(
            self,
            k=5,
            orientation="rows",
            use_argpartition=False,
            print_interval=100,
            min_value=None,
            max_value=None,
            normalizer=None,
            verbose=True)
Parameters
        ----------
        k : int
            Number of neighboring rows to use for imputation.

        orientation : str
            Which axis of the input matrix should be treated as a sample
            (default is "rows" but can also be "columns")

        use_argpartition : bool
           Use a more naive implementation of kNN imputation whichs calls
           numpy.argpartition for each row/column pair. May give NaN if fewer
           than k neighbors are available for a missing value.

        print_interval : int

        min_value : float
            Minimum possible imputed value

        max_value : float
            Maximum possible imputed value

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

        verbose : bool
Parameters
        ----------
        k : int
            Number of neighboring rows to use for imputation.
Parameters ---------- k : int Number of neighboring rows to use for imputation .
def __init__(
            self,
            k=5,
            orientation="rows",
            use_argpartition=False,
            print_interval=100,
            min_value=None,
            max_value=None,
            normalizer=None,
            verbose=True):
        """
        Parameters
        ----------
        k : int
            Number of neighboring rows to use for imputation.

        orientation : str
            Which axis of the input matrix should be treated as a sample
            (default is "rows" but can also be "columns")

        use_argpartition : bool
           Use a more naive implementation of kNN imputation whichs calls
           numpy.argpartition for each row/column pair. May give NaN if fewer
           than k neighbors are available for a missing value.

        print_interval : int

        min_value : float
            Minimum possible imputed value

        max_value : float
            Maximum possible imputed value

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

        verbose : bool
        """
        Solver.__init__(
            self,
            min_value=min_value,
            max_value=max_value,
            normalizer=normalizer)
        self.k = k
        self.verbose = verbose
        self.orientation = orientation
        self.print_interval = print_interval
        if use_argpartition:
            self._impute_fn = knn_impute_with_argpartition
        else:
            self._impute_fn = knn_impute_few_observed
def __init__ ( self , k = 5 , orientation = "rows" , use_argpartition = False , print_interval = 100 , min_value = None , max_value = None , normalizer = None , verbose = True ) : Solver . __init__ ( self , min_value = min_value , max_value = max_value , normalizer = normalizer ) self . k = k self . verbose = verbose self . orientation = orientation self . print_interval = print_interval if use_argpartition : self . _impute_fn = knn_impute_with_argpartition else : self . _impute_fn = knn_impute_few_observed