neuroprob/parametrics.py
def compute_F(self, XZ):
        """
        The input to the ANN will be of shape (samples*timesteps, dims).
        
        :param torch.tensor cov: covariates with shape (samples, timesteps, dims)
        :returns: inner product with shape (samples, neurons, timesteps)
        :rtype: torch.tensor
        """
        XZ = self._XZ(XZ)
        incov = XZ.view(-1, XZ.shape[-1])
        post_mu = self.mu_ANN(incov).view(*XZ.shape[:2], -1).permute(0, 2, 1)
        if self.sigma_ANN is not None:
            post_var  = self.sigma_ANN(incov).view(*XZ.shape[:2], -1).permute(0, 2, 1)
        else:
            post_var = 0

        return post_mu, post_var

        The input to the ANN will be of shape (samples*timesteps, dims).
        
        :param torch.tensor cov: covariates with shape (samples, timesteps, dims)
        :returns: inner product with shape (samples, neurons, timesteps)
        :rtype: torch.tensor
        
The input to the ANN will be of shape (samples*timesteps, dims).
The input to the ANN will be of shape ( samples * timesteps dims ) .
def compute_F(self, XZ):
        XZ = self._XZ(XZ)
        incov = XZ.view(-1, XZ.shape[-1])
        post_mu = self.mu_ANN(incov).view(*XZ.shape[:2], -1).permute(0, 2, 1)
        if self.sigma_ANN is not None:
            post_var  = self.sigma_ANN(incov).view(*XZ.shape[:2], -1).permute(0, 2, 1)
        else:
            post_var = 0
        return post_mu, post_var
def compute_F ( self , XZ ) : XZ = self . _XZ ( XZ ) incov = XZ . view ( - 1 , XZ . shape [ - 1 ] ) post_mu = self . mu_ANN ( incov ) . view ( * XZ . shape [ : 2 ] , - 1 ) . permute ( 0 , 2 , 1 ) if self . sigma_ANN is not None : post_var = self . sigma_ANN ( incov ) . view ( * XZ . shape [ : 2 ] , - 1 ) . permute ( 0 , 2 , 1 ) else : post_var = 0 return post_mu , post_var
The input to the ANN will be of shape (samples*timesteps, dims).
The input to the ANN will be of shape ( samples * timesteps dims ) .
"""
        The input to the ANN will be of shape (samples*timesteps, dims).
        
        :param torch.tensor cov: covariates with shape (samples, timesteps, dims)
        :returns: inner product with shape (samples, neurons, timesteps)
        :rtype: torch.tensor
        """
inner product with shape (samples, neurons, timesteps)
inner product with shape ( samples neurons timesteps )
torch.tensor
self
XZ
cov
covariates with shape (samples, timesteps, dims)
covariates with shape ( samples timesteps dims )