neulab/docprompting-conala huggingface dataset:
numpy.matrix.getA1 method   matrix.getA1()[source]
 
Return self as a flattened ndarray. Equivalent to np.asarray(x).ravel()  Parameters 
 None
  Returns 
 
retndarray


self, 1-D, as an ndarray     Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
>>> x.getA1()
array([ 0,  1,  2, ...,  9, 10, 11])
numpy.matrix.getfield method   matrix.getfield(dtype, offset=0)
 
Returns a field of the given array as a certain type. A field is a view of the array data with a given data-type. The values in the view are determined by the given type and the offset into the current array in bytes. The offset needs to be such that the view dtype fits in the array dtype; for example an array of dtype complex128 has 16-byte elements. If taking a view with a 32-bit integer (4 bytes), the offset needs to be between 0 and 12 bytes.  Parameters 
 
dtypestr or dtype


The data type of the view. The dtype size of the view can not be larger than that of the array itself.  
offsetint


Number of bytes to skip before beginning the element view.     Examples >>> x = np.diag([1.+1.j]*2)
>>> x[1, 1] = 2 + 4.j
>>> x
array([[1.+1.j,  0.+0.j],
       [0.+0.j,  2.+4.j]])
>>> x.getfield(np.float64)
array([[1.,  0.],
       [0.,  2.]])
 By choosing an offset of 8 bytes we can select the complex part of the array for our view: >>> x.getfield(np.float64, offset=8)
array([[1.,  0.],
       [0.,  4.]])
numpy.matrix.getH method   matrix.getH()[source]
 
Returns the (complex) conjugate transpose of self. Equivalent to np.transpose(self) if self is real-valued.  Parameters 
 None
  Returns 
 
retmatrix object


complex conjugate transpose of self     Examples >>> x = np.matrix(np.arange(12).reshape((3,4)))
>>> z = x - 1j*x; z
matrix([[  0. +0.j,   1. -1.j,   2. -2.j,   3. -3.j],
        [  4. -4.j,   5. -5.j,   6. -6.j,   7. -7.j],
        [  8. -8.j,   9. -9.j,  10.-10.j,  11.-11.j]])
>>> z.getH()
matrix([[ 0. -0.j,  4. +4.j,  8. +8.j],
        [ 1. +1.j,  5. +5.j,  9. +9.j],
        [ 2. +2.j,  6. +6.j, 10.+10.j],
        [ 3. +3.j,  7. +7.j, 11.+11.j]])
numpy.matrix.getI method   matrix.getI()[source]
 
Returns the (multiplicative) inverse of invertible self.  Parameters 
 None
  Returns 
 
retmatrix object


If self is non-singular, ret is such that ret * self == self * ret == np.matrix(np.eye(self[0,:].size)) all return True.    Raises 
 numpy.linalg.LinAlgError: Singular matrix

If self is singular.      See also  linalg.inv
  Examples >>> m = np.matrix('[1, 2; 3, 4]'); m
matrix([[1, 2],
        [3, 4]])
>>> m.getI()
matrix([[-2. ,  1. ],
        [ 1.5, -0.5]])
>>> m.getI() * m
matrix([[ 1.,  0.], # may vary
        [ 0.,  1.]])
numpy.matrix.getT method   matrix.getT()[source]
 
Returns the transpose of the matrix. Does not conjugate! For the complex conjugate transpose, use .H.  Parameters 
 None
  Returns 
 
retmatrix object


The (non-conjugated) transpose of the matrix.      See also  
transpose, getH

  Examples >>> m = np.matrix('[1, 2; 3, 4]')
>>> m
matrix([[1, 2],
        [3, 4]])
>>> m.getT()
matrix([[1, 3],
        [2, 4]])
numpy.matrix.item method   matrix.item(*args)
 
Copy an element of an array to a standard Python scalar and return it.  Parameters 
 
