def test_subreuse(self):
mbounds = ((0, 1), (-1, 0))
env = testenvs.RandomEnv(mbounds)
reuse_cfg = explorers.SubReuseExplorer.defcfg._deepcopy()
reuse_cfg.m_channels = env.m_channels
reuse_cfg.reuse.s_channels = env.s_channels
reuse_cfg.reuse.algorithm = 'random'
reuse_cfg.reuse.res = 10
reuse_cfg._strict(True)
dataset = {'m_channels' : [env.m_channels[0]],
's_channels' : env.s_channels,
'explorations': []}
orders = []
for _ in range(100):
m = tools.random_signal(reuse_cfg.m_channels)
m.pop(env.m_channels[1].name)
s = tools.random_signal(reuse_cfg.reuse.s_channels)
dataset['explorations'].append(({'m_signal': m}, {'s_signal': s}))
orders.append(m)
reuse_explorer = explorers.SubReuseExplorer(reuse_cfg, [dataset])
for _ in range(100):
order = reuse_explorer.explore()
order['m_signal'].pop(env.m_channels[1].name)
self.assertTrue(order['m_signal'] in orders)
self.assertEqual(reuse_explorer.explore(), None)
def test_reuseexp_random2(self):
mbounds = ((0, 1), (-1, 0))
env = testenvs.RandomEnv(mbounds)
reuse_cfg = explorers.ReuseExplorer.defcfg._deepcopy()
reuse_cfg.m_channels = env.m_channels
reuse_cfg.reuse.s_channels = env.s_channels
reuse_cfg.reuse.algorithm = 'sensor_uniform'
reuse_cfg.reuse.res = 10
reuse_cfg._strict(True)
dataset = {'m_channels' : env.m_channels,
's_channels' : env.s_channels,
'explorations': []}
orders = []
for _ in range(1000):
m = tools.random_signal(reuse_cfg.m_channels)
s = tools.random_signal(reuse_cfg.reuse.s_channels)
dataset['explorations'].append(({'m_signal': m}, {'s_signal': s}))
orders.append(m)
reuse_explorer = explorers.ReuseExplorer(reuse_cfg, [dataset])
for _ in range(100):
order = reuse_explorer.explore()
self.assertTrue(order['m_signal'] in orders)
def test_subreuse(self):
mbounds = ((0, 1), (-1, 0))
env = testenvs.RandomEnv(mbounds)
reuse_cfg = explorers.SubReuseExplorer.defcfg._deepcopy()
reuse_cfg.m_channels = env.m_channels
reuse_cfg.reuse.s_channels = env.s_channels
reuse_cfg.reuse.algorithm = 'random'
reuse_cfg.reuse.res = 10
reuse_cfg._strict(True)
dataset = {
'm_channels': [env.m_channels[0]],
's_channels': env.s_channels,
'explorations': []
}
orders = []
for _ in range(100):
m = tools.random_signal(reuse_cfg.m_channels)
m.pop(env.m_channels[1].name)
s = tools.random_signal(reuse_cfg.reuse.s_channels)
dataset['explorations'].append(({'m_signal': m}, {'s_signal': s}))
orders.append(m)
reuse_explorer = explorers.SubReuseExplorer(reuse_cfg, [dataset])
for _ in range(100):
order = reuse_explorer.explore()
order['m_signal'].pop(env.m_channels[1].name)
self.assertTrue(order['m_signal'] in orders)
self.assertEqual(reuse_explorer.explore(), None)
def test_reuse_simple(self):
m_channels = (Channel('a', (0, 1)), Channel('b', (-1, 0)))
s_channels = (Channel('x', (4, 9)),)
dataset = {'m_channels' : m_channels,
's_channels' : s_channels,
'explorations': []}
orders = []
for _ in range(1000):
m = tools.random_signal(m_channels)
s = tools.random_signal(s_channels)
dataset['explorations'].append(({'m_signal': m}, {'s_signal': s}))
orders.append(m)
reuse_cfg = reuse.s_reusegen.RandomReuse.defcfg._deepcopy()
rndreuse = reuse.s_reusegen.RandomReuse(reuse_cfg, dataset)
self.assertEqual(len(rndreuse), 1000)
c = 0
for mc in rndreuse:
c += 1
self.assertTrue(mc in orders)
self.assertEqual(c, 1000)
self.assertEqual(len(rndreuse), 0)
def test_reuseexp_random2(self):
mbounds = ((0, 1), (-1, 0))
env = testenvs.RandomEnv(mbounds)
reuse_cfg = explorers.ReuseExplorer.defcfg._deepcopy()
reuse_cfg.m_channels = env.m_channels
reuse_cfg.reuse.s_channels = env.s_channels
reuse_cfg.reuse.algorithm = 'sensor_uniform'
reuse_cfg.reuse.res = 10
reuse_cfg._strict(True)
dataset = {
'm_channels': env.m_channels,
's_channels': env.s_channels,
'explorations': []
}
orders = []
for _ in range(1000):
m = tools.random_signal(reuse_cfg.m_channels)
s = tools.random_signal(reuse_cfg.reuse.s_channels)
dataset['explorations'].append(({'m_signal': m}, {'s_signal': s}))
orders.append(m)
reuse_explorer = explorers.ReuseExplorer(reuse_cfg, [dataset])
for _ in range(100):
order = reuse_explorer.explore()
self.assertTrue(order['m_signal'] in orders)
