def test_beta_dist(): du2 = [randn(n_features, 1) for i in range(n_kernels)] for i in range(len(du2)): du2[i] /= norm(du2[i]) assert_equal(0., beta_dist(du, du)) assert_not_equal(0., beta_dist(du, du2)) du2 = [randn(n_features + 2, 1) for i in range(n_kernels)] for i in range(len(du2)): du2[i] /= norm(du2[i]) assert_raises(ValueError, beta_dist, du, du2)
def test_beta_dist(): du2 = [randn(n_features, 1) for i in range(n_kernels)] for i in range(len(du2)): du2[i] /= norm(du2[i]) assert_equal(0., beta_dist(du, du)) assert_not_equal(0., beta_dist(du, du2)) du2 = [randn(n_features+2, 1) for i in range(n_kernels)] for i in range(len(du2)): du2[i] /= norm(du2[i]) assert_raises(ValueError, beta_dist, du, du2)
def callback_recovery(loc): d = loc['dict_obj'] d.wc.append(emd(loc['dictionary'], d.generating_dict, 'chordal', scale=True)) d.wfs.append(emd(loc['dictionary'], d.generating_dict, 'fubinistudy', scale=True)) d.hc.append(hausdorff(loc['dictionary'], d.generating_dict, 'chordal', scale=True)) d.hfs.append(hausdorff(loc['dictionary'], d.generating_dict, 'fubinistudy', scale=True)) d.bd.append(beta_dist(d.generating_dict, loc['dictionary'])) d.dr99.append(detection_rate(loc['dictionary'], d.generating_dict, 0.99)) d.dr97.append(detection_rate(loc['dictionary'], d.generating_dict, 0.97))
def callback_recovery(loc): d = loc["dict_obj"] d.wc.append( emd(loc["dictionary"], d.generating_dict, "chordal", scale=True)) d.wfs.append( emd(loc["dictionary"], d.generating_dict, "fubinistudy", scale=True)) d.hc.append( hausdorff(loc["dictionary"], d.generating_dict, "chordal", scale=True)) d.hfs.append( hausdorff(loc["dictionary"], d.generating_dict, "fubinistudy", scale=True)) d.bd.append(beta_dist(d.generating_dict, loc["dictionary"])) d.dr99.append(detection_rate(loc["dictionary"], d.generating_dict, 0.99)) d.dr97.append(detection_rate(loc["dictionary"], d.generating_dict, 0.97))
def test_beta_dict_length(): du2 = [randn(n_features, 1) for i in range(n_kernels + 2)] for i in range(len(du2)): du2[i] /= norm(du2[i]) assert_not_equal(0., beta_dist(du, du2))
def test_beta_dict_length(): du2 = [randn(n_features, 1) for i in range(n_kernels+2)] for i in range(len(du2)): du2[i] /= norm(du2[i]) assert_not_equal(0., beta_dist(du, du2))