How to use init_actions method in toolium

Best Python code snippet using toolium_python

test_fictplay.py

Source:test_fictplay.py Github

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...16 # symmetric 2x2 coordination game17 payoff_matrix = [[4, 0],18 [3, 2]]19 self.fp = FictitiousPlay(payoff_matrix)20 def test_set_init_actions_with_given_init_actions(self):21 init_actions = (0, 1)22 self.fp.set_init_actions(init_actions)23 assert_array_equal(self.fp.current_actions, init_actions)24 for i, current_belief in enumerate(self.fp.current_beliefs):25 ok_(current_belief[init_actions[1-i]] == 1 and26 current_belief.sum() == 1)27 def test_set_init_actions_when_init_action_dist_None(self):28 self.fp.set_init_actions() # Action dist randomly chosen29 init_actions = self.fp.current_actions30 for i, current_belief in enumerate(self.fp.current_beliefs):31 ok_(current_belief[init_actions[1-i]] == 1 and32 current_belief.sum() == 1)33 def test_play(self):34 init_actions = (0, 1)35 best_responses = (1, 0)36 self.fp.set_init_actions(init_actions)37 self.fp.play()38 assert_array_equal(self.fp.current_actions, best_responses)39 def test_simulate_rest_point(self):40 beliefs_sequence = \41 self.fp.simulate(ts_length=3, init_actions=(0, 0))42 assert_array_equal(43 beliefs_sequence[0],44 [[1, 0],45 [1, 0],46 [1, 0]]47 )48 def test_simulate(self):49 beliefs_sequence = \50 self.fp.simulate(ts_length=3, init_actions=(0, 1))51 # played actions: (0, 1), (1, 0), (0, 1)52 assert_array_almost_equal_nulp(53 beliefs_sequence[0],54 [[0, 1],55 [1/2, 1/2],56 [1/3, 2/3]]57 )58class TestFictitiousPlay_bimatrix:59 '''Test the methods of FictitiousPlay with bimatrix'''60 def setUp(self):61 '''Setup a FictitiousPlay instance'''62 payoff_bimatrix = np.zeros((2, 3, 2)) # 2 x 3 game63 g = NormalFormGame(payoff_bimatrix)64 self.fp = FictitiousPlay(g)65 def test_set_init_actions_with_given_init_actions(self):66 init_actions = (0, 2)67 self.fp.set_init_actions(init_actions)68 assert_array_equal(self.fp.current_actions, init_actions)69 for i, current_belief in enumerate(self.fp.current_beliefs):70 ok_(current_belief[init_actions[1-i]] == 1 and71 current_belief.sum() == 1)72 def test_set_init_actions_when_init_action_dist_None(self):73 self.fp.set_init_actions() # Action dist randomly chosen74 init_actions = self.fp.current_actions75 for i, current_belief in enumerate(self.fp.current_beliefs):76 ok_(current_belief[init_actions[1-i]] == 1 and77 current_belief.sum() == 1)78# Invalid inputs #79@raises(ValueError)80def test_fp_invalid_input():81 fp = FictitiousPlay(np.zeros((2, 3, 4, 3))) # three-player game82if __name__ == '__main__':83 import sys84 import nose85 argv = sys.argv[:]86 argv.append('--verbose')87 argv.append('--nocapture')...

