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How to use raw_transitions method in hypothesis

Best Python code snippet using hypothesis

process_mona_output.py

Source:process_mona_output.py

...100                ),101            ).group(1)102        )103    @property104    def raw_transitions(self) -> Dict[int, Dict[int, Set[str]]]:105        """106        Get the raw transitions.107        mapping: start state -> end state -> {guard to reach end from start}108        """109        raw_transitions: Dict[int, Dict[int, Set[str]]] = {}110        lines = self.output.splitlines()111        # from the 8th line, the output specifies the transitions.112        transition_strings = lines[7:]113        for t in transition_strings:114            match = cast(115                Match, re.search("State ([0-9]+): ([01X]+|) -> state ([0-9]+)", t)116            )117            if match is None:118                continue...

flowbuilder.py

Source:flowbuilder.py

1from collections import namedtuple2from django.db import transaction3from river.models import State, Workflow4from river.models.factories import TransitionMetaFactory, TransitionApprovalMetaFactory5from river.tests.models.factories import BasicTestModelObjectFactory6RawTransition = namedtuple("RawTransition", ["source_state", "destination_state", "authorization_policies"])7RawState = namedtuple("RawState", ["label"])8AuthorizationPolicy = namedtuple("RawAuthorizationPolicy", ["priority", "permissions", "groups", "user"])9class Flow(object):10    def __init__(self, workflow, states, transitions_metas, transitions_approval_metas, objects):11        self.workflow = workflow12        self.states = states13        self.transitions_metas = transitions_metas14        self.transitions_approval_metas = transitions_approval_metas15        self.objects = objects16    def get_state(self, raw_state):17        return self.states[raw_state.label]18class AuthorizationPolicyBuilder(object):19    def __init__(self):20        self._priority = 021        self._user = None22        self._permissions = []23        self._groups = []24    def with_priority(self, priority):25        self._priority = priority26        return self27    def with_permission(self, permission):28        return self.with_permissions([permission])29    def with_user(self, user):30        self._user = user31        return self32    def with_group(self, group):33        return self.with_groups([group])34    def with_permissions(self, permissions):35        self._permissions.extend(permissions)36        return self37    def with_groups(self, groups):38        self._groups.extend(groups)39        return self40    def build(self):41        return AuthorizationPolicy(self._priority, self._permissions, self._groups, self._user)42class FlowBuilder(object):43    def __init__(self, field_name, content_type):44        self.field_name = field_name45        self.content_type = content_type46        self.raw_transitions = []47        self.additional_raw_states = []48        self.objects_count = 149        self.object_factory = lambda: BasicTestModelObjectFactory().model50    def with_transition(self, source_state, destination_state, authorization_policies=None):51        self.raw_transitions.append(RawTransition(source_state, destination_state, authorization_policies))52        return self53    def with_additional_state(self, state):54        self.additional_raw_states.append(state)55        return self56    def with_objects(self, count):57        self.objects_count = count58        return self59    def with_object_factory(self, factory):60        self.object_factory = factory61        return self62    @transaction.atomic63    def build(self):64        workflow = None65        states = {}66        transition_metas = []67        transitions_approval_metas = []68        workflow_objects = []69        for additional_raw_state in self.additional_raw_states:70            state, _ = State.objects.get_or_create(label=additional_raw_state.label)71            states[state.label] = state72        for raw_transition in self.raw_transitions:73            source_state, _ = State.objects.get_or_create(label=raw_transition.source_state.label)74            if not workflow:75                workflow = Workflow.objects.create(field_name=self.field_name, content_type=self.content_type, initial_state=source_state)76            destination_state, _ = State.objects.get_or_create(label=raw_transition.destination_state.label)77            states[source_state.label] = source_state78            states[destination_state.label] = destination_state79            transition_meta = TransitionMetaFactory.create(80                workflow=workflow,81                source_state=source_state,82                destination_state=destination_state,83            )84            transition_metas.append(transition_meta)85            if raw_transition.authorization_policies:86                for authorization_policy in raw_transition.authorization_policies:87                    transition_approval_meta = TransitionApprovalMetaFactory.create(88                        workflow=workflow,89                        transition_meta=transition_meta,90                        priority=authorization_policy.priority,91                        permissions=authorization_policy.permissions,92                    )93                    transition_approval_meta.groups.set(authorization_policy.groups)94                    transitions_approval_metas.append(transition_approval_meta)95        for i in range(self.objects_count):96            workflow_objects.append(self.object_factory())...

