Showing ValueError: shapes (1,3) and (1,3) not aligned: 3 (dim 1) != 1 (dim 0)
Question:
I am trying to use the following matrices and perform a dot product as shown in the code. I checked the size of the matrices and all are (3, 1) but it is throwing me error for the last two dot products.
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))
Answers:
By converting the matrix to array by using
n12 = np.squeeze(np.asarray(n2))
X12 = np.squeeze(np.asarray(x1))
solved the issue.
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.
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.
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
Solution: Input the transpose of n2 into dot product:
np.dot(x1, n2.T)
For a dot product to work, the dimensions of the input matrices must meet a certain criteria: the size of the column of the first matrix = the size of the row of the second matrix.
You have both matrices with dimensions (1,3):
x1.shape => (1,3), n2.shape => (1,3).
Following the above rule, you need x1.shape => (1,3), n2.shape => (3,1).
This is easily accomplished by taking the transpose of n2, shown above.
I am trying to use the following matrices and perform a dot product as shown in the code. I checked the size of the matrices and all are (3, 1) but it is throwing me error for the last two dot products.
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))
By converting the matrix to array by using
n12 = np.squeeze(np.asarray(n2))
X12 = np.squeeze(np.asarray(x1))
solved the issue.
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.
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.
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
Solution: Input the transpose of n2 into dot product:
np.dot(x1, n2.T)
For a dot product to work, the dimensions of the input matrices must meet a certain criteria: the size of the column of the first matrix = the size of the row of the second matrix.
You have both matrices with dimensions (1,3):
x1.shape => (1,3), n2.shape => (1,3).
Following the above rule, you need x1.shape => (1,3), n2.shape => (3,1).
This is easily accomplished by taking the transpose of n2, shown above.