def __init__(self,cf,input_sheet,x=0.0,y=0.0,template=BoundingBox(radius=0.1),
mask=patterngenerator.Constant(),
min_matrix_radius=1):
"""
From an existing copy of ConnectionField (CF) that acts as a
template, create a new CF that shares weights with the
template CF. Copies all the properties of CF to stay
identical except the weights variable that actually contains
the data.
The only difference from a normal CF is that the weights of
the CF are implemented as a numpy view into the single master
copy of the weights stored in the CF template.
"""
# CEBALERT: There's no call to super's __init__; see JAHACKALERT
# below.
template = copy(template)
if not isinstance(template,Slice):
template = Slice(template,input_sheet,force_odd=True,
min_matrix_radius=min_matrix_radius)
# Note: if passed in, mask is shared between CFs (but not if created here)
if not hasattr(mask,'view'):
mask = _create_mask(patterngenerator.Constant(),
template.compute_bounds(input_sheet),
input_sheet,True,0.5)
self._has_norm_total=False
self.mask=mask
weights_slice = self._create_input_sheet_slice(input_sheet,x,y,template,min_matrix_radius=min_matrix_radius)
self.weights = weights_slice.submatrix(cf.weights)
def __init__(self,cf,input_sheet,x=0.0,y=0.0,template=BoundingBox(radius=0.1),
mask=patterngenerator.Constant(),
min_matrix_radius=1):
"""
From an existing copy of ConnectionField (CF) that acts as a
template, create a new CF that shares weights with the
template CF. Copies all the properties of CF to stay
identical except the weights variable that actually contains
the data.
The only difference from a normal CF is that the weights of
the CF are implemented as a numpy view into the single master
copy of the weights stored in the CF template.
"""
# CEBALERT: There's no call to super's __init__; see JAHACKALERT
# below.
template = copy(template)
if not isinstance(template,Slice):
template = Slice(template,input_sheet,force_odd=True,
min_matrix_radius=min_matrix_radius)
# Note: if passed in, mask is shared between CFs (but not if created here)
if not hasattr(mask,'view'):
mask = _create_mask(patterngenerator.Constant(),
template.compute_bounds(input_sheet),
input_sheet,True,0.5)
self._has_norm_total=False
self.mask=mask
weights_slice = self._create_input_sheet_slice(input_sheet,x,y,template,min_matrix_radius=min_matrix_radius)
self.weights = weights_slice.submatrix(cf.weights)
def _create_cfs(self):
"""
Creates the CF objects, initializing the weights one by one
and adding them to the sparse weights object in chunks.
"""
vectorized_create_cf = simple_vectorize(self._create_cf)
self.cfs = vectorized_create_cf(*self._generate_coords())
self.flatcfs = list(self.cfs.flat)
self.weights = sparse.csarray_float(self.src.activity.shape,
self.dest.activity.shape)
cf_x, cf_y = self.dest.activity.shape
src_x, src_y = self.src.activity.shape
y_array = np.zeros((src_x * src_y * cf_y), dtype=np.int32)
x_array = np.zeros((src_x * src_y * cf_y), dtype=np.int32)
val_array = np.zeros((src_x * src_y * cf_y), dtype=np.float32)
# Iterate over the CFs
for x in range(cf_x):
temp_sparse = sparse.csarray_float(self.src.activity.shape,
self.dest.activity.shape)
idx = 0
for y in range(cf_y):
x1, x2, y1, y2 = self.cfs[x][y].input_sheet_slice.tolist()
if self.same_cf_shape_for_all_cfs:
mask_template = self.mask_template
else:
mask_template = _create_mask(self.cf_shape,
self.bounds_template,
self.src, self.autosize_mask,
self.mask_threshold)
weights = self.cfs[x][y]._init_weights(mask_template)
cn_x, cn_y = weights.shape
y_val = x * cf_y + y
for cnx in range(cn_x):
val_array[idx:idx + cn_y] = weights[cnx, :]
x_val = (x1 + cnx) * src_y + y1
x_array[idx:idx + cn_y] = range(x_val, x_val + cn_y)
y_array[idx:idx + cn_y] = y_val
idx += cn_y
nnz_idx = val_array.nonzero()
temp_sparse.setTriplets(x_array[nnz_idx], y_array[nnz_idx],
val_array[nnz_idx])
self.weights += temp_sparse
x_array *= 0
y_array *= 0
val_array *= 0.0
del temp_sparse
self.weights.compress()
self.apply_learn_output_fns()
print self.name, "loaded"
def _create_cfs(self):
"""
Creates the CF objects, initializing the weights one by one
and adding them to the sparse weights object in chunks.