*argsArguments (variable number and type)


 none: in this case, the method only works for arrays with one element (a.size == 1), which element is copied into a standard Python scalar object and returned. int_type: this argument is interpreted as a flat index into the array, specifying which element to copy and return. tuple of int_types: functions as does a single int_type argument, except that the argument is interpreted as an nd-index into the array.     Returns 
 
zStandard Python scalar object


A copy of the specified element of the array as a suitable Python scalar     Notes When the data type of a is longdouble or clongdouble, item() returns a scalar array object because there is no available Python scalar that would not lose information. Void arrays return a buffer object for item(), unless fields are defined, in which case a tuple is returned. item is very similar to a[args], except, instead of an array scalar, a standard Python scalar is returned. This can be useful for speeding up access to elements of the array and doing arithmetic on elements of the array using Python’s optimized math. Examples >>> np.random.seed(123)
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[2, 2, 6],
       [1, 3, 6],
       [1, 0, 1]])
>>> x.item(3)
1
>>> x.item(7)
0
>>> x.item((0, 1))
2
>>> x.item((2, 2))
1
numpy.matrix.itemset method   matrix.itemset(*args)
 
Insert scalar into an array (scalar is cast to array’s dtype, if possible) There must be at least 1 argument, and define the last argument as item. Then, a.itemset(*args) is equivalent to but faster than a[args] = item. The item should be a scalar value and args must select a single item in the array a.  Parameters 
 
*argsArguments


If one argument: a scalar, only used in case a is of size 1. If two arguments: the last argument is the value to be set and must be a scalar, the first argument specifies a single array element location. It is either an int or a tuple.     Notes Compared to indexing syntax, itemset provides some speed increase for placing a scalar into a particular location in an ndarray, if you must do this. However, generally this is discouraged: among other problems, it complicates the appearance of the code. Also, when using itemset (and item) inside a loop, be sure to assign the methods to a local variable to avoid the attribute look-up at each loop iteration. Examples >>> np.random.seed(123)
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[2, 2, 6],
       [1, 3, 6],
       [1, 0, 1]])
>>> x.itemset(4, 0)
>>> x.itemset((2, 2), 9)
>>> x
array([[2, 2, 6],
       [1, 0, 6],
       [1, 0, 9]])
numpy.matrix.itemsize attribute   matrix.itemsize
 
Length of one array element in bytes. Examples >>> x = np.array([1,2,3], dtype=np.float64)
>>> x.itemsize
8
>>> x = np.array([1,2,3], dtype=np.complex128)
>>> x.itemsize
16
numpy.matrix.max method   matrix.max(axis=None, out=None)[source]
 
Return the maximum value along an axis.  Parameters 
 See `amax` for complete descriptions
    See also  
amax, ndarray.max

  Notes This is the same as ndarray.max, but returns a matrix object where ndarray.max would return an ndarray. Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
>>> x.max()
11
>>> x.max(0)
matrix([[ 8,  9, 10, 11]])
>>> x.max(1)
matrix([[ 3],
        [ 7],
        [11]])
numpy.matrix.mean method   matrix.mean(axis=None, dtype=None, out=None)[source]
 
Returns the average of the matrix elements along the given axis. Refer to numpy.mean for full documentation.  See also  numpy.mean
  Notes Same as ndarray.mean except that, where that returns an ndarray, this returns a matrix object. Examples >>> x = np.matrix(np.arange(12).reshape((3, 4)))
>>> x
matrix([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
>>> x.mean()
5.5
>>> x.mean(0)
matrix([[4., 5., 6., 7.]])
>>> x.mean(1)
matrix([[ 1.5],
        [ 5.5],
        [ 9.5]])
numpy.matrix.min method   matrix.min(axis=None, out=None)[source]
 
Return the minimum value along an axis.  Parameters 
 See `amin` for complete descriptions.
    See also  
amin, ndarray.min

