[pypy-commit] benchmarks default: (cfbolz, arigo)
arigo
noreply at buildbot.pypy.org
Mon Dec 8 17:15:26 CET 2014
Author: Armin Rigo <arigo at tunes.org>
Branch:
Changeset: r284:ed338f71e5f7
Date: 2014-12-08 16:15 +0000
http://bitbucket.org/pypy/benchmarks/changeset/ed338f71e5f7/
Log: (cfbolz, arigo)
Add the MDP code (Robert Grosse on pypy-dev)
diff --git a/benchmarks.py b/benchmarks.py
--- a/benchmarks.py
+++ b/benchmarks.py
@@ -82,7 +82,7 @@
'spectral-norm', 'chaos', 'telco', 'go', 'pyflate-fast',
'raytrace-simple', 'crypto_pyaes', 'bm_mako', 'bm_chameleon',
'json_bench', 'pidigits', 'hexiom2', 'eparse', 'deltablue',
- 'bm_dulwich_log', 'bm_krakatau']:
+ 'bm_dulwich_log', 'bm_krakatau', 'bm_mdp']:
_register_new_bm(name, name, globals(), **opts.get(name, {}))
for name in ['names', 'iteration', 'tcp', 'pb', ]:#'web']:#, 'accepts']:
diff --git a/own/bm_mdp.py b/own/bm_mdp.py
new file mode 100644
--- /dev/null
+++ b/own/bm_mdp.py
@@ -0,0 +1,299 @@
+import collections, time
+from collections import defaultdict as ddict
+from fractions import Fraction as frac
+
+def tarjanSCC(roots, getChildren):
+ """Return a list of strongly connected components in a graph. If getParents is passed instead of getChildren, the result will be topologically sorted.
+
+ roots - list of root nodes to search from
+ getChildren - function which returns children of a given node
+ """
+
+ sccs = []
+ indexCounter = itertools.count()
+ index = {}
+ lowlink = {}
+ removed = set()
+ subtree = []
+
+ #Use iterative version to avoid stack limits for large datasets
+ stack = [(node, 0) for node in roots]
+ while stack:
+ current, state = stack.pop()
+ if state == 0: #before recursing
+ if current not in index: #if it's in index, it was already visited (possibly earlier on the current search stack)
+ lowlink[current] = index[current] = next(indexCounter)
+ subtree.append(current)
+
+ stack.append((current, 1))
+ stack.extend((child, 0) for child in getChildren(current) if child not in removed)
+ else: #after recursing
+ children = [child for child in getChildren(current) if child not in removed]
+ for child in children:
+ if index[child] <= index[current]: #backedge (or selfedge)
+ lowlink[current] = min(lowlink[current], index[child])
+ else:
+ lowlink[current] = min(lowlink[current], lowlink[child])
+ assert(lowlink[current] <= index[current])
+
+ if index[current] == lowlink[current]:
+ scc = []
+ while not scc or scc[-1] != current:
+ scc.append(subtree.pop())
+
+ sccs.append(tuple(scc))
+ removed.update(scc)
+ return sccs
+
+def topoSort(roots, getParents):
+ """Return a topological sorting of nodes in a graph.
