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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,132 @@ | ||
| from __future__ import absolute_import, division, print_function | ||
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| import operator | ||
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| def weight(items, **kwargs): | ||
| if not len(kwargs): | ||
| raise ValueError('Missing attribute for weighting items!') | ||
| scaled = [] | ||
| for attr, weight in kwargs.items(): | ||
| values = [float(getattr(item, attr)) for item in items] | ||
| try: | ||
| s = sum(values) | ||
| scaled.append([weight * (v / s) for v in values]) | ||
| except ZeroDivisionError: | ||
| # s equals to zero, attr wont contribute | ||
| scaled.append([0] * len(items)) | ||
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| return map(sum, zip(*scaled)) | ||
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| def ff(items, targets): | ||
| """First-Fit | ||
| This is perhaps the simplest packing heuristic; | ||
| it simply packs items in the next available bin. | ||
| Complexity O(n^2) | ||
| """ | ||
| bins = [(target, []) for target in targets] | ||
| skip = [] | ||
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| for item in items: | ||
| for target, content in bins: | ||
| if item <= (target - sum(content)): | ||
| content.append(item) | ||
| break | ||
| else: | ||
| skip.append(item) | ||
| return bins, skip | ||
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| def ffd(items, targets, **kwargs): | ||
| """First-Fit Decreasing | ||
| This is perhaps the simplest packing heuristic; | ||
| it simply packs items in the next available bin. | ||
| This algorithm differs only from Next-Fit Decreasing | ||
| in having a 'sort'; that is, the items are pre-sorted | ||
| (largest to smallest). | ||
| Complexity O(n^2) | ||
| """ | ||
| sizes = zip(items, weight(items, **kwargs)) | ||
| sizes = sorted(sizes, key=operator.itemgetter(1), reverse=True) | ||
| items = map(operator.itemgetter(0), sizes) | ||
| return ff(items, targets) | ||
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| def mr(items, targets, **kwargs): | ||
| """Max-Rest | ||
| Complexity O(n^2) | ||
| """ | ||
| bins = [(target, []) for target in targets] | ||
| skip = [] | ||
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| for item in items: | ||
| capacities = [target - sum(content) for target, content in bins] | ||
| weighted = weight(capacities, **kwargs) | ||
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| (target, content), capacity, _ = max(zip(bins, capacities, weighted), | ||
| key=operator.itemgetter(2)) | ||
| if item <= capacity: | ||
| content.append(item) | ||
| else: | ||
| skip.append(item) | ||
| return bins, skip | ||
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| def mrpq(items, targets): | ||
| """Max-Rest Priority Queue | ||
| Complexity O(n*log(n)) | ||
| """ | ||
| raise NotImplementedError() | ||
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| def bf(items, targets, **kwargs): | ||
| """Best-Fit | ||
| Complexity O(n^2) | ||
| """ | ||
| bins = [(target, []) for target in targets] | ||
| skip = [] | ||
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| for item in items: | ||
| containers = [] | ||
| capacities = [] | ||
| for target, content in bins: | ||
| capacity = target - sum(content) | ||
| if item <= capacity: | ||
| containers.append(content) | ||
| capacities.append(capacity - item) | ||
|
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| if len(capacities): | ||
| weighted = zip(containers, weight(capacities, **kwargs)) | ||
| content, _ = min(weighted, key=operator.itemgetter(1)) | ||
| content.append(item) | ||
| else: | ||
| skip.append(item) | ||
| return bins, skip | ||
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| def bfd(items, targets, **kwargs): | ||
| """Best-Fit Decreasing | ||
| Complexity O(n^2) | ||
| """ | ||
| sizes = zip(items, weight(items, **kwargs)) | ||
| sizes = sorted(sizes, key=operator.itemgetter(1), reverse=True) | ||
| items = map(operator.itemgetter(0), sizes) | ||
| return bf(items, targets, **kwargs) | ||
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| def bfh(items, targets): | ||
| """Best-Fit-Heap | ||
| Slightly Improved Complexity | ||
| """ | ||
| raise NotImplementedError() |
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