(
            self,
            shrinkage_value=None,
            convergence_threshold=0.001,
            max_iters=100,
            max_rank=None,
            n_power_iterations=1,
            init_fill_method="zero",
            min_value=None,
            max_value=None,
            normalizer=None,
            verbose=True)
Parameters
        ----------
        shrinkage_value : float
            Value by which we shrink singular values on each iteration. If
            omitted then the default value will be the maximum singular
            value of the initialized matrix (zeros for missing values) divided
            by 50.

        convergence_threshold : float
            Minimum ration difference between iterations (as a fraction of
            the Frobenius norm of the current solution) before stopping.

        max_iters : int
            Maximum number of SVD iterations

        max_rank : int, optional
            Perform a truncated SVD on each iteration with this value as its
            rank.

        n_power_iterations : int
            Number of power iterations to perform with randomized SVD

        init_fill_method : str
            How to initialize missing values of data matrix, default is
            to fill them with zeros.

        min_value : float
            Smallest allowable value in the solution

        max_value : float
            Largest allowable value in the solution

        normalizer : object
            Any object (such as BiScaler) with fit() and transform() methods

        verbose : bool
            Print debugging info
Parameters
        ----------
        shrinkage_value : float
            Value by which we shrink singular values on each iteration. If
            omitted then the default value will be the maximum singular
            value of the initialized matrix (zeros for missing values) divided
            by 50.
Parameters ---------- shrinkage_value : float Value by which we shrink singular values on each iteration . If omitted then the default value will be the maximum singular value of the initialized matrix ( zeros for missing values ) divided by 50 .
def __init__(
            self,
            shrinkage_value=None,
            convergence_threshold=0.001,
            max_iters=100,
            max_rank=None,
            n_power_iterations=1,
            init_fill_method="zero",
            min_value=None,
            max_value=None,
            normalizer=None,
            verbose=True):
        """
        Parameters
        ----------
        shrinkage_value : float
            Value by which we shrink singular values on each iteration. If
            omitted then the default value will be the maximum singular
            value of the initialized matrix (zeros for missing values) divided
            by 50.

        convergence_threshold : float
            Minimum ration difference between iterations (as a fraction of
            the Frobenius norm of the current solution) before stopping.

        max_iters : int
            Maximum number of SVD iterations

        max_rank : int, optional
            Perform a truncated SVD on each iteration with this value as its
            rank.

        n_power_iterations : int
            Number of power iterations to perform with randomized SVD

        init_fill_method : str
            How to initialize missing values of data matrix, default is
            to fill them with zeros.

        min_value : float
            Smallest allowable value in the solution

        max_value : float
            Largest allowable value in the solution

        normalizer : object
            Any object (such as BiScaler) with fit() and transform() methods

        verbose : bool
            Print debugging info
        """
        Solver.__init__(
            self,
            fill_method=init_fill_method,
            min_value=min_value,
            max_value=max_value,
            normalizer=normalizer)
        self.shrinkage_value = shrinkage_value
        self.convergence_threshold = convergence_threshold
        self.max_iters = max_iters
        self.max_rank = max_rank
        self.n_power_iterations = n_power_iterations
        self.verbose = verbose
def __init__ ( self , shrinkage_value = None , convergence_threshold = 0.001 , max_iters = 100 , max_rank = None , n_power_iterations = 1 , init_fill_method = "zero" , min_value = None , max_value = None , normalizer = None , verbose = True ) : Solver . __init__ ( self , fill_method = init_fill_method , min_value = min_value , max_value = max_value , normalizer = normalizer ) self . shrinkage_value = shrinkage_value self . convergence_threshold = convergence_threshold self . max_iters = max_iters self . max_rank = max_rank self . n_power_iterations = n_power_iterations self . verbose = verbose