吴恩达Coursera, 机器学习专项课程, Machine Learning：Unsupervised Learning, Recommenders, Reinforcement Learning第一周编程作业1

吴恩达Coursera, 机器学习专项课程, Machine Learning：Unsupervised Learning, Recommenders, Reinforcement Learning第一周所有jupyter notebook文件1：

吴恩达Coursera, 机器学习专项课程, Machine Learning：Unsupervised Learning, Recommenders, Reinforcement Learning第一周所有jupyter notebook文件(包括实验室练习文件)1

本次作业

Exercise 1

# UNQ_C1
# GRADED FUNCTION: find_closest_centroids

def find_closest_centroids(X, centroids):
    """
    Computes the centroid memberships for every example
    
    Args:
        X (ndarray): (m, n) Input values      
        centroids (ndarray): k centroids
    
    Returns:
        idx (array_like): (m,) closest centroids
    
    """

    # Set K
    K = centroids.shape[0]

    # You need to return the following variables correctly
    idx = np.zeros(X.shape[0], dtype=int)

    ### START CODE HERE ###
    dis = []
    for i in range(len(X)):
        for j in range(len(centroids)):
            dis.append(((X[i][0] - centroids[j][0]) **2 + (X[i][1] - centroids[j][1]) **2 ) ** (1/2))
        idx[i] = np.argmin(dis)
        dis = [] 

    ### END CODE HERE ###
    
    return idx

Exercise 2

# UNQ_C2
# GRADED FUNCTION: compute_centpods

def compute_centroids(X, idx, K):
    """
    Returns the new centroids by computing the means of the 
    data points assigned to each centroid.
    
    Args:
        X (ndarray):   (m, n) Data points
        idx (ndarray): (m,) Array containing index of closest centroid for each 
                       example in X. Concretely, idx[i] contains the index of 
                       the centroid closest to example i
        K (int):       number of centroids
    
    Returns:
        centroids (ndarray): (K, n) New centroids computed
    """
    
    # Useful variables
    m, n = X.shape
    
    # You need to return the following variables correctly
    centroids = np.zeros((K, n))
    
    ### START CODE HERE ###
    for i in range(K):
        indices = np.where(idx == i)
        centroids[i, :] = (np.sum(X[indices, :], axis=1) / len(indices[0])).ravel()
    ### END CODE HERE ## 
    
    return centroids