"""
vectorized_create_cf = simple_vectorize(self._create_cf)
self.cfs = vectorized_create_cf(*self._generate_coords())
self.flatcfs = list(self.cfs.flat)
self.weights = sparse.csarray_float(self.src.activity.shape,self.dest.activity.shape)
cf_x,cf_y = self.dest.activity.shape
src_x,src_y = self.src.activity.shape
y_array = np.zeros((src_x*src_y*cf_y),dtype=np.int32)
x_array = np.zeros((src_x*src_y*cf_y),dtype=np.int32)
val_array = np.zeros((src_x*src_y*cf_y),dtype=np.float32)
# Iterate over the CFs
for x in range(cf_x):
temp_sparse = sparse.csarray_float(self.src.activity.shape,self.dest.activity.shape)
idx = 0
for y in range(cf_y):
cf = self.cfs[x][y]
label = cf.label + ('-%d' % self.seed if self.seed is not None else '')
name = "%s_CF (%.5f, %.5f)" % ('' if label is None else label, cf.x,cf.y)
x1,x2,y1,y2 = cf.input_sheet_slice.tolist()
if self.same_cf_shape_for_all_cfs:
mask_template = self.mask_template
else:
mask_template = _create_mask(self.cf_shape,self.bounds_template,
self.src,self.autosize_mask,
self.mask_threshold, name=name)
weights = self.cfs[x][y]._init_weights(mask_template)
cn_x,cn_y = weights.shape
y_val = x * cf_y + y
for cnx in range(cn_x):
val_array[idx:idx+cn_y] = weights[cnx,:]
x_val = (x1+cnx) * src_y + y1
x_array[idx:idx+cn_y] = range(x_val,x_val+cn_y)
y_array[idx:idx+cn_y] = y_val
idx += cn_y
nnz_idx = val_array.nonzero()
temp_sparse.setTriplets(x_array[nnz_idx],y_array[nnz_idx],val_array[nnz_idx])
self.weights += temp_sparse
x_array *= 0; y_array *= 0; val_array *= 0.0
del temp_sparse
self.weights.compress()
self.debug("Sparse projection %r loaded" % self.name)
def __init__(self,initialize_cfs=True,**params):
"""
Initialize the Projection with a set of cf_type objects
(typically SparseConnectionFields), each located at the
location in the source sheet corresponding to the unit in the
target sheet. The cf_type objects are stored in the 'cfs'
array.
The nominal_bounds_template specified may be altered: the
bounds must be fitted to the Sheet's matrix, and the weights
matrix must have odd dimensions. These altered bounds are
passed to the individual connection fields.
A mask for the weights matrix is constructed. The shape is
specified by cf_shape; the size defaults to the size
of the nominal_bounds_template.
