import numpy as np
from sklearn.datasets import load_iris
from sklearn.covariance import EmpiricalCovariance as skEmpiricalCovariance

class EmpiricalCovariance():
    def fit(self, X):
        self.covariance_ = np.cov(X.T, bias=True)
        return self

X, _ = load_iris(return_X_y=True)
est1 = EmpiricalCovariance().fit(X)
est2 = skEmpiricalCovariance().fit(X)
assert np.allclose(est1.covariance_, est2.covariance_)