I have to calculate a matrix from the alignement of some sequences,which is the library from which I can import the function : MatrixInfo?
with the previous version of biopython (3.7) I have always being using "Bio.SubsMat" as function, now, with the new version (3.8), this function doesn't work anymore.
I tried to use Bio.Align, but the resulting matrices, with global and local values, are exactly the same and they should not.
how can I overcome the problem?
Related
I have a matrix addition with several terms that I want to display in a Jupyter Notebook. I need the order of terms to match the standard notation - in my case, of linear regression. But, the terms do not, by default, appear in the correct order for my purpose, and I would like to ask how to control the order of display of matrices in a matrix addition (MatAdd) term in Sympy. For example, here we see that Sympy selects a particular order for the terms, that appears to be based on the values in the Matrix.
from sympy import MatAdd, Matrix
A = Matrix([1])
B = Matrix([0])
print(MatAdd(A, B, evaluate=False))
This gives
Matrix([[0]]) + Matrix([[1]])
Notice the matrix terms do not follow the order of defintion or the variable names.
Is there anything I can do to control the print output order of Matrix terms in a MatAdd expression?
You can use init_printing to chose from a few options. In particular, the order keyword should control how things are shown on the screen vs how things are stored in SymPy objects.
Now comes the differences: by setting init_printing(order="none") printers behave differently. I believe this is some bug.
For example, I usually use Latex rendering when using Jupyter Notebook:
from sympy import MatAdd, Matrix, init_printing
init_printing(order="none")
A = Matrix([1])
B = Matrix([0])
add = MatAdd(A, B, evaluate=False)
print(add)
# out: Matrix([[0]]) + Matrix([[1]])
display(add)
# out: [1] + [0]
Here you can see that the latex printer is displaying the elements as they are stored (check add.args), whereas the string printer is not following that convention...
I used Rcpp (especially Rcpp Armadillo) to perform a method that returns as result several large matrix, for example of size 10000*10000. How can I save these matrix to use them in R environment. Assume that my code in Rcpp looks like:
list Output (20000);
for( int i(0);i<20000;++1 ){
...
...
// Suppose that the previous lines allow me to compute a matrix Gi of size 10000*10000
Output(i)=Gi;
}
return Output;
The way I programmed is very costly and need enough memory. But I need the 20000 matrix to compute an estimator in R environment. How can I save the matrix ? I do not know if bigmatrix package can help me.
Best,
I finally found a solution. I noticed that I will need 15TB to save the matrices. That is impossible. What I finally did is to save only some features of the matrices, as eigenvalues for example and others. See more details here
I am working on solving the linear algebraic equation Ax = b by using Eigen solvers through mex function of Matlab. Given a complex sparse matrix A and a sparse vector b from Matlab workspace, I want to map matrix A and vector b in Eigen sparse matrix format. After that, I need to use Eigen's linear equation solvers to solve it. At the end I need to transfer the results x to Matlab workspace.
However, since I am not good at C++ and not familiar with Eigen either. I am stuck at the first step, namely constructing the complex sparse matrix in Eigen accepted format.
I have found there is the following function in Eigen,
Eigen::MappedSparseMatrix<double,RowMajor> mat(rows, cols, nnz, row_ptr, col_index, values);
And I can use mxGetPr, mxGetPi, mxGetIr, mxGetJc, etc, these mex functions to get the info for the above "rows, cols, nnz, row_ptr, col_index, values". However, since in my case, matrix A is a complex sparse matrix, I am not sure whether "MappedSparseMatrix" can do that.
If it can, how the format of "MappedSparseMatrix" should be ? Is the following correct ?
Eigen::MappedSparseMatrix<std::complex<double>> mat(rows, cols, nnz, row_ptr, col_index, values_complex);
If so, how should I construct that values_complex ?
I have found about a relevant topic before. I can use the following codes to get a complex dense matrix.
