Python heapq.nsmallest方法代碼示例

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import nsmallest [as 別名]
def _similar_names(owner, attrname, distance_threshold, max_choices):
    """Given an owner and a name, try to find similar names

    The similar names are searched given a distance metric and only
    a given number of choices will be returned.
    """
    possible_names = []
    names = _node_names(owner)

    for name in names:
        if name == attrname:
            continue

        distance = _string_distance(attrname, name)
        if distance <= distance_threshold:
            possible_names.append((name, distance))

    # Now get back the values with a minimum, up to the given
    # limit or choices.
    picked = [name for (name, _) in
              heapq.nsmallest(max_choices, possible_names,
                              key=operator.itemgetter(1))]
    return sorted(picked) 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import nsmallest [as 別名]
def search(self, text, num=20):
        '''
        Recommend some experts in the given text's field.
        :param text: The text.
        :param num: The number of the recommended experts.
        :return: A list of dictionaries:
                {
                    'id': The expert's ID in AMiner(http://www.aminer.cn/),
                    'url': The expert's AMiner homepage.
                    'L2 distance': Similarity. The smaller the L2 distance is , the more likely the expert is interested in the given text's field.
                }
        '''
        vec = self.doc2vec(text)
        dist_mat = self._index_mat - vec.T
        dist = np.linalg.norm(dist_mat, axis=1)
        ret = [{
          'id': self._id2person[i],
          'url': self.base_url.format(self._id2person[i]),
          'L2 distance': d
        } for i, d in enumerate(dist)]
        return heapq.nsmallest(num, ret, lambda x: x['L2 distance']) 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import nsmallest [as 別名]
def pickBestPatch(self, ty, tx, coords):
        """Iterate over a random selection of patches (e.g. 100) and pick a random
        sample of the best (e.g. top 5).  Distance metric is used to rank the patches.
        """
        results = []
        for sy, sx in random.sample(list(coords), min(len(coords), PATCH_COUNT)):
            d = self.D(sy, sx, ty, tx)
            heapq.heappush(results, (d, len(results), (sy,sx)))
        
        # Some unlucky cases with special images cause no patches to be found
        # at all, in this case we just bail out.
        if not results:
            return -1, -1

        choices = heapq.nsmallest(BEST_COUNT, results)
        return random.choice(choices)[2] 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import nsmallest [as 別名]
def prune_conv_layers(self, num=1):
        """Prune one conv2d filter.
        """
        self.register_conv_hooks()
        before_loss, before_accuracy = self.train_fun(self.model)
        ranks = []
        for path, output in self.outputs.items():
            output = output.data
            grad = self.grads[path].data
            v = grad * output
            v = v.sum(0).sum(1).sum(1)  # sum to the channel axis.
            v = torch.abs(v)
            v = v / torch.sqrt(torch.sum(v * v))  # normalize
            for i, e in enumerate(v):
                ranks.append((path, i, e))
        to_prune = nsmallest(num, ranks, key=lambda t: t[2])
        to_prune = sorted(to_prune, key=lambda t: (t[0], -t[1]))  # prune the filters with bigger indexes first to avoid rearrangement.
        for path, filter_index, value in to_prune:
            self.remove_conv_filter(path, filter_index)
        self.deregister_hooks()
        after_loss, after_accuracy = self.train_fun(self.model)
        return after_loss - before_loss, after_accuracy - before_accuracy 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import nsmallest [as 別名]
def prune_linear_layers(self, num=1):
        self.register_linear_hooks()
        before_loss, before_accuracy = self.train_fun(self.model)
        ranks = []
        for path, output in self.outputs.items():
            output = output.data
            grad = self.grads[path].data
            v = grad * output
            v = v.sum(0)  # sum to the channel axis.
            v = torch.abs(v)
            v = v / torch.sqrt(torch.sum(v * v))  # normalize
            for i, e in enumerate(v):
                ranks.append((path, i, e))
        to_prune = nsmallest(num, ranks, key=lambda t: t[2])
        to_prune = sorted(to_prune, key=lambda t: (t[0], -t[1]))
        for path, feature_index, value in to_prune:
            self.remove_linear_feature(path, feature_index)
        self.deregister_hooks()
        after_loss, after_accuracy = self.train_fun(self.model)
        return after_loss - before_loss, after_accuracy - before_accuracy 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import nsmallest [as 別名]
def takeOrdered(self, num, key=None):
        """
        Get the N elements from an RDD ordered in ascending order or as
        specified by the optional key function.

        .. note:: this method should only be used if the resulting array is expected
            to be small, as all the data is loaded into the driver's memory.

