diff --git a/Network/Network.py b/Network/Network.py deleted file mode 100644 index a5c08c5..0000000 --- a/Network/Network.py +++ /dev/null @@ -1,69 +0,0 @@ -import tensorflow as tf -from keras import layers - -# Normalizes the pixel values of an image to the range [0, 1]. - - -def normalize(image, label): - return image / 255, label - - - # Set the paths to the folder containing the training data -train_data_dir = "Network/Training/" -# Set the number of classes and batch size -num_classes = 3 -batch_size = 32 -# Set the image size and input shape -img_width, img_height = 100, 100 -input_shape = (img_width, img_height, 1) -# Load the training and validation data -train_ds = tf.keras.utils.image_dataset_from_directory( - train_data_dir, - validation_split=0.2, - subset="training", - shuffle=True, - seed=123, - image_size=(img_height, img_width), - batch_size=batch_size) - -val_ds = tf.keras.utils.image_dataset_from_directory( - train_data_dir, - validation_split=0.2, - subset="validation", - shuffle=True, - seed=123, - image_size=(img_height, img_width), - batch_size=batch_size) -# Get the class names -class_names = train_ds.class_names -print(class_names) -# Normalize the training and validation data -train_ds = train_ds.map(normalize) -val_ds = val_ds.map(normalize) -# Define the model architecture -model = tf.keras.Sequential([ - layers.Conv2D(16, 3, padding='same', activation='relu', - input_shape=(img_height, img_width, 1)), - layers.MaxPooling2D(), - layers.Conv2D(32, 3, padding='same', activation='relu'), - layers.MaxPooling2D(), - layers.Conv2D(64, 3, padding='same', activation='relu'), - layers.MaxPooling2D(), - layers.Flatten(), - layers.Dense(128, activation='relu'), - layers.Dense(num_classes, activation='softmax') -]) -# Compile the model -model.compile(optimizer='adam', - loss=tf.keras.losses.SparseCategoricalCrossentropy( - from_logits=True), - metrics=['accuracy']) -# Print the model summary -model.summary() -# Train the model -epochs = 10 -model.fit(train_ds, - validation_data=val_ds, - epochs=epochs) -# Save the trained model -model.save('Network/trained_model.h5') diff --git a/Network/Predictor.py b/Network/Predictor.py deleted file mode 100644 index 26273e3..0000000 --- a/Network/Predictor.py +++ /dev/null @@ -1,50 +0,0 @@ -import os -import random -import logging -import numpy as np -import tensorflow as tf -from tensorflow import keras -from termcolor import colored - - -class Predictor: - def __init__(self): - # Turn off interactive logging - tf.get_logger().setLevel(logging.ERROR) - - # Load the trained model - self.model = keras.models.load_model('Network/trained_model.h5') - - # Load the class names - self.class_names = ['table', 'table', 'order'] - - # Path to the folder containing test images - self.test_images_folder = 'Network/Testing/' - - def predict(self, image_path): - # Load and preprocess the test image - test_image = keras.preprocessing.image.load_img( - image_path, target_size=(100, 100)) - test_image = keras.preprocessing.image.img_to_array(test_image) - test_image = np.expand_dims(test_image, axis=0) - test_image = test_image / 255.0 # Normalize the image - - # Reshape the image array to (1, height, width, channels) - test_image = np.reshape(test_image, (1, 100, 100, 3)) - - # Make predictions - predictions = self.model.predict(test_image, verbose=None) - predicted_class_index = np.argmax(predictions[0]) - predicted_class = self.class_names[predicted_class_index] - - print(colored("Predicted class: ", "yellow")+f"{predicted_class}") - return predicted_class - - def random_path_img(self) -> str: - folder_name = random.choice(os.listdir(self.test_images_folder)) - folder_path = os.path.join(self.test_images_folder, folder_name) - filename = "" - while not (filename.endswith('.jpg') or filename.endswith('.jpeg')): - filename = random.choice(os.listdir(folder_path)) - image_path = os.path.join(folder_path, filename) - return image_path diff --git a/Network/TesterRandom.py b/Network/TesterRandom.py deleted file mode 100644 index 