'Transform new data by linear interpolation
Parameters
T : array-like, shape=(n_samples,)
Data to transform.
Returns
T_ : array, shape=(n_samples,)
The transformed data'

def transform(self, T):

    T = as_float_array(T)
    if (len(T.shape) != 1):
        raise ValueError('Isotonic    regression    input    should    be    a    1d    array')
    if (self.out_of_bounds not in ['raise', 'nan', 'clip']):
        raise ValueError("The    argument    ``out_of_bounds``    must    be    in    'nan',    'clip',    'raise';    got    {0}".format(self.out_of_bounds))
    if (self.out_of_bounds == 'clip'):
        T = np.clip(T, self.X_min_, self.X_max_)
    return self.f_(T)
