def fit(self, x, y, feature_sampling=None):
if x.shape[0] != y.shape[0] or len(y) == 0:
raise Error("Invalid x, y!")
if self.type == "CART":
self.root = self.build_CART_tree(x, y, feature_sampling)
elif self.type == "ID3" or self.type == "C4.5":
self.root = self.build_tree(x, y, np.arange(x.shape[1]))
else:
raise Error("Invalid type!")
def fit(self,
x,
y,
use_normal_equation=True,
regularization=None,
lam=0.1,
detailed=False):
if use_normal_equation:
X = np.hstack((np.ones([x.shape[0], 1]), x))
if regularization is None:
inv_matrix = np.dot(X.T, X)
elif regularization == 2:
inv_matrix = np.dot(X.T, X) + lam * np.eye(X.shape[1])
else:
raise Error("invalid regularization!")
if np.linalg.det(inv_matrix) == 0:
raise Error("The matrix is singular, can't do inverse!")
inv_matrix = np.linalg.inv(inv_matrix)
self.w = np.dot(np.dot(inv_matrix, X.T), np.reshape(y, [-1, 1]))
self.w = self.w.flatten()
self.b = self.w[0]
self.w = self.w[range(1, len(self.w))]
else:
if self.batch_size is None:
self.batch_size = x.shape[0]
self.w = np.ones(x.shape[1])
self.b = np.ones(1)
num_of_batch = x.shape[0] // self.batch_size
if x.shape[0] % self.batch_size != 0:
num_of_batch += 1
for i in range(self.epoch):
for j in range(num_of_batch):
start = j * self.batch_size
end = min((j + 1) * self.batch_size, x.shape[0])
error = self.predict(x[start:end, :]) - y[start:end]
error = error.reshape([-1, 1])
if regularization is None:
delta_w = np.sum(error * x[start:end, :], axis=0)
delta_b = np.sum(error)
elif regularization == 1:
delta_w = np.sum(error * x[start:end, :],
axis=0) + lam * np.sign(self.w)
delta_b = np.sum(error) + lam * np.sign(self.b)
elif regularization == 2:
delta_w = np.sum(error * x[start:end, :],
axis=0) + 2 * lam * self.w
delta_b = np.sum(error) + 2 * lam * self.b
else:
raise Error("invalid regularization!")
self.w -= self.eta * delta_w
self.b -= self.eta * delta_b
if detailed:
print("epoch:", i, " w:", self.w, " b:", self.b)
def build_tree(self, x, y, features):
if len(y) < self.pre_pruning[1]:
return None
is_same_kind, label = self.is_same_kind(y)
if is_same_kind or len(features) == 0:
return Node(label, is_leaf=True)
split_feature, split_values, max_info = None, None, 0
for feature in features:
if self.type == "ID3":
info, values = calculate_info_gain(x[:, feature], y)
elif self.type == "C4.5":
info, values = calculate_info_gain_ratio(x[:, feature], y)
else:
raise Error("Invalid type!")
if max_info < info:
split_feature = feature
split_values = values
max_info = info
if max_info < self.pre_pruning[0]:
return Node(label, is_leaf=True)
node = Node(label, is_leaf=False)
for value in split_values:
new_features = np.delete(features, split_feature)
value_x = x[x[:, split_feature] == value]
value_y = y[x[:, split_feature] == value]
child = self.build_tree(value_x, value_y, new_features)
if child is None:
return Node(label, is_leaf=True)
node.add_child(value, child)
return node
async def reload_translation(self, word):
try:
if not await self.db_layer.word.record_is_exists(word):
return Error("Word not found!!!")
await self.producer.send_message(WordKafkaMessage(word))
return "The audio recording will be updated!!!"
except Exception as exp:
return self.__unexpected_exception__(exp)
def __init__(self,
input_attr=0,
output_attr=0,
num=20,
feature_sampling=np.log2):
if input_attr != 0 and input_attr != 1:
raise Error("Invalid input_attr!")
