Fehlermeldung

[Romaxx]

Hallo zusammen,
ich bekomme folgende Fehlermeldung:

Code: Alles auswählen

  File "vSSGP_LVM_model.py", line 64, in get_EPhi
    EPhi = (2 * sf_trf/M)**0.5 * T.exp(-0.5 * ((S_hat**2)[None,:, :] * SIGMA_trf[:, None, :]).sum(2)) * T.cos((S_hat[None,:, :] * (MU[:, None, :] - U[None, :, :])).sum(2) + b[None, :])  # N x M

  File "theano\tensor\var.py", line 556, in __getitem__
    view = self.dimshuffle(pattern)

  File "theano\tensor\var.py", line 355, in dimshuffle
    pattern)

  File "theano\tensor\elemwise.py", line 174, in __init__
    (input_broadcastable, new_order))

ValueError: ('You cannot drop a non-broadcastable dimension.', ((False, False), ('x', 0)))

Der Code:

Code: Alles auswählen

def get_EPhi(self, lengthscale_trf, lengthscale_p_trf, sf_trf, S, MU, SIGMA_trf, U, b, N, M): 
        inv_lengthscale_p_trf, inv_lengthscale_trf = 2 * np.pi * lengthscale_p_trf**-1, lengthscale_trf**-1
        S_hat = inv_lengthscale_trf * S + inv_lengthscale_p_trf # N x Q
        EPhi = (2 * sf_trf/M)**0.5 * T.exp(-0.5 * ((S_hat**2)[None,:, :] * SIGMA_trf[:, None, :]).sum(2)) * T.cos((S_hat[None,:, :] * (MU[:, None, :] - U[None, :, :])).sum(2) + b[None, :])  # N x M
        
        S_hat_U_b =  (S_hat * U).sum(1)[:,None] + b[None,:] # M x M
        big_sum_minus = S_hat_U_b - np.transpose(S_hat_U_b) # M x M
        big_sum_plus = S_hat_U_b + np.transpose(S_hat_U_b) # M x M
        S_hat_minus = S_hat[None,:,:] - S_hat[:,None,:] # M x M x Q
        S_hat_plus = S_hat[None,:,:] + S_hat[:,None,:] # M x M x Q
        
        EPhiTPhi = np.zeros((M,M))
        for i in range(0, N):       
            EPhiTPhi_temp = np.exp(-0.5 * (S_hat_minus**2 * (np.transpose(SIGMA_trf[i , :]))[None, None,:]).sum(2)) * np.cos((S_hat_minus * (np.transpose(MU[i , :]))[None, None,:]).sum(2) + big_sum_minus) + np.exp(-0.5 * (S_hat_plus**2 * (np.transpose(SIGMA_trf[i , :]))[None, None,:]).sum(2)) * np.cos((S_hat_plus * (np.transpose(MU[i , :]))[None, None,:]).sum(2) + big_sum_plus)
            EPhiTPhi = EPhiTPhi + EPhiTPhi_temp;  
            
        EPhiTPhi = (sf_trf/M) * EPhiTPhi # M x M
        
        return EPhi, EPhiTPhi

Vielen Dank für die Hilfe, Romaxx

[Romaxx]

Hallo,

hat sich erledigt.

Nun ist ein anderer Fehler aufgetaucht:

Code: Alles auswählen

  File "vSSGP_LVM_model.py", line 67, in get_EPhi
    big_sum_minus = S_hat_U_b - np.transpose(S_hat_U_b) # M x M

  File "C:\Program Files\Anaconda2\lib\site-packages\numpy\core\fromnumeric.py", line 557, in transpose
    return transpose(axes)

  File "theano\tensor\var.py", line 273, in transpose
    return theano.tensor.basic.transpose(self, axes)

  File "theano\tensor\basic.py", line 3565, in transpose
    ret = DimShuffle(x.broadcastable, axes)(x)

  File "theano\tensor\elemwise.py", line 152, in __init__
    str(j), str(type(j)))

TypeError: ("DimShuffle indices must be python ints. Got: '%s' of type '%s'.", 'None', "<type 'NoneType'>")

Code:

Code: Alles auswählen

    def get_EPhi(self, lengthscale_trf, lengthscale_p_trf, sf_trf, S, MU, SIGMA_trf, U, b, N, M): 
        inv_lengthscale_p_trf, inv_lengthscale_trf = 2 * np.pi * lengthscale_p_trf**-1, lengthscale_trf**-1
        S_hat = inv_lengthscale_trf * S + inv_lengthscale_p_trf # N x Q
        EPhi = (2 * sf_trf/M)**0.5 * T.exp(-0.5 * ((S_hat**2)[None,:, :] * SIGMA_trf[:, None, :]).sum(2)) * T.cos((S_hat[None,:, :] * (MU[:, None, :] - U[None, :, :])).sum(2) + b)  # N x M
        
        S_hat_U_b =  (S_hat * U).sum(1)[:,None] + b # M x M
        big_sum_minus = S_hat_U_b - np.transpose(S_hat_U_b) # M x M
        big_sum_plus = S_hat_U_b + np.transpose(S_hat_U_b) # M x M
        S_hat_minus = S_hat[None,:,:] - S_hat[:,None,:] # M x M x Q
        S_hat_plus = S_hat[None,:,:] + S_hat[:,None,:] # M x M x Q
        
        EPhiTPhi = np.zeros((M,M))
        for i in range(0, N):       
            EPhiTPhi_temp = np.exp(-0.5 * (S_hat_minus**2 * (np.transpose(SIGMA_trf[i , :]))[None, None,:]).sum(2)) * np.cos((S_hat_minus * (np.transpose(MU[i , :]))[None, None,:]).sum(2) + big_sum_minus) + np.exp(-0.5 * (S_hat_plus**2 * (np.transpose(SIGMA_trf[i , :]))[None, None,:]).sum(2)) * np.cos((S_hat_plus * (np.transpose(MU[i , :]))[None, None,:]).sum(2) + big_sum_plus)
            EPhiTPhi = EPhiTPhi + EPhiTPhi_temp;  
            
        EPhiTPhi = (sf_trf/M) * EPhiTPhi # M x M
        
        return EPhi, EPhiTPhi

[Romaxx]

Hat sich nun auch erledigt.