You can take advantage of the complex type : # build a complex array of your cells z = np.array([complex(c.m_x, c.m_y) for c in cells]) First solution # mesh this array so that you will have all combinations m, n = np.meshgrid(z, z) # get the distance via the norm out = abs(m-n) Second solution … Read more
So how does one identify if a matrix is truly singular? (In MATLAB, without using paper and pencil or symbolic computations, or hand written row operations?) The textbooks sometimes tell students to use the determinant, so I’ll start there. In theory, one can simply test if the determinant of your matrix is zero. Thus M … Read more
Matrix was removed from Ruby’s standard library in version 3.1. More info: https://www.ruby-lang.org/en/news/2021/12/25/ruby-3-1-0-released/ With Ruby 3.1, matrix needs to be explicitly added to the Gemfile. You can add it manually or run something like: $ bundle add matrix After it’s added to the Gemfile, bundle your application: $ bundle install Then your application should continue … Read more
With pandas.DataFrame.to_csv you can write the columns and the index to a file: import numpy as np import pandas as pd A = np.random.randint(0, 10, size=36).reshape(6, 6) names = [_ for _ in ‘abcdef’] df = pd.DataFrame(A, index=names, columns=names) df.to_csv(‘df.csv’, index=True, header=True, sep=’ ‘) will give you the following df.csv file: a b c d … Read more
First, your 3rd row is linearly dependent with 1t and 2nd row. However, your 1st and 4th column are linearly dependent. Two methods you could use: Eigenvalue If one eigenvalue of the matrix is zero, its corresponding eigenvector is linearly dependent. The documentation eig states the returned eigenvalues are repeated according to their multiplicity and … Read more
How to fill Matrix with zeros in OpenCV? To fill a pre-existing Mat object with zeros, you can use Mat::zeros() Mat m1 = …; m1 = Mat::zeros(1, 1, CV_64F); To intialize a Mat so that it contains only zeros, you can pass a scalar with value 0 to the constructor: Mat m1 = Mat(1,1, CV_64F, … Read more
The problem described can be solved as follows. Let M = m_11 m_12 m_13 m_21 m_22 m_23 m_31 m_32 m_33 denote the desired rotation matrix. We require 1 0 0 * M + t = x_x x_y x_z 0 1 0 y_x y_y y_z 0 0 1 z_x z_y z_y where t denotes the translation; … Read more
Update I have implemented the Hungarian Algorithm in the same steps provided by the link you posted: Hungarian algorithm Here’s the files with comments: Github Algorithm (Improved greedy) for step 3: (This code is very detailed and good for understanding the concept of choosing line to draw: horizontal vs Vertical. But note that this step … Read more
It is a seldom-used feature from C99 GNU extension (GCC documentation) that is used to forward-declare parameters used in VLA declarators. matrix_* matrix_insert_values(int n; double a[][n], int m, int n); Do you see how int n appears twice? The first int n; is just a forward declaration of the actual int n, which is at … Read more
I finally found, that it can be done using LU decomposition. Here the U matrix represents the reduced form of the linear system. from numpy import array from scipy.linalg import lu a = array([[2.,4.,4.,4.],[1.,2.,3.,3.],[1.,2.,2.,2.],[1.,4.,3.,4.]]) pl, u = lu(a, permute_l=True) Then u reads array([[ 2., 4., 4., 4.], [ 0., 2., 1., 2.], [ 0., 0., … Read more