def get_rate(self, count1, count2):
rate = 0
if (is_empty(count1) or is_empty(count2)):
print('割合を取得する際に必要な二つの数値が取得できていない。')
return rate
try:
if to_int(count1) > 0 or to_int(count2) > 0:
rate = round(
to_int(count1) / (to_int(count1) + to_int(count2)), 2)
return rate
except Exception as e:
print('割合の取得に失敗しました。')
print(str(e))
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 validate_count(self, evaluation_item, count):
try:
if (is_empty(count)):
print(evaluation_item['name'] + 'が取得できていない。')
return False
if ('min' in evaluation_item
and (to_int(count) < evaluation_item['min'])):
print(evaluation_item['name'] + 'が' +
str(evaluation_item['min']) + '未満')
return False
if ('max' in evaluation_item
and (to_int(count) > evaluation_item['max'])):
print(evaluation_item['name'] + 'が' +
str(evaluation_item['max']) + '以上')
return False
return True
except Exception as e:
print('カウントのバリデーションに失敗')
print(sys._getframe().f_code.co_name)
print(e.args)
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 draw_basis_activation(
map
): # This mapping finds the closest neuron for each basis vector and prints the "name" of the basis vector on the neuron position
words = empty_list(map.size, 1)
basis_vectors = []
d = map.space.dimension
for i in range(d):
b = scipy.zeros(d, int)
b[i] = 1
basis_vectors.append(b)
for i, bv in enumerate(basis_vectors):
bmu = map.find_bmu0(bv)
x = map.positions[bmu]
x = fn.to_int(x)
words[x[0]][x[1]] = map.space.words[i]
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 setpositions(self):
x = []
list = flatten(self.neurons)
for neuron in list:
x.append(fn.to_int(neuron.position))
self.positions = scipy.array(x)