Python itertools.izip方法代碼示例

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import izip [as 別名]
def tag(self, data_iter):
        """A tagging function.

        Args:
            data_iter: A iterator for generate batches.

        Returns:
            A generator for tagging result.
        """
        output = []
        for data in data_iter:
            batch = data_to_ids(data, [self.item2id] + [self.word2id] * self.parameters['word_window_size'])
            batch = create_input(batch)
            seq_ids, seq_other_ids_list, seq_lengths = batch[0], batch[1: -1], batch[-1]
            feed_dict = {self.seq_ids_pl: seq_ids.astype(INT_TYPE),
                         self.seq_lengths_pl: seq_lengths.astype(INT_TYPE),
                         self.is_train_pl: False}
            for pl, v in zip(self.seq_other_ids_pls, seq_other_ids_list):
                feed_dict[pl] = v.astype(INT_TYPE)
            scores = self.sess.run(self.scores_op, feed_dict)
            stag_ids = self.inference(scores, seq_lengths)
            for seq, stag_id, length in izip(data[0], stag_ids, seq_lengths):
                output.append((seq, [self.id2tag[t] for t in stag_id[:length]]))
            yield zip(*output)
            output = [] 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import izip [as 別名]
def create_input(batch):
    """
    Take each sentence data in batch and return an input for
    the training or the evaluation function.
    """
    assert len(batch) > 0
    lengths = [len(seq) for seq in batch[0]]
    max_len = max(2, max(lengths))
    ret = []
    for d in batch:
        dd = []
        for seq_id, pos in izip(d, lengths):
            assert len(seq_id) == pos
            pad = [0] * (max_len - pos)
            dd.append(seq_id + pad)
        ret.append(np.array(dd))
    ret.append(np.array(lengths))
    return ret 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import izip [as 別名]
def go(fhs):
  fmt = None
  with open(opt_vocab, 'w') as vocab_out:
    with open(opt_output, 'w') as vecs_out:
      for lines in izip(*fhs):
        parts = [line.split() for line in lines]
        token = parts[0][0]
        if any(part[0] != token for part in parts[1:]):
          raise IOError('vector files must be aligned')

        print >> vocab_out, token

        vec = [sum(float(x) for x in xs) for xs in zip(*parts)[1:]]
        if not fmt:
          fmt = struct.Struct('%df' % len(vec))

        vecs_out.write(fmt.pack(*vec)) 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import izip [as 別名]
def convert_to_graph_tool(G):
  timer = utils.Timer()
  timer.tic()
  gtG = gt.Graph(directed=G.is_directed())
  gtG.ep['action'] = gtG.new_edge_property('int')

  nodes_list = G.nodes()
  nodes_array = np.array(nodes_list)

  nodes_id = np.zeros((nodes_array.shape[0],), dtype=np.int64)

  for i in range(nodes_array.shape[0]):
    v = gtG.add_vertex()
    nodes_id[i] = int(v)

  # d = {key: value for (key, value) in zip(nodes_list, nodes_id)}
  d = dict(itertools.izip(nodes_list, nodes_id))

  for src, dst, data in G.edges_iter(data=True):
    e = gtG.add_edge(d[src], d[dst])
    gtG.ep['action'][e] = data['action']
  nodes_to_id = d
  timer.toc(average=True, log_at=1, log_str='src.graph_utils.convert_to_graph_tool')
  return gtG, nodes_array, nodes_to_id 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import izip [as 別名]
def averageSeries(requestContext, *seriesLists):
    """
    Short Alias: avg()

    Takes one metric or a wildcard seriesList.
    Draws the average value of all metrics passed at each time.

    Example:

    .. code-block:: none

      &target=averageSeries(company.server.*.threads.busy)

    """
    yield defer.succeed(None)
    (seriesList, start, end, step) = normalize(seriesLists)
    name = "averageSeries(%s)" % formatPathExpressions(seriesList)
    values = (safeDiv(safeSum(row), safeLen(row)) for row in izip(*seriesList))
    series = TimeSeries(name, start, end, step, values)
    series.pathExpression = name
    returnValue([series]) 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import izip [as 別名]
def stddevSeries(requestContext, *seriesLists):
    """

    Takes one metric or a wildcard seriesList.
    Draws the standard deviation of all metrics passed at each time.

    Example:

    .. code-block:: none

      &target=stddevSeries(company.server.*.threads.busy)

    """
    yield defer.succeed(None)
    (seriesList, start, end, step) = normalize(seriesLists)
    name = "stddevSeries(%s)" % formatPathExpressions(seriesList)
    values = (safeStdDev(row) for row in izip(*seriesList))
    series = TimeSeries(name, start, end, step, values)
    series.pathExpression = name
    returnValue([series]) 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import izip [as 別名]
def maxSeries(requestContext, *seriesLists):
    """
    Takes one metric or a wildcard seriesList.
    For each datapoint from each metric passed in, pick the maximum value and graph it.

