def test_Model():
""" """
M = sb.Model()
x = np.linspace(0.1, 0.9, 22)
target_mu = 0.5
target_sigma = 1
target_y = sb.cumgauss(x, target_mu, target_sigma)
F = M.fit(x, target_y, initial=[target_mu, target_sigma])
npt.assert_equal(F.predict(x), target_y)
def test_Model():
""" """
M = sb.Model()
x = np.linspace(0.1, 0.9, 22)
target_mu = 0.5
target_sigma = 1
target_y = sb.cumgauss(x, target_mu, target_sigma)
F = M.fit(x, target_y, initial=[target_mu, target_sigma])
npt.assert_equal(F.predict(x), target_y)
def test_cum_gauss():
sigma = 1
mu = 0
x = np.linspace(-1, 1, 12)
y = sb.cumgauss(x, mu, sigma)
# A basic test that the input and output have the same shape:
npt.assert_equal(y.shape, x.shape)
# The function evaluated over items symmetrical about mu should be
# symmetrical relative to 0 and 1:
npt.assert_equal(y[0], 1 - y[-1])
# Approximately 68% of the Gaussian distribution is in mu +/- sigma, so
# the value of the cumulative Gaussian at mu - sigma should be
# approximately equal to (1 - 0.68/2). Note the low precision!
npt.assert_almost_equal(y[0], (1 - 0.68) / 2, decimal=2)
def test_cum_gauss():
sigma = 1
mu = 0
x = np.linspace(-1, 1, 12)
y = sb.cumgauss(x, mu, sigma)
# A basic test that the input and output have the same shape:
npt.assert_equal(y.shape , x.shape)
# The function evaluated over items symmetrical about mu should be
# symmetrical relative to 0 and 1:
npt.assert_equal(y[0], 1 - y[-1])
# Approximately 68% of the Gaussian distribution is in mu +/- sigma, so
# the value of the cumulative Gaussian at mu - sigma should be
# approximately equal to (1 - 0.68/2). Note the low precision!
npt.assert_almost_equal(y[0], (1 - 0.68) / 2, decimal=2)