首先看下dot源码中的注释部分
def dot(a, b, out=None):
"""
dot(a, b, out=None)
Dot product of two arrays. Specifically,
- If both `a` and `b` are 1-D arrays, it is inner product of vectors
(without complex conjugation).
- If both `a` and `b` are 2-D arrays, it is matrix multiplication,
but using :func:`matmul` or ``a @ b`` is preferred.
- If either `a` or `b` is 0-D (scalar), it is equivalent to :func:`multiply`
and using ``numpy.multiply(a, b)`` or ``a * b`` is preferred.
- If `a` is an N-D array and `b` is a 1-D array, it is a sum product over
the last axis of `a` and `b`.
- If `a` is an N-D array and `b` is an M-D array (where ``M>=2``), it is a
sum product over the last axis of `a` and the second-to-last axis of `b`::
dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
.....
关注一下最常用的两种情况：
If bothaandbare 1-D arrays, it is inner product of vectors
这就是两个向量dot，最后得到的两个向量的内积。
If bothaandbare 2-D arrays, it is matrix multiplication, but using :func:matmulor ``a @ b`` is preferred.
2-D arrays指的就是矩阵了。根据上面的解释不难看出，如果是两个矩阵dot，执行的就是矩阵相乘运算。
写段代码测试下
def demo2():
a1 = np.arange(1, 5)
a2 = a1[::-1]
print(a1)
print(a2)
# 两个向量dot为内积
print(a1.dot(a2))
print(np.dot(a1, a2))
print("nn")
b1 = np.arange(1, 5).reshape(2, 2)
b2 = np.arange(5, 9).reshape(2, 2)
b3 = np.arange(9, 15).reshape(3, 2)
print(b1)
print(b2)
print(b3)
print(np.dot(b1, b2))
# 会报错, 不满足矩阵相乘条件
# print(np.dot(b1, b3))
代码执行的结果
[1 2 3 4] [4 3 2 1] 20 20 [[1 2] [3 4]] [[5 6] [7 8]] [[ 9 10] [11 12] [13 14]] [[19 22] [43 50]]2.multiply
同样的看一下multiply对应源码的注释部分。
def multiply(x1, x2, *args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__
"""
multiply(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])
Multiply arguments element-wise.
Parameters
----------
x1, x2 : array_like
Input arrays to be multiplied. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output).
out : ndarray, None, or tuple of ndarray and None, optional
.....
明白multiply方法的关键就是上面的一句注释：
Multiply arguments element-wise.
说人话就是：按对应的元素相乘。
def demo3():
a1 = np.arange(1, 5)
a2 = a1[::-1]
print(a1)
print(a2)
print(np.multiply(a1, a2))
print("nn")
b1 = np.arange(1, 5).reshape(2, 2)
b2 = np.arange(5, 9).reshape(2, 2)
print(b1)
print(b2)
print(np.multiply(b1, b2))
运行得到结果
[1 2 3 4] [4 3 2 1] [4 6 6 4] [[1 2] [3 4]] [[5 6] [7 8]] [[ 5 12] [21 32]]
参考对应的代码，应该就很容易理解了。
3. *运算符乘法运算符，最后得到的结果，跟multiply方法得到的结果是一样的。
def demo4():
a1 = np.arange(1, 5)
a2 = a1[::-1]
print(a1)
print(a2)
print(a1 * a2)
print("nn")
b1 = np.arange(1, 5).reshape(2, 2)
b2 = np.arange(5, 9).reshape(2, 2)
print(b1)
print(b2)
print(b1 * b2)
最终结果
[1 2 3 4] [4 3 2 1] [4 6 6 4] [[1 2] [3 4]] [[5 6] [7 8]] [[ 5 12] [21 32]]