        >>> sc.parallelize([10, 1, 2, 9, 3, 4, 5, 6, 7]).takeOrdered(6)
        [1, 2, 3, 4, 5, 6]
        >>> sc.parallelize([10, 1, 2, 9, 3, 4, 5, 6, 7], 2).takeOrdered(6, key=lambda x: -x)
        [10, 9, 7, 6, 5, 4]
        """

        def merge(a, b):
            return heapq.nsmallest(num, a + b, key)

        return self.mapPartitions(lambda it: [heapq.nsmallest(num, it, key)]).reduce(merge) 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import nsmallest [as 別名]
def search(lattice, ngrams, queues, beam_size, viterbi_size):
    for i in range(len(lattice)):
        for j in range(len(lattice[i])):
            for target, source in lattice[i][j]:

                word_queue = []
                for previous_cost, previous_history in queues[j]:
                    history = previous_history + [(target, source)]
                    cost = previous_cost + get_ngram_cost(ngrams, tuple(history[-3:]))
                    hypothesis = (cost, history)
                    word_queue.append(hypothesis)

                # prune word_queue to viterbi size
                if viterbi_size > 0:
                    word_queue = heapq.nsmallest(viterbi_size, word_queue, key=operator.itemgetter(0))

                queues[i] += word_queue

        # prune queues[i] to beam size
        if beam_size > 0:
            queues[i] = heapq.nsmallest(beam_size, queues[i], key=operator.itemgetter(0))
    return queues 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import nsmallest [as 別名]
def update(self, key=None, l_priority=None, full_sort=False, keep_top_k=None):
        """
        Sort the petridish queue using func

        Args
            func : a function that maps from (idx, petridish_queue_entry) to a float; the default
            is lambda i, _ : i, which means we follow the FIFO order.
        """
        if len(self.entries) == 0:
            return
        assert bool(key) != bool(l_priority), "only one option should be used for updating priority"
        if key:
            for i in range(self.size()):
                self.entries[i][IDX_PV] = key(self.entries[i][IDX_PQE])
        else:
            for i in range(self.size()):
                self.entries[i][IDX_PV] = l_priority[i]
        if full_sort:
            self.entries.sort()
            if keep_top_k is not None:
                self.entries[keep_top_k:] = []
        elif keep_top_k is not None:
            self.entries = heapq.nsmallest(keep_top_k, self.entries)
        else:
            self._update() 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import nsmallest [as 別名]
def kNN_entity(self, vec, topk=10, method=0, self_vec_id=None):
        q = []
        for i in range(len(self.vec_e)):
            #skip self
            if self_vec_id != None and i == self_vec_id:
                continue
            if method == 1:
                dist = SP.distance.cosine(vec, self.vec_e[i])
            else:
                dist = LA.norm(vec - self.vec_e[i])
            if len(q) < topk:
                HP.heappush(q, self.index_dist(i, dist))
            else:
                #indeed it fetches the biggest
                tmp = HP.nsmallest(1, q)[0]
                if tmp.dist > dist:
                    HP.heapreplace(q, self.index_dist(i, dist) )
        rst = []
        while len(q) > 0:
            item = HP.heappop(q)
            rst.insert(0, (self.vocab_e[self.vec2e[item.index]], item.dist))
        return rst

    #given entity name, find kNN 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import nsmallest [as 別名]
def kNN_relation(self, vec, topk=10, method=0, self_vec_id=None):
        q = []
        for i in range(len(self.vec_r)):
            #skip self
            if self_vec_id != None and i == self_vec_id:
                continue
            if method == 1:
                dist = SP.distance.cosine(vec, self.vec_r[i])
            else:
                dist = LA.norm(vec - self.vec_r[i])
            if len(q) < topk:
                HP.heappush(q, self.index_dist(i, dist))
            else:
                #indeed it fetches the biggest
                tmp = HP.nsmallest(1, q)[0]
                if tmp.dist > dist:
                    HP.heapreplace(q, self.index_dist(i, dist) )
        rst = []
        while len(q) > 0:
            item = HP.heappop(q)
            rst.insert(0, (self.vocab_r[self.vec2r[item.index]], item.dist))
        return rst

    #given relation name, find kNN 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import nsmallest [as 別名]
def new_wrapper(cls, func, cache):
        '''Create a new wrapper that will determine the correct function to call.'''
        # define the wrapper...
        def F(*arguments, **keywords):
            heap = [res for _, res in heapq.nsmallest(len(cache), cache)]
            f, (a, w, k) = cls.match((arguments[:], keywords), heap)
            return f(*arguments, **keywords)
            #return f(*(arguments + tuple(w)), **keywords)

