/usr/lib/python3/dist-packages/csb/test/cases/statistics/pdf/parameterized.py is in python3-csb 1.2.3+dfsg-3.
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import csb.test as test
from csb.numeric import log
from csb.statistics.pdf.parameterized import ParameterizedDensity
from csb.statistics.pdf.parameterized import ParameterValueError, ParameterizationError
from csb.statistics.pdf.parameterized import AbstractParameter, Parameter, NonVirtualParameter
class Location(NonVirtualParameter):
def _validate(self, value):
return float(value)
class Scale(Parameter):
def _validate(self, value):
return float(value)
def _compute(self, base_value):
if base_value == 0.0:
return numpy.inf
else:
return 1.0 / base_value ** 0.5
def bind_to(self, base):
if base.name != "precision":
raise ValueError(base)
super(Scale, self).bind_to(base)
class DoubleScale(Parameter):
def _validate(self, value):
return float(value)
def _compute(self, base_value):
return base_value * 2.0
class Precision(Parameter):
def _validate(self, value):
if value < 0:
raise ParameterValueError(self.name, value)
return float(value)
class FancyGaussian(ParameterizedDensity):
def __init__(self, mu=0, precision=1):
super(FancyGaussian, self).__init__()
self._register('mu')
self._register('sigma')
self._register('precision')
loc = Location(mu)
prec = Precision(precision)
sigma = Scale(0)
sigma.bind_to(prec)
self.set_params(mu=loc, sigma=sigma, precision=prec)
@property
def mu(self):
return self['mu'].value
@property
def sigma(self):
return self['sigma'].value
@property
def precision(self):
return self['precision'].value
def log_prob(self, x):
mu = self.mu
sigma = self.sigma
return log(1.0 / numpy.sqrt(2 * numpy.pi * sigma ** 2)) - (x - mu) ** 2 / (2 * sigma ** 2)
@test.unit
class TestAbstractGenericParameter(test.Case):
"""
Use AbstractParameter as a generic class which accepts values
of any type.
"""
def setUp(self):
class Value(object):
pass
class Param(AbstractParameter):
def _validate(self, value):
if not isinstance(value, Value):
raise TypeError(value)
return value
self.value = Value()
self.param = Param(self.value)
def testValue(self):
self.assertIs(self.param.value, self.value)
def testSet(self):
self.assertRaises(TypeError, self.param.set, 3)
@test.unit
class TestParameter(test.Case):
"""
This is the main test case with complete coverage for AbstractParameter's
methods and behavior. Covers also Parameter.
computed -- leaf
/
base -- computed2
\
computed3
"""
def setUp(self):
self.base = Precision(1.2)
self.computed = Scale(100, base=self.base)
self.computed2 = Scale(200, base=self.base)
self.computed3 = Scale(300, base=self.base)
self.leaf = DoubleScale(400, base=self.computed)
def testConstrucor(self):
# make sure newly constructed parameters are left in a consistent state
# to avoid unnecessary consistency updates
self.assertTrue(self.base._consistent)
self.assertTrue(Scale(1)._consistent)
def testName(self):
self.assertEqual(self.base.name, "precision")
self.assertEqual(self.computed.name, "scale")
self.assertEqual(Scale(name="TesT").name, "TesT")
def testValue(self):
self.assertEqual(self.base.value, 1.2)
self.assertEqual(self.computed.value, 1.0 / numpy.sqrt(self.base.value))
self.assertEqual(self.computed2.value, 1.0 / numpy.sqrt(self.base.value))
self.assertEqual(self.leaf.value, self.computed.value * 2)
# turn self.base into a virtual parameter
self.base.bind_to(Precision(12.2))
self.assertEqual(self.base.value, 12.2)
def testIsVirtual(self):
self.assertFalse(self.base.is_virtual)
self.assertTrue(self.computed.is_virtual)
self.base.bind_to(Precision(12.2))
self.assertTrue(self.base.is_virtual)
def testSet(self):
base_initial_value = self.base._value
# recompute all derivatives from the initial value of base
self.assertEqual(self.computed._value, 100)
self.leaf._ensure_consistency()
self.computed2._ensure_consistency()
self.computed3._ensure_consistency()
# set self.base - it should remain consistent because it is not computed
self.assertTrue(self.base._consistent)
self.base.set(2.2)
self.assertTrue(self.base._consistent)
self.assertEqual(self.base.value, 2.2)
