def __init__(self, input_dim,
                 order=0,
                 variance=1.0, weight_variances=1., bias_variance=1.,
                 active_dims=None, ARD=None, name=None):
        """
        - input_dim is the dimension of the input to the kernel
        - order specifies the activation function of the neural network
          the function is a rectified monomial of the chosen order.
        - variance is the initial value for the variance parameter
        - weight_variances is the initial value for the weight_variances parameter
          defaults to 1.0 (ARD=False) or np.ones(input_dim) (ARD=True).
        - bias_variance is the initial value for the bias_variance parameter
          defaults to 1.0.
        - active_dims is a list of length input_dim which controls which
          columns of X are used.
        - ARD specifies whether the kernel has one weight_variance per dimension
          (ARD=True) or a single weight_variance (ARD=False).
        """
        super().__init__(input_dim, active_dims, name=name)

        if order not in self.implemented_orders:
            raise ValueError('Requested kernel order is not implemented.')
        self.order = order

        self.variance = Parameter(variance, transform=transforms.positive,
                                  dtype=settings.float_type)
        self.bias_variance = Parameter(bias_variance, transform=transforms.positive,
                                       dtype=settings.float_type)
        weight_variances, self.ARD = self._validate_ard_shape("weight_variances", weight_variances, ARD)
        self.weight_variances = Parameter(weight_variances, transform=transforms.positive,
                                          dtype=settings.float_type)
def __init__ ( self , input_dim , order = 0 , variance = 1.0 , weight_variances = 1. , bias_variance = 1. , active_dims = None , ARD = None , name = None ) : super ( ) . __init__ ( input_dim , active_dims , name = name ) if order not in self . implemented_orders : raise ValueError ( 'Requested kernel order is not implemented.' ) self . order = order self . variance = Parameter ( variance , transform = transforms . positive , dtype = settings . float_type ) self . bias_variance = Parameter ( bias_variance , transform = transforms . positive , dtype = settings . float_type ) weight_variances , self . ARD = self . _validate_ard_shape ( "weight_variances" , weight_variances , ARD ) self . weight_variances = Parameter ( weight_variances , transform = transforms . positive , dtype = settings . float_type )
- input_dim is the dimension of the input to the kernel
        - order specifies the activation function of the neural network
          the function is a rectified monomial of the chosen order.
        - variance is the initial value for the variance parameter
        - weight_variances is the initial value for the weight_variances parameter
          defaults to 1.0 (ARD=False) or np.ones(input_dim) (ARD=True).
        - bias_variance is the initial value for the bias_variance parameter
          defaults to 1.0.
        - active_dims is a list of length input_dim which controls which
          columns of X are used.
        - ARD specifies whether the kernel has one weight_variance per dimension
          (ARD=True) or a single weight_variance (ARD=False).