def compute_joint_norm_totals(projlist,active_units_mask=True):
"""
Compute norm_total for each CF in each projection from a group to
be normalized jointly.
"""
# Assumes that all Projections in the list have the same r,c size
assert len(projlist)>=1
iterator = CFIter(projlist[0],active_units_mask=active_units_mask)
for junk,i in iterator():
sums = [p.flatcfs[i].norm_total for p in projlist]
joint_sum = numpy.add.reduce(sums)
for p in projlist:
p.flatcfs[i].norm_total=joint_sum
def activate(self, input_activity):
"""Activate using the specified response_fn and output_fn."""
self.input_buffer = input_activity
self.activity *= 0.0
if self.x_avg is None:
self.x_avg = self.target * ones(self.dest.shape, activity_type)
if self.scaled_x_avg is None:
self.scaled_x_avg = self.target * ones(self.dest.shape,
activity_type)
if self.sf is None:
self.sf = ones(self.dest.shape, activity_type)
if self.lr_sf is None:
self.lr_sf = ones(self.dest.shape, activity_type)
self.response_fn(CFIter(self), input_activity, self.activity,
self.strength)
for of in self.output_fns:
of(self.activity)
self.calculate_sf()
self.do_scaling()
def compute_joint_norm_totals_opt(projlist, active_units_mask):
"""
Compute norm_total for each CF in each projections from a
group to be normalized jointly. The same assumptions are
made as in the original function.
"""
# Assumes that all Projections in the list have the same r,c size
length = len(projlist)
assert length >= 1
proj = projlist[0]
iterator = CFIter(proj, active_units_mask=active_units_mask)
num_cfs = len(proj.flatcfs) # pyflakes:ignore (passed to weave C code)
active_units_mask = iterator.get_active_units_mask()
sheet_mask = iterator.get_sheet_mask(
) # pyflakes:ignore (passed to weave C code)
cf_type = iterator.cf_type # pyflakes:ignore (passed to weave C code)
# CEBALERT: Not consistent with other C code. E.g. could be
# simplified to use active_units_mask[] and sheet_mask[]?
code = c_header + """
DECLARE_SLOT_OFFSET(_norm_total,cf_type);
DECLARE_SLOT_OFFSET(_has_norm_total,cf_type);
DECLARE_SLOT_OFFSET(weights,cf_type);
DECLARE_SLOT_OFFSET(input_sheet_slice,cf_type);
DECLARE_SLOT_OFFSET(mask,cf_type);
npfloat *x = active_units_mask;
npfloat *m = sheet_mask;
for (int r=0; r<num_cfs; ++r) {
double load = *x++;
double msk = *m++;
if (msk!=0 && load != 0) {
double nt = 0;
for(int p=0; p<length; p++) {
PyObject *proj = PyList_GetItem(projlist,p);
PyObject *cfs = PyObject_GetAttrString(proj,"flatcfs");
PyObject *cf = PyList_GetItem(cfs,r);
LOOKUP_FROM_SLOT_OFFSET(int,_has_norm_total,cf);
LOOKUP_FROM_SLOT_OFFSET(double,_norm_total,cf);
if (_has_norm_total[0] == 0) {
LOOKUP_FROM_SLOT_OFFSET(float,weights,cf);
LOOKUP_FROM_SLOT_OFFSET(int,input_sheet_slice,cf);
UNPACK_FOUR_TUPLE(int,rr1,rr2,cc1,cc2,input_sheet_slice);
SUM_NORM_TOTAL(cf,weights,_norm_total,rr1,rr2,cc1,cc2);
}
nt += _norm_total[0];
Py_DECREF(cfs);
}
for(int p=0; p<length; p++) {
PyObject *proj = PyList_GetItem(projlist,p);
PyObject *cfs = PyObject_GetAttrString(proj,"flatcfs");
PyObject *cf = PyList_GetItem(cfs,r);
LOOKUP_FROM_SLOT_OFFSET(double,_norm_total,cf);
_norm_total[0] = nt;
LOOKUP_FROM_SLOT_OFFSET(int,_has_norm_total,cf);
_has_norm_total[0] = 1;
Py_DECREF(cfs);
}
}
}
"""
inline(code, [
'projlist', 'active_units_mask', 'sheet_mask', 'num_cfs', 'length',
'cf_type'
],
local_dict=locals(),
headers=['<structmember.h>'])
def compute_joint_norm_totals_opt(projlist,active_units_mask):
"""
Compute norm_total for each CF in each projections from a
group to be normalized jointly. The same assumptions are
made as in the original function.
"""
# Assumes that all Projections in the list have the same r,c size
length = len(projlist)
assert length>=1
proj = projlist[0]
iterator = CFIter(proj,active_units_mask=active_units_mask)
num_cfs = len(proj.flatcfs) # pyflakes:ignore (passed to weave C code)
active_units_mask = iterator.get_active_units_mask()
sheet_mask = iterator.get_sheet_mask() # pyflakes:ignore (passed to weave C code)
cf_type = iterator.cf_type # pyflakes:ignore (passed to weave C code)
# CEBALERT: Not consistent with other C code. E.g. could be
# simplified to use active_units_mask[] and sheet_mask[]?
code = c_header + """
DECLARE_SLOT_OFFSET(_norm_total,cf_type);
DECLARE_SLOT_OFFSET(_has_norm_total,cf_type);
DECLARE_SLOT_OFFSET(weights,cf_type);
DECLARE_SLOT_OFFSET(input_sheet_slice,cf_type);
DECLARE_SLOT_OFFSET(mask,cf_type);
npfloat *x = active_units_mask;
npfloat *m = sheet_mask;
for (int r=0; r<num_cfs; ++r) {
double load = *x++;
double msk = *m++;
if (msk!=0 && load != 0) {
double nt = 0;
for(int p=0; p<length; p++) {
PyObject *proj = PyList_GetItem(projlist,p);
PyObject *cfs = PyObject_GetAttrString(proj,"flatcfs");
PyObject *cf = PyList_GetItem(cfs,r);
LOOKUP_FROM_SLOT_OFFSET(int,_has_norm_total,cf);
LOOKUP_FROM_SLOT_OFFSET(double,_norm_total,cf);
if (_has_norm_total[0] == 0) {
LOOKUP_FROM_SLOT_OFFSET(float,weights,cf);
LOOKUP_FROM_SLOT_OFFSET(int,input_sheet_slice,cf);
UNPACK_FOUR_TUPLE(int,rr1,rr2,cc1,cc2,input_sheet_slice);
SUM_NORM_TOTAL(cf,weights,_norm_total,rr1,rr2,cc1,cc2);
}
nt += _norm_total[0];
Py_DECREF(cfs);
}
for(int p=0; p<length; p++) {
PyObject *proj = PyList_GetItem(projlist,p);
PyObject *cfs = PyObject_GetAttrString(proj,"flatcfs");
PyObject *cf = PyList_GetItem(cfs,r);
LOOKUP_FROM_SLOT_OFFSET(double,_norm_total,cf);
_norm_total[0] = nt;
LOOKUP_FROM_SLOT_OFFSET(int,_has_norm_total,cf);
_has_norm_total[0] = 1;
Py_DECREF(cfs);
}
}
}
"""
inline(code, ['projlist','active_units_mask','sheet_mask','num_cfs','length','cf_type'],
local_dict=locals(),
headers=['<structmember.h>'])
def n_bytes(self):
return self.activity.nbytes + self.__sharedcf.weights.nbytes + \
sum([cf.input_sheet_slice.nbytes
for cf,i in CFIter(self)()])