  Notes This is the same as ndarray.min, but returns a matrix object where ndarray.min would return an ndarray. Examples >>> x = -np.matrix(np.arange(12).reshape((3,4))); x
matrix([[  0,  -1,  -2,  -3],
        [ -4,  -5,  -6,  -7],
        [ -8,  -9, -10, -11]])
>>> x.min()
-11
>>> x.min(0)
matrix([[ -8,  -9, -10, -11]])
>>> x.min(1)
matrix([[ -3],
        [ -7],
        [-11]])
numpy.matrix.nbytes attribute   matrix.nbytes
 
Total bytes consumed by the elements of the array. Notes Does not include memory consumed by non-element attributes of the array object. Examples >>> x = np.zeros((3,5,2), dtype=np.complex128)
>>> x.nbytes
480
>>> np.prod(x.shape) * x.itemsize
480
numpy.matrix.ndim attribute   matrix.ndim
 
Number of array dimensions. Examples >>> x = np.array([1, 2, 3])
>>> x.ndim
1
>>> y = np.zeros((2, 3, 4))
>>> y.ndim
3
numpy.matrix.newbyteorder method   matrix.newbyteorder(new_order='S', /)
 
Return the array with the same data viewed with a different byte order. Equivalent to: arr.view(arr.dtype.newbytorder(new_order))
 Changes are also made in all fields and sub-arrays of the array data type.  Parameters 
 
new_orderstring, optional


Byte order to force; a value from the byte order specifications below. new_order codes can be any of:  ‘S’ - swap dtype from current to opposite endian {‘<’, ‘little’} - little endian {‘>’, ‘big’} - big endian {‘=’, ‘native’} - native order, equivalent to sys.byteorder
 {‘|’, ‘I’} - ignore (no change to byte order)  The default value (‘S’) results in swapping the current byte order.    Returns 
 
new_arrarray


New array object with the dtype reflecting given change to the byte order.
numpy.matrix.nonzero method   matrix.nonzero()
 
Return the indices of the elements that are non-zero. Refer to numpy.nonzero for full documentation.  See also  numpy.nonzero

equivalent function
numpy.matrix.partition method   matrix.partition(kth, axis=- 1, kind='introselect', order=None)
 
Rearranges the elements in the array in such a way that the value of the element in kth position is in the position it would be in a sorted array. All elements smaller than the kth element are moved before this element and all equal or greater are moved behind it. The ordering of the elements in the two partitions is undefined.  New in version 1.8.0.   Parameters 
 
kthint or sequence of ints


Element index to partition by. The kth element value will be in its final sorted position and all smaller elements will be moved before it and all equal or greater elements behind it. The order of all elements in the partitions is undefined. If provided with a sequence of kth it will partition all elements indexed by kth of them into their sorted position at once.  Deprecated since version 1.22.0: Passing booleans as index is deprecated.   
axisint, optional


Axis along which to sort. Default is -1, which means sort along the last axis.  
kind{‘introselect’}, optional


Selection algorithm. Default is ‘introselect’.  
orderstr or list of str, optional


When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need to be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.      See also  numpy.partition

Return a parititioned copy of an array.  argpartition

Indirect partition.  sort

Full sort.    Notes See np.partition for notes on the different algorithms. Examples >>> a = np.array([3, 4, 2, 1])
>>> a.partition(3)
>>> a
array([2, 1, 3, 4])
 >>> a.partition((1, 3))
>>> a
array([1, 2, 3, 4])
numpy.matrix.prod method   matrix.prod(axis=None, dtype=None, out=None)[source]
 
Return the product of the array elements over the given axis. Refer to prod for full documentation.  See also  
prod, ndarray.prod

  Notes Same as ndarray.prod, except, where that returns an ndarray, this returns a matrix object instead. Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
>>> x.prod()
0
>>> x.prod(0)
matrix([[  0,  45, 120, 231]])
>>> x.prod(1)
matrix([[   0],
        [ 840],
        [7920]])
numpy.matrix.ptp method   matrix.ptp(axis=None, out=None)[source]
 
