def fill_in_parameters(self, group_type, random=False, ones=False, universal=False):
"""Separate all possible types of twobodys, threebodys, manybody.
One type per group. And fill in either universal ls and sigma from
pre-defined parameters from set_parameters("sigma", ..) and set_parameters("ls", ..)
or random parameters if random is True.
Args:
group_type (str): "specie", "twobody", "threebody", "cut3b", "manybody"
definition_list (list, dict): list of elements
"""
nspec = len(self.all_group_names["specie"])
if nspec < 1:
raise RuntimeError("the specie group has to be defined in advance")
if random:
for group_name in self.all_group_names[group_type]:
self.set_parameters(group_name, parameters=nprandom(2))
elif ones:
for group_name in self.all_group_names[group_type]:
self.set_parameters(group_name, parameters=np.ones(2))
elif universal:
for group_name in self.all_group_names[group_type]:
self.set_parameters(
group_name,
parameters=[self.universal["sigma"], self.universal["lengthscale"]],
)
def prepare_samples(self, horizon):
if horizon <= 0:
raise Exception("the input horizon is invalid")
else:
self.horizon = horizon
self.prepared_samples = self.lower + (nprandom(self.horizon) *
self.amplitude)
def draw_sample(self, context, t=None):
""" Draw one random sample."""
if self.context != context:
raise Exception(
"the arm corresponding to a different context is called")
if t is None:
# The parameter t is ignored in this Arm. Do sampling right away.
return self.lower + (nprandom() * self.amplitude)
else:
if t >= self.horizon:
raise Exception("the time instance is beyond the horizon")
else:
return self.prepared_samples[t]
'''Test for generic utils
'''
import numpy as np
from chemlab.utils.celllinkedlist import CellLinkedList
from chemlab.libs.ckdtree import cKDTree
from chemlab.utils import distance_matrix
import time
from nose_parameterized import parameterized
from numpy.random import random as nprandom
@parameterized([
(nprandom((5, 3)) * 2, nprandom((5, 3)) * 2, 0.5),
(nprandom((4,3)) - 0.5, nprandom((4,3)) - 0.5, 0.5) # negative nums
])
def test_distances(coords, coords_b, cutoff):
# Consistency checks
print "Simple"
t = time.time()
dist_simple = distance_matrix(coords, coords_b, cutoff, method="simple")
print -t + time.time()
print "Cell-lists"
t = time.time()
dist_clist = distance_matrix(coords, coords_b, cutoff, method="cell-lists")
print -t + time.time()
print dist_simple
print dist_clist.todense()
assert np.allclose(dist_simple, dist_clist.todense())
def draw_nparray(self, shape=(1,)):
""" Draw one random sample. The parameter t is ignored in this Arm."""
return np.minimum((-1. / self.p) * np.log(nprandom(shape)), self.trunc)
from numpy import array, rec
from numpy.random import normal as nprandom
from rpy2.robjects import numpy2ri, r
foo = array(range(10))
bar = foo + nprandom(0,1,10)
d = rec.fromarrays([foo, bar], names=('foo','bar'))
print d
fit = r.lm('bar ~ foo', data=d)
print fit.rx2('coefficients')
def draw_nparray(self, shape=(1, )):
""" Draw a numpy array of random samples, of a certain shape."""
return self.lower + (nprandom(shape) * self.amplitude)
'''Test for generic utils
'''
import numpy as np
from chemlab.utils.celllinkedlist import CellLinkedList
from chemlab.libs.ckdtree import cKDTree
from chemlab.utils import distance_matrix
import time
from nose_parameterized import parameterized
from numpy.random import random as nprandom
@parameterized([
(nprandom((5, 3)) * 2, nprandom((5, 3)) * 2, 0.5),
(nprandom((4, 3)) - 0.5, nprandom((4, 3)) - 0.5, 0.5) # negative nums
])
def test_distances(coords, coords_b, cutoff):
# Consistency checks
print "Simple"
t = time.time()
dist_simple = distance_matrix(coords, coords_b, cutoff, method="simple")
print -t + time.time()
print "Cell-lists"
t = time.time()
dist_clist = distance_matrix(coords, coords_b, cutoff, method="cell-lists")
print -t + time.time()
print dist_simple
print dist_clist.todense()
