def get_predict_result():
diff_number = windows_size - 1
data = spider(str(int(get_current_number()) - diff_number), get_current_number(), "predict")
red_name_list = [(BOLL_NAME[0], i + 1) for i in range(sequence_len)]
red_data = data[["{}号码_{}".format(name[0], i) for name, i in red_name_list]].values.astype(int) - 1
blue_data = data[[BOLL_NAME[1][0]]].values.astype(int) - 1
if len(data) != 3:
print("[WARN] 期号出现跳期，期号不连续！开始查找最近上一期期号！本期预测时间较久！")
last_current_year = (get_year() - 1) * 1000
max_times = 160
while len(data) != 3:
data = spider(last_current_year + max_times, get_current_number(), "predict")[[x[0] for x in BOLL_NAME]]
time.sleep(np.random.random(1).tolist()[0])
max_times -= 1
# 预测红球
with red_graph.as_default():
reverse_sequence = tf.compat.v1.get_default_graph().get_tensor_by_name(pred_key_d[BOLL_NAME[0][0]])
red_pred = red_sess.run(reverse_sequence, feed_dict={
"red_inputs:0": red_data.reshape(batch_size, windows_size, sequence_len),
"sequence_length:0": np.array([sequence_len] * 1)
})
# 预测蓝球
with blue_graph.as_default():
softmax = tf.compat.v1.get_default_graph().get_tensor_by_name(pred_key_d[BOLL_NAME[1][0]])
blue_pred = blue_sess.run(softmax, feed_dict={
"blue_inputs:0": blue_data.reshape(batch_size, windows_size)
})
# 拼接结果
ball_name_list = ["{}_{}".format(name[1], i) for name, i in red_name_list] + [BOLL_NAME[1][1]]
pred_result_list = red_pred[0].tolist() + blue_pred.tolist()
return json.dumps(
{b_name: int(res) + 1 for b_name, res in zip(ball_name_list, pred_result_list)}
).encode('utf-8').decode('unicode_escape')
def get_predict_result():
data = spider(str(int(get_current_number()) - 2), get_current_number(),
"predict")[BOLL_NAME]
result = []
for i, model in enumerate(model_list):
boll_name = BOLL_NAME[i]
data_list = [int(x) for x in data[boll_name].tolist()]
p_data = np.array(data_list).reshape([-1, 3, 1]).astype(np.float32)
result.extend(model.predict_classes(p_data))
return json.dumps(
{b_name: int(res) + 1
for b_name, res in zip(BOLL_NAME, result)})
def get_predict_result():
diff_number = windows_size - 1
data = spider(str(int(get_current_number()) - diff_number),
get_current_number(), "predict")[BOLL_NAME]
if len(data) != 3:
print("[WARN] 期号出现跳期，期号不连续！开始查找最近上一期期号！本期预测时间较久！")
last_current_year = (get_year() - 1) * 1000
max_times = 160
while len(data) != 3:
data = spider(last_current_year + max_times, get_current_number(),
"predict")[BOLL_NAME]
time.sleep(np.random.random(1).tolist()[0])
max_times -= 1
result = []
for i, model in enumerate(model_list):
boll_name = BOLL_NAME[i]
data_list = [int(x) for x in data[boll_name].tolist()]
p_data = np.array(data_list).reshape([-1, windows_size,
1]).astype(np.float32)
result.extend(model.predict_classes(p_data))
return json.dumps({
b_name: int(res) + 1
for b_name, res in zip(BOLL_NAME, result)
}).encode('utf-8').decode('unicode_escape')
def get_predict_result():
if locked():
return "正在计算中，请稍后再请求..."
create_lock()
try:
data = spider(str(int(get_current_number()) - 2), get_current_number(), "predict")[BOLL_NAME]
model_list = load_model_list()
result = []
for i, model in enumerate(model_list):
boll_name = BOLL_NAME[i]
data_list = [int(x) for x in data[boll_name].tolist()]
p_data = np.array(data_list).reshape([-1, 3, 1]).astype(np.float32)
result.extend(model.predict_classes(p_data))
result_json = json.dumps({b_name: int(res) + 1 for b_name, res in zip(BOLL_NAME, result)}, ensure_ascii=False)
remove_lock()
return result_json
except Exception as e:
remove_lock()
return "计算出错，稍后重新请求后再试..."