Peak-to-peak (maximum - minimum) value along the given axis. Refer to numpy.ptp for full documentation.  See also  numpy.ptp
  Notes Same as ndarray.ptp, except, where that would return an ndarray object, this returns a matrix object. Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
>>> x.ptp()
11
>>> x.ptp(0)
matrix([[8, 8, 8, 8]])
>>> x.ptp(1)
matrix([[3],
        [3],
        [3]])
numpy.matrix.put method   matrix.put(indices, values, mode='raise')
 
Set a.flat[n] = values[n] for all n in indices. Refer to numpy.put for full documentation.  See also  numpy.put

equivalent function
numpy.matrix.ravel method   matrix.ravel(order='C')[source]
 
Return a flattened matrix. Refer to numpy.ravel for more documentation.  Parameters 
 
order{‘C’, ‘F’, ‘A’, ‘K’}, optional


The elements of m are read using this index order. ‘C’ means to index the elements in C-like order, with the last axis index changing fastest, back to the first axis index changing slowest. ‘F’ means to index the elements in Fortran-like index order, with the first index changing fastest, and the last index changing slowest. Note that the ‘C’ and ‘F’ options take no account of the memory layout of the underlying array, and only refer to the order of axis indexing. ‘A’ means to read the elements in Fortran-like index order if m is Fortran contiguous in memory, C-like order otherwise. ‘K’ means to read the elements in the order they occur in memory, except for reversing the data when strides are negative. By default, ‘C’ index order is used.    Returns 
 
retmatrix


Return the matrix flattened to shape (1, N) where N is the number of elements in the original matrix. A copy is made only if necessary.      See also  matrix.flatten

returns a similar output matrix but always a copy  matrix.flat

a flat iterator on the array.  numpy.ravel

related function which returns an ndarray
numpy.matrix.repeat method   matrix.repeat(repeats, axis=None)
 
Repeat elements of an array. Refer to numpy.repeat for full documentation.  See also  numpy.repeat

equivalent function
numpy.matrix.reshape method   matrix.reshape(shape, order='C')
 
Returns an array containing the same data with a new shape. Refer to numpy.reshape for full documentation.  See also  numpy.reshape

equivalent function    Notes Unlike the free function numpy.reshape, this method on ndarray allows the elements of the shape parameter to be passed in as separate arguments. For example, a.reshape(10, 11) is equivalent to a.reshape((10, 11)).
numpy.matrix.resize method   matrix.resize(new_shape, refcheck=True)
 
Change shape and size of array in-place.  Parameters 
 
new_shapetuple of ints, or n ints


Shape of resized array.  
refcheckbool, optional


If False, reference count will not be checked. Default is True.    Returns 
 None
  Raises 
 ValueError

If a does not own its own data or references or views to it exist, and the data memory must be changed. PyPy only: will always raise if the data memory must be changed, since there is no reliable way to determine if references or views to it exist.  SystemError

If the order keyword argument is specified. This behaviour is a bug in NumPy.      See also  resize

Return a new array with the specified shape.    Notes This reallocates space for the data area if necessary. Only contiguous arrays (data elements consecutive in memory) can be resized. The purpose of the reference count check is to make sure you do not use this array as a buffer for another Python object and then reallocate the memory. However, reference counts can increase in other ways so if you are sure that you have not shared the memory for this array with another Python object, then you may safely set refcheck to False. Examples Shrinking an array: array is flattened (in the order that the data are stored in memory), resized, and reshaped: >>> a = np.array([[0, 1], [2, 3]], order='C')
>>> a.resize((2, 1))
>>> a
array([[0],
       [1]])
 >>> a = np.array([[0, 1], [2, 3]], order='F')
>>> a.resize((2, 1))
>>> a
array([[0],
       [2]])
 Enlarging an array: as above, but missing entries are filled with zeros: >>> b = np.array([[0, 1], [2, 3]])
>>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
>>> b
array([[0, 1, 2],
       [3, 0, 0]])
 Referencing an array prevents resizing… >>> c = a
>>> a.resize((1, 1))
Traceback (most recent call last):
...
ValueError: cannot resize an array that references or is referenced ...
 Unless refcheck is False: >>> a.resize((1, 1), refcheck=False)
>>> a
array([[0]])
>>> c
array([[0]])
numpy.matrix.round method   matrix.round(decimals=0, out=None)
 
