def fit(self, X, y=None):
        '''
        Learn the transformation to shifted eigenvalues. Only depends
        on the input dimension.

        Parameters
        ----------
        X : array, shape [n, n]
            The *symmetric* input similarities.
        '''
        n = X.shape[0]
        if X.shape != (n, n):
            raise TypeError("Input must be a square matrix.")

        self.train_ = X

        memory = get_memory(self.memory)
        lo, = memory.cache(scipy.linalg.eigvalsh)(X, eigvals=(0, 0))
        self.shift_ = max(self.min_eig - lo, 0)

        return self
def fit(self, X, y=None):
        '''
        Learn the transformation to shifted eigenvalues. Only depends
        on the input dimension.

        Parameters
        ----------
        X : array, shape [n, n]
            The *symmetric* input similarities.
        '''
        n = X.shape[0]
        if X.shape != (n, n):
            raise TypeError("Input must be a square matrix.")

        self.train_ = X

        memory = get_memory(self.memory)
        lo, = memory.cache(scipy.linalg.eigvalsh)(X, eigvals=(0, 0))
        self.shift_ = max(self.min_eig - lo, 0)

        return self
def fit ( self , X , y = None ) : n = X . shape [ 0 ] if X . shape != ( n , n ) : raise TypeError ( "Input must be a square matrix." ) self . train_ = X memory = get_memory ( self . memory ) lo , = memory . cache ( scipy . linalg . eigvalsh ) ( X , eigvals = ( 0 , 0 ) ) self . shift_ = max ( self . min_eig - lo , 0 ) return self
Learn the transformation to shifted eigenvalues. Only depends
        on the input dimension.

        Parameters
        ----------
        X : array, shape [n, n]
            The *symmetric* input similarities.
Learn the transformation to shifted eigenvalues . Only depends on the input dimension .