Python numbers.Number方法代碼示例

# 需要導入模塊: import numbers [as 別名]
# 或者: from numbers import Number [as 別名]
def get_numeric_list(values, typ, expected_len=None):
    if isinstance(values, numbers.Number):
        if expected_len is not None:
            return [typ(values)] * expected_len
        else:
            return [typ(values)]
    elif isinstance(values, (list, tuple)):
        if expected_len is not None:
            assert len(values) == expected_len
        try:
            ret = [typ(value) for value in values]
            return ret
        except(ValueError):
            print("Need iterable with numeric elements, received: %s" %str(values))
            sys.exit(1)
    else:
        raise ValueError("Unaccepted value type, values=%s" %str(values)) 

# 需要導入模塊: import numbers [as 別名]
# 或者: from numbers import Number [as 別名]
def __rdiv__(self, other):
        """x.__rdiv__(y) <=> y/x

        Only `NDArray` is supported for now.

        Example
        -------
        >>> x = mx.nd.ones((2,3))*3
        >>> y = mx.nd.ones((2,3))
        >>> x.__rdiv__(y).asnumpy()
        array([[ 0.33333334,  0.33333334,  0.33333334],
               [ 0.33333334,  0.33333334,  0.33333334]], dtype=float32)
        """
        if isinstance(other, Number):
            return _internal._RDivScalar(self, scalar=other)
        else:
            raise TypeError('type %s not supported' % str(type(other))) 

# 需要導入模塊: import numbers [as 別名]
# 或者: from numbers import Number [as 別名]
def __rmod__(self, other):
        """x.__rmod__(y) <=> y%x

        Only `NDArray` is supported for now.

        Example
        -------
        >>> x = mx.nd.ones((2,3))*3
        >>> y = mx.nd.ones((2,3))
        >>> x.__rmod__(y).asnumpy()
        array([[ 1.,  1.,  1.,
               [ 1.,  1.,  1., dtype=float32)
        """
        if isinstance(other, Number):
            return _internal._RModScalar(self, scalar=other)
        else:
            raise TypeError('type %s not supported' % str(type(other))) 

# 需要導入模塊: import numbers [as 別名]
# 或者: from numbers import Number [as 別名]
def __len__(self):
        """Get number of outputs for the symbol.

        Example
        -------
        >>> a = mx.sym.var('a')
        >>> b = mx.sym.var('b')
        >>> c = a + b
        >>> len(c)

        Returns
        -------
        len(self): Number of outputs
            Number of outputs
        """
        output_count = mx_uint()
        check_call(_LIB.MXSymbolGetNumOutputs(self.handle, ctypes.byref(output_count)))
        return output_count.value 

# 需要導入模塊: import numbers [as 別名]
# 或者: from numbers import Number [as 別名]
def __init__(
            self,
            degrees,
            resample='BILINEAR',
            expand=False,
            center=None):
        if isinstance(degrees, numbers.Number):
            if degrees < 0:
                raise ValueError(
                    "If degrees is a single number, it must be positive.")
            self.degrees = (-degrees, degrees)
        else:
            if len(degrees) != 2:
                raise ValueError(
                    "If degrees is a sequence, it must be of len 2.")
            self.degrees = degrees

        self.resample = resample
        self.expand = expand
        self.center = center 

# 需要導入模塊: import numbers [as 別名]
# 或者: from numbers import Number [as 別名]
def class_balanced_weight(beta, samples_per_class):
    assert 0 <= beta < 1, 'Wrong rang of beta {}'.format(beta)
    if not isinstance(samples_per_class, np.ndarray):
        if isinstance(samples_per_class, (list, tuple)):
            samples_per_class = np.array(samples_per_class)
        elif torch.is_tensor(samples_per_class):
            samples_per_class = samples_per_class.numpy()
        else:
            raise NotImplementedError(
                'Type of samples_per_class should be {}, {} or {} but got {}'.format(
                    (list, tuple), np.ndarray, torch.Tensor, type(samples_per_class)))
    assert isinstance(samples_per_class, np.ndarray) \
        and isinstance(beta, numbers.Number)

    balanced_matrix = (1 - beta) / (1 - np.power(beta, samples_per_class))
    return torch.Tensor(balanced_matrix) 

# 需要導入模塊: import numbers [as 別名]
# 或者: from numbers import Number [as 別名]
def gen_mass(self, n):
        """Generate planetary mass values in units of Earth mass.
        
        The prototype provides a log-uniform distribution between the minimum and 
        maximum values.
        
        Args:
            n (integer):
                Number of samples to generate
                
        Returns:
            astropy Quantity array:
                Planet mass values in units of Earth mass.
        
