Mixing
Transforming a list containing the elements below the diagonal of a matrix into a full matrix
I want to create a full-fledged matrix from a list of the elements that are below the diagonal. The following list contains the elements below the diagonal:
And this would be the desired output:
Up until this point, I tried to make this work with normal sintax in python by implementing the following code:
list_similarities = [1,0.1,0.6,0.4,1,0.1,0.2,1,0.7,1]
the_range = range(0,4)
list_of_lists =
counter_element = 0
counter = -1
for element in the_range:
counter += 1
counter_element += len(the_range)-element
intermediary = (len(the_range)-element)
first_element = counter_element-intermediary
line_list = list_similarities[first_element:counter_element]
# counter = 0, then no need to add an additional element
# print(line_list)
if counter == 0:
"do nothing"
elif counter >0:
for item in range(0,element):
from_list_to_add = list_of_lists[item]
element_to_add = from_list_to_add[item+1]
line_list.insert(0,element_to_add)
print(line_list)
list_of_lists.append(line_list.copy())
# print("final lists:", list_of_lists)
# print(counter_element)
print("final lists:", list_of_lists)
However, the output is the following:
final lists: [[1, 0.1, 0.6, 0.4], [0.1, 1, 0.1, 0.2], [0.1, 0.1, 1, 0.7], [0.7, 0.1, 0.1, 1]]
It does the first 2 lists, which represent the 2 rows from the matrix, but will not do the last 2 because of the way my code works and so far I don't know a solution for that..
This is due to the fact that my counter will make the list go out of range. I looked at a lot of posts on the stack overflow, but I cannot find something that would work in my situation. If you can point me towards a similar example it would be perfect.
Thank you for your time and suggestions!
UPDATE :
My question is not a duplicate of Numpy: convert an array to a triangular matrix because I do not want to create a matrix where my values from the array are part of just the lower triangular matrix, but rather they are also in the upper triangular matrix.
python python-3.x matrix matrix-transform
asked Nov 19 '18 at 12:20
Adrian
698
add a comment |
I want to create a full-fledged matrix from a list of the elements that are below the diagonal. The following list contains the elements below the diagonal:
And this would be the desired output:
Up until this point, I tried to make this work with normal sintax in python by implementing the following code:
list_similarities = [1,0.1,0.6,0.4,1,0.1,0.2,1,0.7,1]
the_range = range(0,4)
list_of_lists =
counter_element = 0
counter = -1
for element in the_range:
counter += 1
counter_element += len(the_range)-element
intermediary = (len(the_range)-element)
first_element = counter_element-intermediary
line_list = list_similarities[first_element:counter_element]
# counter = 0, then no need to add an additional element
# print(line_list)
if counter == 0:
"do nothing"
elif counter >0:
for item in range(0,element):
from_list_to_add = list_of_lists[item]
element_to_add = from_list_to_add[item+1]
line_list.insert(0,element_to_add)
print(line_list)
list_of_lists.append(line_list.copy())
# print("final lists:", list_of_lists)
# print(counter_element)
print("final lists:", list_of_lists)
However, the output is the following:
final lists: [[1, 0.1, 0.6, 0.4], [0.1, 1, 0.1, 0.2], [0.1, 0.1, 1, 0.7], [0.7, 0.1, 0.1, 1]]
It does the first 2 lists, which represent the 2 rows from the matrix, but will not do the last 2 because of the way my code works and so far I don't know a solution for that..
This is due to the fact that my counter will make the list go out of range. I looked at a lot of posts on the stack overflow, but I cannot find something that would work in my situation. If you can point me towards a similar example it would be perfect.
Thank you for your time and suggestions!
UPDATE :
My question is not a duplicate of Numpy: convert an array to a triangular matrix because I do not want to create a matrix where my values from the array are part of just the lower triangular matrix, but rather they are also in the upper triangular matrix.
