# How to get element-wise matrix multiplication (Hadamard product) in numpy?

## Question:

I have two matrices

```
a = np.matrix([[1,2], [3,4]])
b = np.matrix([[5,6], [7,8]])
```

and I want to get the element-wise product, `[[1*5,2*6], [3*7,4*8]]`

, equaling

`[[5,12], [21,32]]`

I have tried

```
print(np.dot(a,b))
```

and

```
print(a*b)
```

but both give the result

`[[19 22], [43 50]]`

which is the matrix product, not the element-wise product. How can I get the the element-wise product (aka Hadamard product) using built-in functions?

## Answers:

just do this:

```
import numpy as np
a = np.array([[1,2],[3,4]])
b = np.array([[5,6],[7,8]])
a * b
```

For elementwise multiplication of `matrix`

objects, you can use `numpy.multiply`

:

```
import numpy as np
a = np.array([[1,2],[3,4]])
b = np.array([[5,6],[7,8]])
np.multiply(a,b)
```

**Result**

```
array([[ 5, 12],
[21, 32]])
```

However, you should really use `array`

instead of `matrix`

. `matrix`

objects have all sorts of horrible incompatibilities with regular ndarrays. With ndarrays, you can just use `*`

for elementwise multiplication:

```
a * b
```

If you’re on Python 3.5+, you don’t even lose the ability to perform matrix multiplication with an operator, because `@`

does matrix multiplication now:

```
a @ b # matrix multiplication
```

```
import numpy as np
x = np.array([[1,2,3], [4,5,6]])
y = np.array([[-1, 2, 0], [-2, 5, 1]])
x*y
Out:
array([[-1, 4, 0],
[-8, 25, 6]])
%timeit x*y
1000000 loops, best of 3: 421 ns per loop
np.multiply(x,y)
Out:
array([[-1, 4, 0],
[-8, 25, 6]])
%timeit np.multiply(x, y)
1000000 loops, best of 3: 457 ns per loop
```

Both `np.multiply`

and `*`

would yield element wise multiplication known as the Hadamard Product

`%timeit`

is ipython magic

Try this:

```
a = np.matrix([[1,2], [3,4]])
b = np.matrix([[5,6], [7,8]])
#This would result a 'numpy.ndarray'
result = np.array(a) * np.array(b)
```

Here, `np.array(a)`

returns a 2D array of type `ndarray`

and multiplication of two `ndarray`

would result element wise multiplication. So the result would be:

```
result = [[5, 12], [21, 32]]
```

If you wanna get a matrix, the do it with this:

```
result = np.mat(result)
```

```
error: OpenCV(4.6.0) /io/opencv/modules/core/src/arithm.cpp:230: error: (-215:Assertion failed) (mtype == CV_8U || mtype == CV_8S) && _mask.sameSize(*psrc1) in function 'binary_op'
```

This is the error When I tried .Implemented the LOGIC

```
def masking_rgb(im_rgb,im_mask):
r1,c1=im_mask.shape
for i in range(r1):
for j in range (c1):
if im_mask[i,j]==0:
im_rgb[i,j,:]=0
return im_rgb
im_rgb=masking_rgb(im_rgb,im_mask)
plt.imshow(im_rgb)
plt.show()
```