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* Remove commented-out print statements in algorithmic functions * Encapsulate non-algorithmic code in __main__ * Remove unused print_matrix function * Remove print statement in __init__ * Remove print statement from doctest * Encapsulate non-algorithmic code in __main__ * Modify algorithm to return instead of print * Encapsulate non-algorithmic code in __main__ * Refactor data_safety_checker to return instead of print * updating DIRECTORY.md * updating DIRECTORY.md * Apply suggestions from code review * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com> Co-authored-by: Christian Clauss <cclauss@me.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
34 lines
1019 B
Python
34 lines
1019 B
Python
"""
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Author : Yvonne
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This is a pure Python implementation of Dynamic Programming solution to the
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longest_sub_array problem.
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The problem is :
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Given an array, to find the longest and continuous sub array and get the max sum of the
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sub array in the given array.
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"""
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class SubArray:
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def __init__(self, arr):
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# we need a list not a string, so do something to change the type
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self.array = arr.split(",")
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def solve_sub_array(self):
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rear = [int(self.array[0])] * len(self.array)
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sum_value = [int(self.array[0])] * len(self.array)
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for i in range(1, len(self.array)):
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sum_value[i] = max(
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int(self.array[i]) + sum_value[i - 1], int(self.array[i])
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)
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rear[i] = max(sum_value[i], rear[i - 1])
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return rear[len(self.array) - 1]
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if __name__ == "__main__":
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whole_array = input("please input some numbers:")
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array = SubArray(whole_array)
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re = array.solve_sub_array()
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print(("the results is:", re))
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