def collapse_weight(stepmon, tolerance=0.005, generations=50, mask=None):
'''return a dict of {measure:indices} where the product_measure
exhibits a dimensional collapse in weight. Dimensional collapse in
weight is defined by: max(weight[i]) <= tolerance over N generations.
collapse will be ignored at (measure,indices) as specified in the mask.
Format of mask will determine the return value for this function. Default
mask format is dict of {measure: indices}, with alternate formatting
available as a set of tuples of (measure,index).
''' #XXX: not mentioned, 'where' format also available
np = _m.numpy
# reject bad masks
if mask is None: pass
elif type(mask) is set:
for i in mask:
if not hasattr(i, '__len__') or len(i) != 2:
msg = "bad element '%s' in mask" % str(i)
raise ValueError(msg)
if type(i[0]) is not int or type(i[1]) is not int:
msg = "bad element '%s' in mask" % str(i)
raise ValueError(msg)
elif type(mask) is dict:
for (i, j) in getattr(mask, 'iteritems', mask.items)():
if type(j) is not set or type(i) is not int:
msg = "bad entry '%s:%s' in mask" % (str(i), str(j))
raise ValueError(msg)
for k in j: # items in the set
if hasattr(k, '__len__'):
msg = "bad entry '%s:%s' in mask" % (str(i), str(j))
raise ValueError(msg)
elif hasattr(mask, '__len__') and len(mask) == 2:
if np.array(mask).ndim != 2:
msg = "%s is not a valid mask" % str(mask)
raise TypeError(msg)
elif hasattr(mask, '__len__') and not len(mask):
mask = type(mask)(((), ())) #XXX: HACK to get empty where mask
else:
msg = "%s is not a valid mask" % str(mask)
raise TypeError(msg)
# is the max weight less than tolerance across all generations?
weights = _m._weights(stepmon, generations).max(axis=0) <= tolerance
# identify mask format and build filter
mask, pairs = _weight_filter(mask)
# get weight collapse in 'where' notation
wts = (tuple(i) for i in np.where(weights) if len(i))
# apply mask and selected format...
if pairs: # return explicit 'pairs' {(measure,index)}
import itertools
return mask(set(getattr(itertools, 'izip', zip)(*wts)))
if pairs is None: # return 'where' format [measures,indices]
return mask(wts)
# returns a dict of {measure:indices}
wts = np.array(tuple(wts))
if not wts.size: return {}
return mask(
dict((i, set(wts[1][wts[0] == i])) for i in range(1 + wts[0][-1])
if i in wts[0]))
def collapse_weight(stepmon, tolerance=0.005, generations=50, mask=None):
'''return a dict of {measure:indices} where the product_measure
exhibits a dimensional collapse in weight. Dimensional collapse in
weight is defined by: max(weight[i]) <= tolerance over N generations.
collapse will be ignored at (measure,indices) as specified in the mask.
Format of mask will determine the return value for this function. Default
mask format is dict of {measure: indices}, with alternate formatting
available as a set of tuples of (measure,index).
''' #XXX: not mentioned, 'where' format also available
np = _m.numpy
# reject bad masks
if mask is None: pass
elif type(mask) is set:
for i in mask:
if not hasattr(i, '__len__') or len(i) != 2:
msg = "bad element '%s' in mask" % str(i)
raise ValueError(msg)
if type(i[0]) is not int or type(i[1]) is not int:
msg = "bad element '%s' in mask" % str(i)
raise ValueError(msg)
elif type(mask) is dict:
for (i,j) in getattr(mask, 'iteritems', mask.items)():
if type(j) is not set or type(i) is not int:
msg = "bad entry '%s:%s' in mask" % (str(i),str(j))
raise ValueError(msg)
for k in j: # items in the set
if hasattr(k, '__len__'):
msg = "bad entry '%s:%s' in mask" % (str(i),str(j))
raise ValueError(msg)
elif hasattr(mask, '__len__') and len(mask) == 2:
if np.array(mask).ndim != 2:
msg = "%s is not a valid mask" % str(mask)
raise TypeError(msg)
elif hasattr(mask, '__len__') and not len(mask):
mask = type(mask)(((),())) #XXX: HACK to get empty where mask
else:
msg = "%s is not a valid mask" % str(mask)
raise TypeError(msg)
# is the max weight less than tolerance across all generations?
weights = _m._weights(stepmon, generations).max(axis=0) <= tolerance
# identify mask format and build filter
mask, pairs = _weight_filter(mask)
# get weight collapse in 'where' notation
wts = (tuple(i) for i in np.where(weights) if len(i))
# apply mask and selected format...
if pairs: # return explicit 'pairs' {(measure,index)}
import itertools
return mask(set(getattr(itertools, 'izip', zip)(*wts)))
if pairs is None: # return 'where' format [measures,indices]
return mask(wts)
# returns a dict of {measure:indices}
wts = np.array(tuple(wts))
if not wts.size: return {}
return mask(dict((i,set(wts[1][wts[0]==i])) for i in range(1+wts[0][-1]) if i in wts[0]))