def fit(self, X, y):
self.w_ = np.zeros(1 + X.shape[1])
"""
注意自在训练神经网络的时候，一次迭代就将取到的所有数据输入，往复输入，往复调整
"""
for i in range(self.n_iter):
output = self.net_input(X)
errors = (y - output)
# print '，errors：',
# print errors
self.w_[1:] += self.eta * X.T.dot(errors) #X.T 转秩 ;dot,矩阵相乘
self.w_[0] += self.eta * errors.sum()
cost = (errors**2).sum() / 2.0 #J(W)
self.cost_.append(cost)
content = []
t = []
t.append(i + 1)
t.append(cost)
content.append(t)
content.append(self.w_.tolist())
mesg = json.dumps(content)
websocket.send_message(mesg) #使用第三方平台(websocket技术),实时数据传递到前端
time.sleep(1.5)
# print output
return self
def _api_answer_pgp_json(self, payload):
request_answer = websocket.encrypt_pgp_json(
{
"request": self._pgpjson_pending_request,
"payload": payload
},
self._pgpjson_client_key,
self._gpg_context,
)
websocket.send_message(self.wfile, request_answer)
self._close_websocket()
def _request_signature(self):
self._pgpjson_nonce = token_urlsafe(NONCE_BYTES)
signature_request = websocket.encrypt_pgp_json(
{
"nonce": self._pgpjson_nonce,
"request": self._pgpjson_pending_request,
},
self._pgpjson_client_key,
self._gpg_context,
)
websocket.send_message(self.wfile, signature_request)
def gen2(websocket):
websocket.send_message("respdata")
return
yield # noqa
def func2(websocket):
websocket.send_message("respdata")