def test_im(self):
mbounds = ((23, 34), (-3, -2))
sbounds = ((0, 1), (-1, -0), (101, 1001))
env = testenvs.BoundedRandomEnv(mbounds, sbounds)
exp_cfg = explorers.IMExplorer.defcfg._deepcopy()
exp_cfg.m_channels = env.m_channels
exp_cfg.s_channels = env.s_channels
exp_cfg.learner = learners.RandomLearner.defcfg._deepcopy()
exp_cfg.res = 100
for im_name in ['explorers.LocalInterestModel',
'explorers.PredictiveNoveltyMotivation',
'explorers.IntermediateLevelOfNoveltyMotivation',
]:
exp_cfg.lim.classname = im_name
exp = explorers.IMExplorer(exp_cfg)
for _ in range(10):
m_signal = tools.random_signal(env.m_channels)
feedback = env.execute(m_signal)
exp.receive({'m_signal': m_signal, 'uuid': exp.cfg.uuid}, feedback)
for _ in range(100):
exploration = exp.explore()
self.assertTrue(all(c.bounds[0] <= exploration['m_signal'][c.name] <= c.bounds[1] for c in env.m_channels))
feedback = env.execute(exploration['m_signal'])
exp.receive(exploration, feedback)
def test_im(self):
mbounds = ((23, 34), (-3, -2))
sbounds = ((0, 1), (-1, -0), (101, 1001))
env = testenvs.BoundedRandomEnv(mbounds, sbounds)
exp_cfg = explorers.IMExplorer.defcfg._deepcopy()
exp_cfg.m_channels = env.m_channels
exp_cfg.s_channels = env.s_channels
exp_cfg.learner = learners.RandomLearner.defcfg._deepcopy()
exp_cfg.res = 100
for im_name in [
'explorers.LocalInterestModel',
'explorers.PredictiveNoveltyMotivation',
'explorers.IntermediateLevelOfNoveltyMotivation',
]:
exp_cfg.lim.classname = im_name
exp = explorers.IMExplorer(exp_cfg)
for _ in range(10):
m_signal = tools.random_signal(env.m_channels)
feedback = env.execute(m_signal)
exp.receive({
'm_signal': m_signal,
'uuid': exp.cfg.uuid
}, feedback)
for _ in range(100):
exploration = exp.explore()
self.assertTrue(
all(c.bounds[0] <= exploration['m_signal'][c.name] <=
c.bounds[1] for c in env.m_channels))
feedback = env.execute(exploration['m_signal'])
exp.receive(exploration, feedback)
def test_multi_random(self):
mbounds = ((0, 1), (-1, 0))
env = testenvs.RandomEnv(mbounds)
reuse_cfg = explorers.MultiReuseExplorer.defcfg._deepcopy()
reuse_cfg.m_channels = env.m_channels
reuse_cfg.s_channels = env.m_channels
reuse_cfg.reuse.s_channels = env.s_channels
reuse_cfg.reuse.algorithm = 'random'
reuse_cfg.reuse.res = 10
reuse_cfg.pick_algorithm = 'diversity'
reuse_cfg.res = 10
reuse_cfg._strict(True)
datasets = []
orders = []
for _ in range(5):
dataset = {
'm_channels': env.m_channels,
's_channels': env.s_channels,
'explorations': []
}
for _ in range(100):
m = tools.random_signal(reuse_cfg.m_channels)
s = tools.random_signal(reuse_cfg.reuse.s_channels)
dataset['explorations'].append(({
'm_signal': m
}, {
's_signal': s
}))
orders.append(m)
datasets.append(dataset)
reuse_explorer = explorers.MultiReuseExplorer(reuse_cfg, datasets)
for _ in range(500):
order = reuse_explorer.explore()
self.assertTrue(order['m_signal'] in orders)
self.assertEqual(reuse_explorer.explore(), None)
def test_multi_random(self):
mbounds = ((0, 1), (-1, 0))
env = testenvs.RandomEnv(mbounds)
reuse_cfg = explorers.MultiReuseExplorer.defcfg._deepcopy()
reuse_cfg.m_channels = env.m_channels
reuse_cfg.s_channels = env.m_channels
reuse_cfg.reuse.s_channels = env.s_channels
reuse_cfg.reuse.algorithm = 'random'
reuse_cfg.reuse.res = 10
reuse_cfg.pick_algorithm = 'diversity'
reuse_cfg.res = 10
reuse_cfg._strict(True)
datasets = []
orders = []
for _ in range(5):
dataset = {'m_channels' : env.m_channels,
's_channels' : env.s_channels,
'explorations': []}
for _ in range(100):
m = tools.random_signal(reuse_cfg.m_channels)
s = tools.random_signal(reuse_cfg.reuse.s_channels)
dataset['explorations'].append(({'m_signal': m}, {'s_signal': s}))
orders.append(m)
datasets.append(dataset)
reuse_explorer = explorers.MultiReuseExplorer(reuse_cfg, datasets)
for _ in range(500):
order = reuse_explorer.explore()
self.assertTrue(order['m_signal'] in orders)
self.assertEqual(reuse_explorer.explore(), None)
def _execute(self, m_signal, meta=None):
return tools.random_signal(self.s_channels)
def _execute(self, m_signal, meta=None):
return tools.random_signal(self.s_channels)
