automatyczny_kelner/src/obj/Waiter.py

import copy
from src.obj.Object import Object


class Waiter(Object):
    def __init__(self, position, orientation, square_size, screen_size, basket=[], memory=[], battery=300):
        super().__init__("waiter", position, orientation, square_size, screen_size)
        self.battery = battery
        self.basket_size = 4
        self.memory_size = 4
        self.basket = basket
        self.memory = memory
        self.prev_position = copy.deepcopy(self.position)
        self.prev_orientation = copy.copy(self.orientation)

    def changeState(self, state):
        self.position = copy.deepcopy(state.position)
        self.orientation = copy.copy(state.orientation)
        self.basket = copy.copy(state.basket)
        self.battery -= state.cost
        # self.calcTree()
        return state

    def dish_in_basket(self, table) -> bool:
        return table in self.basket

    '''    
    def calcTree(self):
        from sklearn import tree
        import pandas as pd  # for manipulating the csv data
        import numpy as np
        import os

        # importing the dataset from the disk
        train_data_m = np.genfromtxt(
            "dataset/converted_dataset.csv", delimiter=",", skip_header=1)

        X_train = [data[:-1] for data in train_data_m]
        y_train = [data[-1] for data in train_data_m]
        # Create the decision tree classifier using the ID3 algorithm
        clf = tree.DecisionTreeClassifier(criterion='entropy')

        # Train the decision tree on the training data
        clf.fit(X_train, y_train)

        # Visualize the trained decision tree
        tree_text = tree.export_text(clf, feature_names=[
                                     'Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables', 'Availability', 'Cleanliness', 'Error'])
        with open('decision_tree.txt', 'w') as f:
            f.write(tree_text)  # Save the visualization as a text file

        # Test the decision tree with a new example
        # Battery Charge,Fullness,Ready orders,Waiting tables,Availability,Cleanliness,Error
        new_example = [self.battery, 0, self.orderReadiness,
                       self.waitingTables, self.availability, self.cleanliness, self.error]
        predicted_label = clf.predict([new_example])
        if predicted_label[0] > 0:
            result = "YES"
        else:
            result = "NO"
        print("Predicted Label:", result)

    def calcTreePDF(self):
        from sklearn import tree
        import pandas as pd  # for manipulating the csv data
        import numpy as np
        import graphviz
        import os
        os.environ["PATH"] += os.pathsep + \
            'C:/Program Files (x86)/Graphviz/bin/'

        # importing the dataset from the disk
        train_data_m = np.genfromtxt(
            "dataset/converted_dataset.csv", delimiter=",", skip_header=1)

        X_train = [data[:-1] for data in train_data_m]
        y_train = [data[-1] for data in train_data_m]
        # Create the decision tree classifier using the ID3 algorithm
        clf = tree.DecisionTreeClassifier(criterion='entropy')

        # Train the decision tree on the training data
        clf.fit(X_train, y_train)

        # Visualize the trained decision tree
        tree_text = tree.export_text(clf, feature_names=[
                                     'Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables', 'Availability', 'Cleanliness', 'Error'])
        with open('decision_tree.txt', 'w') as f:
            f.write(tree_text)  # Save the visualization as a text file
        dot_data = tree.export_graphviz(clf, out_file=None, feature_names=[
                                        'Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables', 'Availability', 'Cleanliness', 'Error'], class_names=['NO', 'YES'], filled=True, rounded=True)
        graph = graphviz.Source(dot_data)
        graph.render("decision_tree")  # Save the visualization as a PDF file

        # Test the decision tree with a new example
        # Battery Charge,Fullness,Ready orders,Waiting tables,Availability,Cleanliness,Error
        new_example = [self.battery, 0, self.orderReadiness,
                       self.waitingTables, self.availability, self.cleanliness, self.error]
        predicted_label = clf.predict([new_example])
        if predicted_label[0] > 0:
            result = "YES"
        else:
            result = "NO"
        print("Predicted Label:", result)
        
    '''

    def basket_is_full(self) -> bool:
        return self.basket_size == 0

    def combine_orders(self):
        while not self.basket_is_full() and not self.memory_is_empty():
            dish = self.memory.pop()
            dish.set_done()
            self.basket.append(dish)
            self.basket_size -= 1
            self.memory_size += 1

    def deliver_dish(self, table):
        if table in self.basket:
            table.reset()
            self.basket.remove(table)
            self.basket_size += 1

    def order_in_memory(self, table) -> bool:
        return table in self.memory

    def memory_is_empty(self) -> bool:
        return not self.memory

    def memory_is_full(self) -> bool:
        return self.memory_size == 0

    def collect_order(self, table):
        if self.memory_is_full():
            return
        if table.agent_role == "order":
            table.set_wait()
            self.memory.append(table)
            self.memory_size -= 1

    def battary_status(self) -> str:
        if self.battery >= 200:
            return "hight"
        elif self.battery >= 100:
            return "medium"
        else:
            return "low"

    def recharge(self):
        self.battery = 300

    def left(self):
        self.orientation = (self.orientation + 1) % 4

    def right(self):
        self.orientation = (self.orientation - 1) % 4

    def front(self):
        if self.orientation % 2:                        # x (1 or 3)
            self.position[0] += self.orientation - 2    # x (-1 or +1)
        else:                                           # y (0 or 2)
            self.position[1] += self.orientation - 1    # y (-1 or +1)