97c6a89..0000000 --- a/Network/TesterRandom.py +++ /dev/null @@ -1,64 +0,0 @@ -import os -from pathlib import Path -import numpy as np -import tensorflow as tf -from tensorflow import keras - -# Load the trained model -model = keras.models.load_model('Network/trained_model.h5') - -# Load the class names -class_names = ['table', 'done', 'order'] - -# Path to the folder containing test images -test_images_folder = 'Network/Testing/' - -# Iterate over the test images -i = 0 -errorcount = 0 -for folder_name in os.listdir(test_images_folder): - folder_path = os.path.join(test_images_folder, folder_name) - if os.path.isdir(folder_path): - print('Testing images in folder:', folder_name) - - # True class based on folder name - if folder_name == 'Empty': - true_class = 'table' - elif folder_name == 'Food': - true_class = 'done' - elif folder_name == 'People': - true_class = 'order' - - # Iterate over the files in the subfolder - for filename in os.listdir(folder_path): - if filename.endswith('.jpg') or filename.endswith('.jpeg'): - i += 1 - # Load and preprocess the test image - image_path = os.path.join(folder_path, filename) - test_image = keras.preprocessing.image.load_img( - image_path, target_size=(100, 100)) - test_image = keras.preprocessing.image.img_to_array(test_image) - test_image = np.expand_dims(test_image, axis=0) - test_image = test_image / 255.0 # Normalize the image - - # Reshape the image array to (1, height, width, channels) - test_image = np.reshape(test_image, (1, 100, 100, 3)) - - # Make predictions - predictions = model.predict(test_image) - predicted_class_index = np.argmax(predictions[0]) - predicted_class = class_names[predicted_class_index] - - direct = 'Network/Results/' - filename = str(i) + predicted_class + '.jpeg' - test_image = np.reshape(test_image, (100, 100, 3)) - Path(direct).mkdir(parents=True, exist_ok=True) - tf.keras.preprocessing.image.save_img( - direct+filename, test_image) - if predicted_class != true_class: - errorcount += 1 - print('Image:', filename) - print('True class:', true_class) - print('Predicted class:', predicted_class) - print() -print('Error count: ', errorcount) diff --git a/Network/testerVal.py b/Network/testerVal.py deleted file mode 100644 index 45140c2..0000000 --- a/Network/testerVal.py +++ /dev/null @@ -1,58 +0,0 @@ -from pathlib import Path -import numpy as np -import tensorflow as tf -from tensorflow import keras - -# Load the trained model -model = keras.models.load_model('Network/trained_model.h5') - -# Load the class names -class_names = ['table', 'done', 'order'] - -# Load and preprocess the validation dataset -data_dir = "Network/Training/" -image_size = (100, 100) -batch_size = 32 - -val_ds = tf.keras.preprocessing.image_dataset_from_directory( - data_dir, - validation_split=0.2, - subset="validation", - seed=123, - image_size=image_size, - batch_size=batch_size, -) - -# Select 20 random images from the validation set -val_images = [] -val_labels = [] -for images, labels in val_ds.unbatch().shuffle(1000).take(60): - val_images.append(images) - val_labels.append(labels) - -# Make predictions on the random images -errorcount = 0 -for i in range(60): - test_image = val_images[i] - test_label = val_labels[i] - test_image = np.expand_dims(test_image, axis=0) - test_image = test_image / 255.0 # Normalize the image - - # Make predictions - predictions = model.predict(test_image) - predicted_class_index = np.argmax(predictions[0]) - predicted_class = class_names[predicted_class_index] - - true_class = class_names[test_label] - - direct = 'Network/Results/' - filename = predicted_class + str(i) + '.jpeg' - Path(direct).mkdir(parents=True, exist_ok=True) - tf.keras.preprocessing.image.save_img(direct+filename, val_images[i]) - if predicted_class != true_class: - errorcount += 1 - print('Image', i+1) - print('True class:', true_class) - print('Predicted class:', predicted_class) - print() -print('Error count: ', errorcount) diff --git a/agent.py b/agent.py index 55b3926..bb24fd9 100644 --- a/agent.py +++ b/agent.py @@ -1,14 +1,9 @@ -import random -from termcolor import colored from src.Engine import Engine from src.obj.Waiter import Waiter from src.obj.Kitchen import Kitchen from src.controller.LayoutController import LayoutController - from src.controller.ImageController import ImageController -print(colored("Initialization...", "green")) - SCREEN_SIZE = [800, 800] SQUARE_SIZE = 80 SLEEP_DURATION = 0.125 @@ -20,6 +15,4 @@ kitchen = Kitchen([0, 0], 0, SQUARE_SIZE, SCREEN_SIZE, store) engine = Engine(SCREEN_SIZE, SQUARE_SIZE, kitchen, waiter, SLEEP_DURATION) layout = LayoutController(engine, store).create_and_subscribe(COUNT_OF_OBJECTS) -print(colored("Starting model...", "green")) - engine.loop() diff --git a/Network/Testing/Empty/OIP (1).jpg b/dataset/testset/Empty/OIP (1).jpg similarity index 100% rename from Network/Testing/Empty/OIP (1).jpg rename to dataset/testset/Empty/OIP 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model/model.h5 index 805575b..b6fdb9e 100644 Binary files a/Network/trained_model.h5 and b/model/model.h5 differ diff --git a/src/Engine.py b/src/Engine.py index cec00f3..5db2f5f 100644 --- a/src/Engine.py +++ b/src/Engine.py @@ -6,8 +6,8 @@ from src.obj.Waiter import Waiter from src.obj.Kitchen import Kitchen from src.controller.UserController import UserController from src.controller.StateController import StateController -from src.decisionTree.TreeConcept import TreeEngine -from Network.Predictor import Predictor +from src.controller.TreeController import TreeController +from src.controller.NeuralNetworkController import NeuralNetworkController from queue import PriorityQueue from termcolor import colored from src.obj.PriorityItem import PriorityItem @@ -16,16 +16,18 @@ from src.obj.PriorityItem import PriorityItem class Engine: def __init__(self, screen_size, square_size, kitchen: Kitchen, waiter: Waiter, sleep_duration): + print(colored("Initialization...", "green")) + pygame.display.set_caption('Waiter Agent') self.action_clock = 0 self.sleep_duration = sleep_duration - self.tree = TreeEngine() + self.tree = TreeController() self.kitchen: Kitchen = kitchen self.user: UserController = UserController(waiter) self.state: StateController = StateController(waiter) - self.predictor: Predictor = Predictor() + self.predictor: NeuralNetworkController = NeuralNetworkController() self.screen_size: list[int] = screen_size self.screen = pygame.display.set_mode(self.screen_size) @@ -57,6 +59,8 @@ class Engine: self.objects.append(object) def loop(self): + print(colored("Starting model...", "green")) + self.running = True while self.running: @@ -148,7 +152,13 @@ class Engine: goal_queue = PriorityQueue() # sorting objects by distance to waiter - self.objects.sort(key=lambda o: o.distance_to(self.user.obj.position)) + self.objects.sort( + key=lambda o: + o.distance_to( + self.user.obj.position, + mode="advanced" + ) + ) for o in self.objects: @@ -198,7 +208,7 @@ class Engine: # yes | no | ] - p = self.tree.make_predict(dataset) + p = self.tree.predict_priority(dataset) goal_queue.put(PriorityItem(o, p[0])) self.goals.clear() diff --git a/src/controller/NeuralNetworkController.py b/src/controller/NeuralNetworkController.py new file mode 100644 index 0000000..f7c20dd --- /dev/null +++ b/src/controller/NeuralNetworkController.py @@ -0,0 +1,125 @@ +import os +from pathlib import Path +import random +import logging +import numpy as np +import tensorflow as tf +from tensorflow import keras +from keras import layers +from termcolor import colored + + +class NeuralNetworkController: + def __init__(self, learn: bool = False): + if learn or not os.path.isfile('model/model.h5'): + self.learn_model() + + # Turn off interactive logging + tf.get_logger().setLevel(logging.ERROR) + + # Load the trained model + self.model = keras.models.load_model('model/model.h5') + + # Load the class names + self.class_names = ['table', 'table', 'order'] + + # Path to the folder containing test images + self.test_images_folder = 'dataset/testset' + + def predict(self, image_path): + # Load