Python collections.defaultdict方法代碼示例

# 需要導入模塊: import collections [as 別名]
# 或者: from collections import defaultdict [as 別名]
def __init__(self):
        self.args = None
        self.alignDistance = 0
        self.samples = collections.OrderedDict()
        self.genome = None
        self.sources = {}
        self.annotationSets = collections.OrderedDict()

        # for storing axes, annotations, etc, by allele
        self.alleleTracks = collections.defaultdict(collections.OrderedDict)
        self.trackCompositor = None

        self.dotplots = {}
        self.info = {}

        self.reset() 

# 需要導入模塊: import collections [as 別名]
# 或者: from collections import defaultdict [as 別名]
def _add_sparse_vector_labes(self, graph, vertex_v, node_feature_list):
        # add the vector with a feature resulting from hashing
        # the discrete labeled graph sparse encoding with the sparse vector
        # feature, the val is then multiplied.
        svec = graph.nodes[vertex_v].get(self.key_svec, None)
        if svec:
            vec_feature_list = defaultdict(lambda: defaultdict(float))
            for radius_dist_key in node_feature_list:
                for feature in node_feature_list[radius_dist_key]:
                    val = node_feature_list[radius_dist_key][feature]
                    for i in svec:
                        vec_val = svec[i]
                        key = fast_hash_2(feature, i, self.bitmask)
                        vec_feature_list[radius_dist_key][key] += val * vec_val
            node_feature_list = vec_feature_list
        return node_feature_list 

# 需要導入模塊: import collections [as 別名]
# 或者: from collections import defaultdict [as 別名]
def extract_sequence_and_score(graph=None):
    # make dict with positions as keys and lists of ids as values
    pos_to_ids = defaultdict(list)
    for u in graph.nodes():
        if 'position' not in graph.node[u]:  # no position attributes in graph, use the vertex id instead
            raise Exception('Missing "position" attribute in node:%s %s' % (u, graph.node[u]))
        else:
            pos = graph.node[u]['position']
        # accumulate all node ids
        pos_to_ids[pos] += [u]

    # extract sequence of labels and importances
    seq = [None] * len(pos_to_ids)
    score = [0] * len(pos_to_ids)
    for pos in sorted(pos_to_ids):
        ids = pos_to_ids[pos]
        labels = [graph.node[u].get('label', 'N/A') for u in ids]
        # check that all labels for the same position are identical
        assert(sum([1 for label in labels if label == labels[0]]) == len(labels)
               ), 'ERROR: non identical labels referring to same position: %s  %s' % (pos, labels)
        seq[pos] = labels[0]
        # average all importance score for the same position
        importances = [graph.node[u].get('importance', 0) for u in ids]
        score[pos] = np.mean(importances)
    return seq, score 

# 需要導入模塊: import collections [as 別名]
# 或者: from collections import defaultdict [as 別名]
def compute_matching_neighborhoods_fraction(GA, GB, pairings):
    count = 0
    matches = dict([(i, j) for i, j in enumerate(pairings)])
    matching_edges = defaultdict(list)
    for i, j in GA.edges():
        ii = matches[i]
        jj = matches[j]
        if (ii, jj) in GB.edges():
            matching_edges[i].append(j)
            matching_edges[j].append(i)
    for u in GA.nodes():
        if matching_edges.get(u, False):
            neighbors = nx.neighbors(GA, u)
            matches_neighborhood = True
            for v in neighbors:
                if v not in matching_edges[u]:
                    matches_neighborhood = False
                    break
            if matches_neighborhood:
                count += 1
    return float(count) / len(GA.nodes()) 

# 需要導入模塊: import collections [as 別名]
# 或者: from collections import defaultdict [as 別名]
def lifecycle(args):
    if args.delete:
        return resources.s3.BucketLifecycle(args.bucket_name).delete()
    rule = defaultdict(list, Prefix=args.prefix, Status="Enabled")
    if args.transition_to_infrequent_access is not None:
        rule["Transitions"].append(dict(StorageClass="STANDARD_IA", Days=args.transition_to_infrequent_access))
    if args.transition_to_glacier is not None:
        rule["Transitions"].append(dict(StorageClass="GLACIER", Days=args.transition_to_glacier))
    if args.expire is not None:
        rule["Expiration"] = dict(Days=args.expire)
    if args.abort_incomplete_multipart_upload is not None:
        rule["AbortIncompleteMultipartUpload"] = dict(DaysAfterInitiation=args.abort_incomplete_multipart_upload)
    if len(rule) > 2:
        clients.s3.put_bucket_lifecycle_configuration(Bucket=args.bucket_name,
                                                      LifecycleConfiguration=dict(Rules=[rule]))
    try:
        for rule in resources.s3.BucketLifecycle(args.bucket_name).rules:
            print(json.dumps(rule))
    except ClientError as e:
        expect_error_codes(e, "NoSuchLifecycleConfiguration")
        logger.error("No lifecycle configuration for bucket %s", args.bucket_name) 