if output_attr != 0 and output_attr != 1:
raise Error("Invalid output_attr!")
self.num = num
self.input_attr = input_attr
self.output_attr = output_attr
self.feature_sampling = feature_sampling
self.trees = list()
for i in range(num):
tree = DecisionTree(input_attr, output_attr, type="CART")
self.trees.append(tree)
async def save_word(self, arg_json):
try:
word_inst = Word.init_form_json(
validate_json(Word.get_json_schema(), arg_json))
if await self.db_layer.word.record_is_exists(word_inst.word):
return Error(
"The dictionary already contains this word. Word - {}".
format(word_inst.word))
await self.db_layer.word.save(vars(word_inst))
await self.producer.send_message(WordKafkaMessage(word_inst.word))
except (TypeError, ValidationError) as exp:
return Error(exp)
except JSONDecodeError:
return Error("Not valid JSON was passed.")
except Exception as exp:
return self.__unexpected_exception__(exp)
else:
return "Word was added!!!"
async def update_word(self, arg_json):
try:
json_dict = validate_json(Word.get_json_schema(), arg_json)
word = json_dict['word']
new_json_dict = exclude_json_fields(
json_dict, fields=['translation', 'phrase', 'synonyms'])
if new_json_dict:
result = await self.db_layer.word.find_one_and_update(
word, new_json_dict)
if result is None:
return Error("Word not found!!!")
except (TypeError, ValidationError) as exp:
return Error(exp)
except JSONDecodeError:
return Error("Not valid JSON was passed.")
except Exception as exp:
return self.__unexpected_exception__(exp)
else:
return "Word was updated!!!"
async def get_word(self, word):
try:
db_record_dict = await self.db_layer.word.find_one_by_word(word)
except Exception as exp:
return self.__unexpected_exception__(exp)
else:
if db_record_dict is None:
return Error("Word not found!!!")
else:
return Word\
.init_form_json(db_record_dict)\
.get_pretty_view()
async def delete_word(self, word):
try:
result = await self.db_layer.word.find_one_and_delete(word)
if result is not None:
sound_record_path = result['sound_record_path']
if sound_record_path is not None:
remove_file(sound_record_path)
else:
return Error("Word not found!!!")
except Exception as exp:
return self.__unexpected_exception__(exp)
else:
return "Word was deleted!!!"
def __init__(self, input_attr=0, output_attr=0, type="CART", pre_pruning=(0, 0)):
"""
:param input_attr: 输入属性，1：连续，0：离散 for CART
:param output_attr: 输出属性，1：连续，0：离散 for CART
:param type: ID3, C4.5, CART
:param pre_pruning: 2元组，第一个元素表示损失变化的最小值，第二个元素表示节点上最小样本数量
## 输出属性连续，表示回归问题，采用均方差度量损失
## 输出属性离散，表示分类问题，采用基尼指数度量损失
## 输入属性连续，则对输入排序后，以任意两个相邻点中位数作为切分点，选取最优切分点，将数据划分为两部分
## 输入属性离散，则依次遍历每个可能取值，以是否等于改值为标准，将数据集划分为两部分，选取最优特征取值
"""
self.root = None
self.type = type
self.input_attr = input_attr
self.output_attr = output_attr
self.pre_pruning = pre_pruning
if input_attr != 0 and input_attr != 1:
raise Error("Invalid input_attr!")
if output_attr != 0 and output_attr != 1:
raise Error("Invalid output_attr!")
