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 setweights(self):
x = []
list = flatten(self.neurons)
for neuron in list:
#x.append(fn.normalize(neuron.weight))
x.append(neuron.weight)
self.weights = scipy.array(x)
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 draw_clusters_per_item(mymap, clusters):
cluster_map = empty_list(mymap.size, 1)
vectors = mymap.vectors
keys = mymap.keys
for neuron in flatten(mymap.neurons):
weight = neuron.weight
match = fn.find_best_match(weight, vectors)
key = keys[match]
cluster = clusters[key]
x = neuron.position
x = fn.to_int(x)
cluster_map[x[0]][x[1]] = key
# cluster_map[x[0]][x[1]] = cluster
return cluster_map
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_clusters(mymap, clusters):
cluster_map = empty_list(mymap.size, 1)
vectors = mymap.vectors
keys = mymap.keys
for neuron in flatten(mymap.neurons):
weight = neuron.weight
match = fn.find_best_match(weight, vectors)
key = keys[match]
cluster = clusters[key]
x = neuron.position
x = fn.to_int(x)
# cluster_map[x[0]][x[1]] = key
cluster_map[x[0]][x[1]] = cluster
return cluster_map
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 flush_map(self):
list = flatten(self.neurons)
for n, neuron in enumerate(list):
neuron.weight = self.weights[n]
def setpositions(self):
x = []
list = flatten(self.neurons)
for neuron in list:
x.append(fn.to_int(neuron.position))
self.positions = scipy.array(x)
