def test_GGGM2(verbose=0):
G = GGGM()
sx = 10000
x = nr.randn(sx)
G.init_fdr(x)
G.estimate(x)
assert(G.mixt[1]>0.9)
def test_GGGM3(verbose=0):
G = GGGM()
sx = 1000
x = 100 + np.array([float(st.t.rvs(5)) for i in range(sx)])
G.init(x)
G.estimate(x)
if verbose:
G.parameters()
assert(np.absolute(G.mixt[0])<1.e-15)
def test_GGGM1(verbose=0):
G = GGGM()
sx = 10000
x = np.array([float(st.t.rvs(5)) for i in range(sx)])
G.init_fdr(x)
G.estimate(x)
if verbose:
G.parameters()
assert(np.absolute(G.mean)<0.1)
def test_GGGM0(verbose=0, seed=1):
G = GGGM()
sx = 1000
#x = np.array([float(st.t.rvs(dof)) for i in range(sx)])
if seed:
nr = np.random.RandomState([seed])
else:
import numpy.random as nr
x = nr.randn(sx)
G.init(x)
G.estimate(x)
if verbose:
G.parameters()
assert(np.absolute(G.mean)<0.3)