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logitdyn.py

Source:logitdyn.py Github

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...36 # cdfs left unnormalized37 player.logit_choice_cdfs = \38 np.exp(payoff_array_rotated*self.beta).cumsum(axis=-1)39 # player.logit_choice_cdfs /= player.logit_choice_cdfs[..., [-1]]40 def set_init_actions(self, init_actions=None):41 if init_actions is None:42 init_actions = np.empty(self.N, dtype=int)43 for i in range(self.N):44 init_actions[i] = np.random.randint(self.nums_actions[i])45 self.current_actions[:] = init_actions46 def play(self, player_ind):47 i = player_ind48 # Tuple of the actions of opponent players i+1, ..., N, 0, ..., i-149 opponent_actions = \50 tuple(self.current_actions[i+1:]) + tuple(self.current_actions[:i])51 cdf = self.players[i].logit_choice_cdfs[opponent_actions]52 random_value = np.random.random()53 next_action = cdf.searchsorted(random_value*cdf[-1], side='right')54 self.current_actions[i] = next_action55 def simulate(self, ts_length, init_actions=None):56 """57 Return array of ts_length arrays of N actions58 """59 actions_sequence = np.empty((ts_length, self.N), dtype=int)60 actions_sequence_iter = \61 self.simulate_iter(ts_length, init_actions=init_actions)62 for t, actions in enumerate(actions_sequence_iter):63 actions_sequence[t] = actions64 return actions_sequence65 def simulate_iter(self, ts_length, init_actions=None):66 """67 Iterator version of `simulate`68 """69 self.set_init_actions(init_actions=init_actions)70 player_ind_sequence = np.random.randint(self.N, size=ts_length)71 for t in range(ts_length):72 yield self.current_actions73 self.play(player_ind=player_ind_sequence[t])74 def replicate(self, T, num_reps, init_actions=None):75 out = np.empty((num_reps, self.N), dtype=int)76 for j in range(num_reps):77 actions_sequence_iter = \78 self.simulate_iter(T+1, init_actions=init_actions)79 for actions in actions_sequence_iter:80 x = actions81 out[j] = x...

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test_hbase.py

Source:test_hbase.py Github

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1import pkg_resources2from absl.testing import absltest3from absl.testing import parameterized4from integration_tests.dataproc_test_case import DataprocTestCase5class HBaseTestCase(DataprocTestCase):6 COMPONENT = 'hbase'7 INIT_ACTIONS = ['hbase/hbase.sh']8 INIT_ACTIONS_FOR_NOT_HA = ['zookeeper/zookeeper.sh']9 GCS_BUCKET = None10 def setUp(self):11 super().setUp()12 self.GCS_BUCKET = "test-hbase-{}-{}".format(self.datetime_str(),13 self.random_str())14 self.assert_command('gsutil mb -c regional -l {} gs://{}'.format(15 self.REGION, self.GCS_BUCKET))16 def tearDown(self):17 self.assert_command('gsutil -m rm -rf gs://{}'.format(self.GCS_BUCKET))18 super().tearDown()19 def verify_instance(self, name):20 self.assert_instance_command(21 name, "hbase {} -r {}".format(22 'org.apache.hadoop.hbase.IntegrationTestsDriver',23 'org.apache.hadoop.hbase.mapreduce.IntegrationTestImportTsv'))24 @parameterized.parameters(25 ("SINGLE", ["m"]),26 ("STANDARD", ["m"]),27 ("HA", ["m-0"]),28 )29 def test_hbase(self, configuration, machine_suffixes):30 # Skip on 2.0+ version of Dataproc because it's not supported31 if self.getImageVersion() >= pkg_resources.parse_version("2.0"):32 return33 init_actions = self.INIT_ACTIONS34 if configuration != "HA":35 init_actions = self.INIT_ACTIONS_FOR_NOT_HA + init_actions36 self.createCluster(configuration, init_actions)37 for machine_suffix in machine_suffixes:38 self.verify_instance("{}-{}".format(self.getClusterName(),39 machine_suffix))40 @parameterized.parameters(41 ("SINGLE", ["m"]),42 ("STANDARD", ["m"]),43 ("HA", ["m-0"]),44 )45 def test_hbase_on_gcs(self, configuration, machine_suffixes):46 # Skip on 2.0+ version of Dataproc because it's not supported47 if self.getImageVersion() >= pkg_resources.parse_version("2.0"):48 return49 init_actions = self.INIT_ACTIONS50 if configuration != "HA":51 init_actions = self.INIT_ACTIONS_FOR_NOT_HA + init_actions52 metadata = 'hbase-root-dir=gs://{}/test-dir'.format(self.GCS_BUCKET)53 if self.getImageVersion() > pkg_resources.parse_version("1.4"):54 self.initClusterName(configuration)55 hdfs_host = self.getClusterName()56 if configuration != "HA":57 hdfs_host += '-m'58 metadata += ',hbase-wal-dir=hdfs://{}/hbase-wal'.format(hdfs_host)59 self.createCluster(60 configuration,61 init_actions,62 metadata=metadata)63 for machine_suffix in machine_suffixes:64 self.verify_instance("{}-{}".format(self.getClusterName(),65 machine_suffix))66if __name__ == '__main__':...

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