def _inversion_forward(self, constants, feature_sparsity):
X, y, XTX, XTy = constants.values()
if feature_sparsity:
sparse_vector = torch.diag(self._sparsify("feature"))
sparse_X = X @ sparse_vector
sparse_XTX = sparse_vector @ XTX @ sparse_vector
sparse_XTy = sparse_vector @ XTy
penality = torch.exp(self._params["lambda"]) * torch.diag(
torch.ones(XTX.shape[0])).float().cuda()
inv = torch.inverse(sparse_XTX + penality)
projection_matrix = sparse_X @ inv
y_hat = projection_matrix @ sparse_XTy
return y_hat, permuted_y_hat
else:
penality = torch.exp(self._params["lambda"]) * torch.diag(
torch.ones(XTX.shape[0])).float().cuda()
inv = torch.inverse(XTX + penality)
projection_matrix = X @ inv
y_hat = projection_matrix @ XTy
return y_hat
def _inversion_coef_without_sparsity(self, penality, XTX, XTy):
inv = torch.inverse(XTX + penality)
return inv @ XTy
def _inversion_coef_with_sparsity(self, penality, XTX, XTy):
sparse_vector = torch.diag(self._sparsify("feature"))
sparse_XTX = sparse_vector @ XTX @ sparse_vector
sparse_XTy = sparse_vector @ XTy
inv = torch.inverse(sparse_XTX + penality)
return sparse_vector @ inv @ sparse_XTy