def transform(self, y):
"""Transform labels to normalized encoding.
Parameters
----------
y : array-like of shape [n_samples]
Target values.
Returns
-------
y : array-like of shape [n_samples]
"""
y = column_or_1d(y, warn=True)
classes = np.unique(y)
if len(np.intersect1d(classes, self.classes_)) < len(classes):
diff = np.setdiff1d(classes, self.classes_)
self.classes_ = np.hstack((self.classes_, diff))
return np.searchsorted(self.classes_, y)[0]
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