总结np.array或np.float

We have a numpy-based algorithm that is supposed to handle data of different type.

我们有一个基于numpy的算法,应该处理不同类型的数据。

def my_fancy_algo(a):
    b = np.sum(a, axis=1)
    # Do something b
    return b

If we pass a=np.array[1.0, 2.0, 3.0] then b evaluates to [6.0].

如果我们传递a = np.array [1.0,2.0,3.0],那么b的计算结果为[6.0]。

If we pass a=6.0 then we get

如果我们通过= 6.0然后我们得到

*** ValueError: 'axis' entry is out of bounds

The desired behavior would be that we get same return value 6.0 not ([6.0]) for both inputs.

期望的行为是我们得到两个输入的相同返回值6.0而不是([6.0])。

What is the correct pythonic and safe way to handle this? type? shape?

什么是正确的pythonic和安全的方法来处理这个?类型?形状?

2 个解决方案

#1

Your example array actually gives the same problem as a scalar:

您的示例数组实际上提供了与标量相同的问题:

>>> a = np.array([1.0,2.0,3.0])
>>> np.sum(a, axis=1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib/python3.4/site-packages/numpy/core/fromnumeric.py", line 1724, in sum
    out=out, keepdims=keepdims)
  File "/usr/lib/python3.4/site-packages/numpy/core/_methods.py", line 32, in _sum
    return umr_sum(a, axis, dtype, out, keepdims)
ValueError: 'axis' entry is out of bounds

The good news is that there's a numpy function exactly for ensuring making numpy calls with axis=1 will work - it's called np.atleast_2d:

好消息是,有一个numpy函数正是为了确保使用axis = 1的numpy调用可以工作 - 它被称为np.atleast_2d:

>>> np.sum(np.atleast_2d(a), axis=1)
array([ 6.])
>>> np.sum(np.atleast_2d(6.0), axis=1)
array([ 6.])

But since you apparently want a scalar answer, you could instead just drop the axis argument entirely:

但是因为你显然想要一个标量答案,所以你可以完全放弃轴参数:

>>> np.sum(a)
6.0
>>> np.sum(6.0)
6.0

#2

np.sum(np.array([1.0, 2.0, 3.0]), axis=1) produces ValueError: 'axis' entry is out of bounds for me.

np.sum(np.array([1.0,2.0,3.0]),axis = 1)产生ValueError:'axis'条目对我来说超出范围。

Did you mean to put axis=0 in line 2? Then it works for arrays as well as scalars:

你的意思是把轴= 0放在第2行吗?然后它适用于数组和标量:

>>> np.sum(np.array([1.0, 2.0, 3.0]), axis=0)
6
>>> np.sum(3, axis=0)
3

#1