# 需要導入模塊: import collections [as 別名]
# 或者: from collections import defaultdict [as 別名]
def sample_latent(self, input, input_latent_mu, input_latent_sigma, pred_latent_mu,
                    pred_latent_sigma, initial_pose_mu, initial_pose_sigma, sample=True):
    '''
    Return latent variables: dictionary containing pose and content.
    Then, crop objects from the images and encode into z.
    '''
    latent = defaultdict(lambda: None)

    beta = self.get_transitions(input_latent_mu, input_latent_sigma,
                                pred_latent_mu, pred_latent_sigma, sample)
    pose = self.accumulate_pose(beta)
    # Sample initial pose
    initial_pose = self.pyro_sample('initial_pose', dist.Normal, initial_pose_mu,
                                    initial_pose_sigma, sample)
    pose += initial_pose.view(-1, 1, self.n_components, self.pose_latent_size)
    pose = self.constrain_pose(pose)

    # Get input objects
    input_pose = pose[:, :self.n_frames_input, :, :]
    input_obj = self.get_objects(input, input_pose)
    # Encode the sampled objects
    z = self.object_encoder(input_obj)
    z = self.sample_content(z, sample)
    latent.update({'pose': pose, 'content': z})
    return latent 

# 需要導入模塊: import collections [as 別名]
# 或者: from collections import defaultdict [as 別名]
def __init__(self, config, flows_dir, ports_dir, num_timesteps, debug=False):
        self.logger = logging.getLogger("LogHistory")
        if debug:
            self.logger.setLevel(logging.DEBUG)

        self.log_entry = namedtuple("LogEntry", "source destination type")
        self.ports = defaultdict(list)
        self.flows = defaultdict(list)

        self.data = defaultdict(lambda: defaultdict(lambda: defaultdict(int)))
        self.current_timestep = 0
        self.total_timesteps = num_timesteps

        self.parse_config(config)
        self.parse_logs(num_timesteps, flows_dir, ports_dir)
        self.info()

        pretty(self.data) 

# 需要導入模塊: import collections [as 別名]
# 或者: from collections import defaultdict [as 別名]
def loadEmbedding(filename):
    """
    加載詞向量文件

    :param filename: 文件名
    :return: embeddings列表和它對應的索引
    """
    embeddings = []
    word2idx = defaultdict(list)
    with open(filename, mode="r", encoding="utf-8") as rf:
        for line in rf:
            arr = line.split(" ")
            embedding = [float(val) for val in arr[1: -1]]
            word2idx[arr[0]] = len(word2idx)
            embeddings.append(embedding)
    return embeddings, word2idx 

# 需要導入模塊: import collections [as 別名]
# 或者: from collections import defaultdict [as 別名]
def load_json_logs(json_logs):
    # load and convert json_logs to log_dict, key is epoch, value is a sub dict
    # keys of sub dict is different metrics, e.g. memory, bbox_mAP
    # value of sub dict is a list of corresponding values of all iterations
    log_dicts = [dict() for _ in json_logs]
    for json_log, log_dict in zip(json_logs, log_dicts):
        with open(json_log, 'r') as log_file:
            for line in log_file:
                log = json.loads(line.strip())
                # skip lines without `epoch` field
                if 'epoch' not in log:
                    continue
                epoch = log.pop('epoch')
                if epoch not in log_dict:
                    log_dict[epoch] = defaultdict(list)
                for k, v in log.items():
                    log_dict[epoch][k].append(v)
    return log_dicts 

# 需要導入模塊: import collections [as 別名]
# 或者: from collections import defaultdict [as 別名]
def __init__(self, tau=0, name="", ds_name=""):
        self.name = name
        self.ds_name = ds_name
        self.tau = tau

        self.ids = set()
        self.ids_correct = set()
        self.ids_correct_fp = set()
        self.ids_agree = set()

        # Legal = there is a fingerprint match below threshold tau
        self.ids_legal = set()

        self.counts = defaultdict(lambda: 0)
        self.counts_legal = defaultdict(lambda: 0)
        self.counts_correct = defaultdict(lambda: 0)

        # Total number of examples
        self.i = 0 

# 需要導入模塊: import collections [as 別名]
# 或者: from collections import defaultdict [as 別名]
def _save_sorted_results(self, run_stats, scores, image_count, filename):
    """Saves sorted (by score) results of the evaluation.

