本文整理匯總了Python中numpy.core.numeric.inexact方法的典型用法代碼示例。如果您正苦於以下問題：Python numeric.inexact方法的具體用法？Python numeric.inexact怎麽用？Python numeric.inexact使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類numpy.core.numeric的用法示例。
在下文中一共展示了numeric.inexact方法的7個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: asfarray
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import inexact [as 別名]
def asfarray(a, dtype=_nx.float_):
"""
Return an array converted to a float type.
Parameters
----------
a : array_like
The input array.
dtype : str or dtype object, optional
Float type code to coerce input array `a`. If `dtype` is one of the
'int' dtypes, it is replaced with float64.
Returns
-------
out : ndarray
The input `a` as a float ndarray.
Examples
--------
>>> np.asfarray([2, 3])
array([ 2., 3.])
>>> np.asfarray([2, 3], dtype='float')
array([ 2., 3.])
>>> np.asfarray([2, 3], dtype='int8')
array([ 2., 3.])
"""
if not _nx.issubdtype(dtype, _nx.inexact):
dtype = _nx.float_
return asarray(a, dtype=dtype) 示例2: asfarray
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import inexact [as 別名]
def asfarray(a, dtype=_nx.float_):
"""
Return an array converted to a float type.
Parameters
----------
a : array_like
The input array.
dtype : str or dtype object, optional
Float type code to coerce input array `a`. If `dtype` is one of the
'int' dtypes, it is replaced with float64.
Returns
-------
out : ndarray
The input `a` as a float ndarray.
Examples
--------
>>> np.asfarray([2, 3])
array([ 2., 3.])
>>> np.asfarray([2, 3], dtype='float')
array([ 2., 3.])
>>> np.asfarray([2, 3], dtype='int8')
array([ 2., 3.])
"""
dtype = _nx.obj2sctype(dtype)
if not issubclass(dtype, _nx.inexact):
dtype = _nx.float_
return asarray(a, dtype=dtype) 示例3: asfarray
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import inexact [as 別名]
def asfarray(a, dtype=_nx.float_):
dtype = _nx.obj2sctype(dtype)
if not issubclass(dtype, _nx.inexact):
dtype = _nx.float_
return afnumpy.asarray(a, dtype=dtype) 示例4: common_type
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import inexact [as 別名]
def common_type(*arrays):
"""
Return a scalar type which is common to the input arrays.
The return type will always be an inexact (i.e. floating point) scalar
type, even if all the arrays are integer arrays. If one of the inputs is
an integer array, the minimum precision type that is returned is a
64-bit floating point dtype.
All input arrays except int64 and uint64 can be safely cast to the
returned dtype without loss of information.
Parameters
----------
array1, array2, ... : ndarrays
Input arrays.
Returns
-------
out : data type code
Data type code.
See Also
--------
dtype, mintypecode
Examples
--------
>>> np.common_type(np.arange(2, dtype=np.float32))
<type 'numpy.float32'>
>>> np.common_type(np.arange(2, dtype=np.float32), np.arange(2))
<type 'numpy.float64'>
>>> np.common_type(np.arange(4), np.array([45, 6.j]), np.array([45.0]))
<type 'numpy.complex128'>
"""
is_complex = False
precision = 0
for a in arrays:
t = a.dtype.type
if iscomplexobj(a):
is_complex = True
if issubclass(t, _nx.integer):
p = 2 # array_precision[_nx.double]
else:
p = array_precision.get(t, None)
if p is None:
raise TypeError("can't get common type for non-numeric array")
precision = max(precision, p)
if is_complex:
return array_type[1][precision]
else:
return array_type[0][precision] 示例5: common_type
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import inexact [as 別名]
def common_type(*arrays):
"""
Return a scalar type which is common to the input arrays.
The return type will always be an inexact (i.e. floating point) scalar
type, even if all the arrays are integer arrays. If one of the inputs is
an integer array, the minimum precision type that is returned is a
64-bit floating point dtype.
All input arrays can be safely cast to the returned dtype without loss
of information.
Parameters
----------
array1, array2, ... : ndarrays
Input arrays.
Returns
-------
out : data type code
Data type code.
See Also
--------
dtype, mintypecode
Examples
--------
>>> np.common_type(np.arange(2, dtype=np.float32))
<type 'numpy.float32'>
>>> np.common_type(np.arange(2, dtype=np.float32), np.arange(2))
<type 'numpy.float64'>
>>> np.common_type(np.arange(4), np.array([45, 6.j]), np.array([45.0]))
<type 'numpy.complex128'>
"""
is_complex = False
precision = 0
for a in arrays:
t = a.dtype.type
if iscomplexobj(a):
is_complex = True
if issubclass(t, _nx.integer):
p = 2 # array_precision[_nx.double]
else:
p = array_precision.get(t, None)
if p is None:
raise TypeError("can't get common type for non-numeric array")
precision = max(precision, p)
if is_complex:
return array_type[1][precision]
else:
return array_type[0][precision] 示例6: common_type
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import inexact [as 別名]
def common_type(*arrays):
"""
Return a scalar type which is common to the input arrays.
The return type will always be an inexact (i.e. floating point) scalar
type, even if all the arrays are integer arrays. If one of the inputs is
an integer array, the minimum precision type that is returned is a
64-bit floating point dtype.
All input arrays except int64 and uint64 can be safely cast to the
returned dtype without loss of information.
Parameters
----------
array1, array2, ... : ndarrays
Input arrays.
Returns
-------
out : data type code
Data type code.
See Also
--------
dtype, mintypecode
Examples
--------
>>> np.common_type(np.arange(2, dtype=np.float32))
<type 'numpy.float32'>
>>> np.common_type(np.arange(2, dtype=np.float32), np.arange(2))
<type 'numpy.float64'>
>>> np.common_type(np.arange(4), np.array([45, 6.j]), np.array([45.0]))
<type 'numpy.complex128'>
"""
is_complex = False
precision = 0
for a in arrays:
t = a.dtype.type
if iscomplexobj(a):
is_complex = True
if issubclass(t, _nx.integer):
p = 2 # array_precision[_nx.double]
else:
p = array_precision.get(t, None)
if p is None:
raise TypeError("can't get common type for non-numeric array")
precision = max(precision, p)
if is_complex:
return array_type[1][precision]
else:
return array_type[0][precision] 示例7: common_type
# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import inexact [as 別名]
def common_type(*arrays):
"""
Return a scalar type which is common to the input arrays.
The return type will always be an inexact (i.e. floating point) scalar
type, even if all the arrays are integer arrays. If one of the inputs is
an integer array, the minimum precision type that is returned is a
64-bit floating point dtype.
All input arrays can be safely cast to the returned dtype without loss
of information.
Parameters
----------
array1, array2, ... : ndarrays
Input arrays.
Returns
-------
out : data type code
Data type code.
See Also
--------
dtype, mintypecode
Examples
--------
>>> np.common_type(np.arange(2, dtype=np.float32))
<type 'numpy.float32'>
>>> np.common_type(np.arange(2, dtype=np.float32), np.arange(2))
<type 'numpy.float64'>
>>> np.common_type(np.arange(4), np.array([45, 6.j]), np.array([45.0]))
<type 'numpy.complex128'>
"""
is_complex = False
precision = 0
for a in arrays:
t = a.dtype.type
if iscomplexobj(a):
is_complex = True
if issubclass(t, _nx.integer):
p = 1
else:
p = array_precision.get(t, None)
if p is None:
raise TypeError("can't get common type for non-numeric array")
precision = max(precision, p)
if is_complex:
return array_type[1][precision]
else:
return array_type[0][precision]