def predict(self, params):
        if not hasattr(params[0], '__len__'):
            params = [params]

        x, y = self.make_xy(params, [])
        try:
            return self.classifier.predict(x)
        except __HOLE__:  # not yet fit
            #having an empty model predict 1
            #makes it convenient to generate
            #initial false positives
            return np.ones(len(params), dtype=np.int)
dataset/ETHPy150Open ChrisBeaumont/brut/bubbly/model.py/Model.predict