AI人工智能中的数学基础原理与Python实战：自动驾驶领域的人工智能应用

    import cv2
    import numpy as np
    import tensorflow as tf
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import accuracy_score
    from keras.models import Sequential
    from keras.layers import Dense, Activation, Dropout
    from keras.optimizers import Adam
    
    # 计算机视觉
    def gray_image(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    return gray
    
    def edge_detection(image):
    laplacian = cv2.Laplacian(image, cv2.CV_64F)
    return laplacian
    
    def feature_matching(features, labels):
    model = Sequential()
    model.add(Dense(32, input_dim=features.shape[1], activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(16, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1, activation='sigmoid'))
    
    model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.001), metrics=['accuracy'])
    model.fit(features, labels, epochs=10, batch_size=32, verbose=0)
    return model
    
    # 机器学习
    def train_test_split(X, y):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    return X_train, X_test, y_train, y_test
    
    def accuracy_score(y_true, y_pred):
    return accuracy_score(y_true, y_pred)
    
    # 深度学习
    def create_model():
    model = Sequential()
    model.add(Dense(64, input_dim=32, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(32, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1, activation='sigmoid'))
    return model
    
    def train_model(model, X_train, y_train, X_test, y_test):
    model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.001), metrics=['accuracy'])
    model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
    y_pred = model.predict(X_test)
    return accuracy_score(y_test, y_pred)
    
    # 路径规划
    def a_star(graph, start, goal):
    open_list = [start]
    g_score = {start: 0}
    f_score = {start: heuristic(start, goal)}
    came_from = {}
    
    while open_list:
        current = min(open_list, key=lambda x: g_score[x] + f_score[x])
        if current == goal:
            path = []
            while current in came_from:
                path.append(current)
                current = came_from[current]
            return path
        open_list.remove(current)
        for neighbor in graph[current]:
            tentative_g_score = g_score[current] + distance(current, neighbor)
            if neighbor not in g_score or tentative_g_score < g_score[neighbor]:
                came_from[neighbor] = current
                g_score[neighbor] = tentative_g_score
                f_score[neighbor] = tentative_g_score + heuristic(neighbor, goal)
                if neighbor not in open_list:
                    open_list.append(neighbor)
    return None
    
    # 控制理论
    def pid_control(kp, ki, kd, error):
    return kp * error + ki * np.integrate(error, 0) + kd * np.diff(error)
    
    def model_predictive_control(kp, ki, kd, km, y_pred, y_true):
    error = y_true - y_pred
    control = pid_control(kp, ki, kd, error) + km * error
    return control
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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