Hi, there

trying to so linear regression with multi variable.

train_data = np.array([

[40, 6, 4],

[44, 10, 4],

[46, 12, 5],

[48, 14, 7],

[52, 16, 9],

[58, 18, 12],

[60, 22, 14],

[68, 24, 20],

[74, 26, 21],

[80, 32, 24]])

test_data = np.array([

[6, 4],

[10, 5],

[4, 8]])

x_train = train_data[:,1:3]

y_train = train_data[:,0]

POLY_DEGREE = 3

input_size = 2

output_size = 1

poly = PolynomialFeatures(input_size * POLY_DEGREE, include_bias=False)

x_train_poly = poly.fit_transform(x_train)

class LinearRegression(nn.Module):

def **init**(self,input_size,output_size):

super(LinearRegression,self).**init**()

self.linear = nn.Linear(input_size,output_size)

```
def forward(self,x):
out = self.linear(x)
return out
```

model = LinearRegression(input_size,output_size)

criterion = torch.nn.MSELoss()

optimizer = torch.optim.SGD(model.parameters(), lr=0.001)

losses = []

for epoch in range(50):

inputs = Variable(torch.from_numpy(x_train))

targets = Variable(torch.from_numpy(y_train))

```
#feedforward + Backward + Optimize
optimizer.zero_grad()
outputs = model(inputs)
```

==============================================

error is following

TypeError: addmm_ received an invalid combination of arguments - got (int, int, torch.LongTensor, torch.FloatTensor), but expected one of:

- (torch.LongTensor mat1, torch.LongTensor mat2)
- (torch.SparseLongTensor mat1, torch.LongTensor mat2)
- (int beta, torch.LongTensor mat1, torch.LongTensor mat2)
- (int alpha, torch.LongTensor mat1, torch.LongTensor mat2)
- (int beta, torch.SparseLongTensor mat1, torch.LongTensor mat2)
- (int alpha, torch.SparseLongTensor mat1, torch.LongTensor mat2)
- (int beta, int alpha, torch.LongTensor mat1, torch.LongTensor mat2)
- (int beta, int alpha, torch.SparseLongTensor mat1, torch.LongTensor mat2)