def test_covariances(self):
"""
Compare the equal and unequal length handeling of `HasCovarianceMatrix`.
If it works properly, the unequal length method should reduce to the
equal length one if called qith equal length data. Computing unequal
dataset length covariances remains something to be careful with, but
this backwards compatibility provides some validation.
"""
N = 10000
a, b, c = parameters('a, b, c')
a_i, b_i, c_i = variables('a_i, b_i, c_i')
model = {a_i: a, b_i: b, c_i: c}
np.random.seed(1)
# Sample from a multivariate normal with correlation.
pcov = 1e-1 * np.array([[0.4, 0.3, 0.5], [0.3, 0.8, 0.4], [0.5, 0.4, 1.2]])
xdata = np.random.multivariate_normal([10, 100, 70], pcov, N).T
fit = Fit(
model=model,
a_i=xdata[0],
b_i=xdata[1],
c_i=xdata[2],
absolute_sigma=False
)
fit_result = fit.execute()
cov_equal = fit._cov_mat_equal_lenghts(fit_result.params)
cov_unequal = fit._cov_mat_unequal_lenghts(fit_result.params)
np.testing.assert_array_almost_equal(cov_equal, cov_unequal)
# Try with absolute_sigma=True
fit = Fit(
model=model,
a_i=xdata[0],
b_i=xdata[1],
c_i=xdata[2],
sigma_a_i=np.sqrt(pcov[0, 0]),
sigma_b_i=np.sqrt(pcov[1, 1]),
sigma_c_i=np.sqrt(pcov[2, 2]),
absolute_sigma=True
)
fit_result = fit.execute()
cov_equal = fit._cov_mat_equal_lenghts(fit_result.params)
cov_unequal = fit._cov_mat_unequal_lenghts(fit_result.params)
np.testing.assert_array_almost_equal(cov_equal, cov_unequal)
