Python six.integer_types方法代碼示例

# 需要導入模塊: import six [as 別名]
# 或者: from six import integer_types [as 別名]
def to_example(dictionary):
  """Helper: build tf.Example from (string -> int/float/str list) dictionary."""
  features = {}
  for (k, v) in six.iteritems(dictionary):
    if not v:
      raise ValueError("Empty generated field: %s" % str((k, v)))
    if isinstance(v[0], six.integer_types):
      features[k] = tf.train.Feature(int64_list=tf.train.Int64List(value=v))
    elif isinstance(v[0], float):
      features[k] = tf.train.Feature(float_list=tf.train.FloatList(value=v))
    elif isinstance(v[0], six.string_types):
      if not six.PY2:  # Convert in python 3.
        v = [bytes(x, "utf-8") for x in v]
      features[k] = tf.train.Feature(bytes_list=tf.train.BytesList(value=v))
    elif isinstance(v[0], bytes):
      features[k] = tf.train.Feature(bytes_list=tf.train.BytesList(value=v))
    else:
      raise ValueError("Value for %s is not a recognized type; v: %s type: %s" %
                       (k, str(v[0]), str(type(v[0]))))
  return tf.train.Example(features=tf.train.Features(feature=features)) 

# 需要導入模塊: import six [as 別名]
# 或者: from six import integer_types [as 別名]
def _check_len(self, node):
        inferred = _safe_infer_call_result(node, node)
        if not inferred or inferred is astroid.Uninferable:
            return

        if (isinstance(inferred, astroid.Instance)
                and inferred.name == 'int'
                and not isinstance(inferred, astroid.Const)):
            # Assume it's good enough, since the int() call might wrap
            # something that's uninferable for us
            return

        if not isinstance(inferred, astroid.Const):
            self.add_message('invalid-length-returned', node=node)
            return

        value = inferred.value
        if not isinstance(value, six.integer_types) or value < 0:
            self.add_message('invalid-length-returned', node=node) 

# 需要導入模塊: import six [as 別名]
# 或者: from six import integer_types [as 別名]
def _render_parts(self, value, parts=None):
        if not parts:
            parts = []

        if isinstance(value, six.string_types):
            parts.append(utils.xhtml_escape(value))
        elif isinstance(value, six.integer_types):
            parts.append(str(value))
        elif isinstance(value, datetime.datetime):
            parts.append(value.strftime("%Y-%m-%dT%H:%M:%S.000Z"))
        elif isinstance(value, dict):
            for name, subvalue in value.items():
                if not isinstance(subvalue, list):
                    subvalue = [subvalue]
                for subsubvalue in subvalue:
                    parts.append('<' + name + '>')
                    self._render_parts(subsubvalue, parts)
                    parts.append('</' + name + '>')
        else:
            raise Exception("Unknown S3 value type %r", value) 

# 需要導入模塊: import six [as 別名]
# 或者: from six import integer_types [as 別名]
def _dense_inner_flatten(inputs, new_rank):
  """Helper function for `inner_flatten`."""
  rank_assertion = check_ops.assert_rank_at_least(
      inputs, new_rank, message='inputs has rank less than new_rank')
  with ops.control_dependencies([rank_assertion]):
    outer_dimensions = array_ops.strided_slice(
        array_ops.shape(inputs), [0], [new_rank - 1])
    new_shape = array_ops.concat((outer_dimensions, [-1]), 0)
    reshaped = array_ops.reshape(inputs, new_shape)

  # if `new_rank` is an integer, try to calculate new shape.
  if isinstance(new_rank, six.integer_types):
    static_shape = inputs.get_shape()
    if static_shape is not None and static_shape.dims is not None:
      static_shape = static_shape.as_list()
      static_outer_dims = static_shape[:new_rank - 1]
      static_inner_dims = static_shape[new_rank - 1:]
      flattened_dimension = 1
      for inner_dim in static_inner_dims:
        if inner_dim is None:
          flattened_dimension = None
          break
        flattened_dimension *= inner_dim
      reshaped.set_shape(static_outer_dims + [flattened_dimension])
  return reshaped 

# 需要導入模塊: import six [as 別名]
# 或者: from six import integer_types [as 別名]
def _convert_to_native_type(self, value):
        if isinstance(value, list):
            return [self._convert_to_native_type(x) for x in value]
        elif isinstance(value, SudsObject):
            d = {}
            for attr_name, attr_value in value:
                d[attr_name] = self._convert_to_native_type(attr_value)
            return d
        elif isinstance(value, six.string_types):
            # This handles suds.sax.text.Text as well, as it derives from
            # unicode.
            if PY2:
                return str(value.encode('utf-8'))
            else:
                return str(value)
        elif isinstance(value, six.integer_types):
            return int(value)
        return value 

