import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torch.autograd import Variable


x_train=np.array([[3.3],[4.4],[5.5],[6.71],[6.93],[4.168],
                 [9.779],[6.182],[7.59],[2.167],[7.042],
                  [10.791],[5.313],[7.997],[3.1]],dtype=np.float32)
y_train=np.array([[1.7],[2.76],[2.09],[3.19],[1.694],[1.573],
                 [3.366],[2.596],[2.53],[1.221],[2.827],
                  [3.465],[1.65],[2.904],[1.3]],dtype=np.float32)
#將numpy.array轉換成Tensor
x_train=torch.from_numpy(x_train)
y_train=torch.from_numpy(y_train)

#建立模型
class LinearRegression(nn.Module):
    def __init__(self):
        super(LinearRegression,self).__init__()
        self.linear=nn.Linear(1,1)
        
    def forward(self,x):
        out=self.linear(x)
        return out
    
if torch.cuda.is_available():
    model=LinearRegeression().cuda()
else:
    model=LinearRegression()
    
criterion=nn.MSELoss()
optimizer=torch.optim.SGD(model.parameters(),lr=1e-3)

#訓練模型
num_epochs=1000
for epoch in range(num_epochs):
    if torch.cuda.is_available():
        inputs=Variable(x_train).cuda()
        target=Variable(y_train).cuda()    
    else:
        inputs=Variable(x_train)
        target=Variable(y_train)
    #forward
    out=model(inputs)
    loss=criterion(out,target)
    #backward
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    if (epoch+1)%20==0:
        print('Epoch[{}/{}],loss:{:.6f}'.format(epoch+1,num_epochs,loss.data[0]))
        
        
#預測
model.eval()   #轉化為預測模式
predict=model(Variable(x_train))
predict=predict.data.numpy()
plt.plot(x_train.numpy(),y_train.numpy(),'ro',label='Original data')
plt.plot(x_train.numpy(),predict,label='Fitting Line')
plt.show()