fix code docs

RELEASE.md

@@ -31,7 +31,7 @@ However, currently MindSpore Parser cannot parse numpy.ndarray in JIT-graph. To
 
 <table>
 <tr>
-<td style="text-align:center"> 1.1.0 </td> <td style="text-align:center"> 1.2.0 </td>
+<td style="text-align:center"> 1.1.1 </td> <td style="text-align:center"> 1.2.0-rc1 </td>
 </tr>
 <tr>
 <td>
@@ -64,7 +64,7 @@ Previously, we have incomplete support for keyword arguments `out` and `where` i
 
 <table>
 <tr>
-<td style="text-align:center"> 1.1.0 </td> <td style="text-align:center"> 1.2.0 </td>
+<td style="text-align:center"> 1.1.1 </td> <td style="text-align:center"> 1.2.0-rc1 </td>
 </tr>
 <tr>
 <td>
@@ -107,7 +107,7 @@ Previously, we have incomplete support for keyword arguments `out` and `where` i
 
 <table>
 <tr>
-<td style="text-align:center"> 1.1.0 </td> <td style="text-align:center"> 1.2.0 </td>
+<td style="text-align:center"> 1.1.1 </td> <td style="text-align:center"> 1.2.0-rc1 </td>
 </tr>
 <tr>
 <td>

mindspore/numpy/array_creations.py

@@ -1293,14 +1293,14 @@ def meshgrid(*xi, sparse=False, indexing='xy'):
         >>> y = np.linspace(0, 1, 2)
         >>> xv, yv = np.meshgrid(x, y)
         >>> print(xv)
-        [[0. , 0.5, 1. ],
-        [0. , 0.5, 1. ]]
+        [[0.  0.5 1. ]
+        [0.  0.5 1. ]]
         >>> print(yv)
-        [[0.,  0.,  0.],
-        [1.,  1.,  1.]]
+        [[0.  0.  0.],
+        [1.  1.  1.]]
         >>> xv, yv = np.meshgrid(x, y, sparse=True)
         >>> print(xv)
-        [[0. ,  0.5,  1. ]]
+        [[0.  0.5  1. ]]
         >>> print(yv)
         [[0.],
         [1.]
@@ -1426,19 +1426,19 @@ class mGridClass(nd_grid):
         >>> from mindspore.numpy import mgrid
         >>> output = mgrid[0:5, 0:5]
         >>> print(output)
-        [[[0, 0, 0, 0, 0],
-        [1, 1, 1, 1, 1],
-        [2, 2, 2, 2, 2],
-        [3, 3, 3, 3, 3],
-        [4, 4, 4, 4, 4]],
-        [[0, 1, 2, 3, 4],
-        [0, 1, 2, 3, 4],
-        [0, 1, 2, 3, 4],
-        [0, 1, 2, 3, 4],
-        [0, 1, 2, 3, 4]]]
+        [[[0 0 0 0 0]
+        [1 1 1 1 1]
+        [2 2 2 2 2]
+        [3 3 3 3 3]
+        [4 4 4 4 4]]
+        [[0 1 2 3 4]
+        [0 1 2 3 4]
+        [0 1 2 3 4]
+        [0 1 2 3 4]
+        [0 1 2 3 4]]]
         >>> output = mgrid[-1:1:5j]
         >>> print(output)
-        [-1. , -0.5,  0. ,  0.5,  1. ]
+        [-1.  -0.5  0.   0.5  1. ]
     """
     def __init__(self):
         super(mGridClass, self).__init__(sparse=False)
@@ -1473,13 +1473,13 @@ class oGridClass(nd_grid):
         [Tensor(shape=[5, 1], dtype=Int32, value=
         [[0],
         [1],
-        [2],
+        [2]
         [3],
         [4]]), Tensor(shape=[1, 5], dtype=Int32, value=
         [[0, 1, 2, 3, 4]])]
         >>> output = ogrid[-1:1:5j]
         >>> print(output)
-        [-1. , -0.5,  0. ,  0.5,  1. ]
+        [-1.  -0.5  0.   0.5  1. ]
     """
     def __init__(self):
         super(oGridClass, self).__init__(sparse=True)
@@ -1684,10 +1684,10 @@ def ix_(*args):
         >>> import mindspore.numpy as np
         >>> ixgrid = np.ix_(np.array([0, 1]), np.array([2, 4]))
         >>> print(ixgrid)
-        [Tensor(shape=[2, 1], dtype=Int32, value=
+        (Tensor(shape=[2, 1], dtype=Int32, value=
         [[0],
         [1]]), Tensor(shape=[1, 2], dtype=Int32, value=
-        [[2, 4]])]
+        [[2, 4]]))
     """
     # TODO boolean mask
     _check_input_tensor(*args)
@@ -1784,8 +1784,9 @@ def indices(dimensions, dtype=mstype.int32, sparse=False):
         ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
+        >>> import mindspore.numpy as np
         >>> grid = np.indices((2, 3))
-        >>> print(indices)
+        >>> print(grid)
         [Tensor(shape=[2, 3], dtype=Int32, value=
         [[0, 0, 0],
         [1, 1, 1]]), Tensor(shape=[2, 3], dtype=Int32, value=

mindspore/numpy/array_ops.py

@@ -492,16 +492,14 @@ def column_stack(tup):
         ValueError: If `tup` is empty.
 
