def predict_proba(self, a, b, device=None):
        """Infer causal directions using the trained NCC pairwise model.

        Args:
            a (numpy.ndarray): Variable 1
            b (numpy.ndarray): Variable 2
            device (str): Device to run the algorithm on (defaults to ``cdt.SETTINGS.default_device``)

        Returns:
            float: Causation score (Value : 1 if a->b and -1 if b->a)
        """
        device = SETTINGS.get_default(device=device)
        if self.model is None:
            print('Model has to be trained before doing any predictions')
            raise ValueError
        if len(np.array(a).shape) == 1:
            a = np.array(a).reshape((-1, 1))
            b = np.array(b).reshape((-1, 1))
        m = np.hstack((a, b))
        m = scale(m)
        m = m.astype('float32')
        m = th.from_numpy(m).t().unsqueeze(0)

        if th.cuda.is_available():
            m = m.cuda()

        return (self.model(m).data.cpu().numpy()-.5) * 2
def predict_proba(self, a, b, device=None):
        """Infer causal directions using the trained NCC pairwise model.

        Args:
            a (numpy.ndarray): Variable 1
            b (numpy.ndarray): Variable 2
            device (str): Device to run the algorithm on (defaults to ``cdt.SETTINGS.default_device``)

        Returns:
            float: Causation score (Value : 1 if a->b and -1 if b->a)
        """
        device = SETTINGS.get_default(device=device)
        if self.model is None:
            print('Model has to be trained before doing any predictions')
            raise ValueError
        if len(np.array(a).shape) == 1:
            a = np.array(a).reshape((-1, 1))
            b = np.array(b).reshape((-1, 1))
        m = np.hstack((a, b))
        m = scale(m)
        m = m.astype('float32')
        m = th.from_numpy(m).t().unsqueeze(0)

        if th.cuda.is_available():
            m = m.cuda()

        return (self.model(m).data.cpu().numpy()-.5) * 2
def predict_proba ( self , a , b , device = None ) : device = SETTINGS . get_default ( device = device ) if self . model is None : print ( 'Model has to be trained before doing any predictions' ) raise ValueError if len ( np . array ( a ) . shape ) == 1 : a = np . array ( a ) . reshape ( ( - 1 , 1 ) ) b = np . array ( b ) . reshape ( ( - 1 , 1 ) ) m = np . hstack ( ( a , b ) ) m = scale ( m ) m = m . astype ( 'float32' ) m = th . from_numpy ( m ) . t ( ) . unsqueeze ( 0 ) if th . cuda . is_available ( ) : m = m . cuda ( ) return ( self . model ( m ) . data . cpu ( ) . numpy ( ) - .5 ) * 2
Infer causal directions using the trained NCC pairwise model.

        Args:
            a (numpy.ndarray): Variable 1
            b (numpy.ndarray): Variable 2
            device (str): Device to run the algorithm on (defaults to ``cdt.SETTINGS.default_device``)

        Returns:
            float: Causation score (Value : 1 if a->b and -1 if b->a)
Infer causal directions using the trained NCC pairwise model .