python python-3.x matrix matrix-transform
asked Nov 19 '18 at 12:20
Adrian
698
1
Possible duplicate of Numpy: convert an array to a triangular matrix
– Matthieu Brucher
Nov 19 '18 at 12:24
1
@Andrian see my answer and let me know if it helps
– seralouk
Nov 19 '18 at 13:00
@seralouk thank you for taking the time to come up with a solution! It works as expected :)
– Adrian
Nov 19 '18 at 13:08
add a comment |
I want to create a full-fledged matrix from a list of the elements that are below the diagonal. The following list contains the elements below the diagonal:
And this would be the desired output:
Up until this point, I tried to make this work with normal sintax in python by implementing the following code:
list_similarities = [1,0.1,0.6,0.4,1,0.1,0.2,1,0.7,1]
the_range = range(0,4)
list_of_lists =
counter_element = 0
counter = -1
for element in the_range:
counter += 1
counter_element += len(the_range)-element
intermediary = (len(the_range)-element)
first_element = counter_element-intermediary
line_list = list_similarities[first_element:counter_element]
# counter = 0, then no need to add an additional element
# print(line_list)
if counter == 0:
"do nothing"
elif counter >0:
for item in range(0,element):
from_list_to_add = list_of_lists[item]
element_to_add = from_list_to_add[item+1]
line_list.insert(0,element_to_add)
print(line_list)
list_of_lists.append(line_list.copy())
# print("final lists:", list_of_lists)
# print(counter_element)
print("final lists:", list_of_lists)
However, the output is the following:
final lists: [[1, 0.1, 0.6, 0.4], [0.1, 1, 0.1, 0.2], [0.1, 0.1, 1, 0.7], [0.7, 0.1, 0.1, 1]]
It does the first 2 lists, which represent the 2 rows from the matrix, but will not do the last 2 because of the way my code works and so far I don't know a solution for that..
This is due to the fact that my counter will make the list go out of range. I looked at a lot of posts on the stack overflow, but I cannot find something that would work in my situation. If you can point me towards a similar example it would be perfect.
Thank you for your time and suggestions!
UPDATE :
My question is not a duplicate of Numpy: convert an array to a triangular matrix because I do not want to create a matrix where my values from the array are part of just the lower triangular matrix, but rather they are also in the upper triangular matrix.
python python-3.x matrix matrix-transform
asked Nov 19 '18 at 12:20
Adrian
698
I want to create a full-fledged matrix from a list of the elements that are below the diagonal. The following list contains the elements below the diagonal:
And this would be the desired output:
Up until this point, I tried to make this work with normal sintax in python by implementing the following code:
list_similarities = [1,0.1,0.6,0.4,1,0.1,0.2,1,0.7,1]
the_range = range(0,4)
list_of_lists =
counter_element = 0
counter = -1
for element in the_range:
counter += 1
counter_element += len(the_range)-element
intermediary = (len(the_range)-element)
first_element = counter_element-intermediary
line_list = list_similarities[first_element:counter_element]
# counter = 0, then no need to add an additional element
# print(line_list)
if counter == 0:
"do nothing"
elif counter >0:
for item in range(0,element):
from_list_to_add = list_of_lists[item]
element_to_add = from_list_to_add[item+1]
line_list.insert(0,element_to_add)
print(line_list)
list_of_lists.append(line_list.copy())
# print("final lists:", list_of_lists)
# print(counter_element)
print("final lists:", list_of_lists)
However, the output is the following:
final lists: [[1, 0.1, 0.6, 0.4], [0.1, 1, 0.1, 0.2], [0.1, 0.1, 1, 0.7], [0.7, 0.1, 0.1, 1]]
It does the first 2 lists, which represent the 2 rows from the matrix, but will not do the last 2 because of the way my code works and so far I don't know a solution for that..
This is due to the fact that my counter will make the list go out of range. I looked at a lot of posts on the stack overflow, but I cannot find something that would work in my situation. If you can point me towards a similar example it would be perfect.
Thank you for your time and suggestions!
UPDATE :
My question is not a duplicate of Numpy: convert an array to a triangular matrix because I do not want to create a matrix where my values from the array are part of just the lower triangular matrix, but rather they are also in the upper triangular matrix.
python python-3.x matrix matrix-transform
python python-3.x matrix matrix-transform
asked Nov 19 '18 at 12:20
Adrian
698
asked Nov 19 '18 at 12:20
Adrian
698
edited Nov 19 '18 at 12:48
asked Nov 19 '18 at 12:20
Adrian
698
asked Nov 19 '18 at 12:20
Adrian
698
asked Nov 19 '18 at 12:20
Adrian
698
698
1
Possible duplicate of Numpy: convert an array to a triangular matrix
– Matthieu Brucher
Nov 19 '18 at 12:24
1
@Andrian see my answer and let me know if it helps
– seralouk
Nov 19 '18 at 13:00
@seralouk thank you for taking the time to come up with a solution! It works as expected :)
– Adrian
Nov 19 '18 at 13:08
add a comment |
1
Possible duplicate of Numpy: convert an array to a triangular matrix
– Matthieu Brucher
Nov 19 '18 at 12:24
1
@Andrian see my answer and let me know if it helps
– seralouk
Nov 19 '18 at 13:00
@seralouk thank you for taking the time to come up with a solution! It works as expected :)
– Adrian
Nov 19 '18 at 13:08
1
1
Possible duplicate of Numpy: convert an array to a triangular matrix
– Matthieu Brucher
Nov 19 '18 at 12:24
Possible duplicate of Numpy: convert an array to a triangular matrix
– Matthieu Brucher
Nov 19 '18 at 12:24
1
1
@Andrian see my answer and let me know if it helps
– seralouk
Nov 19 '18 at 13:00
@Andrian see my answer and let me know if it helps
– seralouk
Nov 19 '18 at 13:00
@seralouk thank you for taking the time to come up with a solution! It works as expected :)
– Adrian
Nov 19 '18 at 13:08
@seralouk thank you for taking the time to come up with a solution! It works as expected :)
– Adrian
Nov 19 '18 at 13:08
add a comment |
2 Answers
2
active
oldest
votes
A solution using numpy.triu_indices and numpy.tril_indices. I have guided each step with comments. The key is to first find the upper right indices, assign the value from the list, then make the matrix symmetric.
