updates in closest pair of points algorithm #979
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cclauss
reviewed
Jul 9, 2019
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| points = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (0, 2), (5, 6), (1, 2)] | ||
| points = column_based_sort(points) | ||
| points = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)] |
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Please consider adding if __name__ == "__main__": before this line and then indent this line and any lines below it. This will allow our automated testing to run doctests without executing your code.
Erfaniaa
suggested changes
Jul 9, 2019
| & by applying divide and conquer approach, | ||
| minimum distance is obtained recursively. | ||
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| >> closest points lie on different sides of partition | ||
| This case handled by forming a strip of points | ||
| whose Xco-ords distance is less than closest_pair_dis | ||
| from mid-point's Xco-ords. | ||
| from mid-point's Xco-ords. points sorted based on Yco-ords | ||
| are used in this step to reduce sorting time. | ||
| Closest pair distance is found in the strip of points. (closest_in_strip) |
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Put the period at the end of the line.
| Closest pair distance is found in the strip of points. (closest_in_strip) | ||
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| min(closest_pair_dis, closest_in_strip) would be the final answer. | ||
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| Time complexity: O(n * (logn)^2) | ||
| Time complexity: O(n * logn) |
| @@ -1,25 +1,24 @@ | |||
| """ | |||
| The algorithm finds distance btw closest pair of points in the given n points. | |||
| Approach used -> Divide and conquer | |||
| The points are sorted based on Xco-ords | |||
| The points are sorted based on Xco-ords and | |||
| then based on Yco-ords separetely. | |||
| """ | ||
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| import math | ||
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| def euclidean_distance_sqr(point1, point2): | ||
| return pow(point1[0] - point2[0], 2) + pow(point1[1] - point2[1], 2) |
| @@ -1,25 +1,24 @@ | |||
| """ | |||
| The algorithm finds distance btw closest pair of points in the given n points. | |||
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Use "between" instead of "btw".
Erfaniaa
suggested changes
Jul 9, 2019
| closest_in_strip = dis_between_closest_in_strip(cross_strip, | ||
| len(cross_strip), closest_pair_dis) | ||
| return min(closest_pair_dis, closest_in_strip) | ||
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| def closest_pair_of_points(points, points_counts): | ||
| return math.sqrt(closest_pair_of_points_sqr(points, points_counts)) | ||
| points_sorted_on_X = column_based_sort(points, column = 0) |
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Make it lower case. Use "points_sorted_on_x" and "points_sorted_on_y" with small x and y in all parts of your code.
| closest_in_strip = dis_between_closest_in_strip(cross_strip, | ||
| len(cross_strip), closest_pair_dis) | ||
| return min(closest_pair_dis, closest_in_strip) | ||
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| def closest_pair_of_points(points, points_counts): | ||
| return math.sqrt(closest_pair_of_points_sqr(points, points_counts)) | ||
| points_sorted_on_X = column_based_sort(points, column = 0) | ||
| points_sorted_on_Y = column_based_sort(points, column = 1) |
| @@ -66,7 +65,7 @@ def dis_between_closest_in_strip(points, points_counts, min_dis = float("inf")): | |||
| return min_dis | |||
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| def closest_pair_of_points_sqr(points, points_counts): | |||
| def closest_pair_of_points_sqr(points_sorted_on_X, points_sorted_on_Y, points_counts): | |||
Erfaniaa
reviewed
Jul 9, 2019
| @@ -1,25 +1,24 @@ | |||
| """ | |||
| The algorithm finds distance btw closest pair of points in the given n points. | |||
| Approach used -> Divide and conquer | |||
| The points are sorted based on Xco-ords | |||
| The points are sorted based on Xco-ords and | |||
| then based on Yco-ords separetely. | |||
| & by applying divide and conquer approach, | |||
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I think here "And " is better than "& ".
Erfaniaa
suggested changes
Jul 9, 2019
| & by applying divide and conquer approach, | ||
| minimum distance is obtained recursively. | ||
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| >> closest points lie on different sides of partition | ||
| >> closest points can lie on different sides of partition. |
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It's better to make the first character of each sentence upper case. Please check other sentences too.
Erfaniaa
approved these changes
Jul 9, 2019
stokhos
pushed a commit
to stokhos/Python
that referenced
this pull request
Jan 3, 2021
* updated closest pair of points (n*(logn)^2) to (n*logn)
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n*logn algorithm for closest pair of points.