MatrixXcd mat(m,n);
mat.real() = Map<MatrixXd>(realData,m,n);
mat.imag() = Map<MatrixXd>(imagData,m,n);
However, since my matrix A is a sparse matrix, it seems that it will produce errors if I define mat as a complex sparse matrix like the following:
SparseMatrix<std::complex<double> > mat;
mat.real() = Map<SparseMatrix>(rows, cols, nnz, row_ptr, col_index, realData);
mat.imag() = Map<SparseMatrix>(rows, cols, nnz, row_ptr, col_index, imagData);
So can anyone provide some advice for that?
MatlLab stores complex entries in two separate buffers: one for the real components and one for the imaginary components, whereas Eigen needs them to be interleaved:
value_ptr = [r0,i0,r1,i1,r2,i2,...]
so that it is compatible with std::complex<>. So in your case, you will have to create yourself a temporary buffer holding the values in that interleaved format to be passed to MappedSparseMatrix, or, if using Eigen 3.3, to Map<SparseMatrix<double,RowMajor> >.
Moreover, you will have to adjust the buffer of indices so that they are zero-based. To this end, decrement by one all entries of col_ptr and row_ptr before passing them to Eigen, and increment them by one afterward.
I want to generate a sequence of random numbers that will be used to pick tiles for a "maze". Each maze will have an id and I want to use that id as a seed to a pseudo random function. That way I can generate the same maze over and over given it's maze id. Preferably I do not want to use a built in pseudo random function in a language since I do not have control over the algorithm and it could change from platform to platform. As such, I would like to know:
How should I go about implementing my own pseudo random function?
Is it even feasible to generate platform independent pseudo random numbers?
Yes, it is possible.
Here is an example of such an algorithm (and its use) for noise generation.
Those particular random functions (Noise1, Noise2, Noise3, ..) use input parameters and calculate the pseudo random values from there.
Their output range is from 0.0 to 1.0.
And there are many more out there (Like mentioned in the comments).
UPDATE 2019
Looking back at this answer, a better suited choice would be the below-mentioned mersenne twister. Or you could find any implementation of xorshift.
The Mersenne Twister may be a good pick for this. As you can see from the pseudocode on wikipedia, you can seed the RNG with whatever you prefer to produce identical values for any instance with that seed. In your case, the maze ID or the hash of the maze ID.
If you are using Python, you can use the random module by typing at the beginning,
import random. Then, to use it, you type-
var = random.randint(1000, 9999)
This gives the var a 4 digit number that can be used for its id
If you are using another language, there is likely a similar module
My question is similar to this one Multiply a 3D matrix with a 2D matrix. However, I'm coding in Fortran.
Say, if I have a RxSxT matrix A and an SxU matrix B, where R,S,T,U are integers, and I want to multiply A(:,:,0) with B. How can I do this with matmul? When I do something like
C(:,:,0) = matmul(A(:,:,0),B)
The compiler (gfortran) gives:
Warning:Array reference at (1) is out of bounds (0 < 1) in dimension 3
f951: internal compiler error: Segmentation fault
Is there a way around this?
Thanks.
EDIT: I should add that I'm actually transposing the second matrix. Say, A a RxSxT matrix and B a UxS matrix. Then
C(:,:,0) = matmul(B,transpose(A(:,:,0))
That transpose might be part of the problem. Does it convert A(i,j,k) to A(k,i,j)?
Remember that in Fortran your array indices start from 1 by default. So unless you have specified your array A to have a non-default lower bound on the 3rd dimension, gfortran is entirely correct in pointing out your error.
Of course, an internal compiler error is always a compiler bug; unless you have some ancient version of gfortran please file a bug at http://gcc.gnu.org/bugzilla
transpose (A(:,:,0)) should interchange the indices A(i,j,0) to A(j,i,0). A(:,:,0) is a rank two matrix.
The compiler should never crash, whether or not the input source code is correct. Are you using the latest version of gfortran? You could report this "internal compiler error: Segmentation fault" to the gfortran development team: http://gcc.gnu.org/wiki/GFortran#bugs