and preprocess the test image + test_image = keras.preprocessing.image.load_img( + image_path, target_size=(100, 100)) + test_image = keras.preprocessing.image.img_to_array(test_image) + test_image = np.expand_dims(test_image, axis=0) + test_image = test_image / 255.0 # Normalize the image + + # Reshape the image array to (1, height, width, channels) + test_image = np.reshape(test_image, (1, 100, 100, 3)) + + # Make predictions + predictions = self.model.predict(test_image, verbose=None) + predicted_class_index = np.argmax(predictions[0]) + predicted_class = self.class_names[predicted_class_index] + + print(colored("Predicted class: ", "yellow")+f"{predicted_class}") + return predicted_class + + def random_path_img(self) -> str: + folder_name = random.choice(os.listdir(self.test_images_folder)) + folder_path = os.path.join(self.test_images_folder, folder_name) + filename = "" + while not (filename.endswith('.jpg') or filename.endswith('.jpeg')): + filename = random.choice(os.listdir(folder_path)) + image_path = os.path.join(folder_path, filename) + return image_path + + def learn_model(self): + print(colored("Model training... ", "green")) + + # Normalizes the pixel values of an image to the range [0, 1]. + + def normalize(image, label): + return image / 255, label + + # Set the paths to the folder containing the training data + train_data_dir = "dataset/trainset" + # Set the number of classes and batch size + num_classes = 3 + batch_size = 32 + # Set the image size and input shape + img_width, img_height = 100, 100 + input_shape = (img_width, img_height, 3) + # Load the training and validation data + train_ds = tf.keras.utils.image_dataset_from_directory( + train_data_dir, + validation_split=0.2, + subset="training", + shuffle=True, + seed=123, + image_size=(img_height, img_width), + batch_size=batch_size) + + val_ds = tf.keras.utils.image_dataset_from_directory( + train_data_dir, + validation_split=0.2, + subset="validation", + shuffle=True, + seed=123, + image_size=(img_height, img_width), + batch_size=batch_size) + # Get the class names + class_names = train_ds.class_names + print(class_names) + # Normalize the training and validation data + train_ds = train_ds.map(normalize) + val_ds = val_ds.map(normalize) + # Define the model architecture + model = tf.keras.Sequential([ + layers.Conv2D(16, 3, padding='same', activation='relu', + input_shape=input_shape), + layers.MaxPooling2D(), + layers.Conv2D(32, 3, padding='same', activation='relu'), + layers.MaxPooling2D(), + layers.Conv2D(64, 3, padding='same', activation='relu'), + layers.MaxPooling2D(), + layers.Flatten(), + layers.Dense(128, activation='relu'), + layers.Dense(num_classes, activation='softmax') + ]) + # Compile the model + model.compile(optimizer='adam', + loss=tf.keras.losses.SparseCategoricalCrossentropy( + from_logits=True), + metrics=['accuracy']) + # Print the model summary + model.summary() + # Train the model + epochs = 10 + model.fit(train_ds, + validation_data=val_ds, + epochs=epochs) + + # Save the trained model + Path("model/").mkdir(parents=True, exist_ok=True) + model.save('model/model.h5') diff --git a/src/controller/TreeController.py b/src/controller/TreeController.py new file mode 100644 index 0000000..5f62bc4 --- /dev/null +++ b/src/controller/TreeController.py @@ -0,0 +1,235 @@ +import csv +from pathlib import Path +from sklearn import tree +import numpy as np +import graphviz + + +class TreeController(): + + def __init__(self, newTree: bool = True, exportText: bool = True, exportPDF: bool = False): + if newTree: + self.generateRawDataset() + self.convertDataset() + + # importing the dataset from the disk + train_data_m = np.genfromtxt( + "out/dataset.csv", delimiter=",", skip_header=1) + + # Separate the attributes and labels + self.X_train = [data[:-1] for data in train_data_m] + self.y_train = [data[-1] for data in train_data_m] + + # Create the decision tree classifier using the ID3 algorithm + self.clf = tree.DecisionTreeClassifier( + criterion='entropy', splitter="best") + # clf = tree.DecisionTreeClassifier(criterion='gini') + + # Train the decision tree on the training data + self.clf.fit(self.X_train, self.y_train) + + if exportText: + self.exportText() + + if exportPDF: + self.exportPdf() + + def generateRawDataset(self): + battery_values = ['high', 'low'] + distance_values = ['far', 'close'] + mood_values = ['undefined', 'good', 'bad'] + status_values = ["empty", "new order", + "waiting for cooking", "waiting for the dish"] + other = ['no', 'yes'] + + training_data = [] + training_data.append([ + "Battery level", + "Distance between kitchen and table", + "Customers mood", + "Basket is empty", + "Dish is ready", + "Dish in basket", + "Table status", + "Is actual", + "Priority" + ]) + + for battery in battery_values: + for distance in distance_values: + for mood in mood_values: + for basket in other: + for ready in other: + for dish in other: + for status in status_values: + for actual in other: + + dataset = self.buildDataset( + battery, + distance, + mood, + basket, + ready, + dish, + status, + actual + ) + + training_data.append(dataset) + + Path("out/").mkdir(parents=True, exist_ok=True) + with open('out/rawDataset.csv', 'w', newline='') as file: + writer = csv.writer(file) + writer.writerows(training_data) + + def buildDataset(self, b, d, m, e, r, i, s, a) -> list: + dataset = [ + b, # battery + d, # distance + m, # mood + e, # basket is empty + r, # ready + i, # dish in basket + s, # status + a, # actual + ] + + dataset.append(self.getPriority(dataset)) + + return dataset + + def getPriority(self, dataset) -> str: + PRIORITY = { + 'high': 'high priority', + 'low': 'low priority', + 'kitchen': 'return to kitchen' + } + + BATTERY_LEVEL = dataset[0] + DISTANCE_TO_OBJECT = dataset[1] + MOOD = dataset[2] + BASKET_IS_EMPTY = dataset[3] + DISH_IS_READY = dataset[4] + DISH_IN_BASKET = dataset[5] + STATUS_OF_TABLE = dataset[6] + IS_ACTUAL = dataset[7] + + def mood_and_distance() -> str: + if MOOD == "undefined": + return PRIORITY['kitchen'] + elif MOOD == "good": + return PRIORITY['low'] + elif DISTANCE_TO_OBJECT == "far": + return PRIORITY['high'] + else: + return PRIORITY['low'] + + if BATTERY_LEVEL == "low": + return PRIORITY['kitchen'] + elif IS_ACTUAL == "no": + return PRIORITY['high'] + elif STATUS_OF_TABLE == "empty" or STATUS_OF_TABLE == "waiting for cooking": + return PRIORITY['kitchen'] + elif STATUS_OF_TABLE == "new order" or STATUS_OF_TABLE == "waiting for the dish": + return mood_and_distance() + elif BASKET_IS_EMPTY == "yes": + return PRIORITY['kitchen'] + elif DISH_IS_READY == "no": + return PRIORITY['kitchen'] + elif DISH_IN_BASKET == "no": + return PRIORITY['kitchen'] + else: + return mood_and_distance() + + def convertDataset(self): + attributes = { + 1: ["low", "high"], + 2: ["far", "close"], + 3: ['bad', 'good', 'undefined'], + 4: ["no", "yes"], + 5: ["no", "yes"], + 6: ["no", "yes"], + 7: ["empty", "new order", "waiting for cooking", "waiting for the dish"], + 8: ["no", "yes"], + 9: ["high priority", "low priority", "return to kitchen"] + } + + # Create a mapping dictionary for attribute values + mapping = {} + for attr, values in attributes.items(): + mapping[attr] = {value: index for index, + value in enumerate(values)} + + converted_dataset = [] + + # Read the input CSV file + Path("out/").mkdir(parents=True, exist_ok=True) + with open("out/rawDataset.csv", "r") as csvfile: + reader = csv.reader(csvfile) + + header = next(reader) # Skip the header row + converted_dataset.append(header) + + # Convert the data rows + for row in reader: + converted_row = [] + for i, value in enumerate(row): + # Convert the attribute values + if i + 1 in mapping: + converted_value = mapping[i + 1][value] + else: + converted_value = value + + converted_row.append(converted_value) + + converted_dataset.append(converted_row) + + # Write the converted dataset to a new CSV file + Path("out/").mkdir(parents=True, exist_ok=True) + with open("out/dataset.csv", "w", newline="") as csvfile: + writer = csv.writer(csvfile) + + # Write the converted