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)