def test_fit_with_constraints():
p = Normal()
X = st.norm(loc=0.05, scale=1.0).rvs(5000, random_state=0).reshape(-1, 1)
p.fit(X,
constraints=[{
"param": p.mu,
"type": "ineq",
"fun": lambda mu: mu
}, {
"param": p.mu,
"type": "ineq",
"fun": lambda mu: 0.1 - mu
}, {
"param": p.sigma,
"type": "ineq",
"fun": lambda sigma: sigma
}, {
"param": (p.mu, p.sigma),
"type": "ineq",
"fun": lambda mu, sigma: mu * sigma
}])
assert p.mu.get_value() >= 0.0
assert p.mu.get_value() <= 0.1
assert p.sigma.get_value() >= 0.0
assert p.mu.get_value() * p.sigma.get_value() >= 0.0
def check_fit(mu, sigma):
p = Normal()
X = st.norm(loc=mu, scale=sigma).rvs(5000, random_state=0).reshape(-1, 1)
s0 = p.score(X)
p.fit(X)
assert np.abs(p.mu.get_value() - mu) <= 0.1
assert np.abs(p.sigma.get_value() - sigma) <= 0.1
assert p.score(X) >= s0
def test_fit_with_bounds():
p = Normal()
X = st.norm(loc=0.05, scale=1.0).rvs(5000, random_state=0).reshape(-1, 1)
p.fit(X, bounds=[{"param": p.sigma, "bounds": (0, None)}])
assert p.sigma.get_value() >= 0.0