def search(self, q):
"""
Prepare elements for request and play it
"""
self.param = urlencode(self.param.myParam.get())
self.recherche = urlparse(self.recherche.my_recherche.get())
url = "%s%s%s" % (
self.recherche[1],
self.recherche[2],
self.recherche[3]
)
ok, response, timer = self._requester()
if not ok:
self.error = Error()
self.error.add_param(
GT_('Erreur'),
GT_('Url injoignable : %s\n%s') % (url, response)
)
else:
self.result = Result()
self.result.unset()
# Request information (url, execution time)
self.result.add_param(
GT_('http://%s en %d.%06d secondes') % (
url,
timer.seconds,
timer.microseconds
),
'%s : %s' % (response.status, response.reason)
)
# Request information (url, execution time)
self.result.add_param(
GT_('Entetes'),
response.getheaders()
)
version = 1.0
if response.version == 11:
version = 1.1
self.result.add_param(
GT_('Version du protocole'),
GT_('HTTP/%s' % version)
)
q.task_done()
def __unexpected_exception__(self, exp):
self.module_logger.error("Exception : {}".format(exp))
return Error("Something was wrong")
n_data, n_input = X.shape
n_class = np.unique(t).size
T = create_label(t, n_data, n_class)
print 'make train/test data'
n_train, n_test = 1000, 50
i = np.random.permutation(n_data)[:n_train+n_test]
i_train, i_test = np.hsplit(i, [n_train])
X_train, X_test = X[i_train, :].reshape(n_train, 1, 28, 28), X[i_test, :].reshape(n_test, 1, 28, 28)
T_train, T_test = T[i_train, :], T[i_test, :]
print 'initialize...'
linear, sigmoid, softmax, relu = act.linear(), act.sigmoid(), act.softmax(), act.relu()
conv1, conv2 = conv(20, 1, 5, 5, relu), conv(50, 20, 5, 5, relu)
pool1, pool2 = pool(2, 2, 2), pool(2, 2, 2)
neural = NN(800, 500, 10, linear, sigmoid, softmax)
error = err.cross_entropy()
cnn = CNN(conv1, pool1, conv2, pool2, neural, error)
print 'train...'
cnn.train(X_train, T_train, epsilon = 0.005, lam = 0.0001, gamma = 0.9, s_batch = 5, epochs = 50)
print 'predict...'
Y_test = cnn.predict(X_test)
accuracy = cnn.accuracy(Y_test, T_test)
print 'accuracy: {0}'.format(accuracy)
print 'save figure of loss...'
cnn.save_lossfig()
class Action:
"""
Class Recherche
Classe de controle de la vue "Recherche"
"""
def __init__(self):
"""
Constructeur du controleur de la vue "Recherche"
A à sa charge la gestion des événements sur la vue "vue.Recherche"
"""
self.param = Parametre()
self.recherche = Recherche()
self.header = Header()
def search(self, q):
"""
Prepare elements for request and play it
"""
self.param = urlencode(self.param.myParam.get())
self.recherche = urlparse(self.recherche.my_recherche.get())
url = "%s%s%s" % (
self.recherche[1],
self.recherche[2],
self.recherche[3]
)
ok, response, timer = self._requester()
if not ok:
self.error = Error()
self.error.add_param(
GT_('Erreur'),
GT_('Url injoignable : %s\n%s') % (url, response)
)
else:
self.result = Result()
self.result.unset()
# Request information (url, execution time)
self.result.add_param(
GT_('http://%s en %d.%06d secondes') % (
url,
timer.seconds,
timer.microseconds
),
'%s : %s' % (response.status, response.reason)
)
# Request information (url, execution time)
self.result.add_param(
GT_('Entetes'),
response.getheaders()
)
version = 1.0
if response.version == 11:
version = 1.1
self.result.add_param(
GT_('Version du protocole'),
GT_('HTTP/%s' % version)
)
q.task_done()
def _requester(self):
"""
Execute request and return result
"""
try:
methode = 'POST'
if not self.param:
methode = 'GET'
debut = datetime.now()
connection = httplib.HTTPConnection(self.recherche[1])
connection.request(
methode,
'%s/%s' % (self.recherche[2], self.recherche[3]),
self.param,
self.header.my_header.get()
)
response = connection.getresponse()
connection.close()
timer = datetime.now() - debut
return True, response, timer
except socket.gaierror:
return False, GT_("Socket erreur"), None
except http.client.InvalidURL:
return False, GT_("Url non valide"), None
