def fit(self, X, y=None):
n = X.shape[<NUM_LIT:0>]<EOL>if X.shape != (n, n):<EOL><INDENT>raise TypeError("<STR_LIT>")<EOL><DEDENT>self.train_ = X<EOL>memory = get_memory(self.memory)<EOL>lo, = memory.cache(scipy.linalg.eigvalsh)(X, eigvals=(<NUM_LIT:0>, <NUM_LIT:0>))<EOL>self.shift_ = max(self.min_eig - lo, <NUM_LIT:0>)<EOL>return self<EOL>
Learn the transformation to shifted eigenvalues. Only depends
on the input dimension.

Parameters
----------
X : array, shape [n, n]
    The *symmetric* input similarities.