MarkovDecisionProcess.py

Source:MarkovDecisionProcess.py

1import sys2class Action:3    def __init__(self, action_val, transitions, j_value):4        self.action_val = action_val5        self.transitions = transitions6        self.j_value = j_value7class State:8    def __init__(self, state_val, actions, reward):9        self.state_val = state_val10        self.actions = actions11        self.reward = reward12def create_state_from_line(line):13    state_val = int (line [0].replace("s", ""))14    reward = int (line[1])15    raw_transitions = line [2:]16    full_length = len (raw_transitions) 17    transitions = []18    for i in range(0,full_length, 3):19        action = int (raw_transitions[i].replace("(a", ""))20        transition_state = int (raw_transitions[i + 1].replace("s", ""))21        transition_probability = float (raw_transitions[i + 2].replace(")", ""))22        transitions.append([action, transition_state, transition_probability])23    actions = create_actions_from_transitions(transitions)24    return State(state_val, actions, reward)25def create_actions_from_transitions(transitions):26    actions = {}27    # initialize the dic28    for transition in transitions:29        actions[transition[0]] = []30    for transition in transitions:31        curr_transitions = actions[transition[0]]32        curr_transitions.append([transition[1], transition[2]])33        actions[transition[0]] = curr_transitions34    action_objects = []35    for action in actions:36        action_objects.append (Action(action, actions[action], 0))37    return action_objects38def get_optimal_action_and_j_val(state, gamma, prev_j_val):39    actions = state.actions40    max_j = -100000041    maximised_action = None42    for action in actions:43        temp_j = state.reward44        for transition in action.transitions:45            # transition 0 is the state to, and transition 1 is the probability46            temp_j += gamma * (prev_j_val[transition[0]] * transition[1])47        if (temp_j > max_j):48            max_j = temp_j49            maximised_action = action50    return (maximised_action, max_j)51def value_iteration(state_dict, gamma):52    j_one = {}53    for key, state in state_dict.items():54        j_one[state.state_val] = state.reward55    iteration_one_string = ""56    for key, state in state_dict.items():57        iteration_one_string += "(s%s"%state.state_val + " a%s"%state.actions[0].action_val + " %s"%state.reward + ") "58    print("After iteration 1 : ")59    print(iteration_one_string)60    iteration = 0;61    j_value_matrix = [j_one]62    # use delta to stop iteration63    while iteration < 19:64        next_j_row = {}65        current_iteration_as_string = ""66        for key, state in state_dict.items():67            (max_action, max_j) = get_optimal_action_and_j_val(state, gamma, j_value_matrix[iteration])68            current_iteration_as_string += "(s%s"%state.state_val + " a%s"%max_action.action_val + " %s"%round (max_j, 4) + ") "69            next_j_row[state.state_val] = max_j70        print("After iteration %s"%(iteration + 2) + " : ")71        print(current_iteration_as_string)72        j_value_matrix.append(next_j_row)73        iteration += 174            75if len (sys.argv) != 5:76    raise ValueError("Please provide five arguments")77training_set_file_path = sys.argv[3]78gamma = float (sys.argv[4])79raw_states = [i.strip().split() for i in open(training_set_file_path).readlines()]80state_dict = {}81for idx, line in enumerate (raw_states):82    state_dict [idx + 1] = create_state_from_line(line)...

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