forked from mindspore-Ecosystem/mindspore
!49691 fixed_std_mean
Merge pull request !49691 from yide12/tensor_nansum_master
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@ -11,7 +11,7 @@ mindspore.ops.std
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参数：
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- **input** (Tensor[Number]) - 输入Tensor，其数据类型为数值型。shape： :math:`(N, *)` ，其中 :math:`*` 表示任意数量的附加维度。秩应小于8。
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- **axis** (Union[int, tuple(int), list(int)]，可选) - 要减少的维度。只允许常量值。假设 `input` 的秩为r，取值范围[-r,r)。默认值: None，缩小所有维度。
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- **ddof** (Union[int, bool]，可选) - δ自由度。如果为整数，计算中使用的除数是 :math:`N - ddof` ，其中 :math:`N` 表示元素的数量。如果为True，使用Bessel校正。如果是False，使用偏置估计来计算方差。默认值：0。
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- **ddof** (Union[int, bool]，可选) - δ自由度。如果为整数，计算中使用的除数是 :math:`N - ddof` ，其中 :math:`N` 表示元素的数量。如果为True，使用Bessel校正。如果是False，使用偏置估计来计算标准差。默认值：0。
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- **keepdims** (bool，可选) - 是否保留输出Tensor的维度。如果为True，则保留缩小的维度，大小为1。否则移除维度。默认值：False。
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返回：
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@ -11,9 +11,9 @@ mindspore.ops.std_mean
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参数：
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- **input** (Tensor[Number]) - 输入Tensor，其数据类型为数值型。shape： :math:`(N, *)` ，其中 :math:`*` 表示任意数量的附加维度。秩应小于8。
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- **axis** (Union[int, tuple(int), list(int)]，可选) - 要减少的维度。只允许常量值。假设 `input` 的秩为r，取值范围[-r,r)。默认值: None，缩小所有维度。
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- **ddof** (Union[int, bool]，可选) - δ自由度。如果为整数，计算中使用的除数是 :math:`N - ddof` ，其中 :math:`N` 表示元素的数量。如果为True，使用Bessel校正。如果是False，使用偏置估计来计算方差。默认值：0。
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- **ddof** (Union[int, bool]，可选) - δ自由度。如果为整数，计算中使用的除数是 :math:`N - ddof` ，其中 :math:`N` 表示元素的数量。如果为True，使用Bessel校正。如果是False，使用偏置估计来计算标准差。默认值：0。
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- **keepdims** (bool，可选) - 是否保留输出Tensor的维度。如果为True，则保留缩小的维度，大小为1。否则移除维度。默认值：False。
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返回：
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包含标准差和均值的tuple。
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假设输入 `input` 的shape为 :math:`(x_0, x_1, ..., x_R)` ：
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@ -4708,10 +4708,12 @@ def var(input, axis=None, ddof=0, keepdims=False): # pylint: disable=redefined-o
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``Ascend`` ``GPU`` ``CPU``
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Examples:
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>>> input = Tensor(np.array([[1, 2, 3], [-1, 1, 4]]).astype(np.float32))
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>>> output = ops.var(input, 1, True, False)
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>>> import mindspore as ms
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>>> input = ms.Tensor([[1, 2, 3, 4], [-1, 1, 4, -10]], ms.float32)
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>>> output = ms.ops.var(input, 1, 2, True)
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>>> print(output)
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[1. 6.3333325]
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[[ 2.5]
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[54.5]]
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"""
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axis = _check_var_std_input(input, ddof, keepdims, axis, "var")
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output = var_mean(input, axis, ddof, keepdims)
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@ -4737,7 +4739,7 @@ def var_mean(input, axis=None, ddof=0, keepdims=False):
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If ddof is an integer, the divisor used in calculations is :math:`N - ddof`,
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where :math:`N` represents the number of elements.
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If ddof is True, will use the Bessel correction unbiased estimation.
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If ddof is False, will through the biased estimation to calculate the variance and mean.
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If ddof is False, will through the biased estimation to calculate the variance.
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Default: 0.
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keepdims (bool, optional): Whether the output Tensor has dim retained or not.
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If true, keep these reduced dimensions and the length is 1.
