云模型多个云图合并Python代码

以下是一个简单的用Python实现的云模型多个云图合并的示例代码：

import numpy as np

# 加权平均法
def merge_clouds_weighted_average(clouds, weights):
    merged_cloud = np.zeros_like(clouds[0])
    for i, cloud in enumerate(clouds):
        merged_cloud += cloud * weights[i]
    return merged_cloud / np.sum(weights)

# 相似度比较法
def merge_clouds_similarity(clouds):
    similarities = np.zeros((len(clouds), len(clouds)))
    for i in range(len(clouds)):
        for j in range(i+1, len(clouds)):
            similarities[i,j] = similarities[j,i] = compute_similarity(clouds[i], clouds[j])
    while len(clouds) > 1:
        i, j = np.unravel_index(np.argmax(similarities), similarities.shape)
        new_cloud = merge_two_clouds(clouds[i], clouds[j])
        clouds.pop(max(i,j))
        clouds.pop(min(i,j))
        clouds.append(new_cloud)
        similarities = np.delete(similarities, max(i,j), axis=0)
        similarities = np.delete(similarities, max(i,j), axis=1)
        similarities = np.delete(similarities, min(i,j), axis=0)
        similarities = np.delete(similarities, min(i,j), axis=1)
        new_similarities = np.zeros((len(clouds), len(clouds)))
        for i in range(len(clouds)):
            for j in range(i+1, len(clouds)):
                new_similarities[i,j] = new_similarities[j,i] = compute_similarity(clouds[i], clouds[j])
        similarities = np.vstack((similarities, new_similarities))
        similarities = np.hstack((similarities, np.zeros((len(similarities), 1))))
        similarities = np.hstack((similarities, np.zeros((len(similarities), 1)).T))
        similarities[-len(clouds):, :-len(clouds)] = new_similarities
    return clouds[0]

# 聚类分析法
def merge_clouds_clustering(clouds, k):
    from sklearn.cluster import AgglomerativeClustering
    clustering = AgglomerativeClustering(n_clusters=k, affinity='precomputed', linkage='average')
    similarities = np.zeros((len(clouds), len(clouds)))
    for i in range(len(clouds)):
        for j in range(i+1, len(clouds)):
            similarities[i,j] = similarities[j,i] = compute_similarity(clouds[i], clouds[j])
    clustering.fit(similarities)
    merged_clouds = []
    for i in range(k):
        indices = np.where(clustering.labels_ == i)[0]
        weights = np.zeros(len(indices))
        for j, index in enumerate(indices):
            weights[j] = np.sum(similarities[index, indices]) / (len(indices) - 1)
        merged_clouds.append(merge_clouds_weighted_average(clouds[indices], weights))
    return merged_clouds

# 计算云图相似度
def compute_similarity(cloud1, cloud2):
    return np.sum(np.minimum(cloud1, cloud2))

# 合并两个云图
def merge_two_clouds(cloud1, cloud2):
    return np.maximum(cloud1, cloud2)

其中，`clouds`是一个包含多个云图的列表，`weights`是各个云图的权值。`merge_clouds_weighted_average`函数实现了加权平均法，`merge_clouds_similarity`函数实现了相似度比较法，`merge_clouds_clustering`函数实现了聚类分析法。`compute_similarity`函数计算云图相似度，`merge_two_clouds`函数实现了合并两个云图的操作。