# self.computed and self.leaf should be inconsistent now that their base is updated
self.assertFalse(self.computed._consistent)
self.assertFalse(self.leaf._consistent)
self.assertEqual(self.computed._value, 1.0 / numpy.sqrt(base_initial_value))
self.assertEqual(self.leaf._value, 2.0 / numpy.sqrt(base_initial_value))
# retrieving self.computed's value should trigger updates up to self.computed
recomputed = self.computed.value
self.assertTrue(self.computed._consistent)
self.assertEqual(recomputed, 1.0 / numpy.sqrt(self.base._value))
# self.leaf is still inconsistent
self.assertFalse(self.leaf._consistent)
self.assertEqual(self.leaf._value, 2.0 / numpy.sqrt(base_initial_value))
self.assertIs(self.leaf._nearest_consistent_base()[-1], self.computed)
# until we request its value
recomputed = self.leaf.value
self.assertTrue(self.leaf._consistent)
self.assertEqual(recomputed, 2.0 / numpy.sqrt(self.base._value))
self.assertEqual(recomputed, 2.0 * self.computed._value)
# make sure the other two branches are still inconsistent
initial_value = 1.0 / numpy.sqrt(base_initial_value)
self.assertEqual(self.computed2._value, initial_value)
self.assertEqual(self.computed3._value, initial_value)
# until they get used
recomputed = self.computed2.value
self.assertTrue(self.computed2._consistent)
self.assertEqual(recomputed, 1.0 / numpy.sqrt(self.base._value))
# attempt to set self.computed - not allowed
self.assertRaises(ParameterizationError, self.computed.set, 2)
# attempt to set a negative Precision
self.assertRaises(ParameterValueError, self.base.set, -2)
# attempt to assigned non-float - not allowed in the Parameter specialization
self.assertRaises(ParameterValueError, Parameter().set, object())
def testBindTo(self):
# can't bind self.base to itself
self.assertRaises(ParameterizationError, self.base.bind_to, self.base)
# deeper circular dependency
self.assertRaises(ParameterizationError, self.base.bind_to, self.computed)
# self.base is not virtual and therefore must be consistent
self.assertTrue(self.base._consistent)
# make it virtual - should get inconsistent now
self.base.bind_to(Precision(12.2))
self.assertFalse(self.base._consistent)
self.assertTrue(self.base.is_virtual)
# retrieving its value should trigger the consistency cascade
self.assertEqual(self.base.value, 12.2)
self.assertTrue(self.base._consistent)
def testFindBaseParameter(self):
self.assertIs(self.base.find_base_parameter(), self.base)
self.assertIs(self.computed.find_base_parameter(), self.base)
@test.unit
class TestNonVirtualParameter(test.Case):
"""
Make sure explicit NonVirtualParameter-s are updatable and
refuse binding requests
"""
def setUp(self):
self.param = Location()
def testConstructor(self):
base = Parameter()
self.assertRaises(ParameterizationError, lambda: Location(base=base))
def testIsVirtual(self):
self.assertFalse(self.param.is_virtual)
def testBindTo(self):
base = Parameter()
self.assertRaises(ParameterizationError, self.param.bind_to, base)
def testSet(self):
self.param.set(22)
self.assertEqual(self.param.value, 22)
@test.unit
class TestParameterizedDensity(test.Case):
def setUp(self):
self.pdf = FancyGaussian(2, 5)
def testConstructor(self):
class Density(ParameterizedDensity):
def __init__(self, p):
super(Density, self).__init__()
self._register('p')
self.set_params(p=p)
def log_prob(self, x):
return x
self.assertRaises(TypeError, Density, 4)
def testProperties(self):
self.assertEqual(self.pdf.mu, 2)
self.assertEqual(self.pdf.precision, 5)
self.assertAlmostEqual(self.pdf.sigma, 0.4472, places=3)
def testParameterChaining(self):
self.assertEqual(self.pdf.precision, 5)
self.assertAlmostEqual(self.pdf.sigma, 0.4472, places=3)
self.pdf['precision'].set(2)
self.assertEqual(self.pdf.precision, 2)
self.assertAlmostEqual(self.pdf.sigma, 0.7071, places=3)
def testAssignment(self):
self.pdf['sigma'] = Scale(55)
self.assertEqual(self.pdf.sigma, 55)
self.assertEqual(self.pdf['sigma'].name, 'scale')
def assign(i):
self.pdf['sigma'] = i
self.assertRaises(TypeError, assign, 55)
if __name__ == '__main__':
test.Console()
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