Showing ValueError: shapes (1,3) and (1,3) not ali

Question

I am trying to run this code,and the last 2 dot products are showing error as suggested in the heading. I checked the size of the matrices and both are (3, 1), then why it is showing me an error while doing dot product?

coordinate1 = [-7.173, -2.314, 2.811] 
coordinate2 = [-5.204, -3.598, 3.323] 
coordinate3 = [-3.922, -3.881, 4.044] 
coordinate4 = [-2.734, -3.794, 3.085] 

import numpy as np 
from numpy import matrix
coordinate1i=matrix(coordinate1)
coordinate2i=matrix(coordinate2)
coordinate3i=matrix(coordinate3)
coordinate4i=matrix(coordinate4)

b0 = coordinate1i - coordinate2i
b1 = coordinate3i - coordinate2i
b2 = coordinate4i - coordinate3i

n1 = np.cross(b0, b1)
n2 = np.cross(b2, b1)

n12cross = np.cross(n1,n2)
x1= np.cross(n1,b1)/np.linalg.norm(b1)
print np.shape(x1)
print np.shape(n2)
np.asarray(x1)
np.asarray(n2)

y = np.dot(x1,n2)
x = np.dot(n1,n2)

return np.degrees(np.arctan2(y, x))

Answer 1

By converting the matrix to array by using

n12 = np.squeeze(np.asarray(n2))

X12 = np.squeeze(np.asarray(x1))

solved the issue.

Answer 2

The column of the first matrix and the row of the second matrix should be equal and the order should be like this only

column of first matrix = row of second matrix

and do not follow the below step

row of first matrix  = column of second matrix

it will throw an error

Answer 3

numpy.dot(a, b, out=None)

Dot product of two arrays.

For N dimensions it is a sum product over the last axis of a and the second-to-last of b.

Documentation: numpy.dot.

Answer 4

Unlike standard arithmetic, which desires matching dimensions, dot products require that the dimensions are one of:

• (X..., A, B) dot (Y..., B, C) -> (X..., Y..., A, C), where ... means "0 or more different values
• (B,) dot (B, C) -> (C,)
• (A, B) dot (B,) -> (A,)
• (B,) dot (B,) -> ()

Your problem is that you are using np.matrix, which is totally unnecessary in your code - the main purpose of np.matrix is to translate a * b into np.dot(a, b). As a general rule, np.matrix is probably not a good choice.