Return a with each element rounded to the given number of decimals. Refer to numpy.around for full documentation.  See also  numpy.around

equivalent function
numpy.matrix.searchsorted method   matrix.searchsorted(v, side='left', sorter=None)
 
Find indices where elements of v should be inserted in a to maintain order. For full documentation, see numpy.searchsorted  See also  numpy.searchsorted

equivalent function
numpy.matrix.setfield method   matrix.setfield(val, dtype, offset=0)
 
Put a value into a specified place in a field defined by a data-type. Place val into a’s field defined by dtype and beginning offset bytes into the field.  Parameters 
 
valobject


Value to be placed in field.  
dtypedtype object


Data-type of the field in which to place val.  
offsetint, optional


The number of bytes into the field at which to place val.    Returns 
 None
    See also  getfield
  Examples >>> x = np.eye(3)
>>> x.getfield(np.float64)
array([[1.,  0.,  0.],
       [0.,  1.,  0.],
       [0.,  0.,  1.]])
>>> x.setfield(3, np.int32)
>>> x.getfield(np.int32)
array([[3, 3, 3],
       [3, 3, 3],
       [3, 3, 3]], dtype=int32)
>>> x
array([[1.0e+000, 1.5e-323, 1.5e-323],
       [1.5e-323, 1.0e+000, 1.5e-323],
       [1.5e-323, 1.5e-323, 1.0e+000]])
>>> x.setfield(np.eye(3), np.int32)
>>> x
array([[1.,  0.,  0.],
       [0.,  1.,  0.],
       [0.,  0.,  1.]])
numpy.matrix.setflags method   matrix.setflags(write=None, align=None, uic=None)
 
Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY), respectively. These Boolean-valued flags affect how numpy interprets the memory area used by a (see Notes below). The ALIGNED flag can only be set to True if the data is actually aligned according to the type. The WRITEBACKIFCOPY and (deprecated) UPDATEIFCOPY flags can never be set to True. The flag WRITEABLE can only be set to True if the array owns its own memory, or the ultimate owner of the memory exposes a writeable buffer interface, or is a string. (The exception for string is made so that unpickling can be done without copying memory.)  Parameters 
 
writebool, optional


Describes whether or not a can be written to.  
alignbool, optional


Describes whether or not a is aligned properly for its type.  
uicbool, optional


Describes whether or not a is a copy of another “base” array.     Notes Array flags provide information about how the memory area used for the array is to be interpreted. There are 7 Boolean flags in use, only four of which can be changed by the user: WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED. WRITEABLE (W) the data area can be written to; ALIGNED (A) the data and strides are aligned appropriately for the hardware (as determined by the compiler); UPDATEIFCOPY (U) (deprecated), replaced by WRITEBACKIFCOPY; WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced by .base). When the C-API function PyArray_ResolveWritebackIfCopy is called, the base array will be updated with the contents of this array. All flags can be accessed using the single (upper case) letter as well as the full name. Examples >>> y = np.array([[3, 1, 7],
...               [2, 0, 0],
...               [8, 5, 9]])
>>> y
array([[3, 1, 7],
       [2, 0, 0],
       [8, 5, 9]])
>>> y.flags
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False
>>> y.setflags(write=0, align=0)
>>> y.flags
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : False
  ALIGNED : False
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False
>>> y.setflags(uic=1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: cannot set WRITEBACKIFCOPY flag to True
numpy.matrix.size attribute   matrix.size
 
