matrix
Simple 3×3 matrix inverse code (C++)
Here’s a version of batty’s answer, but this computes the correct inverse. batty’s version computes the transpose of the inverse. // computes the inverse of a matrix m double det = m(0, 0) * (m(1, 1) * m(2, 2) – m(2, 1) * m(1, 2)) – m(0, 1) * (m(1, 0) * m(2, 2) – … Read more
How can I sort a 2-D array in MATLAB with respect to one column?
I think the sortrows function is what you’re looking for. >> sortrows(data,1) ans = -1 4 1 3 5 7
Flattening SVG matrix transforms in Inkscape
Short answer When typing the transformation matrix params in Inkscape, make sure you have “Edit current matrix” checked, since if you apply a new transformation matrix to an object, you’re actually multiplying this new matrix with the existing transformation matrix of the object, so make sure you edit it instead. Long Answer How to recalculate … Read more
Bind Poses, Joint Transforms in Collada
I know what file format you are referring to. The file format you use (.anim) has one more skeletal file with (.inv_bind_mats) extension and same name, you don’t need to calculate anything but just read .inv_bind_mats file.
Singular matrix issue with Numpy
The matrix you pasted [[ 1, 8, 50], [ 8, 64, 400], [ 50, 400, 2500]] Has a determinant of zero. This is the definition of a Singular matrix (one for which an inverse does not exist) http://en.wikipedia.org/wiki/Invertible_matrix
A simple algorithm for generating positive-semidefinite matrices
generate random matrix multiply it by its own transposition you have obtained a positive semi-definite matrix. Example code (Python): import numpy as np matrixSize = 10 A = np.random.rand(matrixSize, matrixSize) B = np.dot(A, A.transpose()) print ‘random positive semi-define matrix for today is’, B
Convolve2d just by using Numpy
You could generate the subarrays using as_strided: import numpy as np a = np.array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14], [15, 16, 17, 18, 19], [20, 21, 22, 23, 24]]) sub_shape = (3,3) view_shape = tuple(np.subtract(a.shape, sub_shape) + 1) + sub_shape strides = a.strides + … Read more
How to check if all values in the columns of a numpy matrix are the same?
In [45]: a Out[45]: array([[1, 1, 0], [1, 0, 0], [1, 0, 0], [1, 1, 0]]) Compare each value to the corresponding value in the first row: In [46]: a == a[0,:] Out[46]: array([[ True, True, True], [ True, False, True], [ True, False, True], [ True, True, True]], dtype=bool) A column shares a common … Read more