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Self-normalised Distance with Don't Cares

机译:自我无关的距离

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We present O(n log m) algorithms for a new class of problems termed self-normalised distance with don't cares. The input is a pattern p of length m and text t of length n > m. The elements of these strings are either integers or wild card symbols. In the shift version, the problem is to compute min_α ∑_(j=0)~(m-1) (α + P_j - t_(i+j))~2 for all i, where wild cards do not contribute to the sum. In the shift-scale version, the objective is to compute min_α,β ∑_(j=0)~(m-1) (α + βp_j - t_(i+j))~2 for all i similarly. We show that the algorithms have the additional benefit of providing simple O(n log to) solutions for the problems of exact matching with don't cares, exact shift matching with don't cares and exact shift-scale matching with don't cares.
机译:我们提出了O(n log m)算法,用于一类新的问题,该问题称为“自相关距离”。输入是长度为m的模式p和长度为n> m的文本t。这些字符串的元素是整数或通配符。在移位版本中,问题在于为所有i计算min_α∑_(j = 0)〜(m-1)(α+ P_j-t_(i + j))〜2,其中通配符不和。在移位比例版本中,目标是类似地为所有i计算min_α,β∑_(j = 0)〜(m-1)(α+βp_j-t_(i + j))〜2。我们证明了该算法的额外好处是,提供了简单的O(n log to)解决方案,以解决“无所谓”的精确匹配,“无所谓”的精确移位匹配和“无所谓”的精确移位尺度匹配的问题。 。

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