can't understand scipy.sparse.csr_matrix example
I can't wrap my head around csr_matrix
examples in scipy documentation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html
Can someone explain how this example work?
>>> row = np.array([0, 0, 1, 2, 2, 2])
>>> col = np.array([0, 2, 2, 0, 1, 2])
>>> data = np.array([1, 2, 3, 4, 5, 6])
>>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray()
array([[1, 0, 2],
[0, 0, 3],
[4, 5, 6]])
I believe this is following this format.
csr_matrix((data, (row_ind, col_ind)), [shape=(M, N)])
where data, row_ind and col_ind satisfy the relationship a[row_ind[k], col_ind[k]] = data[k].
What is a
here?
python scipy
add a comment |
I can't wrap my head around csr_matrix
examples in scipy documentation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html
Can someone explain how this example work?
>>> row = np.array([0, 0, 1, 2, 2, 2])
>>> col = np.array([0, 2, 2, 0, 1, 2])
>>> data = np.array([1, 2, 3, 4, 5, 6])
>>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray()
array([[1, 0, 2],
[0, 0, 3],
[4, 5, 6]])
I believe this is following this format.
csr_matrix((data, (row_ind, col_ind)), [shape=(M, N)])
where data, row_ind and col_ind satisfy the relationship a[row_ind[k], col_ind[k]] = data[k].
What is a
here?
python scipy
1
a
is the matrix.
– Warren Weckesser
Nov 11 '18 at 23:44
add a comment |
I can't wrap my head around csr_matrix
examples in scipy documentation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html
Can someone explain how this example work?
>>> row = np.array([0, 0, 1, 2, 2, 2])
>>> col = np.array([0, 2, 2, 0, 1, 2])
>>> data = np.array([1, 2, 3, 4, 5, 6])
>>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray()
array([[1, 0, 2],
[0, 0, 3],
[4, 5, 6]])
I believe this is following this format.
csr_matrix((data, (row_ind, col_ind)), [shape=(M, N)])
where data, row_ind and col_ind satisfy the relationship a[row_ind[k], col_ind[k]] = data[k].
What is a
here?
python scipy
I can't wrap my head around csr_matrix
examples in scipy documentation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html
Can someone explain how this example work?
>>> row = np.array([0, 0, 1, 2, 2, 2])
>>> col = np.array([0, 2, 2, 0, 1, 2])
>>> data = np.array([1, 2, 3, 4, 5, 6])
>>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray()
array([[1, 0, 2],
[0, 0, 3],
[4, 5, 6]])
I believe this is following this format.
csr_matrix((data, (row_ind, col_ind)), [shape=(M, N)])
where data, row_ind and col_ind satisfy the relationship a[row_ind[k], col_ind[k]] = data[k].
What is a
here?
python scipy
python scipy
edited Nov 12 '18 at 2:04
Aerin
asked Nov 11 '18 at 23:00
AerinAerin
3,76143458
3,76143458
1
a
is the matrix.
– Warren Weckesser
Nov 11 '18 at 23:44
add a comment |
1
a
is the matrix.
– Warren Weckesser
Nov 11 '18 at 23:44
1
1
a
is the matrix.– Warren Weckesser
Nov 11 '18 at 23:44
a
is the matrix.– Warren Weckesser
Nov 11 '18 at 23:44
add a comment |
2 Answers
2
active
oldest
votes
This is a sparse matrix. So, it stores the explicit indices and values at those indices. So for example, since row=0 and col=0 corresponds to 1 (the first entries of all three arrays in your example). Hence, the [0,0] entry of the matrix is 1. And so on.
add a comment |
As far as I understand, in row and col arrays we have indices which corrensponds to non-zero values in matrix. a[0, 0] = 1, a[0, 2] = 2, a[1, 2] = 3 and so on. As we have no indices for a[0, 1], a[1, 0], a[1, 1] so appropriate values in matrix are equal to 0.
Also, maybe this little intro will be helpful for you:
https://www.youtube.com/watch?v=Lhef_jxzqCg
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
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active
oldest
votes
This is a sparse matrix. So, it stores the explicit indices and values at those indices. So for example, since row=0 and col=0 corresponds to 1 (the first entries of all three arrays in your example). Hence, the [0,0] entry of the matrix is 1. And so on.
add a comment |
This is a sparse matrix. So, it stores the explicit indices and values at those indices. So for example, since row=0 and col=0 corresponds to 1 (the first entries of all three arrays in your example). Hence, the [0,0] entry of the matrix is 1. And so on.
add a comment |
This is a sparse matrix. So, it stores the explicit indices and values at those indices. So for example, since row=0 and col=0 corresponds to 1 (the first entries of all three arrays in your example). Hence, the [0,0] entry of the matrix is 1. And so on.
This is a sparse matrix. So, it stores the explicit indices and values at those indices. So for example, since row=0 and col=0 corresponds to 1 (the first entries of all three arrays in your example). Hence, the [0,0] entry of the matrix is 1. And so on.
answered Nov 12 '18 at 2:14
Rohit PandeyRohit Pandey
98151639
98151639
add a comment |
add a comment |
As far as I understand, in row and col arrays we have indices which corrensponds to non-zero values in matrix. a[0, 0] = 1, a[0, 2] = 2, a[1, 2] = 3 and so on. As we have no indices for a[0, 1], a[1, 0], a[1, 1] so appropriate values in matrix are equal to 0.
Also, maybe this little intro will be helpful for you:
https://www.youtube.com/watch?v=Lhef_jxzqCg
add a comment |
As far as I understand, in row and col arrays we have indices which corrensponds to non-zero values in matrix. a[0, 0] = 1, a[0, 2] = 2, a[1, 2] = 3 and so on. As we have no indices for a[0, 1], a[1, 0], a[1, 1] so appropriate values in matrix are equal to 0.
Also, maybe this little intro will be helpful for you:
https://www.youtube.com/watch?v=Lhef_jxzqCg
add a comment |
As far as I understand, in row and col arrays we have indices which corrensponds to non-zero values in matrix. a[0, 0] = 1, a[0, 2] = 2, a[1, 2] = 3 and so on. As we have no indices for a[0, 1], a[1, 0], a[1, 1] so appropriate values in matrix are equal to 0.
Also, maybe this little intro will be helpful for you:
https://www.youtube.com/watch?v=Lhef_jxzqCg
As far as I understand, in row and col arrays we have indices which corrensponds to non-zero values in matrix. a[0, 0] = 1, a[0, 2] = 2, a[1, 2] = 3 and so on. As we have no indices for a[0, 1], a[1, 0], a[1, 1] so appropriate values in matrix are equal to 0.
Also, maybe this little intro will be helpful for you:
https://www.youtube.com/watch?v=Lhef_jxzqCg
edited Jan 24 at 10:25
answered Jan 24 at 9:55
AutoRunAutoRun
12
12
add a comment |
add a comment |
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1
a
is the matrix.– Warren Weckesser
Nov 11 '18 at 23:44