
col.sqr()
denom = col.sum()
if denom == 0:
print("Denominator is zero")
value = 0
else:
value = num / denom
return value
def calc_indicator_value(self, index):
numData = self.trainData.getNrows()
numCoeff = self.grid.getSize()
seq = self.grid.getStorage().seq(index)
num = 0
denom = 0
tmp = DataVector(numCoeff)
self.multEval.multTranspose(self.errors, tmp)
num = tmp.__getitem__(seq)
num **= 2
alpha = DataVector(numCoeff)
col = DataVector(numData)
alpha.__setitem__(seq, 1.0)
self.multEval.mult(alpha, col)
col.sqr()
denom = col.sum()
if denom == 0:
print "Denominator is zero"
value = 0
else:
value = num/denom
return value
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 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 naive_calc_single(self, index):
numData = self.trainData.getNrows()
numCoeff = self.grid.getSize()
seq = self.grid.getStorage().seq(index)
num = 0
denom = 0
tmp = DataVector(numCoeff)
self.multEval.multTranspose(self.errors, tmp)
num = tmp.__getitem__(seq)
num **= 2
alpha = DataVector(numCoeff)
alpha.setAll(0.0)
alpha.__setitem__(seq, 1.0)
col = DataVector(numData)
self.multEval.mult(alpha, col)
print col
col.sqr()
denom = col.sum()
print num
print denom
if denom == 0:
print "Denominator is zero"
value = 0
else:
value = num / denom
return value
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 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.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)
