I have a NumPy array with integer values. Values of matrix range from 0 to max element in matrix(in other words, all numbers from 0 to max data element presented in it). I need to build effective( effective means fast fully-vectorized solution) for searching number of elements in each row and encode them according to matrix values.
Let data = [[3,7,2],[1,4,5],[9,8,7]]
I am studying image-processing using Numpy and facing a problem with filtering with convolution.
I have a list of lists, something like
Is there a smart and space-efficient symmetric matrix in numpy which automatically (and transparently) fills the position at [j][i] when [i][j] is written to?
I want to convert a 1-dimensional array into a 2-dimensional array by specifying the number of columns in the 2D array. Something that would work like this:
I have two matrices
>>> ex=np.arange(30) >>> e=np.reshape(ex,[3,10]) >>> e 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, 25, 26, 27, 28, 29]]) >>> e>15 array([[False, False, False, False, False, False, False, False, False, False], [False, False, False, False, False, False, … Read more
I tried to create this code to input an m by n matrix. I intended to input [[1,2,3],[4,5,6]] but the code yields [[4,5,6],[4,5,6]. Same things happen when I input other m by n matrix, the code yields an m by n matrix whose rows are identical.
What is the status of the matrix class in NumPy?