def test_huber_huberT(self):
n = scale.norms.HuberT()
n.t = 1.5
h = scale.Huber(norm=n)
assert_almost_equal(scale.huber(self.chem)[0], h(self.chem)[0],
DECIMAL)
assert_almost_equal(scale.huber(self.chem)[1], h(self.chem)[1],
DECIMAL)
def test_huber_huberT(self):
n = scale.norms.HuberT()
n.t = 1.5
h = scale.Huber(norm=n)
assert_almost_equal(scale.huber(self.chem)[0], h(self.chem)[0],
DECIMAL)
assert_almost_equal(scale.huber(self.chem)[1], h(self.chem)[1],
DECIMAL)
def get_location_estimator(estimator):
if hasattr(estimator, "__call__"):
f = estimator
elif type(estimator) == str:
if estimator == "HL" or estimator == "hl" or estimator == "hodges_lehmann":
f = hodges_lehmann_mean
elif estimator == "median":
f = np.median
elif estimator == "Huber" or estimator == "huber":
f = lambda x: np.asscalar(huber(x)[0])
else:
f = np.mean
else:
f = np.mean
return f
def get_scale_estimator(estimator):
if hasattr(estimator, "__call__"):
f = estimator
elif type(estimator) == str:
if estimator == "Huber" or estimator == "huber":
f = lambda x: np.asscalar(huber(x)[1])
elif estimator == "MAD" or estimator == "mad" or estimator == "mean_absolute_deviation":
f = median_absolute_deviation
elif estimator == "apdS" or estimator == "S":
f = lambda x: absolute_pairwise_difference(x, "S")
elif estimator == "apdQ" or estimator == "Q":
f = lambda x: absolute_pairwise_difference(x, "Q")
elif estimator == "biweight_midvariance" or estimator == "bmv":
f = biweight_midvariance
else:
f = np.var
else:
f = np.var
return f
def test_huber_location(self):
assert_almost_equal(scale.huber(self.chem)[1], 0.67365, DECIMAL)
def test_huber_scale(self):
assert_almost_equal(scale.huber(self.chem)[0], 3.20549, DECIMAL)
def test_huber_location(self):
assert_almost_equal(scale.huber(self.chem)[1], 0.67365, DECIMAL)
def test_huber_scale(self):
assert_almost_equal(scale.huber(self.chem)[0], 3.20549, DECIMAL)