     Examples:
-        >>> import mindspore.numpy as mnp
-        >>> import numpy as onp
-        >>> from mindspore import Tensor
-        >>> x1 = Tensor(onp.array([1, 2, 3]).astype('int32'))
-        >>> x2 = Tensor(onp.array([4, 5, 6]).astype('int32'))
-        >>> output = mnp.column_stack((x1, x2))
+        >>> import mindspore.numpy as np
+        >>> x1 = np.array([1, 2, 3]).astype('int32')
+        >>> x2 = np.array([4, 5, 6]).astype('int32')
+        >>> output = np.column_stack((x1, x2))
         >>> print(output)
-        [[1, 4],
-         [2, 5],
-         [3, 6]]
+        [[1 4]
+         [2 5]
+         [3 6]]
     """
     if isinstance(tup, Tensor):
         return tup
@@ -541,15 +539,13 @@ def vstack(tup):
         ValueError: If `tup` is empty.
 
     Examples:
-        >>> import mindspore.numpy as mnp
-        >>> import numpy as onp
-        >>> from mindspore import Tensor
-        >>> x1 = Tensor(onp.array([1, 2, 3]).astype('int32'))
-        >>> x2 = Tensor(onp.array([4, 5, 6]).astype('int32'))
-        >>> output = mnp.vstack((x1, x2))
+        >>> import mindspore.numpy as np
+        >>> x1 = np.array([1, 2, 3]).astype('int32')
+        >>> x2 = np.array([4, 5, 6]).astype('int32')
+        >>> output = np.vstack((x1, x2))
         >>> print(output)
-        [[1, 2, 3],
-         [4, 5, 6]]
+        [[1 2 3]
+         [4 5 6]]
     """
     if isinstance(tup, Tensor):
         return tup
@@ -690,12 +686,13 @@ def where(condition, x=None, y=None):
         >>> y = np.full((2, 1, 1), 7)
         >>> output = np.where(condition, x, y)
         >>> print(output)
-        [[[7, 5],
-        [7, 5],
-        [7, 5]],
-        [[7, 5],
-        [7, 5],
-        [7, 5]]]
+        [[[7 5]
+        [7 5]
+        [7 5]]
+
+        [[7 5]
+        [7 5]
+        [7 5]]]
     """
     # type promotes input tensors
     dtype1 = F.dtype(x)
@@ -978,7 +975,7 @@ def unique(x, return_inverse=False):
         >>> input_x = np.asarray([1, 2, 2, 2, 3, 4, 5]).astype('int32')
         >>> output_x = np.unique(input_x)
         >>> print(output_x)
-        [1, 2, 3, 4, 5]
+        [1 2 3 4 5]
         >>> output_x = np.unique(input_x, return_inverse=True)
         >>> print(output_x)
         (Tensor(shape=[5], dtype=Int32, value= [ 1, 2, 3, 4, 5]), Tensor(shape=[7], dtype=Int32,
@@ -1055,7 +1052,7 @@ def roll(a, shift, axis=None):
         Tensor, with the same shape as a.
 
     Supported Platforms:
-        ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Raises:
         TypeError: If input arguments have types not specified above.

mindspore/numpy/logic_ops.py

@@ -57,8 +57,8 @@ def not_equal(x1, x2, dtype=None):
         >>> a = np.asarray([1, 2])
         >>> b = np.asarray([[1, 3],[1, 4]])
         >>> print(np.not_equal(a, b))
-        >>> [[False  True]
-             [False  True]]
+        [[False  True]
+        [False  True]]
     """
     _check_input_tensor(x1, x2)
     return _apply_tensor_op(F.not_equal, x1, x2, dtype=dtype)
@@ -253,9 +253,6 @@ def isfinite(x, dtype=None):
         >>> output = np.isfinite(np.array([np.inf, 1., np.nan]).astype('float32'))
         >>> print(output)
         [False  True False]
-        >>> output = np.isfinite(np.log(np.array(-1.).astype('float32')))
-        >>> print(output)
-        False
     """
     return _apply_tensor_op(F.isfinite, x, dtype=dtype)
 

mindspore/numpy/math_ops.py

@@ -258,9 +258,9 @@ def add(x1, x2, dtype=None):
         >>> x2 = np.full((3, 2), [3, 4])
         >>> output = np.add(x1, x2)
         >>> print(output)
-        [[4, 6],
-        [4, 6],
-        [4, 6]]
+        [[4 6]
+        [4 6]
+        [4 6]]
     """
     # broadcast is not fully supported in tensor_add on CPU,
     # so we use tensor_sub as a substitute solution
@@ -297,9 +297,9 @@ def subtract(x1, x2, dtype=None):
         >>> x2 = np.full((3, 2), [3, 4])
         >>> output = np.subtract(x1, x2)
         >>> print(output)
-        [[-2, -2],
-        [-2, -2],
-        [-2, -2]]
+        [[-2 -2]
+        [-2 -2]
+        [-2 -2]]
     """
     return _apply_tensor_op(F.tensor_sub, x1, x2, dtype=dtype)
 