# 需要導入模塊: import six [as 別名]
# 或者: from six import integer_types [as 別名]
def cmp(self, other, abs_vals=False):
        if isinstance(other, six.integer_types):
            if (other < 0 and abs_vals):
                other = abs(other)
            if 0 <= other <= MAXUINT64:
                return self._c_size.cmp_bytes(other, abs_vals)
            else:
                other = SizeStruct.new_from_str(str(other))
        elif isinstance(other, (Decimal, float)):
            other = SizeStruct.new_from_str(str(other))
        elif isinstance(other, Size):
            other = other._c_size
        elif other is None:
            return 1

        return self._c_size.cmp(other, abs_vals)


    ## INTERNAL METHODS ## 

# 需要導入模塊: import six [as 別名]
# 或者: from six import integer_types [as 別名]
def _get_kind_name(item):
  """Returns the kind name in CollectionDef.

  Args:
    item: A data item.

  Returns:
    The string representation of the kind in CollectionDef.
  """
  if isinstance(item, (six.string_types, six.binary_type)):
    kind = "bytes_list"
  elif isinstance(item, six.integer_types):
    kind = "int64_list"
  elif isinstance(item, float):
    kind = "float_list"
  elif isinstance(item, Any):
    kind = "any_list"
  else:
    kind = "node_list"
  return kind 

# 需要導入模塊: import six [as 別名]
# 或者: from six import integer_types [as 別名]
def to_example(dictionary):
  """Helper: build tf.Example from (string -> int/float/str list) dictionary."""
  features = {}
  for (k, v) in six.iteritems(dictionary):
    if not v:
      raise ValueError("Empty generated field: %s" % str((k, v)))
    # Subtly in PY2 vs PY3, map is not scriptable in py3. As a result,
    # map objects will fail with TypeError, unless converted to a list.
    if six.PY3 and isinstance(v, map):
      v = list(v)
    if (isinstance(v[0], six.integer_types) or
        np.issubdtype(type(v[0]), np.integer)):
      features[k] = tf.train.Feature(int64_list=tf.train.Int64List(value=v))
    elif isinstance(v[0], float):
      features[k] = tf.train.Feature(float_list=tf.train.FloatList(value=v))
    elif isinstance(v[0], six.string_types):
      if not six.PY2:  # Convert in python 3.
        v = [bytes(x, "utf-8") for x in v]
      features[k] = tf.train.Feature(bytes_list=tf.train.BytesList(value=v))
    elif isinstance(v[0], bytes):
      features[k] = tf.train.Feature(bytes_list=tf.train.BytesList(value=v))
    else:
      raise ValueError("Value for %s is not a recognized type; v: %s type: %s" %
                       (k, str(v[0]), str(type(v[0]))))
  return tf.train.Example(features=tf.train.Features(feature=features)) 

# 需要導入模塊: import six [as 別名]
# 或者: from six import integer_types [as 別名]
def make_node(self, x, index):
        x = as_sparse_variable(x)
        assert x.format in ["csr", "csc"]
        assert len(index) == 2

        input_op = [x]

        for ind in index:

            if isinstance(ind, slice):
                raise Exception("GetItemScalar called with a slice as index!")

            # in case of indexing using int instead of theano variable
            elif isinstance(ind, integer_types):
                ind = theano.tensor.constant(ind)
                input_op += [ind]

            # in case of indexing using theano variable
            elif ind.ndim == 0:
                input_op += [ind]
            else:
                raise NotImplemented()

        return gof.Apply(self, input_op, [tensor.scalar(dtype=x.dtype)]) 

# 需要導入模塊: import six [as 別名]
# 或者: from six import integer_types [as 別名]
def __init__(self, scalar_op, axis=None):
        if scalar_op.nin not in [-1, 2] or scalar_op.nout != 1:
            raise NotImplementedError((
                "CAReduce only supports binary functions with a single "
                "output."))
        self.scalar_op = scalar_op

        if axis is None:
            self.axis = axis
        # There is a bug in numpy that results in isinstance(x,
        # integer_types) returning False for numpy integers.  See
        # <http://projects.scipy.org/numpy/ticket/2235>.
        elif isinstance(axis, (integer_types, numpy.integer)):
            self.axis = (axis,)
        elif isinstance(axis, numpy.ndarray) and axis.ndim == 0:
            self.axis = (int(axis),)
        else:
            self.axis = list(set(int(a) for a in axis))
            self.axis.sort()
            self.axis = tuple(self.axis)

        self.set_ufunc(scalar_op) 

