99f6a9b9b366e20ebc300fc5be904308c17c484f,src/spn/experiments/RandomSPNs_layerwise/distributions.py,RatNormal,__init__,#RatNormal#Any#Any#Any#Any#Any#Any#Any#,20

Before Change

        super().__init__(multiplicity, in_features, dropout)

        // Create gaussian means and stds
        self.means = nn.Parameter(torch.randn(1, in_features, multiplicity))
        self.stds = nn.Parameter(torch.rand(1, in_features, multiplicity))

        self.min_sigma = check_valid(min_sigma, float, 0.0, max_sigma)

After Change

        
        super(IndependentMultivariate, self).__init__(in_features, out_channels, num_repetitions, dropout)
        if leaf_base_kwargs is None:
            leaf_base_kwargs = {}

        self.base_leaf = leaf_base_class(
            out_channels=out_channels,
            in_features=in_features,
            dropout=dropout,
            num_repetitions=num_repetitions,
            **leaf_base_kwargs,
        )
        self.prod = Product(in_features=in_features, cardinality=cardinality, num_repetitions=num_repetitions)
        self._pad = (cardinality - self.in_features % cardinality) % cardinality

        self.cardinality = check_valid(cardinality, int, 2, in_features + 1)

Instances