Matrix Format [ [ row ], [ row ], … [ row ] ]
linspace(start, stop, number of points desired + 1)
arange(start, stop, step size); default step size: 1; returns a 1 row array from start to stop using step size
identity(n): returns an identity matrix as n x n
transpose(matrix): transpose of a matrix
inv(matrix): inverse of a matrix
shape(matrix): returns the dimensions of the matrix in an ordered pair (row, columns)
rref(matrix): row reduced echelon form of a matrix
det(matrix): determinant of a square matrix
peval(array of coefficients, x): polynomial evaluation (order is from high to low), can take complex arguments
horner(array of coefficients, x): polynomial evaluation using Horner’s method
pceoff(array of roots): returns an array representing a polynomial’s coefficients, can take complex arguments
proot(array of coefficients): returns an array of roots, can take complex arguments
add(array, array) or add(matrix, matrix): addition element by element
sub(array, array) or sub(matrix, matrix): subtraction element by element
dot(array, array): dot product
cross(array, array): cross product
imag(complex number): imaginary part – works on arrays and matrices
real(complex number): real part – works on arrays and matrices
I believe that fft and ifft have to do with fast fourier transforms.
thanks Eddie.
(05-08-2021 01:49 PM)Eddie W. Shore Wrote: [ -> ]I believe that fft and ifft have to do with fast fourier transforms.
yup; the integrated Help has a few examples…
Those sound like the function names from the Prime CAS and the 50g.
BTW, pcoeff is misspelled.
oops, sorry, my comment re Help of course refers to the CAS commands, whereas this thread is about Python… I need more coffee.
(05-08-2021 01:49 PM)Eddie W. Shore Wrote: [ -> ]Matrix Format [ [ row ], [ row ], … [ row ] ]
linspace(start, stop, number of points desired + 1)
arange(start, stop, step size); default step size: 1; returns a 1 row array from start to stop using step size
identity(n): returns an identity matrix as n x n
transpose(matrix): transpose of a matrix
inv(matrix): inverse of a matrix
shape(matrix): returns the dimensions of the matrix in an ordered pair (row, columns)
rref(matrix): row reduced echelon form of a matrix
det(matrix): determinant of a square matrix
peval(array of coefficients, x): polynomial evaluation (order is from high to low), can take complex arguments
horner(array of coefficients, x): polynomial evaluation using Horner’s method
pceoff(array of roots): returns an array representing a polynomial’s coefficients, can take complex arguments
proot(array of coefficients): returns an array of roots, can take complex arguments
add(array, array) or add(matrix, matrix): addition element by element
sub(array, array) or sub(matrix, matrix): subtraction element by element
dot(array, array): dot product
cross(array, array): cross product
imag(complex number): imaginary part – works on arrays and matrices
real(complex number): real part – works on arrays and matrices
I believe that fft and ifft have to do with fast fourier transforms.
Hi, I found it difficult to transpose a matrix with PYTHON. For instance:
transpose ([[1,2,3], [4,5,6]]) gives me this result: [[1,4], [3,2], [5,6]]
The correct result is: [[1,4], [2,5], [3,6]].
Is there a bag perhaps?
There is indeed a bug in transpose for non square matrices.
(09-10-2021 04:55 AM)parisse Wrote: [ -> ]There is indeed a bug in transpose for non square matrices.
Thanks for the answer, Parisse. Since I had to use the "transpose" command in my Python program, I had to write a subroutine to transpose the arrays:
Code:
from linalg import *
def transposeRr(matriceRr):
L=shape(matriceRr);
r=L[0];
c=L[1];
matricezero=zeros(c,r);
for u in range(0,r):
for uu in range (0,c):
matricezero[uu][u]=matriceRr[u][uu];
return matricezero;
(09-11-2021 09:13 AM)robmio Wrote: [ -> ]Since I had to use the "transpose" command in my Python program, I had to write a subroutine to transpose the arrays ...
Is matrix simply list of list ? If yes, we can transpose with a 1-liner.
>>> transpose = lambda a: [list(r) for r in zip(*a)]
>>> transpose([[1,2,3], [4,5,6]])
[[1, 4], [2, 5], [3, 6]]
(09-11-2021 10:36 AM)Albert Chan Wrote: [ -> ] (09-11-2021 09:13 AM)robmio Wrote: [ -> ]Since I had to use the "transpose" command in my Python program, I had to write a subroutine to transpose the arrays ...
Is matrix simply list of list ? If yes, we can transpose with a 1-liner.
>>> transpose = lambda a: [list(r) for r in zip(*a)]
>>> transpose([[1,2,3], [4,5,6]])
[[1, 4], [2, 5], [3, 6]]
Congratulations! This short solution you proposed works very well in my program
matrix() doesn't follow rules of matrix dot product.
Possible bug related to post#7
Code:
from linalg import *
M1= matrix([ [1,2,3], [10,100,1000] ])
# Sum of rows of any Matrix
EachRowSum= dot( M1, ones( len(M1[0]), 1 ) )
# Sum of columns of any Matrix ... NOT WORKING
EachColSum= dot( ones( 1, len(M1) ), M1 )
In the absence of multi-dimensional arrays (numpy) following commands using native zip(), map(), lambda, list constructions ...can be used to have similar effect while considering dimensions as well.
Few array examples & their equivalents
Code:
# x + b21 * k1 + b32 * k2
# where x, k1 & k2 are numpy arrays while b21 & b32 is some scalar value
[sum(i) for i in zip(x, [b21*i for i in k1], [b32*i for i in k2] )]
# r / ( q*( abs(x)+abs(k1) ) )
# where r, x & k1 are equal shaped arrays & q is scalar value
[ (r[i] / ( q*( abs(x[i]) + abs(k1[i]) ) ) ) for i in range(len(x) ) ]