Python heapq.heappushpop方法代碼示例

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import heappushpop [as 別名]
def _addResult(self, test, *args):
        try:
            name = test.id()
        except AttributeError:
            name = 'Unknown.unknown'
        test_class, test_name = name.rsplit('.', 1)

        elapsed = (self._now() - self.start_time).total_seconds()
        item = (elapsed, test_class, test_name)
        if len(self.slow_tests) >= self.num_slow_tests:
            heapq.heappushpop(self.slow_tests, item)
        else:
            heapq.heappush(self.slow_tests, item)

        self.results.setdefault(test_class, [])
        self.results[test_class].append((test_name, elapsed) + args)
        self.last_time[test_class] = self._now()
        self.writeTests() 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import heappushpop [as 別名]
def search_docs(inputs, max_ex=5, opts=None):
    """Given a set of document ids (returned by ranking for a question), search
    for top N best matching (by heuristic) paragraphs that contain the answer.
    """
    if not opts:
        raise RuntimeError('Options dict must be supplied.')

    doc_ids, q_tokens, answer = inputs
    examples = []
    for i, doc_id in enumerate(doc_ids):
        for j, paragraph in enumerate(re.split(r'\n+', fetch_text(doc_id))):
            found = find_answer(paragraph, q_tokens, answer, opts)
            if found:
                # Reverse ranking, giving priority to early docs + paragraphs
                score = (found[0], -i, -j, random.random())
                if len(examples) < max_ex:
                    heapq.heappush(examples, (score, found[1]))
                else:
                    heapq.heappushpop(examples, (score, found[1]))
    return [e[1] for e in examples] 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import heappushpop [as 別名]
def push(self, score, item, extra_data=None):
    """Push an item onto the queue.

    If the queue is at capacity, the item with the smallest score will be
    dropped. Note that it is assumed each item has exactly one score. The same
    item with a different score will still be dropped.

    Args:
      score: Number used to prioritize items in the queue. Largest scores are
          kept in the queue.
      item: A hashable item to be stored. Duplicates of this item will not be
          added to the queue.
      extra_data: An extra (possible not hashable) data to store with the item.
    """
    if item in self.unique_items:
      return
    if len(self.heap) >= self.capacity:
      _, popped_item, _ = heapq.heappushpop(
          self.heap, MPQItemContainer(score, item, extra_data))
      self.unique_items.add(item)
      self.unique_items.remove(popped_item)
    else:
      heapq.heappush(self.heap, MPQItemContainer(score, item, extra_data))
      self.unique_items.add(item) 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import heappushpop [as 別名]
def scored_dict_ranking(candidate_doc_list, scored_dict, top_k):
        scored_doc = []
        v_terms = scored_dict.keys()

        for cur_doc in candidate_doc_list:
            cur_doc_score = 0
            for cur_term in v_terms:
                if cur_doc not in scored_dict[cur_term]:
                    cur_doc_score += 0
                else:
                    cur_doc_score += scored_dict[cur_term][cur_doc]

            if top_k is not None and 0 <= top_k == len(scored_doc):
                heapq.heappushpop(scored_doc, (cur_doc_score, cur_doc))
            else:
                heapq.heappush(scored_doc, (cur_doc_score, cur_doc))

        return scored_doc 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import heappushpop [as 別名]
def nearest(self, x, near_k=1, p=2):
        # use the max heap builtin library heapq
        # init the elements with -inf, and use the minus distance for comparison
        # the top of the max heap is the min distance.
        self.knn = [(-np.inf, None)]*near_k
        def visit(node):
            if not node == None:
                # cal the distance to the split point, i.e. the hyperplane
                dis = x[node.sp] - node.data[node.sp]
                # visit the child node recursively
                # if returned, we get the current nearest point
                visit(node.left if dis < 0 else node.right)
                # cal the distance to the current nearest point
                curr_dis = np.linalg.norm(x-node.data, p)
                # push the minus distance to the heap
                heapq.heappushpop(self.knn, (-curr_dis, node))
                # compare the distance to the hyperplane with the min distance
                # if less, visit another node.
                if -(self.knn[0][0]) > abs(dis):
                    visit(node.right if dis < 0 else node.left)
        visit(self.root)
        self.knn = np.array(
            [i[1].data for i in heapq.nlargest(near_k, self.knn)])
        return self.knn 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import heappushpop [as 別名]
def top(self, num):
        """
        Get the top N elements from a RDD.