Number of elements in the array. Equal to np.prod(a.shape), i.e., the product of the array’s dimensions. Notes a.size returns a standard arbitrary precision Python integer. This may not be the case with other methods of obtaining the same value (like the suggested np.prod(a.shape), which returns an instance of np.int_), and may be relevant if the value is used further in calculations that may overflow a fixed size integer type. Examples >>> x = np.zeros((3, 5, 2), dtype=np.complex128)
>>> x.size
30
>>> np.prod(x.shape)
30
numpy.matrix.sort method   matrix.sort(axis=- 1, kind=None, order=None)
 
Sort an array in-place. Refer to numpy.sort for full documentation.  Parameters 
 
axisint, optional


Axis along which to sort. Default is -1, which means sort along the last axis.  
kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, optional


Sorting algorithm. The default is ‘quicksort’. Note that both ‘stable’ and ‘mergesort’ use timsort under the covers and, in general, the actual implementation will vary with datatype. The ‘mergesort’ option is retained for backwards compatibility.  Changed in version 1.15.0: The ‘stable’ option was added.   
orderstr or list of str, optional


When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.      See also  numpy.sort

Return a sorted copy of an array.  numpy.argsort

Indirect sort.  numpy.lexsort

Indirect stable sort on multiple keys.  numpy.searchsorted

Find elements in sorted array.  numpy.partition

Partial sort.    Notes See numpy.sort for notes on the different sorting algorithms. Examples >>> a = np.array([[1,4], [3,1]])
>>> a.sort(axis=1)
>>> a
array([[1, 4],
       [1, 3]])
>>> a.sort(axis=0)
>>> a
array([[1, 3],
       [1, 4]])
 Use the order keyword to specify a field to use when sorting a structured array: >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
>>> a.sort(order='y')
>>> a
array([(b'c', 1), (b'a', 2)],
      dtype=[('x', 'S1'), ('y', '<i8')])
numpy.matrix.squeeze method   matrix.squeeze(axis=None)[source]
 
Return a possibly reshaped matrix. Refer to numpy.squeeze for more documentation.  Parameters 
 
axisNone or int or tuple of ints, optional


Selects a subset of the axes of length one in the shape. If an axis is selected with shape entry greater than one, an error is raised.    Returns 
 
squeezedmatrix


The matrix, but as a (1, N) matrix if it had shape (N, 1).      See also  numpy.squeeze

related function    Notes If m has a single column then that column is returned as the single row of a matrix. Otherwise m is returned. The returned matrix is always either m itself or a view into m. Supplying an axis keyword argument will not affect the returned matrix but it may cause an error to be raised. Examples >>> c = np.matrix([[1], [2]])
>>> c
matrix([[1],
        [2]])
>>> c.squeeze()
matrix([[1, 2]])
>>> r = c.T
>>> r
matrix([[1, 2]])
>>> r.squeeze()
matrix([[1, 2]])
>>> m = np.matrix([[1, 2], [3, 4]])
>>> m.squeeze()
matrix([[1, 2],
        [3, 4]])
numpy.matrix.std method   matrix.std(axis=None, dtype=None, out=None, ddof=0)[source]
 
Return the standard deviation of the array elements along the given axis. Refer to numpy.std for full documentation.  See also  numpy.std
  Notes This is the same as ndarray.std, except that where an ndarray would be returned, a matrix object is returned instead. Examples >>> x = np.matrix(np.arange(12).reshape((3, 4)))
>>> x
matrix([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
>>> x.std()
3.4520525295346629 # may vary
>>> x.std(0)
matrix([[ 3.26598632,  3.26598632,  3.26598632,  3.26598632]]) # may vary
>>> x.std(1)
matrix([[ 1.11803399],
        [ 1.11803399],
        [ 1.11803399]])