data rows + for row in converted_dataset: + writer.writerow(row) + + def exportText(self): + # Visualize the trained decision tree + tree_text = tree.export_text(self.clf, feature_names=[ + "Battery level", + "Distance between kitchen and table", + "Customers mood", + "Basket is empty", + "Dish is ready", + "Dish in basket", + "Table status", + "Is actual", + ]) + + Path("out/").mkdir(parents=True, exist_ok=True) + with open('out/decision_tree.txt', 'w') as f: + f.write(tree_text) # Save the visualization as a text file + + def exportPdf(self): + dot_data = tree.export_graphviz(self.clf, out_file=None, feature_names=[ + "Battery level", + "Distance between kitchen and table", + "Customers mood", + "Basket is empty", + "Dish is ready", + "Dish in basket", + "Table status", + "Is actual", + ], class_names=[ + 'High priority', + 'Low priority', + 'Return to kitchen', + ], filled=True, rounded=True) + + graph = graphviz.Source(dot_data) + # Save the visualization as a PDF file + Path("out/").mkdir(parents=True, exist_ok=True) + graph.render("out/decision_tree") + + def predict_priority(self, dataset): + return self.clf.predict([dataset]) diff --git a/src/decisionTree/TreeConcept.py b/src/decisionTree/TreeConcept.py deleted file mode 100644 index b77fb39..0000000 --- a/src/decisionTree/TreeConcept.py +++ /dev/null @@ -1,70 +0,0 @@ -import time -from sklearn import tree -import numpy as np -import graphviz -from src.decisionTree.datasetGenerator import generateRawDataset -from src.decisionTree.datasetConverter import convertDataset - - -class TreeEngine(): - def __init__(self): - generateRawDataset() - convertDataset() - - # importing the dataset from the disk - train_data_m = np.genfromtxt( - "out/dataset.csv", delimiter=",", skip_header=1) - - # Separate the attributes and labels - self.X_train = [data[:-1] for data in train_data_m] - self.y_train = [data[-1] for data in train_data_m] - - # Create the decision tree classifier using the ID3 algorithm - self.clf = tree.DecisionTreeClassifier( - criterion='entropy', splitter="best") - # clf = tree.DecisionTreeClassifier(criterion='gini') - - # Train the decision tree on the training data - self.clf.fit(self.X_train, self.y_train) - - self.exportText() - self.exportPdf() - - def exportText(self): - # Visualize the trained decision tree - tree_text = tree.export_text(self.clf, feature_names=[ - "Battery level", - "Distance between kitchen and table", - "Customers mood", - "Basket is empty", - "Dish is ready", - "Dish in basket", - "Table status", - "Is actual", - ]) - - with open('out/decision_tree.txt', 'w') as f: - f.write(tree_text) # Save the visualization as a text file - - def exportPdf(self): - dot_data = tree.export_graphviz(self.clf, out_file=None, feature_names=[ - "Battery level", - "Distance between kitchen and table", - "Customers mood", - "Basket is empty", - "Dish is ready", - "Dish in basket", - "Table status", - "Is actual", - ], class_names=[ - 'High priority', - 'Low priority', - 'Return to kitchen', - ], filled=True, rounded=True) - - graph = graphviz.Source(dot_data) - # Save the visualization as a PDF file - graph.render("out/decision_tree") - - def make_predict(self, dataset): - return self.clf.predict([dataset]) diff --git a/src/decisionTree/datasetConverter.py b/src/decisionTree/datasetConverter.py deleted file mode 100644 index 9d5a57a..0000000 --- a/src/decisionTree/datasetConverter.py +++ /dev/null @@ -1,51 +0,0 @@ -import csv - - -def convertDataset(): - attributes = { - 1: ["low", "high"], - 2: ["far", "close"], - 3: ['bad', 'good', 'undefined'], - 4: ["no", "yes"], - 5: ["no", "yes"], - 6: ["no", "yes"], - 7: ["empty", "new order", "waiting for cooking", "waiting for the dish"], - 8: ["no", "yes"], - 9: ["high priority", "low priority", "return to kitchen"] - } - - # Create a mapping dictionary for attribute values - mapping = {} - for attr, values in attributes.items(): - mapping[attr] = {value: index for index, value in enumerate(values)} - - converted_dataset = [] - - # Read the input