        Note: It returns the list sorted in descending order.
        >>> sc.parallelize([10, 4, 2, 12, 3]).top(1)
        [12]
        >>> sc.parallelize([2, 3, 4, 5, 6], 2).cache().top(2)
        [6, 5]
        """
        def topIterator(iterator):
            q = []
            for k in iterator:
                if len(q) < num:
                    heapq.heappush(q, k)
                else:
                    heapq.heappushpop(q, k)
            yield q

        def merge(a, b):
            return next(topIterator(a + b))

        return sorted(self.mapPartitions(topIterator).reduce(merge), reverse=True) 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import heappushpop [as 別名]
def topKFrequent(self, nums, K):
        """
        Count and Maintain a heap with size k -> O(n lg k)
        Since python heapq does not support cmp, need to wrap data in a struct
        Need to use min heap instead of max heap, since we need to pop the minimal one
        :type nums: List[int]
        :type K: int
        :rtype: List[int]
        """
        cnt = defaultdict(int)
        for e in nums:
            cnt[e] += 1

        lst = []
        for k, v in cnt.items():
            lst.append(Counter(k, v))

        ret = []
        for elt in lst:
            if len(ret) < K:
                heapq.heappush(ret, elt)
            else:
                heapq.heappushpop(ret, elt)

        return map(lambda x: x.val, ret) 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import heappushpop [as 別名]
def thirdMax(self, nums):
        """
        It is an easy question but error prone:
          1. Choice of min heap or max heap: use min heap (not max heap) because
          we want to know the smallest maximum number
          2. Duplicate number
        :type nums: List[int]
        :rtype: int
        """
        if not nums:
            return None

        h = []
        for e in set(nums):
            if len(h) < 3:
                heapq.heappush(h, e)
            elif len(h) == 3 and e > h[0]:
                heapq.heappushpop(h, e)

        assert len(h) <= 3
        if len(h) == 3:
            ret = min(h)
        else:
            ret = max(h)
        return ret 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import heappushpop [as 別名]
def data_gen(self):
        time_data_buffer = []

        for time_data in self.reader:
            # Drop "old" detections
            if len(time_data_buffer) >= self.buffer_size and \
                    time_data < time_data_buffer[0]:
                warn('"Old" detection dropped')
                continue

            # Yield oldest when buffer full
            if len(time_data_buffer) >= self.buffer_size:
                yield heapq.heappushpop(time_data_buffer, time_data)
            else:
                # Else just insert
                heapq.heappush(time_data_buffer, time_data)

        # No more new data: yield remaining buffer
        while time_data_buffer:
            yield heapq.heappop(time_data_buffer) 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import heappushpop [as 別名]
def push(self, x):
    """Pushes a new element."""
    assert self._data is not None
    if len(self._data) < self._n:
      heapq.heappush(self._data, x)
    else:
      heapq.heappushpop(self._data, x) 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import heappushpop [as 別名]
def shuf_file(f, shuf_win):
    heap = []
    for line in f:
        key = hash(line)
        if len(heap) < shuf_win:
            heapq.heappush(heap, (key, line))
        else:
            _, out = heapq.heappushpop(heap, (key, line))
            yield out

    while len(heap) > 0:
        _, out = heapq.heappop(heap)
        yield out 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import heappushpop [as 別名]
def k_closest(points, k, origin=(0, 0)):
    # Time: O(k+(n-k)logk)
    # Space: O(k)
    """Initialize max heap with first k points.
    Python does not support a max heap; thus we can use the default min heap where the keys (distance) are negated.
    """
    heap = [(-distance(p, origin), p) for p in points[:k]]
    heapify(heap)

    """
    For every point p in points[k:],
    check if p is smaller than the root of the max heap;
    if it is, add p to heap and remove root. Reheapify.
    """
    for p in points[k:]:
        d = distance(p, origin)

        heappushpop(heap, (-d, p))  # heappushpop does conditional check
        """Same as:
            if d < -heap[0][0]:
                heappush(heap, (-d,p))
                heappop(heap)

        Note: heappushpop is more efficient than separate push and pop calls.
        Each heappushpop call takes O(logk) time.
        """

    return [p for nd, p in heap]  # return points in heap 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import heappushpop [as 別名]
def push(self, x):
        """Pushes a new element."""
        assert self._data is not None
        if len(self._data) < self._n:
            heapq.heappush(self._data, x)
        else:
            heapq.heappushpop(self._data, x) 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import heappushpop [as 別名]
def push(self, item):
        if len(self.q) < self.maxlen:
            heapq.heappush(self.q, item)
        else:
            heapq.heappushpop(self.q, item) 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import heappushpop [as 別名]
def add(self, item):
        if item is None:
            return
        if len(self._heap) < self._n:
            heapq.heappush(self._heap, item)
        else:
            heapq.heappushpop(self._heap, item)