+
+ roots - list of root nodes to search from
+ getParents - function which returns the parents of a given node
+ """
+
+ results = []
+ visited = set()
+
+ #Use iterative version to avoid stack limits for large datasets
+ stack = [(node,0) for node in roots]
+ while stack:
+ current, state = stack.pop()
+ if state == 0: #before recursing
+ if current not in visited:
+ visited.add(current)
+ stack.append((current,1))
+ stack.extend((parent,0) for parent in getParents(current))
+ else: #after recursing
+ assert(current in visited)
+ results.append(current)
+ return results
+
+def getDamages(L, A, D, B, stab, te):
+ x = (2*L)//5
+ x = ((x+2)*A*B)//(D*50) + 2
+ if stab:
+ x += x // 2
+ x = int(x * te)
+ return [(x*z)//255 for z in range(217,256)]
+
+def getCritDist(L, p, A1, A2, D1, D2, B, stab, te):
+ p = min(p, frac(1))
+ norm = getDamages(L, A1, D1, B, stab, te)
+ crit = getDamages(L*2, A2, D2, B, stab, te)
+
+ dist = ddict(frac)
+ for mult, vals in zip([1-p, p], [norm, crit]):
+ mult /= len(vals)
+ for x in vals:
+ dist[x] += mult
+ return dist
+
+def getAvg(dd):
+ return float(sum(k*v for k,v in dd))
+
+def plus12(x): return x + x//8
+def plus50(x): return x + x//2
+
+def replace(t, i, v):
+ if t[i] != v:
+ temp = t[:i] + (v,) + t[i+1:]
+ t = type(t)._make(temp)
+ return t
+
+
+poke_stats_t = collections.namedtuple('poke_stats_t', ['lvl', 'basespeed', 'hp', 'atk', 'df', 'speed', 'spec'])
+stats_t = collections.namedtuple('stats_t', ['atk', 'df', 'speed', 'spec'])
+NOMODS = stats_t(0,0,0,0)
+
+
+fixeddata_t = collections.namedtuple('fixeddata_t', ['maxhp', 'stats', 'lvl', 'badges','basespeed'])
+halfstate_t = collections.namedtuple('halfstate_t', ['fixed','hp','status','statmods','stats'])
+
+
+_statmod_table = [frac(i,2) for i in range(3,9)]
+_statmod_table = [1/x for x in reversed(_statmod_table)] + [frac(1)] + _statmod_table
+
+def applyHPChange(hstate, change):
+ hp = min(hstate.fixed.maxhp, max(0, hstate.hp + change))
+ return hstate._replace(hp=hp)
+
+def applyBadgeBoosts(badges, stats):
+ return stats_t(*[(plus12(x) if b else x) for x,b in zip(stats, badges)])
+
+attack_stats_t = collections.namedtuple('attack_stats_t', ['power', 'isspec', 'stab', 'te', 'crit'])
+attack_data = {
+ 'Ember': attack_stats_t(40, True, True, 0.5, False),
+ 'Dig': attack_stats_t(100, False, False, 1, False),
+ 'Slash': attack_stats_t(70, False, False, 1, True),
+ 'Water Gun': attack_stats_t(40, True, True, 2, False),
+ 'Bubblebeam': attack_stats_t(65, True, True, 2, False),
+}
+
+def _applyActionSide1(state, act):
+ me, them, extra = state
+
+ if act == 'Super Potion':
+ me = applyHPChange(me, 50)
+ return {(me, them, extra): frac(1)}
+
+ mdata = attack_data[act]
+ aind = 3 if mdata.isspec else 0
+ dind = 3 if mdata.isspec else 1
+ pdiv = 64 if mdata.crit else 512
+ dmg_dist = getCritDist(me.fixed.lvl, frac(me.fixed.basespeed, pdiv),
+ me.stats[aind], me.fixed.stats[aind], them.stats[dind], them.fixed.stats[dind],
+ mdata.power, mdata.stab, mdata.te)
+
+ dist = ddict(frac)
+ for dmg, p in dmg_dist.items():
+ them2 = applyHPChange(them, -dmg)
+ dist[me, them2, extra] += p
+ return dist
+
+def _applyAction(state, side, act):
+ if side == 0:
+ return _applyActionSide1(state, act)
+ else:
+ me, them, extra = state
+ dist = _applyActionSide1((them, me, extra), act)
+ return {(k[1], k[0], k[2]): v for k,v in dist.items()}
+
+def printstate(state):
+ return 'char {} star {}'.format(state[0].hp, state[1].hp)
+def printsp(sp):
+ return '[{}, {}, {}]'.format(sp[0], printstate(sp[1]), ', '.join(map(str, sp[2:])))
+
+class Battle(object):
+ def __init__(self):
+ self.successors = {}
+ self.min = ddict(float)
+ self.max = ddict(lambda:1.0)
+ self.frozen = set()
+
+ self.win = 4, True
+ self.loss = 4, False
+ self.max[self.loss] = 0.0
+ self.min[self.win] = 1.0
+ self.frozen.update([self.win, self.loss])