    Example:

    .. code-block:: none

      &target=maxSeries(Server*.connections.total)

    """
    yield defer.succeed(None)
    (seriesList, start, end, step) = normalize(seriesLists)
    name = "maxSeries(%s)" % formatPathExpressions(seriesList)
    values = (safeMax(row) for row in izip(*seriesList))
    series = TimeSeries(name, start, end, step, values)
    series.pathExpression = name
    returnValue([series]) 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import izip [as 別名]
def rangeOfSeries(requestContext, *seriesLists):
    """
    Takes a wildcard seriesList.
    Distills down a set of inputs into the range of the series

    Example:

    .. code-block:: none

        &target=rangeOfSeries(Server*.connections.total)

    """
    yield defer.succeed(None)
    (seriesList, start, end, step) = normalize(seriesLists)
    name = "rangeOfSeries(%s)" % formatPathExpressions(seriesList)
    values = (safeSubtract(max(row), min(row)) for row in izip(*seriesList))
    series = TimeSeries(name, start, end, step, values)
    series.pathExpression = name
    returnValue([series]) 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import izip [as 別名]
def percentileOfSeries(requestContext, seriesList, n, interpolate=False):
    """
    percentileOfSeries returns a single series which is composed of the n-percentile
    values taken across a wildcard series at each point. Unless `interpolate` is
    set to True, percentile values are actual values contained in one of the
    supplied series.
    """
    yield defer.succeed(None)
    if n <= 0:
        raise ValueError(
            'The requested percent is required to be greater than 0')

    name = 'percentileOfSeries(%s,%g)' % (seriesList[0].pathExpression, n)
    (start, end, step) = normalize([seriesList])[1:]
    values = [_getPercentile(row, n, interpolate) for row in izip(*seriesList)]
    resultSeries = TimeSeries(name, start, end, step, values)
    resultSeries.pathExpression = name

    returnValue([resultSeries]) 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import izip [as 別名]
def countSeries(requestContext, *seriesLists):
    """
    Draws a horizontal line representing the number of nodes found in the seriesList.

    .. code-block:: none

      &target=countSeries(carbon.agents.*.*)

    """
    yield defer.succeed(None)
    (seriesList, start, end, step) = normalize(seriesLists)
    name = "countSeries(%s)" % formatPathExpressions(seriesList)
    values = (int(len(row)) for row in izip(*seriesList))
    series = TimeSeries(name, start, end, step, values)
    series.pathExpression = name
    returnValue([series]) 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import izip [as 別名]
def _create_config_parameters():
  """Creates a config value pair for parameterized test cases.

  Yields:
    A list containing the list of configs and their values.
  """
  string_config_value = 'config value 1'
  integer_config_value = 1
  bool_config_value = True
  list_config_value = ['email1', 'email2']
  config_ids = ['string_config', 'integer_config', 'bool_config', 'list_config']
  config_values = [
      string_config_value, integer_config_value, bool_config_value,
      list_config_value
  ]
  for i in itertools.izip(config_ids, config_values):
    yield [i] 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import izip [as 別名]
def _calculate_annual_sunlight_exposure(
            values, hoys, threshhold=None, blinds_state_ids=None, occ_schedule=None,
            target_hours=None):
        threshhold = threshhold or 1000
        target_hours = target_hours or 250
        schedule = occ_schedule or Schedule.eight_am_to_six_pm()
        ase = 0
        problematic_hours = []
        for h, v in zip(hoys, values):
            if h not in schedule:
                continue
            if v > threshhold:
                ase += 1
                problematic_hours.append(h)

        return ase < target_hours, ase, problematic_hours 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import izip [as 別名]
def check_extract_features_returns_correct_shape(
      self, batch_size, image_height, image_width, depth_multiplier,
      pad_to_multiple, expected_feature_map_shapes, use_explicit_padding=False,
      use_keras=False):
    def graph_fn(image_tensor):
      return self._extract_features(image_tensor,
                                    depth_multiplier,
                                    pad_to_multiple,
                                    use_explicit_padding,
                                    use_keras=use_keras)

    image_tensor = np.random.rand(batch_size, image_height, image_width,
                                  3).astype(np.float32)
    feature_maps = self.execute(graph_fn, [image_tensor])
    for feature_map, expected_shape in itertools.izip(
        feature_maps, expected_feature_map_shapes):
      self.assertAllEqual(feature_map.shape, expected_shape) 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import izip [as 別名]
def check_extract_features_returns_correct_shapes_with_dynamic_inputs(
      self, batch_size, image_height, image_width, depth_multiplier,
      pad_to_multiple, expected_feature_map_shapes, use_explicit_padding=False,
      use_keras=False):
    def graph_fn(image_height, image_width):
      image_tensor = tf.random_uniform([batch_size, image_height, image_width,
                                        3], dtype=tf.float32)
      return self._extract_features(image_tensor,
                                    depth_multiplier,
                                    pad_to_multiple,
                                    use_explicit_padding,
                                    use_keras=use_keras)

    feature_maps = self.execute_cpu(graph_fn, [
        np.array(image_height, dtype=np.int32),
        np.array(image_width, dtype=np.int32)
    ])
    for feature_map, expected_shape in itertools.izip(
        feature_maps, expected_feature_map_shapes):
      self.assertAllEqual(feature_map.shape, expected_shape) 

# 需要導入模塊: import itertools [as 別名]
# 或者: from itertools import izip [as 別名]
def _check_returns_correct_shape(
      self, batch_size, image_height, image_width, depth_multiplier,
      expected_feature_map_shapes, use_explicit_padding=False, min_depth=None,
      layer_names=None):
    def graph_fn(image_tensor):
      model = self._create_application_with_layer_outputs(
          layer_names=layer_names,
          batchnorm_training=False, use_explicit_padding=use_explicit_padding,
          min_depth=min_depth,
          alpha=depth_multiplier)
      return model(image_tensor)

    image_tensor = np.random.rand(batch_size, image_height, image_width,
                                  3).astype(np.float32)
    feature_maps = self.execute(graph_fn, [image_tensor])

    for feature_map, expected_shape in itertools.izip(
        feature_maps, expected_feature_map_shapes):
      self.assertAllEqual(feature_map.shape, expected_shape)