"""
super(CFProjection,self).__init__(**params)
self.weights_generator.set_dynamic_time_fn(None,sublistattr='generators')
# get the actual bounds_template by adjusting a copy of the
# nominal_bounds_template to ensure an odd slice, and to be
# cropped to sheet if necessary
self._slice_template = Slice(copy(self.nominal_bounds_template),
self.src,force_odd=True,
min_matrix_radius=self.min_matrix_radius)
self.bounds_template = self._slice_template.compute_bounds(self.src)
self.mask_template = _create_mask(self.cf_shape,self.bounds_template,
self.src,self.autosize_mask,
self.mask_threshold)
self.n_units = self._calc_n_units()
self.activity = np.array(self.dest.activity)
self.norm_total = np.array(self.dest.activity,dtype=np.float64)
self.has_norm_total = False
if initialize_cfs:
self._create_cfs()
if self.apply_output_fns_init:
self.apply_learn_output_fns()
self.input_buffer = None
def _init_weights(self,mask_template):
if not hasattr(mask_template,'view'):
mask = _create_mask(mask_template,self.weights_slice.compute_bounds(self.input_sheet),self.input_sheet,True,0.5)
mask = self.weights_slice.submatrix(mask_template)
mask = np.array(mask,copy=1)
w = self.weights_generator(x=self.x,y=self.y,bounds=self.get_bounds(self.input_sheet),
xdensity=self.input_sheet.xdensity,
ydensity=self.input_sheet.ydensity,
mask=mask)
w = w.astype(sparse_type)
for of in self.output_fns:
of(w)
return w
def _init_weights(self,mask_template):
if not hasattr(mask_template,'view'):
mask = _create_mask(mask_template,self.weights_slice.compute_bounds(self.input_sheet),self.input_sheet,True,0.5)
mask = self.weights_slice.submatrix(mask_template)
mask = np.array(mask,copy=1)
w = self.weights_generator(x=self.x,y=self.y,bounds=self.get_bounds(self.input_sheet),
xdensity=self.input_sheet.xdensity,
ydensity=self.input_sheet.ydensity,
mask=mask)
w = w.astype(sparse_type)
for of in self.output_fns:
of(w)
return w
def _init_weights(self, mask_template):
if not hasattr(mask_template, "view"):
mask = _create_mask(
mask_template, self.weights_slice.compute_bounds(self.input_sheet), self.input_sheet, True, 0.5
)
mask = self.weights_slice.submatrix(mask_template)
mask = np.array(mask, copy=1)
pattern_params = dict(
x=self.x,
y=self.y,
bounds=self.get_bounds(self.input_sheet),
xdensity=self.input_sheet.xdensity,
ydensity=self.input_sheet.ydensity,
mask=mask,
)
controlled_weights = (
param.Dynamic.time_dependent
and isinstance(param.Dynamic.time_fn, param.Time)
and self.independent_weight_generation
)
if controlled_weights:
with param.Dynamic.time_fn as t:
t(0) # Initialize at time zero.
# Controls random streams
label = "" if self.label is None else self.label
name = "%s_CF (%.5f, %.5f)" % (label, self.x, self.y)
w = self.weights_generator(**dict(pattern_params, name=name))
else:
w = self.weights_generator(**pattern_params)
w = w.astype(sparse_type)
for of in self.output_fns:
of(w)
return w
def _init_weights(self,mask_template):
if not hasattr(mask_template,'view'):
mask = _create_mask(mask_template,
self.weights_slice.compute_bounds(
self.input_sheet),
self.input_sheet,True,0.5)
mask = self.weights_slice.submatrix(mask_template)
mask = np.array(mask,copy=1)
pattern_params = dict(x=self.x,y=self.y,
bounds=self.get_bounds(self.input_sheet),
xdensity=self.input_sheet.xdensity,
ydensity=self.input_sheet.ydensity,
mask=mask)
controlled_weights = (param.Dynamic.time_dependent
and isinstance(param.Dynamic.time_fn,
param.Time)
and self.independent_weight_generation)
if controlled_weights:
with param.Dynamic.time_fn as t:
t(0) # Initialize at time zero.
# Controls random streams
label = '' if self.label is None else self.label
name = "%s_CF (%.5f, %.5f)" % (label, self.x, self.y)
w = self.weights_generator(**dict(pattern_params,
name=name))
else:
w = self.weights_generator(**pattern_params)
w = w.astype(sparse_type)
for of in self.output_fns:
of(w)
return w