@@ -331,9 +331,9 @@ def multiply(x1, x2, dtype=None):
         >>> x2 = np.full((3, 2), [3, 4])
         >>> output = np.multiply(x1, x2)
         >>> print(output)
-        [[3, 8],
-        [3, 8],
-        [3, 8]]
+        [[3 8]
+        [3 8]
+        [3 8]]
     """
     if _get_device() == 'CPU':
         _check_input_tensor(x1, x2)
@@ -374,9 +374,9 @@ def divide(x1, x2, dtype=None):
         >>> x2 = np.full((3, 2), [3, 4])
         >>> output = np.divide(x1, x2)
         >>> print(output)
-        [[0.33333333, 0.5],
-        [0.33333333, 0.5],
-        [0.33333333, 0.5]]
+        [[0.33333334 0.5       ]
+        [0.33333334 0.5       ]
+        [0.33333334 0.5       ]]
     """
     if not _check_is_float(F.dtype(x1)) and not _check_is_float(F.dtype(x2)):
         x1 = F.cast(x1, mstype.float32)
@@ -413,9 +413,9 @@ def true_divide(x1, x2, dtype=None):
         >>> x2 = np.full((3, 2), [3, 4])
         >>> output = np.true_divide(x1, x2)
         >>> print(output)
-        [[0.33333333, 0.5],
-        [0.33333333, 0.5],
-        [0.33333333, 0.5]]
+        [[0.33333334 0.5       ]
+        [0.33333334 0.5       ]
+        [0.33333334 0.5       ]]
     """
     return divide(x1, x2, dtype=dtype)
 
@@ -450,9 +450,9 @@ def power(x1, x2, dtype=None):
         >>> x2 = np.full((3, 2), [3, 4]).astype('float32')
         >>> output = np.power(x1, x2)
         >>> print(output)
-        [[ 1, 16],
-        [ 1, 16],
-        [ 1, 16]]
+        [[ 1 16]
+        [ 1 16]
+        [ 1 16]]
     """
     return _apply_tensor_op(F.tensor_pow, x1, x2, dtype=dtype)
 
@@ -708,8 +708,8 @@ def dot(a, b):
         >>> b = np.full((2, 3, 4), 5).astype('float32')
         >>> output = np.dot(a, b)
         >>> print(output)
-        [[[105, 105, 105, 105],
-        [105, 105, 105, 105]]]
+        [[[105. 105. 105. 105.]
+        [105. 105. 105. 105.]]]
     """
     ndim_a, ndim_b = F.rank(a), F.rank(b)
     if ndim_a > 0 and ndim_b >= 2:
@@ -760,13 +760,13 @@ def outer(a, b):
         >>> b = np.full(4, 3).astype('float32')
         >>> output = np.outer(a, b)
         >>> print(output)
-        [[6, 6, 6, 6],
-        [6, 6, 6, 6],
-        [6, 6, 6, 6],
-        [6, 6, 6, 6],
-        [6, 6, 6, 6],
-        [6, 6, 6, 6],
-        [6, 6, 6, 6]]
+        [[6. 6. 6. 6.]
+        [6. 6. 6. 6.]
+        [6. 6. 6. 6.]
+        [6. 6. 6. 6.]
+        [6. 6. 6. 6.]
+        [6. 6. 6. 6.]
+        [6. 6. 6. 6.]]
     """
     _check_input_tensor(a, b)
     if F.rank(a) != 1:
@@ -1478,7 +1478,7 @@ def amin(a, axis=None, keepdims=False, initial=None, where=True):
         [0. 1.]
         >>> output = np.amin(a, axis=1)
         >>> print(output)
-        [0, 2]
+        [0. 2.]
         >>> output = np.amin(a, where=np.array([False, True]), initial=10, axis=0)
         >>> print(output)
         [10.  1.]
@@ -3733,7 +3733,7 @@ def promote_types(type1, type2):
         >>> import mindspore.numpy as np
         >>> output = np.promote_types(np.float32, np.float64)
         >>> print(output)
-        np.float64
+        Float64
     """
     type1 = _check_dtype(type1)
     type2 = _check_dtype(type2)