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How to use initialize_rules method in hypothesis

Best Python code snippet using hypothesis

garden.py

Source:garden.py

...35        file = open(file_name)36        lines = file.readlines()37        file.close()38        self.pots = self.initialize_pots(lines[0])39        self.rules = self.initialize_rules(lines[2:])40        self.initial_loc = 041        self.repeating = False42        self.num_generations = 043    def initialize_pots(self, line):44        return list(re.search('([.#]+)', line)[0])45    def initialize_rules(self, rules):46        return [Rule(rule) for rule in rules]47    def grow(self):48        next_generation = []49        #check first index...50        for rule in self.rules:51            if rule.matches(self.pots, -1) and rule.result == '#':52                next_generation.append(rule.result)53                self.initial_loc += 154        for index in enumerate(self.pots):55            for rule in self.rules:56                if rule.matches(self.pots, index[0]):57                    next_generation.append(rule.result)58        #check last index...59        for rule in self.rules:...

main.py

Source:main.py

...21            for ele in transformed_rules[element]:22                new_list.append(lst[:index] + [ele] + lst[index+1:])23            return new_list24    return [lst]25def initialize_rules(rules_list: list[str]) -> tuple[dict[str, list[list[str]]], dict[str, list[str]]]:26    rules: dict[str, list[list[str]]] = {}27    for rule in rules_list:28        rule_split = rule.split(": ")29        rules[rule_split[0]] = [values.split(30            " ") for values in rule_split[1].split(" | ")]31    transformed_rules: dict[str, list[str]] = {}32    for key, value_list in rules.items():33        for values in value_list:34            for value in values:35                if value == '"a"':36                    transformed_rules[key] = ["a"]37                if value == '"b"':38                    transformed_rules[key] = ["b"]39    for key in transformed_rules:40        del rules[key]41    return rules, transformed_rules42def check_complete_transformation(rule: list[list[str]]) -> bool:43    for option in rule:44        for element in option:45            if not element.isalpha():46                return False47    return True48def determine_rules(rules_list: list[str]) -> dict[str, list[str]]:49    rules, transformed_rules = initialize_rules(rules_list)50    while rules:51        delete_keys: set[str] = set()52        for key, rule in rules.items():53            rules[key] = determine_new_rule(rule, transformed_rules)54            if check_complete_transformation(rules[key]):55                new_list: list[str] = []56                for lst in rules[key]:57                    new_list.append("".join(lst))58                transformed_rules[key] = new_list59                delete_keys.add(key)60        for key in delete_keys:61            del rules[key]62    return transformed_rules63def valid_string(string: str, transformed_rules: dict[str, list[str]], version: int) -> bool:...

rule_provider.py

Source:rule_provider.py

...13        self.possible_classes = None14        self.class_encoder = preprocessing.LabelEncoder()15        self.tag_encoder = preprocessing.LabelEncoder()16        self.value_encoder = preprocessing.LabelEncoder()17    def initialize_rules(self):18        self.rules = pd.read_csv("config/rules.csv", sep=';', header=None, names = ['tag', 'class', 'value'])19        self.initialize_possible_elements()20        self.value_encoder.fit(m.all_mappings)21        self.rules['value'] = self.value_encoder.transform(self.rules['value'])22        self.tag_encoder.fit(self.rules["tag"])23        self.rules['tag'] = self.tag_encoder.transform(self.rules['tag'])24        classes_with_deleted_spaces = self.rules['class'].map(lambda a: a.replace(" ", ""))25        self.class_encoder.fit(classes_with_deleted_spaces)26        self.rules['class'] = self.class_encoder.transform(classes_with_deleted_spaces)27    def prepare_model(self):28        self.initialize_rules()29        self.classifier = tree.DecisionTreeClassifier()30        np_rules = np.array(self.rules)31        self.classifier.fit = self.classifier.fit(np_rules[:,0:2], np_rules[:,2])32    def predict(self, tag, classes):33        classes_as_string = "".join(classes)34        if classes_as_string.replace(" ", "") not in self.possible_classes:35            return max(self.value_encoder.transform(self.value_encoder.classes_)) + 136        return self.classifier.predict([[self.tag_encoder.transform([tag])[0],37                                         self.class_encoder.transform([classes_as_string])[0]]])38    def initialize_possible_elements(self):39        self.possible_tags = set(self.rules['tag'])40        self.possible_tags_topics = set(self.rules[self.rules['value'].str.contains('topic')]['tag'])41        self.possible_tags_posts = set(self.rules[self.rules['value'].str.contains('post')]['tag'])42        possible_tags_with_author = set(self.rules[self.rules['value'].str.contains('author')]['tag'])...

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