def __call__(self,x):
rows,cols = x.shape
radius = self.density*self.kernel_radius
crop_radius = int(max(1.25,radius*self.crop_radius_multiplier))
# find out the matrix coordinates of the winner
wr,wc = array_argmax(x)
# convert to sheet coordinates
wy = rows-wr-1
# Optimization: Calculate the bounding box around the winner
# in which weights will be changed
cmin = max(wc-crop_radius, 0)
cmax = min(wc+crop_radius+1,cols)
rmin = max(wr-crop_radius, 0)
rmax = min(wr+crop_radius+1,rows)
ymin = max(wy-crop_radius, 0)
ymax = min(wy+crop_radius+1,rows)
bb = BoundingBox(points=((cmin,ymin), (cmax,ymax)))
# generate the kernel matrix and insert it into the correct
# part of the output array
kernel = self.neighborhood_kernel_generator(bounds=bb,xdensity=1,ydensity=1,
size=2*radius,x=wc+0.5,y=wy+0.5)
x *= 0.0
x[rmin:rmax,cmin:cmax] = kernel
def determine_next_position(self,img):
self.maximum=array_argmax(img)
#Coordinates for the box around the brightest pixel
self.coormin_bbox=(self.maximum[1]-1,self.maximum[0]-1)
self.coormax_bbox=(self.maximum[1]+1,self.maximum[0]+1)
self.brightpixel=True
return(self.maximum,self.coormin_bbox,self.coormax_bbox,self.brightpixel)
def determine_next_position(self,img):
self.maximum=array_argmax(img)
#Coordinates for the box around the brightest pixel
self.coormin_bbox=(self.maximum[1]-1,self.maximum[0]-1)
self.coormax_bbox=(self.maximum[1]+1,self.maximum[0]+1)
self.brightpixel=True
return(self.maximum,self.coormin_bbox,self.coormax_bbox,self.brightpixel)
def __call__(self, iterator, input_activity, output_activity, learning_rate, **params):
cfs = iterator.proj.cfs.tolist() # CEBALERT: convert to use flatcfs
rows,cols = output_activity.shape
# This learning function does not need to scale the learning
# rate like some do, so it does not use constant_sum_connection_rate()
single_connection_learning_rate = learning_rate
### JABALERT: The learning_radius is normally set by
### the learn() function of CFSOM, so it doesn't matter
### much that the value accepted here is in matrix and
### not sheet coordinates. It's confusing that anything
### would accept matrix coordinates, but the learning_fn
### doesn't have access to the sheet, so it can't easily
### convert from sheet coords.
radius = self.learning_radius
crop_radius = max(1.25,radius*self.crop_radius_multiplier)
# find out the matrix coordinates of the winner
#
# NOTE: when there are multiple projections, it would be
# slightly more efficient to calculate the winner coordinates
# within the Sheet, e.g. by moving winner_coords() to CFSOM
# and passing in the results here. However, finding the
# coordinates does not take much time, and requiring the
# winner to be passed in would make it harder to mix and match
# Projections and learning rules with different Sheets.
wr,wc = array_argmax(output_activity)
# Optimization: Calculate the bounding box around the winner
# in which weights will be changed, to avoid considering those
# units below.
cmin = int(max(wc-crop_radius,0))
cmax = int(min(wc+crop_radius+1,cols)) # at least 1 between cmin and cmax
rmin = int(max(wr-crop_radius,0))
rmax = int(min(wr+crop_radius+1,rows))
# generate the neighborhood kernel matrix so that the values
# can be read off easily using matrix coordinates.
nk_generator = self.neighborhood_kernel_generator
radius_int = int(ceil(crop_radius))
rbound = radius_int + 0.5
bb = BoundingBox(points=((-rbound,-rbound), (rbound,rbound)))
# Print parameters designed to match fm2d's output
#print "%d rad= %d std= %f alpha= %f" % (topo.sim._time, radius_int, radius, single_connection_learning_rate)
neighborhood_matrix = nk_generator(bounds=bb,xdensity=1,ydensity=1,
size=2*radius)
for r in range(rmin,rmax):
for c in range(cmin,cmax):
cwc = c - wc
rwr = r - wr
lattice_dist = L2norm((cwc,rwr))
if lattice_dist <= crop_radius:
cf = cfs[r][c]
rate = single_connection_learning_rate * neighborhood_matrix[rwr+radius_int,cwc+radius_int]
X = cf.get_input_matrix(input_activity)
cf.weights += rate * (X - cf.weights)
# CEBHACKALERT: see ConnectionField.__init__()
cf.weights *= cf.mask
def __call__(self, iterator, input_activity, output_activity, learning_rate, **params):
cfs = iterator.proj.cfs.tolist() # CEBALERT: convert to use flatcfs
rows,cols = output_activity.shape
# This learning function does not need to scale the learning
# rate like some do, so it does not use constant_sum_connection_rate()
single_connection_learning_rate = learning_rate
### JABALERT: The learning_radius is normally set by
### the learn() function of CFSOM, so it doesn't matter
### much that the value accepted here is in matrix and
### not sheet coordinates. It's confusing that anything
### would accept matrix coordinates, but the learning_fn
### doesn't have access to the sheet, so it can't easily
### convert from sheet coords.
radius = self.learning_radius
crop_radius = max(1.25,radius*self.crop_radius_multiplier)
# find out the matrix coordinates of the winner
#
# NOTE: when there are multiple projections, it would be
# slightly more efficient to calculate the winner coordinates
# within the Sheet, e.g. by moving winner_coords() to CFSOM
# and passing in the results here. However, finding the
# coordinates does not take much time, and requiring the
# winner to be passed in would make it harder to mix and match
# Projections and learning rules with different Sheets.
wr,wc = array_argmax(output_activity)
# Optimization: Calculate the bounding box around the winner
# in which weights will be changed, to avoid considering those
# units below.
cmin = int(max(wc-crop_radius,0))
cmax = int(min(wc+crop_radius+1,cols)) # at least 1 between cmin and cmax
rmin = int(max(wr-crop_radius,0))
rmax = int(min(wr+crop_radius+1,rows))
# generate the neighborhood kernel matrix so that the values
# can be read off easily using matrix coordinates.
nk_generator = self.neighborhood_kernel_generator
radius_int = int(ceil(crop_radius))
rbound = radius_int + 0.5
bb = BoundingBox(points=((-rbound,-rbound), (rbound,rbound)))
# Print parameters designed to match fm2d's output
#print "%d rad= %d std= %f alpha= %f" % (topo.sim._time, radius_int, radius, single_connection_learning_rate)
neighborhood_matrix = nk_generator(bounds=bb,xdensity=1,ydensity=1,
size=2*radius)
for r in range(rmin,rmax):
for c in range(cmin,cmax):
cwc = c - wc
rwr = r - wr
lattice_dist = L2norm((cwc,rwr))
if lattice_dist <= crop_radius:
cf = cfs[r][c]
rate = single_connection_learning_rate * neighborhood_matrix[rwr+radius_int,cwc+radius_int]
X = cf.get_input_matrix(input_activity)
cf.weights += rate * (X - cf.weights)
# CEBHACKALERT: see ConnectionField.__init__()
cf.weights *= cf.mask