        # swap out the original code object with our wrapper's
        f, c = F, F.func_code
        cargs = c.co_argcount, c.co_nlocals, c.co_stacksize, c.co_flags, \
                c.co_code, c.co_consts, c.co_names, c.co_varnames, \
                c.co_filename, '.'.join((func.__module__, func.func_name)), \
                c.co_firstlineno, c.co_lnotab, c.co_freevars, c.co_cellvars
        newcode = types.CodeType(*cargs)
        res = types.FunctionType(newcode, f.func_globals, f.func_name, f.func_defaults, f.func_closure)
        res.func_name, res.func_doc = func.func_name, func.func_doc

        # assign the specified cache to it
        setattr(res, cls.cache_name, cache)
        # ...and finally add a default docstring
        setattr(res, '__doc__', '')
        return res 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import nsmallest [as 別名]
def lookup(self, key: Any, func: Callable[[Any], Any]) -> Any:
        """ Lookup a key in the cache, calling func(key)
            to obtain the data if not already there """
        with self.lock:
            self.use_count[key] += 1
            # Get cache entry or compute if not found
            try:
                result = self.cache[key]
                self.hits += 1
            except KeyError:
                result = func(key)
                self.cache[key] = result
                self.misses += 1

                # Purge the 10% least frequently used cache entries
                if len(self.cache) > self.maxsize:
                    for key, _ in nsmallest(self.maxsize // 10,
                        self.use_count.items(), key = itemgetter(1)):

                        del self.cache[key], self.use_count[key]

            return result 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import nsmallest [as 別名]
def lowest_ranking_filters(self, num):
        data_1 = []
        for i in sorted(self.filter_ranks_1.keys()):
            for j in range(self.filter_ranks_1[i].size(0)):
                data_1.append((self.activation_to_layer_1[i], j, self.filter_ranks_1[i][j]))
        data_2 = []
        for i in sorted(self.filter_ranks_2.keys()):
            for j in range(self.filter_ranks_2[i].size(0)):
                data_2.append((self.activation_to_layer_2[i], j, self.filter_ranks_2[i][j]))
        data_3 = []
        data_3.extend(data_1)
        data_3.extend(data_2)
        dic = {}
        c = nsmallest(num*2, data_3, itemgetter(2))
        for i in range(len(c)):
            nm = str(c[i][0]) + '_' + str(c[i][1])
            if dic.get(nm)!=None:
                dic[nm] = min(dic[nm], c[i][2].item())
            else:
                dic[nm] = c[i][2].item()
        newc = []
        for i in range(len(list(dic.items()))):
            lyer = int(list(dic.items())[i][0].split('_')[0])
            filt = int(list(dic.items())[i][0].split('_')[1])
            val = torch.tensor(list(dic.items())[i][1])
            newc.append((lyer, filt, val))
        return nsmallest(num, newc, itemgetter(2)) 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import nsmallest [as 別名]
def _similar_names(owner, attrname, distance_threshold, max_choices):
    """Given an owner and a name, try to find similar names

    The similar names are searched given a distance metric and only
    a given number of choices will be returned.
    """
    possible_names = []
    names = _node_names(owner)

    for name in names:
        if name == attrname:
            continue

        distance = _string_distance(attrname, name)
        if distance <= distance_threshold:
            possible_names.append((name, distance))

    # Now get back the values with a minimum, up to the given
    # limit or choices.
    picked = [
        name
        for (name, _) in heapq.nsmallest(
            max_choices, possible_names, key=operator.itemgetter(1)
        )
    ]
    return sorted(picked) 

# 需要導入模塊: import heapq [as 別名]
# 或者: from heapq import nsmallest [as 別名]
def _gen_loopblocking_perprocess(
        nested_loop_desc, resource, bufshr, constraint, cost, options,
        gen_tifm, gen_tofm, gen_tbat, gen_ords):

    def _gen_bl_ts():
        '''
        Generator for blocking factors.

        Transpose LoopEnum-major to BL-major.
        '''
        gen_lp_ts = [None] * le.NUM
        gen_lp_ts[le.IFM], gen_lp_ts[le.OFM], gen_lp_ts[le.BAT] = \
                constraint.filter_gen_ts(gen_tifm, gen_tofm, gen_tbat)
        for lp_ts in itertools.product(*gen_lp_ts):
            bl_ts = tuple(zip(*lp_ts))
            yield bl_ts

    def _sweep():
        ''' Sweep all. '''
        is_conv_loops = (nested_loop_desc.data_loops == ConvLayer.data_loops())
        for bl_ts, bl_ords in itertools.product(_gen_bl_ts(), gen_ords):
            if is_conv_loops and skip_conv(bl_ts, bl_ords):
                continue
            if not constraint.is_valid_top_bl(bl_ts[0], bl_ords[0]):
                continue
            lbs = LoopBlockingScheme(
                nested_loop_desc, bl_ts, bl_ords, resource, bufshr,
                options)
            yield lbs

    return heapq.nsmallest(options.ntops, _sweep(),
                           key=_loop_blocking_cmp_key(options, cost))