练习一

# 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 ###
    for i in range(X.shape[0]):
        distance = []
        for j in range(centroids.shape[0]):
            norm_ij = np.linalg.norm(X[i] - centroids[j])
            distance.append(norm_ij)
        idx[i] = np.argmin(distance)

    ### END CODE HERE ###
    
    return idx

练习二：

# 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 k in range(K):
        points = X[idx == k]
        if(len(points) > 0):
            centroids[k] = np.mean(points, axis = 0)
        else:
            centroids[k] = X[np.random.choice(m, 1)].flatten()
    ### END CODE HERE ## 
    
    return centroids

K-means应用：图片压缩，通过改变颜色的数量，颜色的数量就是K，以下是K= 16时，对图片进行压缩：

当K = 32时，

K越大，图片越清晰。

想要资料可以私信。