def draw_neuron_activation(mymap, named=True, symbols=False): # iterates through EACH neuron and finds closest vector
words = distances = empty_list(mymap.size, 1)
if named:
vectors = mymap.vectors
keys = mymap.keys
else:
vectors = []
keys = []
idea_names = mymap.space.idea_names
for item in mymap.space.table:
keys.append(idea_names[item])
vectors.append(mymap.space.table[item])
if symbols:
s = mymap.space.symbol_vectors
keys = []
vectors = []
for item in s:
keys.append(mymap.space.idea_names[item])
vectors.append(s[item])
for neuron in flatten(mymap.neurons):
weight = neuron.weight
match = fn.find_best_match(weight, vectors)
distance = fn.distance(weight, vectors[match])
x = neuron.position
x = fn.to_int(x)
words[x[0]][x[1]] = keys[match]
# distances[x[0]][x[1]] = distance
word_plot(words)
return words
def get_umatrix(mymap, radius=1):
umatrix = empty_list(mymap.size, 1)
xmax = mymap.size[1]
ymax = mymap.size[0]
rad = range(-radius, radius + 1)
# print rad
for neuron in flatten(mymap.neurons):
weight = neuron.weight
position = neuron.position
x = position[0]
y = position[1]
xrange = []
yrange = []
for i in rad:
xrange.append(int((x + i) % xmax))
yrange.append(int((y + i) % ymax))
average_dist = 0
for x in xrange:
for y in yrange:
neighbour_weight = mymap.neurons[x][y].weight
d = fn.distance(neighbour_weight, weight)
average_dist += d
umatrix[x][y] = average_dist
return umatrix
def get_umatrix(mymap, radius=1):
umatrix = empty_list(mymap.size, 1)
xmax = mymap.size[1]
ymax = mymap.size[0]
rad = range(-radius, radius + 1)
# print rad
for neuron in flatten(mymap.neurons):
weight = neuron.weight
position = neuron.position
x = position[0]
y = position[1]
xrange = []
yrange = []
for i in rad:
xrange.append(int((x + i) % xmax))
yrange.append(int((y + i) % ymax))
average_dist = 0
for x in xrange:
for y in yrange:
neighbour_weight = mymap.neurons[x][y].weight
d = fn.distance(neighbour_weight, weight)
average_dist += d
umatrix[x][y] = average_dist
return umatrix
def get_distances_to_nearest(mymap):
distances = empty_list(mymap.size, 1)
vectors = mymap.vectors
matches = []
for neuron in flatten(mymap.neurons):
weight = neuron.weight
match = fn.find_best_match(weight, vectors)
matches.append(match)
distance = fn.distance(weight, vectors[match])
x = neuron.position
x = fn.to_int(x)
distances[x[0]][x[1]] = distance
c = Counter(matches)
print c
print 'items mapped : ' + str(len(sorted(c)))
return distances
def get_distances_to_nearest(mymap):
distances = empty_list(mymap.size, 1)
vectors = mymap.vectors
matches = []
for neuron in flatten(mymap.neurons):
weight = neuron.weight
match = fn.find_best_match(weight, vectors)
matches.append(match)
distance = fn.distance(weight, vectors[match])
x = neuron.position
x = fn.to_int(x)
distances[x[0]][x[1]] = distance
c = Counter(matches)
print c
print 'items mapped : ' + str(len(sorted(c)))
return distances
def draw_neuron_activation(
mymap,
named=True,
symbols=False
): # iterates through EACH neuron and finds closest vector
words = distances = empty_list(mymap.size, 1)
if named:
vectors = mymap.vectors
keys = mymap.keys
else:
vectors = []
keys = []
idea_names = mymap.space.idea_names
for item in mymap.space.table:
keys.append(idea_names[item])
vectors.append(mymap.space.table[item])
if symbols:
s = mymap.space.symbol_vectors
keys = []
vectors = []
for item in s:
keys.append(mymap.space.idea_names[item])
vectors.append(s[item])
for neuron in flatten(mymap.neurons):
weight = neuron.weight
match = fn.find_best_match(weight, vectors)
distance = fn.distance(weight, vectors[match])
x = neuron.position
x = fn.to_int(x)
words[x[0]][x[1]] = keys[match]
# distances[x[0]][x[1]] = distance
word_plot(words)
return words
def average_distance_to_bmu0(self):
dd = 0.0
for vector in self.vectors:
bmu = self.find_bmu0(vector)
dd += fn.distance(self.weights[bmu], vector)
return dd / len(self.vectors)
def average_distance_to_bmu(self):
dd = 0.0
for item in self.space.table:
bmu = self.find_bmu(item)
dd += fn.distance(bmu.weight , self.space.table[item])
return dd / len(self.space.table)