Created
November 16, 2017 15:35
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| import numpy as np | |
| ITEMS_COUNT = 100 | |
| MAX_SIMILARITY = 100 | |
| CLUSTER_THRESHOLD = 80 | |
| SPLIT_THRESHOLD = 50 | |
| # 随机生成一个相似度矩阵 | |
| # 相似度（0-100），数值越大越相似 | |
| mat = np.random.randint(MAX_SIMILARITY, size=(ITEMS_COUNT, ITEMS_COUNT)) | |
| group = np.arange(ITEMS_COUNT) | |
| while True: | |
| # 取相似度矩阵中最大元素，直到相似度矩阵中没有大于阈值（CLUSTER_THRESHOLD）的元素 | |
| idx = np.unravel_index(np.argmax(mat), mat.shape) | |
| if mat[idx] < CLUSTER_THRESHOLD: | |
| break | |
| print(group) | |
| print(idx, mat[idx]) | |
| # 当且仅当两组元素之间两两相似度均大于阈值（SPLIT_THRESHOLD），合并两组 | |
| # 判断两组元素之间两两相似度是为了防止类内有相似度过低的元素 | |
| # 应该可以缓存一下不能被合并的两个组的编号减少重复运算 | |
| # 两组元素之间两两相似度矩阵 | |
| submat = mat[group == group[idx[0]]][:, group == group[idx[1]]] | |
| # 上述矩阵所有元素都大于阈值（SPLIT_THRESHOLD），或等于 -1 | |
| if np.all(np.logical_or(submat > SPLIT_THRESHOLD, submat == -1)): | |
| group[group == group[idx[1]]] = group[idx[0]] | |
| # 把相似度矩阵当前搜索到的元素置为-1，这样下次继续搜索相似度矩阵中最大元素就可以了 | |
| mat[idx[0],idx[1]] = -1 | |
| mat[idx[1],idx[0]] = -1 | |
| print(group) |
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