def test_USVDAMatrix():
da_matrix = URVDAMatrix(n_datasets=200,
n_algos=20,
normalized=True,
name='USV-0_1-200-20')
da_matrix.save()
inspect_da_matrix(da_matrix)
def test_TransposeDirichletDistributionDAMatrix():
n_datasets = 200
mu = 100
alpha = mu * (np.arange(n_datasets) + 1)
da_matrix = TransposeDirichletDistributionDAMatrix(alpha)
da_matrix.save()
inspect_da_matrix(da_matrix)
def test_BetaDistributionDAMatrix():
n_algos = 20
delta = np.random.rand(n_algos) * 2 # U[0, 2]
s = 10 # alpha + beta
alpha = (s + delta) / 2
alpha = np.sort(alpha) # Ascending alpha
beta = s - alpha
alpha_beta_pairs = [(alpha[i], beta[i]) for i in range(n_algos)]
da_matrix = BetaDistributionDAMatrix(alpha_beta_pairs,
name='IndepBetaDist')
da_matrix.save()
inspect_da_matrix(da_matrix)
def test_DirichletDistributionDAMatrix():
alpha = np.arange(20) + 1
da_matrix = DirichletDistributionDAMatrix(alpha)
da_matrix.save()
inspect_da_matrix(da_matrix)
def test_NFLBetaDist():
n_algos = 20
alpha_beta_pairs = [(5, 5)] * n_algos
da_matrix = BetaDistributionDAMatrix(alpha_beta_pairs, name='NFLBetaDist')
da_matrix.save()
inspect_da_matrix(da_matrix)
def test_parse_cepairs_data():
da_matrix = parse_cepairs_data()
inspect_da_matrix(da_matrix)
def test_TrigonometricPolynomialDAMatrix():
da_matrix = TrigonometricPolynomialDAMatrix(n_datasets=2000, n_algos=20)
da_matrix.save()
inspect_da_matrix(da_matrix)