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92 lines
2.8 KiB
Python
92 lines
2.8 KiB
Python
"""Unittests for heapq."""
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from test.test_support import verify, vereq, verbose, TestFailed
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from heapq import heappush, heappop, heapify, heapreplace
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import random
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def check_invariant(heap):
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# Check the heap invariant.
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for pos, item in enumerate(heap):
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if pos: # pos 0 has no parent
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parentpos = (pos-1) >> 1
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verify(heap[parentpos] <= item)
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# An iterator returning a heap's elements, smallest-first.
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class heapiter(object):
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def __init__(self, heap):
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self.heap = heap
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def next(self):
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try:
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return heappop(self.heap)
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except IndexError:
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raise StopIteration
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def __iter__(self):
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return self
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def test_main():
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# 1) Push 100 random numbers and pop them off, verifying all's OK.
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heap = []
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data = []
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check_invariant(heap)
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for i in range(256):
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item = random.random()
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data.append(item)
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heappush(heap, item)
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check_invariant(heap)
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results = []
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while heap:
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item = heappop(heap)
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check_invariant(heap)
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results.append(item)
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data_sorted = data[:]
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data_sorted.sort()
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vereq(data_sorted, results)
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# 2) Check that the invariant holds for a sorted array
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check_invariant(results)
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# 3) Naive "N-best" algorithm
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heap = []
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for item in data:
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heappush(heap, item)
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if len(heap) > 10:
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heappop(heap)
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heap.sort()
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vereq(heap, data_sorted[-10:])
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# 4) Test heapify.
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for size in range(30):
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heap = [random.random() for dummy in range(size)]
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heapify(heap)
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check_invariant(heap)
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# 5) Less-naive "N-best" algorithm, much faster (if len(data) is big
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# enough <wink>) than sorting all of data. However, if we had a max
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# heap instead of a min heap, it could go faster still via
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# heapify'ing all of data (linear time), then doing 10 heappops
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# (10 log-time steps).
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heap = data[:10]
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heapify(heap)
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for item in data[10:]:
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if item > heap[0]: # this gets rarer the longer we run
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heapreplace(heap, item)
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vereq(list(heapiter(heap)), data_sorted[-10:])
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# 6) Exercise everything with repeated heapsort checks
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for trial in xrange(100):
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size = random.randrange(50)
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data = [random.randrange(25) for i in range(size)]
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if trial & 1: # Half of the time, use heapify
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heap = data[:]
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heapify(heap)
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else: # The rest of the time, use heappush
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heap = []
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for item in data:
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heappush(heap,item)
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data.sort()
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sorted = [heappop(heap) for i in range(size)]
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vereq(data, sorted)
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# Make user happy
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if verbose:
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print "All OK"
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if __name__ == "__main__":
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test_main()
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