Python DFS solution similar to combination sum, ETL, help wanted

  • 0

    At first glance, this question reminds me of a typical dfs/backtrack question, such as combination/permutation/combination sum. My code can pass small set test, but it failed for large data set. Can anyone help me optimize it? Thanks

    class Solution(object):
        def minCost(self, costs):
            :type costs: List[List[int]]
            :rtype: int
            if not costs:
                return 0
            self.result = sys.maxint
            # start with R/G/B
            self.dfs(0, 0, costs[0][0], costs)
            self.dfs(0, 1, costs[0][1], costs)
            self.dfs(0, 2, costs[0][2], costs)
            return self.result
        def dfs(self, depth, color, currsum, costs):
            if depth == len(costs)-1:
                self.result = min(self.result, currsum)
            for c in (set([0,1,2]) - set([color])):
                self.dfs(depth+1, c, currsum+costs[depth+1][c], costs)

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