def activity_sample(sheet,activity=None):
"""
Sample from the sheet activity as if it were a probability distribution.
Returns the sheet coordinates of the sampled unit. If
activity is not None, it is used instead of sheet.activity.
"""
if activity is None:
activity = sheet.activity
idx = util.weighted_sample_idx(activity.ravel())
r,c = util.idx2rowcol(idx,activity.shape)
return sheet.matrix2sheet(r,c)
def activity_sample(sheet, activity=None):
"""
Sample from the sheet activity as if it were a probability distribution.
Returns the sheet coordinates of the sampled unit. If
activity is not None, it is used instead of sheet.activity.
"""
if activity is None:
activity = sheet.activity
idx = util.weighted_sample_idx(activity.ravel())
r, c = util.idx2rowcol(idx, activity.shape)
return sheet.matrix2sheet(r, c)
def activity_mode(sheet,activity=None):
"""
Returns the sheet coordinates of the mode (highest value) of
the sheet activity.
"""
# JPHACKALERT: The mode is computed using numpy.argmax, and
# thus for distributions with multiple equal-valued modes, the
# result will have a systematic bias toward higher x and lower
# y values. (in that order). Function may still be useful for
# unimodal activity distributions, or sheets without limiting/squashing
# output functions.
if activity is None:
activity = sheet.activity
idx = argmax(activity.flat)
r,c = util.idx2rowcol(idx,activity.shape)
return sheet.matrix2sheet(r,c)
def activity_mode(sheet, activity=None):
"""
Returns the sheet coordinates of the mode (highest value) of
the sheet activity.
"""
# JPHACKALERT: The mode is computed using numpy.argmax, and
# thus for distributions with multiple equal-valued modes, the
# result will have a systematic bias toward higher x and lower
# y values. (in that order). Function may still be useful for
# unimodal activity distributions, or sheets without limiting/squashing
# output functions.
if activity is None:
activity = sheet.activity
idx = argmax(activity.flat)
r, c = util.idx2rowcol(idx, activity.shape)
return sheet.matrix2sheet(r, c)
