matrix-multiplication

I have a matrix of disease states states0CommercialA , where the columns are states (i.e., "no disease", "disease", "dead") and the rows are model cycles (i.e., 1, 2, 3, 4, etc.). I'm trying to multiply this by a vector of costs commercialMedExp , wherein each cost corresponds to a disease state. I've attempted the following: commercialMedExp * states0CommercialA but it appears as though the multiplication is occurring across columns instead of across rows. Could someone help me with the correct syntax? Something like commercialMedExp0A <- t(apply(states0CommercialA, 1, function(x){ x *

As always trying to learn more from you, I was hoping I could receive some help with the following code. I need to accomplish the following: 1) I have a vector: x = [1 2 3 4 5 6 7 8 9 10 11 12] 2) and a matrix: A =[11 14 1 5 8 18 10 8 19 13 20 16] I need to be able to multiply each value from x with every value of A , this means: new_matrix = [1* A 2* A 3* A ... 12* A] This will give me this new_matrix of size (12*m x n) assuming A (mxn) . And in this case (12*4x3) How can I do this using bsxfun from matlab? and, would this method be faster than a for-loop ? Regarding my for-loop , I need some

Suppose I have an AxBxC matrix X and a BxD matrix Y . Is there a non-loop method by which I can multiply each of the C AxB matrices with Y ? You can do this in one line using the functions NUM2CELL to break the matrix X into a cell array and CELLFUN to operate across the cells: Z = cellfun(@(x) x*Y,num2cell(X,[1 2]),'UniformOutput',false); The result Z is a 1-by-C cell array where each cell contains an A-by-D matrix. If you want Z to be an A-by-D-by-C matrix, you can use the CAT function: Z = cat(3,Z{:}); NOTE: My old solution used MAT2CELL instead of NUM2CELL , which wasn't as succinct: [A,B

I recently moved to Python 3.5 and noticed the new matrix multiplication operator (@) sometimes behaves differently from the numpy dot operator. In example, for 3d arrays: import numpy as np a = np.random.rand(8,13,13) b = np.random.rand(8,13,13) c = a @ b # Python 3.5+ d = np.dot(a, b) The @ operator returns an array of shape: c.shape (8, 13, 13) while the np.dot() function returns: d.shape (8, 13, 8, 13) How can I reproduce the same result with numpy dot? Are there any other significant differences? The @ operator calls the array's __matmul__ method, not dot . This method is also present in

I am trying to find the most efficient implementation of 4x4 matrix (M) multiplication with a vector (u) using SSE. I mean Mu = v. As far as I understand there are two primary ways to go about this: method 1) v1 = dot(row1, u), v2 = dot(row2, u), v3 = dot(row3, u), v4 = dot(row4, u) method 2) v = u1 col1 + u2 col2 + u3 col3 + u4 col4. Method 2 is easy to implement in SSE2. Method 1 can be implement with either the horizontal add instruction in SSE3 or the dot product instruction in SSE4. However, in all my tests method 2 always outperforms method 1. One place where I though method 1 would have

I'm trying to multiply two matrices together using pure python. Input (X1 is a 3x3 and Xt is a 3x2): X1 = [[1.0016, 0.0, -16.0514], [0.0, 10000.0, -40000.0], [-16.0514, -40000.0, 160513.6437]] Xt = [(1.0, 1.0), (0.0, 0.25), (0.0, 0.0625)] where Xt is the zip transpose of another matrix. Now here is the code: def matrixmult (A, B): C = [[0 for row in range(len(A))] for col in range(len(B[0]))] for i in range(len(A)): for j in range(len(B[0])): for k in range(len(B)): C[i][j] += A[i][k]*B[k][j] return C The error that python gives me is this: IndexError: list index out of range. Now I'm not sure