import numpy as np
n = 4
l = [1,0.1,0.6,0.4,1,0.1,0.2,1,0.7,1]
a = np.zeros((n,n)) # Initialize nxn matrix
triu = np.triu_indices(n) # Find upper right indices of a triangular nxn matrix
tril = np.tril_indices(n, -1) # Find lower left indices of a triangular nxn matrix
a[triu] = l # Assign list values to upper right matrix
a[tril] = a.T[tril] # Make the matrix symmetric
print(a)
Output
[[1. 0.1 0.6 0.4]
[0.1 1. 0.1 0.2]
[0.6 0.1 1. 0.7]
[0.4 0.2 0.7 1. ]]
answered Nov 19 '18 at 12:46
b-fg
1,96411422
Thank you for the answer. It works.
– Adrian
Nov 19 '18 at 13:00
1
i posted a slightly different and shorter answer
– seralouk
Nov 19 '18 at 13:02
1
@Adrian, you are welcome.
– b-fg
Nov 19 '18 at 13:10
1
@Adrian, I have simplified my answer, which works too and it's cleaner.
– b-fg
Nov 19 '18 at 13:17
add a comment |
Its very simple using numpy.triu_indices_from.
Use this:
import numpy as np
list_similarities = [1,0.1,0.6,0.4,1,0.1,0.2,1,0.7,1]
n = 4
Full_matrix = np.zeros((n,n))
inds = np.triu_indices_from(Full_matrix, k = 0)
# use [:] to copy the list and avoid any problem if the initial list is further needed
Full_matrix[inds] = list_similarities[:]
Full_matrix[(inds[1], inds[0])] = list_similarities[:]
Results
array([[1. , 0.1, 0.6, 0.4],
[0.1, 1. , 0.1, 0.2],
[0.6, 0.1, 1. , 0.7],
[0.4, 0.2, 0.7, 1. ]])
P.S: More details about the reason I copy the list using list_similarities[:] here
answered Nov 19 '18 at 12:58
seralouk
5,66522338
I am creating a copy of the list. and this is the best way to do it. There is a huge difference betweenlist_similarities = list_similarities[:]andlist_similarities = list_similarities. This is the safiest way
– seralouk
Nov 19 '18 at 13:22
add a comment |
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2 Answers
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oldest
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2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
A solution using numpy.triu_indices and numpy.tril_indices. I have guided each step with comments. The key is to first find the upper right indices, assign the value from the list, then make the matrix symmetric.
import numpy as np
n = 4
l = [1,0.1,0.6,0.4,1,0.1,0.2,1,0.7,1]
a = np.zeros((n,n)) # Initialize nxn matrix
triu = np.triu_indices(n) # Find upper right indices of a triangular nxn matrix
tril = np.tril_indices(n, -1) # Find lower left indices of a triangular nxn matrix
a[triu] = l # Assign list values to upper right matrix
a[tril] = a.T[tril] # Make the matrix symmetric
print(a)
Output
[[1. 0.1 0.6 0.4]
[0.1 1. 0.1 0.2]
[0.6 0.1 1. 0.7]
[0.4 0.2 0.7 1. ]]
answered Nov 19 '18 at 12:46
b-fg
1,96411422
Thank you for the answer. It works.
– Adrian
Nov 19 '18 at 13:00
1
i posted a slightly different and shorter answer
– seralouk
Nov 19 '18 at 13:02
1
@Adrian, you are welcome.
– b-fg
Nov 19 '18 at 13:10
1
@Adrian, I have simplified my answer, which works too and it's cleaner.