CSV file - with open("out/rawDataset.csv", "r") as csvfile: - reader = csv.reader(csvfile) - - header = next(reader) # Skip the header row - converted_dataset.append(header) - - # Convert the data rows - for row in reader: - converted_row = [] - for i, value in enumerate(row): - # Convert the attribute values - if i + 1 in mapping: - converted_value = mapping[i + 1][value] - else: - converted_value = value - - converted_row.append(converted_value) - - converted_dataset.append(converted_row) - - # Write the converted dataset to a new CSV file - with open("out/dataset.csv", "w", newline="") as csvfile: - writer = csv.writer(csvfile) - - # Write the converted data rows - for row in converted_dataset: - writer.writerow(row) diff --git a/src/decisionTree/datasetGenerator.py b/src/decisionTree/datasetGenerator.py deleted file mode 100644 index 984840a..0000000 --- a/src/decisionTree/datasetGenerator.py +++ /dev/null @@ -1,112 +0,0 @@ -import csv -from pathlib import Path - - -def generateRawDataset(): - battery_values = ['high', 'low'] - distance_values = ['far', 'close'] - mood_values = ['undefined', 'good', 'bad'] - status_values = ["empty", "new order", - "waiting for cooking", "waiting for the dish"] - other = ['no', 'yes'] - - training_data = [] - training_data.append([ - "Battery level", - "Distance between kitchen and table", - "Customers mood", - "Basket is empty", - "Dish is ready", - "Dish in basket", - "Table status", - "Is actual", - "Priority" - ]) - - for battery in battery_values: - for distance in distance_values: - for mood in mood_values: - for basket in other: - for ready in other: - for dish in other: - for status in status_values: - for actual in other: - - dataset = buildDataset( - battery, - distance, - mood, - basket, - ready, - dish, - status, - actual - ) - - training_data.append(dataset) - - Path("out/").mkdir(parents=True, exist_ok=True) - with open('out/rawDataset.csv', 'w', newline='') as file: - writer = csv.writer(file) - writer.writerows(training_data) - - -def buildDataset(b, d, m, e, r, i, s, a) -> list: - dataset = [ - b, # battery - d, # distance - m, # mood - e, # basket is empty - r, # ready - i, # dish in basket - s, # status - a, # actual - ] - - dataset.append(getPriority(dataset)) - - return dataset - - -def getPriority(dataset) -> str: - PRIORITY = { - 'high': 'high priority', - 'low': 'low priority', - 'kitchen': 'return to kitchen' - } - - BATTERY_LEVEL = dataset[0] - DISTANCE_TO_OBJECT = dataset[1] - MOOD = dataset[2] - BASKET_IS_EMPTY = dataset[3] - DISH_IS_READY = dataset[4] - DISH_IN_BASKET = dataset[5] - STATUS_OF_TABLE = dataset[6] - IS_ACTUAL = dataset[7] - - def mood_and_distance() -> str: - if MOOD == "undefined": - return PRIORITY['kitchen'] - elif MOOD == "good": - return PRIORITY['low'] - elif DISTANCE_TO_OBJECT == "far": - return PRIORITY['high'] - else: - return PRIORITY['low'] - - if BATTERY_LEVEL == "low": - return PRIORITY['kitchen'] - elif IS_ACTUAL == "no": - return PRIORITY['high'] - elif STATUS_OF_TABLE == "empty" or STATUS_OF_TABLE == "waiting for cooking": - return PRIORITY['kitchen'] - elif STATUS_OF_TABLE == "new order" or STATUS_OF_TABLE == "waiting for the dish": - return mood_and_distance() - elif BASKET_IS_EMPTY == "yes": - return PRIORITY['kitchen'] - elif DISH_IS_READY == "no": - return PRIORITY['kitchen'] - elif DISH_IN_BASKET == "no": - return PRIORITY['kitchen'] - else: - return mood_and_distance() diff --git a/src/obj/Object.py b/src/obj/Object.py index 86eabc2..040d7fd 100644 --- a/src/obj/Object.py +++ b/src/obj/Object.py @@ -1,3 +1,4 @@ +import math import pygame from src.controller.ImageController import ImageController @@ -46,11 +47,15 @@ class Object: def compare_pos(self, pos) -> bool: return self.position == pos - def distance_to(self, pos) -> int: + def distance_to(self, pos, mode="basic") -> int: x = abs(self.position[0] - pos[0]) y = abs(self.position[1] - pos[1]) - self.h = x + y + self.h = ( + math.sqrt(pow(x, 2) + pow(y, 2)) + if mode == "advanced" + else x + y + ) return self.h