# 需要導入模塊: import six [as 別名]
# 或者: from six import integer_types [as 別名]
def __init__(self, border_mode="valid", subsample=(1, 1)):
        if isinstance(border_mode, integer_types):
            if border_mode < 0:
                raise ValueError(
                    'invalid border_mode {}, which must be a '
                    'non-negative integer'.format(border_mode))
            border_mode = (border_mode, border_mode)
        if isinstance(border_mode, tuple):
            if len(border_mode) != 2 or border_mode[0] < 0 or border_mode[1] < 0:
                raise ValueError(
                    'invalid border_mode {}, which must be a '
                    'pair of non-negative integers'.format(border_mode))
            pad_h, pad_w = map(int, border_mode)
            border_mode = (pad_h, pad_w)
        if not ((isinstance(border_mode, tuple) and min(border_mode) >= 0) or
                border_mode in ('valid', 'full', 'half')):
            raise ValueError(
                'invalid border_mode {}, which must be either '
                '"valid", "full", "half", an integer or a pair of'
                ' integers'.format(border_mode))
        self.border_mode = border_mode
        if len(subsample) != 2:
            raise ValueError("subsample must have two elements")
        self.subsample = tuple(subsample) 

# 需要導入模塊: import six [as 別名]
# 或者: from six import integer_types [as 別名]
def __init__(self, ds, ignore_border, st=None, padding=(0, 0), mode='max'):
        self.ds = tuple(ds)
        if not all([isinstance(d, integer_types) for d in ds]):
            raise ValueError(
                "Pool downsample parameters must be ints."
                " Got %s" % str(ds))
        if st is None:
            st = ds
        assert isinstance(st, (tuple, list))
        self.st = tuple(st)
        self.ignore_border = ignore_border
        self.padding = tuple(padding)
        if self.padding != (0, 0) and not ignore_border:
            raise NotImplementedError(
                'padding works only with ignore_border=True')
        if self.padding[0] >= self.ds[0] or self.padding[1] >= self.ds[1]:
            raise NotImplementedError(
                'padding_h and padding_w must be smaller than strides')
        self.mode = mode
        assert self.mode == 'max' 

# 需要導入模塊: import six [as 別名]
# 或者: from six import integer_types [as 別名]
def make_constant(args):
    """
    Convert python litterals to theano constants in subtensor arguments.

    """
    def conv(a):
            if a is None:
                return a
            elif isinstance(a, slice):
                return slice(conv(a.start),
                             conv(a.stop),
                             conv(a.step))
            elif isinstance(a, (integer_types, numpy.integer)):
                return scal.ScalarConstant(scal.int64, a)
            else:
                return a
    return tuple(map(conv, args)) 

# 需要導入模塊: import six [as 別名]
# 或者: from six import integer_types [as 別名]
def reshape(self, shape, ndim=None):
        """Return a reshaped view/copy of this variable.

        Parameters
        ----------
        shape
            Something that can be converted to a symbolic vector of integers.
        ndim
            The length of the shape. Passing None here means for
            Theano to try and guess the length of `shape`.

        .. warning:: This has a different signature than numpy's
                     ndarray.reshape!
                     In numpy you do not need to wrap the shape arguments
                     in a tuple, in theano you do need to.

        """

        if ndim is not None:
            if not isinstance(ndim, integer_types):
                raise ValueError("Expected ndim to be an integer, is " +
                                 str(type(ndim)))

        return theano.tensor.basic.reshape(self, shape, ndim=ndim) 

# 需要導入模塊: import six [as 別名]
# 或者: from six import integer_types [as 別名]
def makeKeepDims_local(self, x, y, axis):
        if axis is None:
            newaxis = list(range(x.ndim))
        elif isinstance(axis, integer_types):
            if axis < 0:
                newaxis = [axis + x.type.ndim]
            else:
                newaxis = [axis]
        else:
            newaxis = []
            for a in axis:
                if a < 0:
                    a += x.type.ndim
                newaxis.append(a)
        i = 0
        new_dims = []
        for j, _ in enumerate(x.shape):
            if j in newaxis:
                new_dims.append('x')
            else:
                new_dims.append(i)
                i += 1

        return tensor.DimShuffle(y.type.broadcastable, new_dims)(y)