(self, x)
return torch.ones((n_samples, n_assets), dtype=x.dtype, device=x.device) / n_assets
Predict weights.

        Parameters
        ----------
        x : torch.Tensor
            Tensor of shape `(n_samples, n_channels, lookback, n_assets)`.

        Returns
        -------
        weights : torch.Tensor
            Tensor of shape `(n_samples, n_assets)` representing the predicted weights.
Predict weights.
Predict weights .
def __call__(self, x):
        """Predict weights.

        Parameters
        ----------
        x : torch.Tensor
            Tensor of shape `(n_samples, n_channels, lookback, n_assets)`.

        Returns
        -------
        weights : torch.Tensor
            Tensor of shape `(n_samples, n_assets)` representing the predicted weights.

        """
        n_samples, n_channels, lookback, n_assets = x.shape

        return torch.ones((n_samples, n_assets), dtype=x.dtype, device=x.device) / n_assets
def __call__ ( self , x ) : n_samples , n_channels , lookback , n_assets = x . shape return torch . ones ( ( n_samples , n_assets ) , dtype = x . dtype , device = x . device ) / n_assets