def construct_hyper_grids(X, n_grid=30):
grids = dict()
grids['m'] = gu.log_linspace(1e-4, max(X), n_grid)
grids['r'] = gu.log_linspace(.1, float(len(X)), n_grid)
grids['nu'] = gu.log_linspace(.1, float(len(X)), n_grid)
grids['s'] = gu.log_linspace(.1, float(len(X)), n_grid)
return grids
def construct_hyper_grids(X, n_grid=300):
grids = dict()
# Plus 1 for single observation case.
N = len(X) + 1.
ssqdev = np.var(X) * len(X) + 1.
# Data dependent heuristics.
grids['a'] = gu.log_linspace(1./(10*N), 10*N, n_grid)
grids['b'] = gu.log_linspace(ssqdev/100., ssqdev, n_grid)
return grids
def construct_hyper_grids(X, n_grid=30):
grids = dict()
# Plus 1 for single observation case.
N = len(X) + 1.
ssqdev = np.var(X) * len(X) + 1.
# Data dependent heuristics.
grids['m'] = np.linspace(min(X), max(X) + 5, n_grid)
grids['r'] = gu.log_linspace(1. / N, N, n_grid)
grids['s'] = gu.log_linspace(ssqdev / 100., ssqdev, n_grid)
grids['nu'] = gu.log_linspace(1., N, n_grid) # df >= 1
return grids
def construct_hyper_grids(X, n_grid=30):
grids = dict()
# Plus 1 for single observation case.
N = len(X) if len(X) > 0 else 5
minX = min(X) if len(X) > 0 else 0
maxX = max(X) if len(X) > 0 else 1
ssqdev = np.var(X) * N + 1 if len(X) > 0 else 1
# Data dependent heuristics.
grids['m'] = np.linspace(minX, maxX + 5, n_grid)
grids['r'] = log_linspace(1. / N, N, n_grid)
grids['s'] = log_linspace(ssqdev / 100., ssqdev, n_grid)
grids['nu'] = log_linspace(1., N, n_grid) # df >= 1
return grids
def construct_hyper_grids(X, n_grid=30):
grids = dict()
# only use integers for a so we can nicely draw from a negative binomial
# in predictive_draw
grids['a'] = np.unique(np.round(np.linspace(1, len(X), n_grid)))
grids['b'] = gu.log_linspace(.1, float(len(X)), n_grid)
return grids
def construct_hyper_grids(X, n_grid=30):
grids = dict()
N = float(len(X))
ssx = np.sum(np.sin(X))
scx = np.sum(np.cos(X))
k = Vonmises.estimate_kappa(N, ssx, scx)
grids['a'] = gu.log_linspace(1. / N, N, n_grid)
grids['b'] = np.linspace(2 * pi / n_grid, 2 * pi, n_grid)
grids['k'] = np.linspace(k, N * k, n_grid)
return grids
def construct_hyper_grids(X, n_grid=30):
grids = dict()
grids['alpha'] = gu.log_linspace(1./len(X), len(X), n_grid)
return grids
def construct_hyper_grids(X, n_grid=30):
grids = dict()
N = len(X) if len(X) > 0 else 5
grids['alpha'] = log_linspace(1. / N, N, n_grid)
return grids
def construct_hyper_grids(X, n_grid=30):
grids = dict()
grids['alpha'] = gu.log_linspace(1., float(len(X)), n_grid)
grids['beta'] = gu.log_linspace(1., float(len(X)), n_grid)
return grids
def construct_hyper_grids(X, n_grid=30):
grids = dict()
grids['a'] = gu.log_linspace(1, float(len(X)) / 2., n_grid)
grids['b'] = gu.log_linspace(.1, float(len(X)) / 2., n_grid)
return grids
def construct_hyper_grids(X, n_grid=30):
grids = dict()
N = len(X) if len(X) > 0 else 2
grids['a'] = np.unique(np.round(np.linspace(1, N, n_grid)))
grids['b'] = log_linspace(.1, float(N), n_grid)
return grids
probe_values = set(P).union({max(P) + 1})
assert Nk == crp.counts.values()
# Table predictive probabilities.
assert np.allclose(gu.logp_crp_fresh(N, Nk, alpha),
[crp.logpdf(-1, {0: v}, None) for v in probe_values])
# Data probability.
assert np.allclose(gu.logp_crp(N, Nk, alpha), crp.logpdf_score())
# Gibbs transition probabilities.
Z = crp.data.values()
for i, rowid in enumerate(crp.data):
assert np.allclose(gu.logp_crp_gibbs(Nk, Z, i, alpha, 1),
crp.gibbs_logps(rowid))
N = [2**i for i in xrange(8)]
alpha = gu.log_linspace(.001, 100, 10)
seed = [5]
@pytest.mark.parametrize('N, alpha, seed', itertools.product(N, alpha, seed))
def test_crp_simple(N, alpha, seed):
# Obtain the partitions.
A = gu.simulate_crp(N, alpha, rng=gu.gen_rng(seed))
Nk = list(np.bincount(A))
crp = simulate_crp_gpm(N, alpha, rng=gu.gen_rng(seed))
assert A == crp.data.values()
assert_crp_equality(alpha, Nk, crp)
