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cs231 numpy介绍
这个定义比较准确，可以先看看这个，再看后面的例子。
Broadcasting two arrays together follows these rules:
- If the arrays do not have the same rank, prepend the shape of the lower rank array with 1s until both shapes have the same length.
- The two arrays are said to be compatible in a dimension if they have the same size in the dimension, or if one of the arrays has size 1 in that dimension.
- The arrays can be broadcast together if they are compatible in all dimensions.
- After broadcasting, each array behaves as if it had shape equal to the elementwise maximum of shapes of the two input arrays.
- In any dimension where one array had size 1 and the other array had size greater than 1, the first array behaves as if it were copied along that dimension
- numpy 数组广播:例子比较多
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Numpy中的广播(Broadcasting):这个例子比较清晰。
a.shape得到的是(3,) b是一个浮点数，如果转换成array，则b.shape是一个()，a的1轴对齐，补齐为1，a.shape(3,1)，b对齐，则对齐也为(3,1)，然后按照一一对应的方式计算
但是这一段说的不太对，根据后面的介绍，正确流程应该是这样：
- b的维度小于a, 因此首先增加维度对齐，b.shape变成(1,),
- 然后b再在维度为１上tail变成(3,)
- 两个shape相同的array最后相加.最后的shape也是(3,0)
ps：如果按照原文说的，最后的shape将是(3,1),这个可以自己实验一下
- 总结:
1.对齐
先看数组维度，维度不相同在短维度的数组前面连续增加１维，直到两个数组维度相同，转向第２步
2.检查
两个维度数目相同的数组，如果二者各个维度的长度相同或者一个长度为１，那么可以进行第３步执行tail(拓展)．如果不满足，则不可广播
3.拓展
对于检查无误的a,b两数组，输出的数组的shape是a.shape*b.shape(按位乘)．将两数组沿着维度长度为１的轴进行拓展(copy)，拓展成与输出一致的shape，然后进行运算.
