# condig:utf-8
import torch as th
import numpy as np
class GM():
def __init__(self):
# 判断是否可用 gpu 编程 , 大量级计算使用GPU
self._is_gpu = False # th.cuda.is_available()
def fit(self,dt:list or np.ndarray):
self._df :th.Tensor = th.from_numpy(np.array(dt,dtype=np.float32))
if self._is_gpu:
self._df.cuda()
self._n:int = len(self._df)
self._x,self._max_value = self._sigmod(self._df)
z:th.Tensor = self._next_to_mean(th.cumsum(self._x,dim=0))
self.coef:th.Tensor = self._coefficient(self._x, z)
del z
self._x0:th.Tensor = self._x[0]
self._pre:th.Tensor = self._pred()
# 归一化
def _sigmod(self,x:th.Tensor):
_maxv:th.Tensor = th.max(x)
return th.div(x,_maxv),_maxv
# 计算紧邻均值数列
def _next_to_mean(self, x_1:th.Tensor):
z:th.Tensor = th.zeros(self._n-1)
if self._is_gpu:
z.cuda()
for i in range(1,self._n): # 下标从0开始,取不到最大值
z[i - 1] = 0.5 * x_1[i] + 0.5 * x_1[i - 1]
return z
# 计算系数 a,b
def _coefficient(self,x:th.Tensor,z:th.Tensor):
B:th.Tensor = th.stack((-1*z, th.ones(self._n-1)),dim=1)
Y:th.Tensor = th.tensor(x[1:],dtype=th.float32).reshape((-1,1))
if self._is_gpu:
B.cuda()
Y.cuda()
# 返回的是a和b的向量转置,第一个是a 第二个是b;
return th.matmul(th.matmul(th.inverse(th.matmul(B.t(), B)), B.t()),Y)
def _pred(self,start:int=1,end:int=0):
les:int = self._n+end
resut:th.Tensor = th.zeros(les)
if self._is_gpu:
resut.cuda()
resut[0] = self._x0
for i in range(start,les):
resut[i] = (self._x0 - (self.coef[1] / self.coef[0])) * \
(1 - th.exp(self.coef[0])) * th.exp(-1 * self.coef[0] * (i))
del les
return resut
# 计算绝对误差
def confidence(self):
return round((th.sum(th.abs(th.div((self._x-self._pre),self._x)))/self._n).item(),4)
# 预测个数,默认个数大于等于0,
def predict(self,m:int=1,decimals:int=4):
y_pred:th.Tensor = th.mul(self._pre,self._max_value)
y_pred_ = th.zeros(1)
if m<0:
return "预测个数需大于等于0"
elif m>0:
y_pred_:th.Tensor = self._pred(self._n,m)[-m:].mul(self._max_value)
else:
if self._is_gpu:
return list(map(lambda _: round(_, decimals), y_pred.cpu().numpy().tolist()))
else:
return list(map(lambda _:round(_,decimals),y_pred.numpy().tolist()))
# cat 拼接 0 x水平拼接,1y垂直拼接
result:th.Tensor = th.cat((y_pred,y_pred_),dim=0)
del y_pred,y_pred_
if self._is_gpu:
return list(map(lambda _: round(_, decimals), result.cpu().numpy().tolist()))
return list(map(lambda _:round(_,decimals),result.numpy().tolist()))
if __name__=="__main__":
ls = np.arange(91,100,2)
print(type(ls))
# ls = list(range(91, 100, 2))
gm = GM()
gm.fit(ls)
print(gm.confidence())
print(ls)
print(gm.predict(m=2))