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@ -4764,12 +4766,15 @@ def var_mean(input, axis=None, ddof=0, keepdims=False):
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``Ascend`` ``GPU`` ``CPU``
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Examples:
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>>> input = Tensor(np.array([[1, 2, 3], [-1, 1, 4]]).astype(np.float32))
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>>> output_var, output_mean = ops.var_mean(input, 1, True, False)
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>>> import mindspore as ms
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>>> input = ms.Tensor([[1, 2, 3, 4], [-1, 1, 4, -10]], ms.float32)
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>>> output_var, output_mean = ms.ops.var_mean(input, 1, 2, True)
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>>> print(output_var)
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[1. 6.3333325]
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[[ 2.5]
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[54.5]]
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>>> print(output_mean)
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[2. 1.3333334]
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[[ 2.5]
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[-1.5]]
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"""
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axis = _check_var_std_input(input, ddof, keepdims, axis, "var_mean")
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if ddof in (0, 1):
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@ -4779,13 +4784,14 @@ def var_mean(input, axis=None, ddof=0, keepdims=False):
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x_sub = _get_cache_prim(P.Sub)()(input, x_mean)
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x_pow = _get_cache_prim(P.Pow)()(x_sub, 2)
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x_sum = sum(x_pow, axis, keepdims)
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res_mean = mean(input, axis, keepdims)
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nums = 1
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if axis == ():
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nums = input.size
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else:
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for ax in axis:
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nums *= input.shape[ax]
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return true_divide(x_sum, nums - ddof), x_mean
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return true_divide(x_sum, nums - ddof), res_mean
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def std(input, axis=None, ddof=0, keepdims=False):
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@ -4833,10 +4839,12 @@ def std(input, axis=None, ddof=0, keepdims=False):
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``Ascend`` ``GPU`` ``CPU``
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Examples:
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>>> input = Tensor(np.array([[1, 2, 3], [-1, 1, 4]]).astype(np.float32))
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>>> output = ops.std(input, 1, True, False)
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>>> import mindspore as ms
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>>> input = ms.Tensor([[1, 2, 3, 4], [-1, 1, 4, -10]], ms.float32)
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>>> output = ms.ops.std(input, 1, 2, True)
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>>> print(output)
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[1. 2.5166113]
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[[1.5811388]
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[7.3824115]]
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"""
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axis = _check_var_std_input(input, ddof, keepdims, axis, "std")
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output = std_mean(input, axis, ddof, keepdims)
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@ -4862,7 +4870,7 @@ def std_mean(input, axis=None, ddof=0, keepdims=False):
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If ddof is an integer, the divisor used in calculations is :math:`N - ddof`,
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where :math:`N` represents the number of elements.
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If ddof is True, will use the Bessel correction unbiased estimation.
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If ddof is False, will through the biased estimation to calculate the standard deviation and mean.
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If ddof is False, will through the biased estimation to calculate the standard deviation.
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Default: 0.
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keepdims (bool, optional): Whether the output Tensor has dim retained or not.
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If true, keep these reduced dimensions and the length is 1.
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@ -4889,12 +4897,15 @@ def std_mean(input, axis=None, ddof=0, keepdims=False):
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``Ascend`` ``GPU`` ``CPU``
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Examples:
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>>> input = Tensor(np.array([[1, 2, 3], [-1, 1, 4]]).astype(np.float32))
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>>> output_std, output_mean = ops.std_mean(input, 1, True, False)
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>>> import mindspore as ms
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>>> input = ms.Tensor([[1, 2, 3, 4], [-1, 1, 4, -10]], ms.float32)
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>>> output_std, output_mean = ms.ops.std_mean(input, 1, 2, True)
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>>> print(output_std)
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[1. 2.5166113]
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[[1.5811388]
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[7.3824115]]
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>>> print(output_mean)
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[2. 1.3333334]
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[[ 2.5]
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[-1.5]]
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"""
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axis = _check_var_std_input(input, ddof, keepdims, axis, "std_mean")
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if ddof in (0, 1):
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@ -9388,7 +9399,7 @@ def einsum(equation, *operands):
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You can use this operator to perform diagonal, reducesum, transpose, matmul, mul, inner product operations, etc.
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Note:
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The sublist format is alse supported. For example, ops.einsum(op1, sublist1, op2, sublist2, ..., sublist_out).
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The sublist format is also supported. For example, ops.einsum(op1, sublist1, op2, sublist2, ..., sublist_out).
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In this format, equation can be derived by the sublists which are made up of Python's Ellipsis and list of
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integers in [0, 52). Each operand is followed by a sublist and an output sublist is at the end.
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