        """
        n = self.gen_input_check(n)
        Mpr = self.Mprange.to('earthMass').value
        Mp = np.exp(np.random.uniform(low=np.log(Mpr[0]), high=np.log(Mpr[1]), 
                size=n))*u.earthMass
        
        return Mp 

# 需要導入模塊: import numbers [as 別名]
# 或者: from numbers import Number [as 別名]
def next_sample(self):
        """Helper function for reading in next sample."""
        #set total batch size, for example, 1800, and maximum size for each people, for example 45
        if self.seq is not None:
          while True:
            if self.cur >= len(self.seq):
                raise StopIteration
            idx = self.seq[self.cur]
            self.cur += 1
            if self.imgrec is not None:
              s = self.imgrec.read_idx(idx)
              header, img = recordio.unpack(s)
              label = header.label
              if not isinstance(label, numbers.Number):
                label = label[0]
              return label, img, None, None
            else:
              label, fname, bbox, landmark = self.imglist[idx]
              return label, self.read_image(fname), bbox, landmark
        else:
            s = self.imgrec.read()
            if s is None:
                raise StopIteration
            header, img = recordio.unpack(s)
            return header.label, img, None, None 

# 需要導入模塊: import numbers [as 別名]
# 或者: from numbers import Number [as 別名]
def _check_input(self, value, name, center=1, bound=(0, float('inf')), clip_first_on_zero=True):
        if isinstance(value, numbers.Number):
            if value < 0:
                raise ValueError("If {} is a single number, it must be non negative.".format(name))
            value = [center - value, center + value]
            if clip_first_on_zero:
                value[0] = max(value[0], 0)
        elif isinstance(value, (tuple, list)) and len(value) == 2:
            if not bound[0] <= value[0] <= value[1] <= bound[1]:
                raise ValueError("{} values should be between {}".format(name, bound))
        else:
            raise TypeError("{} should be a single number or a list/tuple with lenght 2.".format(name))

        # if value is 0 or (1., 1.) for brightness/contrast/saturation
        # or (0., 0.) for hue, do nothing
        if value[0] == value[1] == center:
            value = None
        return value 

# 需要導入模塊: import numbers [as 別名]
# 或者: from numbers import Number [as 別名]
def random_crop(img, gt, size):
    if isinstance(size, numbers.Number):
        size = (int(size), int(size))
    else:
        size = size

    h, w = img.shape[:2]
    crop_h, crop_w = size[0], size[1]

    if h > crop_h:
        x = random.randint(0, h - crop_h + 1)
        img = img[x:x + crop_h, :, :]
        gt = gt[x:x + crop_h, :]

    if w > crop_w:
        x = random.randint(0, w - crop_w + 1)
        img = img[:, x:x + crop_w, :]
        gt = gt[:, x:x + crop_w]

    return img, gt 

# 需要導入模塊: import numbers [as 別名]
# 或者: from numbers import Number [as 別名]
def __init__(self, size):
    if isinstance(size, numbers.Number):
      self.size = (int(size), int(size))
    else:
      self.size = size 

# 需要導入模塊: import numbers [as 別名]
# 或者: from numbers import Number [as 別名]
def __init__(self, size, padding=0):
        if isinstance(size, numbers.Number):
            self.size = (int(size), int(size))
        else:
            self.size = size # h, w
        self.padding = padding 

# 需要導入模塊: import numbers [as 別名]
# 或者: from numbers import Number [as 別名]
def __init__(self, size):
        if isinstance(size, numbers.Number):
            self.size = (int(size), int(size))
        else:
            self.size = size 

# 需要導入模塊: import numbers [as 別名]
# 或者: from numbers import Number [as 別名]
def pack(header, s):
    """Pack a string into MXImageRecord.

    Parameters
    ----------
    header : IRHeader
        Header of the image record.
        ``header.label`` can be a number or an array. See more detail in ``IRHeader``.
    s : str
        Raw image string to be packed.

    Returns
    -------
    s : str
        The packed string.

    Examples
    --------
    >>> label = 4 # label can also be a 1-D array, for example: label = [1,2,3]
    >>> id = 2574
    >>> header = mx.recordio.IRHeader(0, label, id, 0)
    >>> with open(path, 'r') as file:
    ...     s = file.read()
    >>> packed_s = mx.recordio.pack(header, s)
    """
    header = IRHeader(*header)
    if isinstance(header.label, numbers.Number):
        header = header._replace(flag=0)
    else:
        label = np.asarray(header.label, dtype=np.float32)
        header = header._replace(flag=label.size, label=0)
        s = label.tostring() + s
    s = struct.pack(_IR_FORMAT, *header) + s
    return s 

# 需要導入模塊: import numbers [as 別名]
# 或者: from numbers import Number [as 別名]
def __add__(self, other):
        """x.__add__(y) <=> x+y

        Scalar input is supported.
        Broadcasting is not supported. Use `broadcast_add` instead. """
        if isinstance(other, Symbol):
            return _internal._Plus(self, other)
        if isinstance(other, Number):
            return _internal._PlusScalar(self, scalar=other)
        else:
            raise TypeError('type %s not supported' % str(type(other)))