def collapse_at(stepmon, target=None, tolerance=0.005, \
generations=50, mask=None):
'''return a set of indices where the parameters exhibit a dimensional
collapse at the specified target. Dimensional collapse is defined by:
change(param[i]) <= tolerance over N generations, where:
change(param[i]) = max(param[i]) - min(param[i]) if target = None, or
change(param[i]) = abs(param[i] - target) otherwise.
target can be None, a single value, or a list of values of param length
collapse will be ignored at any indices specififed in the mask
'''
np = _m.numpy
# reject bad masks
if mask is None: pass
elif type(mask) is set:
for i in mask:
if hasattr(i, '__len__'):
msg = "bad element '%s' in mask" % str(i)
raise ValueError(msg)
else:
msg = "%s is not a valid mask" % str(mask)
raise TypeError(msg)
# is max distance from target less than tolerance across all generations?
tolerance = np.asarray(tolerance)
tolerance.shape = (-1, 1)
params = _m._solutions(stepmon, generations)
if target is None: params = params.ptp(axis=0) <= tolerance
else: params = abs(params - target).max(axis=0) <= tolerance
# get tuple of indices of where collapsed
params = np.where(params)[-1]
# apply mask
if mask is None: return set(params)
mask = set(mask)
return set(i for i in params if i not in mask)
def collapse_at(stepmon, target=None, tolerance=0.005, \
generations=50, mask=None):
'''return a set of indices where the parameters exhibit a dimensional
collapse at the specified target. Dimensional collapse is defined by:
change(param[i]) <= tolerance over N generations, where:
change(param[i]) = max(param[i]) - min(param[i]) if target = None, or
change(param[i]) = abs(param[i] - target) otherwise.
target can be None, a single value, or a list of values of param length
collapse will be ignored at any indices specififed in the mask
'''
np = _m.numpy
# reject bad masks
if mask is None: pass
elif type(mask) is set:
for i in mask:
if hasattr(i, '__len__'):
msg = "bad element '%s' in mask" % str(i)
raise ValueError(msg)
else:
msg = "%s is not a valid mask" % str(mask)
raise TypeError(msg)
# is max distance from target less than tolerance across all generations?
tolerance = np.asarray(tolerance)
tolerance.shape = (-1,1)
params = _m._solutions(stepmon, generations)
if target is None: params = params.ptp(axis=0) <= tolerance
else: params = abs(params - target).max(axis=0) <= tolerance
# get tuple of indices of where collapsed
params = np.where(params)[-1]
# apply mask
if mask is None: return set(params)
mask = set(mask)
return set(i for i in params if i not in mask)
def collapse_as(stepmon, offset=False, tolerance=0.005, \
generations=50, mask=None):
'''return a set of pairs of indices where the parameters exhibit a
dimensional collapse. Dimensional collapse is defined by:
max(pairwise(parameters)) <= tolerance over N generations (offset=False),
ptp(pairwise(parameters)) <= tolerance over N generations (offset=True).
collapse will be ignored at any pairs of indices specififed in the mask.
If single indices are provided, ignore all pairs with the given indices.
'''
np = _m.numpy
# reject bad masks
if mask is None: pass
elif type(mask) is set:
for i in mask:
if hasattr(i, '__len__') and len(i) != 2:
msg = "bad element '%s' in mask" % str(i)
raise ValueError(msg)
else:
msg = "%s is not a valid mask" % str(mask)
raise TypeError(msg)
# is the max position difference less than tolerance across all generations?
distances = _m._solutions(stepmon, generations)
#FIXME: HACK: array should be ndim=2... apparently sometimes it's ndim=3
if distances.ndim == 3 and distances.shape[-1] == 1:
distances.shape = distances.shape[:-1]
elif distances.ndim < 3:
pass
else:
msg = 'could not extract pairwise distances from array with shape %s' % distances.shape
raise ValueError(msg)
nindices = distances.shape[-1]
# get distances and pairs of indices
from mystic.tools import pairwise
distances, pairs = pairwise(distances, True)
if offset: # tracking at a distance
distances = distances.ptp(axis=0) <= tolerance
else: # tracking with the same position
distances = distances.max(axis=0) <= tolerance
# get the (index1,index2) pairs where the collapse occurs
if distances.ndim > 1:
distances.shape = tuple(i for i in distances.shape if i != 1) or (1, )
distances = np.array(pairs)[distances]
# apply mask
if mask is None: return set(tuple(i) for i in distances)
mask = selector(mask)
return set(tuple(i) for i in distances if not mask(i))
def collapse_as(stepmon, offset=False, tolerance=0.005, \
generations=50, mask=None):
'''return a set of pairs of indices where the parameters exhibit a
dimensional collapse. Dimensional collapse is defined by:
max(pairwise(parameters)) <= tolerance over N generations (offset=False),
ptp(pairwise(parameters)) <= tolerance over N generations (offset=True).
collapse will be ignored at any pairs of indices specififed in the mask.
If single indices are provided, ignore all pairs with the given indices.
'''
np = _m.numpy
# reject bad masks
if mask is None: pass
elif type(mask) is set:
for i in mask:
if hasattr(i, '__len__') and len(i) != 2:
msg = "bad element '%s' in mask" % str(i)
raise ValueError(msg)
else:
msg = "%s is not a valid mask" % str(mask)
raise TypeError(msg)
# is the max position difference less than tolerance across all generations?
distances = _m._solutions(stepmon, generations)
#FIXME: HACK: array should be ndim=2... apparently sometimes it's ndim=3
if distances.ndim == 3 and distances.shape[-1] == 1:
distances.shape = distances.shape[:-1]
elif distances.ndim < 3:
pass
else:
msg = 'could not extract pairwise distances from array with shape %s' % distances.shape
raise ValueError(msg)
nindices = distances.shape[-1]
# get distances and pairs of indices
from mystic.tools import pairwise
distances, pairs = pairwise(distances, True)
if offset: # tracking at a distance
distances = distances.ptp(axis=0) <= tolerance
else: # tracking with the same position
distances = distances.max(axis=0) <= tolerance
# get the (index1,index2) pairs where the collapse occurs
if distances.ndim > 1:
distances.shape = tuple(i for i in distances.shape if i != 1) or (1,)
distances = np.array(pairs)[distances]
# apply mask
if mask is None: return set(tuple(i) for i in distances)
mask = selector(mask)
return set(tuple(i) for i in distances if not mask(i))
