def test_cart_prod():
x = irm.cart_prod([5, 8, 12])
set_x = set(tuple(a) for a in x)
assert_equal(len(x), 5*8*12)
assert_equal(len(x), len(set_x))
assert_equal(set_x, set(util.cart_prod([range(5), range(8), range(12)])))
# test with singletons
x = irm.cart_prod([5, 1, 1])
set_x = set(tuple(a) for a in x)
assert_equal(len(x), 5*1*1)
assert_equal(len(x), len(set_x))
assert_equal(set_x, set(util.cart_prod([range(5), range(1), range(1)])))
def test_cart_prod():
x = irm.cart_prod([5, 8, 12])
set_x = set(tuple(a) for a in x)
assert_equal(len(x), 5 * 8 * 12)
assert_equal(len(x), len(set_x))
assert_equal(set_x, set(util.cart_prod([range(5), range(8), range(12)])))
# test with singletons
x = irm.cart_prod([5, 1, 1])
set_x = set(tuple(a) for a in x)
assert_equal(len(x), 5 * 1 * 1)
assert_equal(len(x), len(set_x))
assert_equal(set_x, set(util.cart_prod([range(5), range(1), range(1)])))
import numpy as np
from irm import util
def grid_gibbs(set_func, get_score, vals_list):
scores = []
for v in vals_list:
set_func(v)
scores.append(get_score())
i = util.sample_from_scores(scores)
set_func(vals_list[i])
DEFAULT_RELATION_GRIDS = {}
DEFAULT_RELATION_GRIDS['BetaBernoulli'] = [{'alpha' : a, 'beta' : b} for a, b in util.cart_prod([[0.1, 0.5, 1.0, 2.0, 5.0], [0.1, 0.5, 1.0, 2.0, 5.0]])]
DEFAULT_RELATION_GRIDS['BetaBernoulliNonConj'] = [{'alpha' : a, 'beta' : b} for a, b in util.cart_prod([[0.1, 0.5, 1.0, 2.0, 5.0], [0.1, 0.5, 1.0, 2.0, 5.0]])]
DEFAULT_RELATION_GRIDS['GammaPoisson'] = [{'alpha' : a, 'beta' : b} for a, b in util.cart_prod([[0.1, 0.5, 1.0, 2.0, 5.0, 10.0], [0.1, 0.5, 1.0, 2.0, 5.0, 10.0]])]
def default_grid_logistic_distance(scale=1.0):
space_vals = np.logspace(-1.5, 1.8, 10)*scale
p_mins = np.array([0.001, 0.01, 0.02])
p_maxs = np.array([0.90, 0.80, 0.50, 0.20])
res = []
for s in space_vals:
for p_min in p_mins:
for p_max in p_maxs:
res.append({'lambda_hp' : s, 'mu_hp' : s,
'p_min' : p_min, 'p_max' : p_max})
return res
def grid_gibbs(set_func, get_score, vals_list):
scores = []
for v in vals_list:
set_func(v)
scores.append(get_score())
i = util.sample_from_scores(scores)
set_func(vals_list[i])
DEFAULT_RELATION_GRIDS = {}
DEFAULT_RELATION_GRIDS['BetaBernoulli'] = [{
'alpha': a,
'beta': b
} for a, b in util.cart_prod([[0.1, 0.5, 1.0, 2.0, 5.0],
[0.1, 0.5, 1.0, 2.0, 5.0]])]
DEFAULT_RELATION_GRIDS['BetaBernoulliNonConj'] = [{
'alpha': a,
'beta': b
} for a, b in util.cart_prod([[0.1, 0.5, 1.0, 2.0, 5.0],
[0.1, 0.5, 1.0, 2.0, 5.0]])]
DEFAULT_RELATION_GRIDS['GammaPoisson'] = [{
'alpha': a,
'beta': b
} for a, b in util.cart_prod([[0.1, 0.5, 1.0, 2.0, 5.0, 10.0],
[0.1, 0.5, 1.0, 2.0, 5.0, 10.0]])]
def default_grid_logistic_distance(scale=1.0):
space_vals = np.logspace(-1.5, 1.8, 10) * scale
