This example looks like the six particle optimization example except a filter is used instead of a restraint to remove the flip degree of freedom. The filter is written is Python, which makes for quick prototyping, but slower run times.
18 def create_scoring(m, ps):
19 pairs = [[0, 1], [0, 2], [1, 3], [2, 3], [3, 4], [4, 5], [1, 5]]
23 [(ps[p[0]], ps[p[1]])
for p
in pairs],
26 pr.set_maximum_score(.01)
32 def create_representation(m):
35 p = m.add_particle(
"P%d" % i)
41 def create_discrete_states(m, ps):
48 vs = vs + [-v
for v
in vs]
49 print(len(vs),
"states for each particle")
54 pst.set_particle_states(m.get_particle(p), states)
64 def __init__(self, pos, value):
66 IMP.domino.SubsetFilter.__init__(
67 self,
"MF" + str(pos) +
" " + str(value))
71 def get_next_state(self, pos, s):
75 if s[self.pos] > self.value:
81 def get_is_ok(self, state):
83 ret = state[self.pos] == self.value
86 def get_strength(self, s, excluded):
90 def __init__(self, p, s):
92 IMP.domino.SubsetFilterTable.__init__(
93 self,
"MFT" + p.get_name() +
" at " + str(s))
97 def get_subset_filter(self, subset, excluded):
100 and self.p
not in sum([list(x)
for x
in excluded], [])):
102 return self.MyFilter(list(subset).index(self.p), self.s)
107 def create_sampler(m, ps, rs, pst):
117 rc.add_restraints(rs)
121 mf = MyFilterTable(m.get_particle(ps[1]), 0)
126 s.set_assignments_table(states)
127 s.set_subset_filter_tables(filters)
133 m.set_log_level(IMP.SILENT)
135 print(
"creating representation")
136 ps = create_representation(m)
137 print(
"creating discrete states")
138 pst = create_discrete_states(m, ps)
139 print(
"creating score function")
140 rs = create_scoring(m, ps)
142 print(
"creating sampler")
143 s = create_sampler(m, ps, rs, pst)
147 cs = s.create_sample()
149 print(
"found ", cs.get_number_of_configurations(),
"solutions")
151 for i
in range(cs.get_number_of_configurations()):
152 cs.load_configuration(i)
153 print(
"solution number:", i,
" is:", sf.evaluate(
False))