def _update_measurement(self, var, measurement, member):
"""
Returns an updated_measurement.
This is used to update :class:`LazyDict` instances.
"""
if not member:
if var in self.transforms:
transform = self.transforms[var]
else:
# attempt to parse var as a tuple of vars
# and construct derived product transform if
# successful
product_t = [self.transforms[v] for v in var]
def transform(state):
"""
product transform function, generated by recorder
"""
return sum((t(state) for t in product_t), ())
measurement = Measurement(var, transform)
start = len(measurement)
end = len(self.times)
for i in range(start, end):
measurement.write(self.times[i], self.distributions[i])
return measurement
def _update_measurement(self, var, measurement, member):
"""
Returns an updated_measurement.
This is used to update :class:`LazyDict` instances.
"""
if not member:
if var in self.transforms:
transform = self.transforms[var]
else:
# attempt to parse var as a tuple of vars
# and construct derived product transform if
# successful
product_t = [self.transforms[v] for v in var]
def transform(state):
"""
product transform function, generated by recorder
"""
return sum((t(state) for t in product_t), ())
measurement = Measurement(var, transform)
start = len(measurement)
end = len(self.times)
for i in xrange(start, end):
measurement.write(self.times[i], self.distributions[i])
return measurement
def test_attribute_trickery(self):
m = Measurement()
m.write(0.0, Distribution({(0, ) : 1.0, (1, ) : 0.0}))
m.write(0.5, Distribution({(0, ) : 0.5, (1, ) : 0.5}))
m.write(1.0, Distribution({(0, ) : 0.0, (1, ) : 1.0}))
assert_almost_equal(numpy.array(m.expectation),
[[0], [0.5], [1]])
assert_almost_equal(numpy.array(m.variance),
[0, 0.25, 0])
def test_attribute_trickery(self):
m = Measurement()
m.write(0.0, Distribution({(0, ): 1.0, (1, ): 0.0}))
m.write(0.5, Distribution({(0, ): 0.5, (1, ): 0.5}))
m.write(1.0, Distribution({(0, ): 0.0, (1, ): 1.0}))
assert_almost_equal(numpy.array(m.expectation), [[0], [0.5], [1]])
assert_almost_equal(numpy.array(m.variance), [0, 0.25, 0])