# Get ting the index of the non zero connections in scipy csr graph

Active3 hr before
Viewed126 times

indexscipygraph
90%

A sparse matrix is a matrix in which most elements are zeroes, This is in contrast to a dense matrix, the differentiating characteristic of which you can likely figure out at this point without any help

Example_snippet/controller/utility/_index.js/ import numpy as np from scipy . . .
import numpy as np
from scipy
import sparse

X = np.random.uniform(size = (6, 6))
print(X)
88%

Example_snippet/controller/utility/_index.js/ >>> from sknetwork.topology im. . .
>>> from sknetwork.topology
import CoreDecomposition
>>>
from sknetwork.data
import karate_club
>>>
kcore = CoreDecomposition() >>>
kcore.core_value_
4
72%

Perform a shortest-path graph search on a positive directed or undirected graph,,dijkstra(csgraph[, directed, indices, …]),Dijkstra algorithm using Fibonacci Heaps,johnson(csgraph[, directed, indices, …])

Example_snippet/controller/utility/_index.js/ G (0) / \ . . .
G

(0) /
\
1 2 /
\
(2)(1)
65%

skan,csr: CSR Graph representation of skeletons,M (scipy

Example_snippet/controller/utility/_index.js/ >>> image = np.array([[1, 0, 1. . .
>>> image = np.array([
[1, 0, 1, 0, 0, 1, 1],
...[1, 0, 0, 1, 0, 0, 0]
]) >>>
labels, centroids = compute_centroids(image) >>>
print(labels)[[1 0 2 0 0 3 3]
[1 0 0 2 0 0 0]] >>>
centroids
array([
[0.5, 0.],
[0.5, 2.5],
[0., 5.5]
])
75%

CSR (and also CSC, a,k

Example_snippet/controller/utility/_scipy.js/ import numpy as npfrom scipy i. . .
import numpy as npfrom scipy
import sparsefrom sys
import getsizeof # Matrix 1: Create a dense matrix(stored as a full matrix).A_full = np.random.rand(600, 600) # Matrix 2: Store A_full as a sparse matrix(though it is dense).A_sparse = sparse.csc_matrix(A_full) # Matrix 3: Create a sparse matrix(stored as a full matrix).B_full = np.diag(np.random.rand(600)) # Matrix 4: Store B_full as a sparse matrix.B_sparse = sparse.csc_matrix(B_full) # Create a square
function to
return the square of the matrixdef square(A): return np.power(A, 2)