+
+ def _getSuccessorsA(self, statep):
+ st, state = statep
+ for action in ['Dig', 'Super Potion']:
+ yield (1, state, action)
+
+ def _applyActionPair(self, state, side1, act1, side2, act2, dist, pmult):
+ for newstate, p in _applyAction(state, side1, act1).items():
+ if newstate[0].hp == 0:
+ newstatep = self.loss
+ elif newstate[1].hp == 0:
+ newstatep = self.win
+ else:
+ newstatep = 2, newstate, side2, act2
+ dist[newstatep] += p*pmult
+
+ def _getSuccessorsB(self, statep):
+ st, state, action = statep
+ dist = ddict(frac)
+ for eact, p in [('Water Gun', frac(64, 130)), ('Bubblebeam', frac(66, 130))]:
+ priority1 = state[0].stats.speed + 10000*(action == 'Super Potion')
+ priority2 = state[1].stats.speed + 10000*(action == 'X Defend')
+
+ if priority1 > priority2:
+ self._applyActionPair(state, 0, action, 1, eact, dist, p)
+ elif priority1 < priority2:
+ self._applyActionPair(state, 1, eact, 0, action, dist, p)
+ else:
+ self._applyActionPair(state, 0, action, 1, eact, dist, p/2)
+ self._applyActionPair(state, 1, eact, 0, action, dist, p/2)
+
+ return {k:float(p) for k,p in dist.items() if p>0}
+
+ def _getSuccessorsC(self, statep):
+ st, state, side, action = statep
+ dist = ddict(frac)
+ for newstate, p in _applyAction(state, side, action).items():
+ if newstate[0].hp == 0:
+ newstatep = self.loss
+ elif newstate[1].hp == 0:
+ newstatep = self.win
+ else:
+ newstatep = 0, newstate
+ dist[newstatep] += p
+ return {k:float(p) for k,p in dist.items() if p>0}
+
+ def getSuccessors(self, statep):
+ try:
+ return self.successors[statep]
+ except KeyError:
+ st = statep[0]
+ if st == 0:
+ result = list(self._getSuccessorsA(statep))
+ else:
+ if st == 1:
+ dist = self._getSuccessorsB(statep)
+ elif st == 2:
+ dist = self._getSuccessorsC(statep)
+ result = sorted(dist.items(), key=lambda t:(-t[1], t[0]))
+ self.successors[statep] = result
+ return result
+
+ def getSuccessorsList(self, statep):
+ if statep[0] == 4:
+ return []
+ temp = self.getSuccessors(statep)
+ if statep[0] != 0:
+ temp = zip(*temp)[0] if temp else []
+ return temp
+
+ def evaluate(self, tolerance=0.15):
+ badges = 1, 0, 0, 0
+
+ starfixed = fixeddata_t(59, stats_t(40, 44, 56, 50), 11, NOMODS, 115)
+ starhalf = halfstate_t(starfixed, 59, 0, NOMODS, stats_t(40, 44, 56, 50))
+ charfixed = fixeddata_t(63, stats_t(39, 34, 46, 38), 26, badges, 65)
+ charhalf = halfstate_t(charfixed, 63, 0, NOMODS, applyBadgeBoosts(badges, stats_t(39, 34, 46, 38)))
+ initial_state = charhalf, starhalf, 0
+ initial_statep = 0, initial_state
+
+ dmin, dmax, frozen = self.min, self.max, self.frozen
+ stateps = topoSort([initial_statep], self.getSuccessorsList)
+
+ itercount = 0
+ while dmax[initial_statep] - dmin[initial_statep] > tolerance:
+ itercount += 1
+ #print itercount, dmax[initial_statep] - dmin[initial_statep], len(frozen)
+
+ for sp in stateps:
+ if sp in frozen:
+ continue
+
+ if sp[0] == 0: #choice node
+ dmin[sp] = max(dmin[sp2] for sp2 in self.getSuccessors(sp))
+ dmax[sp] = max(dmax[sp2] for sp2 in self.getSuccessors(sp))
+ else:
+ dmin[sp] = sum(dmin[sp2]*p for sp2,p in self.getSuccessors(sp))
+ dmax[sp] = sum(dmax[sp2]*p for sp2,p in self.getSuccessors(sp))
+ if dmin[sp] >= dmax[sp]:
+ dmax[sp] = dmin[sp] = (dmin[sp] + dmax[sp])/2
+ frozen.add(sp)
+ return (dmax[initial_statep] + dmin[initial_statep])/2
+
+def main(n):
+ l = []
+ for i in range(n):
+ t0 = time.time()
+ Battle().evaluate(0.192)
+ time_elapsed = time.time() - t0
+ l.append(time_elapsed)
+ return l
+
+if __name__ == "__main__":
+ import util, optparse
+ parser = optparse.OptionParser(
+ usage="%prog [options]",
+ description="Test the performance of the MDP benchmark")
+ util.add_standard_options_to(parser)
+ options, args = parser.parse_args()
+
+ util.run_benchmark(options, options.num_runs, main)
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