    Args:
      run_stats: dictionary with runtime statistics for submissions,
        can be generated by WorkPiecesBase.compute_work_statistics
      scores: dictionary mapping submission ids to scores
      image_count: dictionary with number of images processed by submission
      filename: output filename
    """
    with open(filename, 'w') as f:
      writer = csv.writer(f)
      writer.writerow(['SubmissionID', 'ExternalTeamId', 'Score',
                       'MedianTime', 'ImageCount'])
      get_second = lambda x: x[1]
      for s_id, score in sorted(iteritems(scores),
                                key=get_second, reverse=True):
        external_id = self.submissions.get_external_id(s_id)
        stat = run_stats.get(
            s_id, collections.defaultdict(lambda: float('NaN')))
        writer.writerow([s_id, external_id, score,
                         stat['median_eval_time'],
                         image_count[s_id]]) 

# 需要導入模塊: import collections [as 別名]
# 或者: from collections import defaultdict [as 別名]
def metric_values(metric, by_labels=()):
    """Return values for the metric."""
    if metric._type == "gauge":
        suffix = ""
    elif metric._type == "counter":
        suffix = "_total"

    values = defaultdict(list)
    for sample_suffix, labels, value in metric._samples():
        if sample_suffix == suffix:
            if by_labels:
                label_values = tuple(labels[label] for label in by_labels)
                values[label_values] = value
            else:
                values[sample_suffix].append(value)

    return values if by_labels else values[suffix] 

# 需要導入模塊: import collections [as 別名]
# 或者: from collections import defaultdict [as 別名]
def __init__(self,corpus_dir,datastore_type='file',db_name='corpus.db'):
        '''
        Read links and associated categories for specified articles 
        in text file seperated by a space

        Args:
            corpus_dir (str): The directory to save the generated corpus
            datastore_type (Optional[str]): Format to save generated corpus.
                                            Specify either 'file' or 'sqlite'.
            db_name (Optional[str]): Name of database if 'sqlite' is selected.
        '''

        self.g = Goose({'browser_user_agent': 'Mozilla','parser_class':'soup'})
        #self.g = Goose({'browser_user_agent': 'Mozilla'})
        self.corpus_dir = corpus_dir
        self.datastore_type = datastore_type
        self.db_name = db_name
        self.stats = defaultdict(int)

        self._create_corpus_dir(self.corpus_dir)

        self.db = None
        if self.datastore_type == 'sqlite':
            self.db = self.corpus_dir + '/' + self.db_name
            self._set_up_db(self.db) 

# 需要導入模塊: import collections [as 別名]
# 或者: from collections import defaultdict [as 別名]
def _create_unique_fields_cache(cells):
    primary_key_column_numbers = []
    cache = {}

    # Unique
    for _, cell in enumerate(cells, start=1):
        field = cell.get('field')
        column_number = cell.get('column-number')
        if field is not None:
            if field.descriptor.get('primaryKey'):
                primary_key_column_numbers.append(column_number)
            if field.constraints.get('unique'):
                cache[tuple([column_number])] = defaultdict(list)

    # Primary key
    if primary_key_column_numbers:
        cache[tuple(primary_key_column_numbers)] = defaultdict(list)

    return cache 

# 需要導入模塊: import collections [as 別名]
# 或者: from collections import defaultdict [as 別名]
def __init__(self, annotation_file=None):
        """
        Constructor of Microsoft COCO helper class for reading and visualizing annotations.
        :param annotation_file (str): location of annotation file
        :param image_folder (str): location to the folder that hosts images.
        :return:
        """
        # load dataset
        self.dataset,self.anns,self.cats,self.imgs = dict(),dict(),dict(),dict()
        self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list)
        if not annotation_file == None:
            print('loading annotations into memory...')
            tic = time.time()
            dataset = json.load(open(annotation_file, 'r'))
            assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset))
            print('Done (t={:0.2f}s)'.format(time.time()- tic))
            self.dataset = dataset
            self.createIndex()