def sample_randomly(self, count=None, random_state=None, seed=None):
"""Iterator sampling random |Parameters| from the space."""
assert not random_state or seed is None
c = 0
ranges = self.ranges
random_state = random_state or new_random_state(seed)
while count is None or c < count:
yield Parameter(((k, random_state.uniform(ranges[k][0], ranges[k][1], shp))
for k, shp in self.parameter_type.iteritems()))
c += 1
def sample_randomly(self, count=None, random_state=None, seed=None):
"""Iterator sampling random |Parameters| from the space."""
assert not random_state or seed is None
c = 0
ranges = self.ranges
random_state = random_state or new_random_state(seed)
while count is None or c < count:
yield Parameter(
((k, random_state.uniform(ranges[k][0], ranges[k][1], shp))
for k, shp in self.parameter_type.iteritems()))
c += 1
def sample_randomly(self, count=None, random_state=None, seed=None):
"""Randomly sample |Parameters| from the space.
.. warning::
When neither `random_state` nor `seed` are specified,
repeated calls to this method will return the same sequence
of parameters!
Parameters
----------
count
`None` or number of random parameters (see below).
random_state
:class:`~numpy.random.RandomState` to use for sampling.
If `None`, a new random state is generated using `seed`
as random seed.
seed
Random seed to use. If `None`, the
:func:`default <pymor.tools.random.new_random_state>` random seed
is used.
Returns
-------
If `count` is `None`, an inexhaustible iterator returning random
|Parameters|.
Otherwise a list of `count` random |Parameters|.
"""
assert not random_state or seed is None
ranges = self.ranges
random_state = random_state or new_random_state(seed)
get_param = lambda: Parameter(
((k, random_state.uniform(ranges[k][0], ranges[k][1], shp))
for k, shp in sorted(self.parameter_type.items())))
# sorted is needed to ensure that the sampling of parameter components happens in an
# deterministic order
if count is None:
def param_generator():
while True:
yield get_param()
return param_generator()
else:
return [get_param() for _ in range(count)]
def sample_randomly(self, count=None, random_state=None, seed=None):
"""Randomly sample |Parameters| from the space.
.. warning::
When neither `random_state` nor `seed` are specified,
repeated calls to this method will return the same sequence
of parameters!
Parameters
----------
count
`None` or number of random parameters (see below).
random_state
:class:`~numpy.random.RandomState` to use for sampling.
If `None`, a new random state is generated using `seed`
as random seed.
seed
Random seed to use. If `None`, the
:func:`default <pymor.tools.random.new_random_state>` random seed
is used.
Returns
-------
If `count` is `None`, an inexhaustible iterator returning random
|Parameters|.
Otherwise a list of `count` random |Parameters|.
"""
assert not random_state or seed is None
ranges = self.ranges
random_state = random_state or new_random_state(seed)
get_param = lambda: Parameter(((k, random_state.uniform(ranges[k][0], ranges[k][1], shp))
for k, shp in sorted(self.parameter_type.items())))
# sorted is needed to ensure that the sampling of parameter components happens in an
# deterministic order
if count is None:
def param_generator():
while True:
yield get_param()
return param_generator()
else:
return [get_param() for _ in range(count)]
