PORKBUN: support CAA

[imlonghao]

close #3199

Testes passed

CAA with whitespace not supported.

=== RUN   TestDNSProviders/example.com/33:CAA:CAA_whitespace
    integration_test.go:248:
        ± MODIFY example.com CAA (128 issuewild ";" ttl=600) -> (0 issue "letsencrypt.org; validationmethods=dns-01; accounturi=https://acme-v02.api.letsencrypt.org/acme/acct/1234" ttl=600), porkbun ID: 437665127
    integration_test.go:269: Expected 0 corrections on second run, but found 1.
    integration_test.go:271: UNEXPECTED #0: ± MODIFY example.com CAA (0 issue "letsencrypt.org;" ttl=600) -> (0 issue "letsencrypt.org; validationmethods=dns-01; accounturi=https://acme-v02.api.letsencrypt.org/acme/acct/1234" ttl=600), porkbun ID: 437665127

[tlimoncelli]

I love when a new feature can be added so simply!

Please run go generate and commit the new documentation/providers.md file.

Thanks!

[imlonghao]

done.

[Ryehdhdhd]

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import tensorflow as tf

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import classification_report, accuracy_score

from keras.layers import Input, Dense

from keras.models import Model


class VIZOR:

    def __init__(self, data_file):

        self.data_file = data_file

        self.gestures = {}

        self.voice_commands = {}

        self.user_preferences = {}

        self.data_analysis_model = NeuralNetwork()

        self.context_model = ContextAwareness()

        self.device_integration = []

        self.power_status = True

        self.state_indicator = "OFF"

        self.logic_agent = LogicAgent()

        self.model = RandomForestClassifier(n_estimators=100, random_state=42)

        self.data = None

        self.project_manager = ProjectManager()


    def initialize(self):

        self.load_gestures()

        self.load_voice_commands()

        self.load_user_preferences()

        self.load_data()

        self.state_indicator = "ON"


    def load_gestures(self):

        self.gestures = {

            "swipe_left": self.handle_swipe_left,

            "swipe_right": self.handle_swipe_right,

        }


    def load_voice_commands(self):

        self.voice_commands = {

            "open_app": self.open_application,

            "close_app": self.close_application,

        }


    def load_user_preferences(self):

        self.user_preferences = self.data_analysis_model.analyze_user_data()


    def load_data(self):

        self.data = pd.read_csv(self.data_file)

        print("Данные загружены.")


    def handle_gesture(self, gesture):

        if gesture in self.gestures:

            self.gestures[gesture]()


    def handle_voice_command(self, command):

        if command in self.voice_commands:

            self.voice_commands[command]()


    def open_application(self):

        print("Приложение открыто.")


    def close_application(self):

        print("Приложение закрыто.")


    def analyze_user_data(self, data):

        self.user_preferences = self.data_analysis_model.train(data)


    def provide_contextual_suggestions(self):

        suggestions = self.context_model.get_suggestions()

        return suggestions


    def integrate_device(self, device):

        self.device_integration.append(device)


    def analyze_data(self):

        X = self.data.drop('target', axis=1)

        y = self.data['target']

        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)


        self.model.fit(X_train, y_train)

        y_pred = self.model.predict(X_test)

        print(classification_report(y_test, y_pred))

        print("Точность:", accuracy_score(y_test, y_pred))


        self.visualize_feature_importance(X)


    def visualize_feature_importance(self, X):

        importances = self.model.feature_importances_

        indices = np.argsort(importances)[::-1]


        plt.figure(figsize=(12, 6))

        plt.title("Важность признаков")

        plt.bar(range(X.shape[1]), importances[indices], align="center")

        plt.xticks(range(X.shape[1]), X.columns[indices], rotation=90)

        plt.xlim([-1, X.shape[1]])

        plt.show()


class NeuralNetwork:

    def train(self, data):

        processed_preferences = []  # Логика обработки

        return processed_preferences


class ContextAwareness:

    def get_suggestions(self):

        return ["предложение_1", "предложение_2"]


class LogicAgent:

    def __init__(self):

        self.model = self.create_model()


    def create_model(self):

        model = tf.keras.Sequential([

            tf.keras.layers.Dense(64, activation='relu', input_shape=(None, )),

            tf.keras.layers.Dense(64, activation='relu'),

            tf.keras.layers.Dense(1, activation='sigmoid')

        ])

        model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

        return model


    def predict(self, input_data):

        return self.model.predict(input_data)


    def train(self, input_data, labels):

        self.model.fit(input_data, labels, epochs=10)


class ProjectManager:

    def __init__(self):

        self.projects = []


    def create_project(self, name, description):

        project = {"name": name, "description": description, "status": "active"}

        self.projects.append(project)

        print(f"Проект '{name}' создан.")


    def track_projects(self):

        for project in self.projects:

            print(f"Проект: {project['name']}, Статус: {project['status']}")


    def update_project_status(self, name, status):

        for project in self.projects:

            if project['name'] == name:

                project['status'] = status

                print(f"Статус проекта '{name}' обновлён на '{status}'.")


    def generate_report(self):

        report = pd.DataFrame(self.projects)

        print(report)


    def schedule_meeting(self, project_name, date_time):

        print(f"Встреча для проекта '{project_name}' запланирована на {date_time}.")


    def manage_risks(self, project_name, risks):

        print(f"Управление рисками для проекта '{project_name}': {risks}")


    def document_project(self, project_name, documentation):

        print(f"Документация для проекта '{project_name}': {documentation}")


# Пример использования

if __name__ == "__main__":

    vizor = VIZOR('data.csv')  # Замените на ваш файл данных

    vizor.initialize()


    # Обработка жеста

    vizor.handle_gesture("swipe_left")


    # Обработка голосовой команды

    vizor.handle_voice_command("open_app")


    # Анализ данных пользователя

    user_data = ["data_point_1", "data_point_2"]

    vizor.analyze_user_data(user_data)


    # Получение предложений на основе контекста

    suggestions = vizor.provide_contextual_suggestions()

    print(suggestions)


    # Интеграция с устройством

    vizor.integrate_device("smart_home_device")


    # Анализ данных

    vizor.analyze_data()


    # Управление проектами

    vizor.project_manager.create_project("Проект A", "Описание проекта A")

    vizor.project_manager.track_projects()

    vizor.project_manager.update_project_status("Проект A", "завершён")

    vizor.project_manager.generate_report()

    vizor.project_manager.schedule_meeting("Проект A", "2024-12-01 10:00")

    vizor.project_manager.manage_risks("Проект A", ["Риск 1", "Риск 2"])

    vizor.project_manager.document_project("Проект A", "Документация по проекту A")