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 draw_item_activation(mymap, named=True, overwrite=False, symbols=False):
words = empty_list(mymap.size, 1)
mymap.renormalize()
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(item)
vectors.append(s[item])
for i, vector in enumerate(vectors):
match = fn.find_best_match(vector, mymap.weights)
x = mymap.positions[match]
x = fn.to_int(x)
w = words[x[0]][x[1]]
if w == "" or overwrite:
if overwrite:
winner = fn.find_best_match(mymap.weights[match], mymap.vectors)
w = keys[winner]
else:
w = keys[i]
else:
w = w + "," + keys[i]
words[x[0]][x[1]] = w
word_plot(words)
return words
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
