thanks for sharing your code, sir.
I tried your code with some other time series data, I found torch.gels function is not stable.
when passing through first layer of anfis, it returns nans, or after several iterations, anfis.coeff went too large, loss become inf. maybe gaussmembership functions mu and sigma which i used are not suitable for these data.
Im wondering whats the suitable range of mu and sigma for setting membership functions, do you have any good suggetions?
thank you in advance.
here is some error
217 y_actual_2d = y_actual.view(y_actual.shape[0], -1)
218 # Use gels to do LSE, then pick out the solution rows:
--> 219 coeff_2d, _ = torch.gels(y_actual_2d, weighted_x_2d)
220 coeff_2d = coeff_2d[0:weighted_x_2d.shape[1]]
221 # Reshape to 3D tensor: divide by rules, n_in+1, then swap last 2 dims
RuntimeError: Lapack Error in gels : Illegal Argument 5 at c:\a\w\1\s\windows\pytorch\aten\src\th\generic/THTensorLapack.cpp:252
thanks for sharing your code, sir.
I tried your code with some other time series data, I found torch.gels function is not stable.
when passing through first layer of anfis, it returns nans, or after several iterations, anfis.coeff went too large, loss become inf. maybe gaussmembership functions mu and sigma which i used are not suitable for these data.
I
m wondering whats the suitable range of mu and sigma for setting membership functions, do you have any good suggetions?thank you in advance.
here is some error
217 y_actual_2d = y_actual.view(y_actual.shape[0], -1)
218 # Use gels to do LSE, then pick out the solution rows:
--> 219 coeff_2d, _ = torch.gels(y_actual_2d, weighted_x_2d)
220 coeff_2d = coeff_2d[0:weighted_x_2d.shape[1]]
221 # Reshape to 3D tensor: divide by rules, n_in+1, then swap last 2 dims
RuntimeError: Lapack Error in gels : Illegal Argument 5 at c:\a\w\1\s\windows\pytorch\aten\src\th\generic/THTensorLapack.cpp:252