import numpy as np
from sklearn.datasets import load_iris
from sklearn.preprocessing import RobustScaler as skRobustScaler

class RobustScaler:
    def __init__(self, quantile_range=(25, 75)):
        self.quantile_range = quantile_range

    def fit(self, X):
        self.center_ = np.median(X, axis=0)
        quantiles = np.percentile(X, self.quantile_range, axis=0)
        self.scale_ = quantiles[1] - quantiles[0]
        return self

    def transform(self, X):
        return (X - self.center_) / self.scale_

X, _ = load_iris(return_X_y=True)
sc1 = RobustScaler().fit(X)
sc2 = skRobustScaler().fit(X)
assert np.allclose(sc1.center_, sc2.center_)
assert np.allclose(sc1.scale_, sc2.scale_)
Xt1 = sc1.transform(X)
Xt2 = sc2.transform(X)
assert np.allclose(Xt1, Xt2)