def test__covariance__shape_and_scale_specified__calculated_based_on_shape_and_scale(
self):
for (shape, scale) in utils.get_shape_scale_pairs():
gamma_model = sm.GammaStochasticModel(shape=shape, scale=scale)
desired_variance = gamma.var(shape, scale=scale)
assert_array_almost_equal(gamma_model.covariance,
desired_variance,
decimal=5)
def __init__(self, mode=0, elem=None, sample=None):
if mode == 0:
self.a = elem[0]
self.mu = elem[1]
self.sigma = elem[2]
else:
self.a, self.mu, self.sigma = gamma.fit(sample)
self.math_average = gamma.mean(self.a, loc=self.mu, scale=self.sigma)
self.dispersion = gamma.var(self.a, loc=self.mu, scale=self.sigma)
def get_variance(self):
'''
Returns variance of gamma random variable
'''
return scipygamma.var(self._alpha, self._shift, self._scale)
def var(self, dist):
return gamma.var(*self._get_params(dist))
def var(self, n, p):
var = gamma.var(self, n, p)
return var
def covariance(self):
return np.atleast_2d(
gamma.var(self._shape, scale=self._scale)
)