def test_fit_transform_pf(self):
"""Checks that in the case of normalization by columns,
the result is the same as in the case of using the package from sklearn
"""
X = np.random.uniform(0, 100, size=(3, 3))
model = StandardFlexibleScaler(column_wise=True)
transformed_skcosmo = model.fit_transform(X)
transformed_sklearn = StandardScaler().fit_transform(X)
self.assertTrue((np.isclose(transformed_sklearn,
transformed_skcosmo,
atol=1e-12)).all())
def test_fit_transform_npf(self):
"""Checks that the entire matrix is correctly normalized
(not column-wise). Compare with the value calculated
directly from the equation.
"""
X = np.random.uniform(0, 100, size=(3, 3))
model = StandardFlexibleScaler(column_wise=False)
X_tr = model.fit_transform(X)
mean = X.mean(axis=0)
var = ((X - mean)**2).mean(axis=0)
scale = np.sqrt(var.sum())
X_ex = (X - mean) / scale
self.assertTrue((np.isclose(X_ex, X_tr, atol=1e-12)).all())