– b-fg
Nov 19 '18 at 13:17
add a comment |
A solution using numpy.triu_indices and numpy.tril_indices. I have guided each step with comments. The key is to first find the upper right indices, assign the value from the list, then make the matrix symmetric.
import numpy as np
n = 4
l = [1,0.1,0.6,0.4,1,0.1,0.2,1,0.7,1]
a = np.zeros((n,n)) # Initialize nxn matrix
triu = np.triu_indices(n) # Find upper right indices of a triangular nxn matrix
tril = np.tril_indices(n, -1) # Find lower left indices of a triangular nxn matrix
a[triu] = l # Assign list values to upper right matrix
a[tril] = a.T[tril] # Make the matrix symmetric
print(a)
Output
[[1. 0.1 0.6 0.4]
[0.1 1. 0.1 0.2]
[0.6 0.1 1. 0.7]
[0.4 0.2 0.7 1. ]]
answered Nov 19 '18 at 12:46
b-fg
1,96411422
Thank you for the answer. It works.
– Adrian
Nov 19 '18 at 13:00
1
i posted a slightly different and shorter answer
– seralouk
Nov 19 '18 at 13:02
1
@Adrian, you are welcome.
– b-fg
Nov 19 '18 at 13:10
1
@Adrian, I have simplified my answer, which works too and it's cleaner.
– b-fg
Nov 19 '18 at 13:17
add a comment |
A solution using numpy.triu_indices and numpy.tril_indices. I have guided each step with comments. The key is to first find the upper right indices, assign the value from the list, then make the matrix symmetric.
import numpy as np
n = 4
l = [1,0.1,0.6,0.4,1,0.1,0.2,1,0.7,1]
a = np.zeros((n,n)) # Initialize nxn matrix
triu = np.triu_indices(n) # Find upper right indices of a triangular nxn matrix
tril = np.tril_indices(n, -1) # Find lower left indices of a triangular nxn matrix
a[triu] = l # Assign list values to upper right matrix
a[tril] = a.T[tril] # Make the matrix symmetric
print(a)
Output
[[1. 0.1 0.6 0.4]
[0.1 1. 0.1 0.2]
[0.6 0.1 1. 0.7]
[0.4 0.2 0.7 1. ]]
answered Nov 19 '18 at 12:46
b-fg
1,96411422
A solution using numpy.triu_indices and numpy.tril_indices. I have guided each step with comments. The key is to first find the upper right indices, assign the value from the list, then make the matrix symmetric.
import numpy as np
n = 4
l = [1,0.1,0.6,0.4,1,0.1,0.2,1,0.7,1]
a = np.zeros((n,n)) # Initialize nxn matrix
triu = np.triu_indices(n) # Find upper right indices of a triangular nxn matrix
tril = np.tril_indices(n, -1) # Find lower left indices of a triangular nxn matrix
a[triu] = l # Assign list values to upper right matrix
a[tril] = a.T[tril] # Make the matrix symmetric
print(a)
Output
[[1. 0.1 0.6 0.4]
[0.1 1. 0.1 0.2]
[0.6 0.1 1. 0.7]
[0.4 0.2 0.7 1. ]]
answered Nov 19 '18 at 12:46
b-fg
1,96411422
edited Nov 19 '18 at 13:16
answered Nov 19 '18 at 12:46
b-fg
1,96411422
answered Nov 19 '18 at 12:46
b-fg
1,96411422
answered Nov 19 '18 at 12:46
b-fg
1,96411422
1,96411422
Thank you for the answer. It works.
– Adrian
Nov 19 '18 at 13:00
1
i posted a slightly different and shorter answer
– seralouk
Nov 19 '18 at 13:02
1
@Adrian, you are welcome.
– b-fg
Nov 19 '18 at 13:10
1
@Adrian, I have simplified my answer, which works too and it's cleaner.
– b-fg
Nov 19 '18 at 13:17
add a comment |
Thank you for the answer. It works.
– Adrian
Nov 19 '18 at 13:00
1
i posted a slightly different and shorter answer
– seralouk
Nov 19 '18 at 13:02
1
@Adrian, you are welcome.
– b-fg
Nov 19 '18 at 13:10
1
@Adrian, I have simplified my answer, which works too and it's cleaner.
– b-fg
Nov 19 '18 at 13:17
Thank you for the answer. It works.
– Adrian
Nov 19 '18 at 13:00
Thank you for the answer. It works.
– Adrian
Nov 19 '18 at 13:00
1
1
i posted a slightly different and shorter answer
– seralouk
Nov 19 '18 at 13:02
i posted a slightly different and shorter answer
– seralouk
Nov 19 '18 at 13:02
1
1
@Adrian, you are welcome.
– b-fg
Nov 19 '18 at 13:10
@Adrian, you are welcome.
– b-fg
Nov 19 '18 at 13:10
1
1
@Adrian, I have simplified my answer, which works too and it's cleaner.
– b-fg
Nov 19 '18 at 13:17
@Adrian, I have simplified my answer, which works too and it's cleaner.
– b-fg
Nov 19 '18 at 13:17
add a comment |
Its very simple using numpy.triu_indices_from.
Use this:
import numpy as np
list_similarities = [1,0.1,0.6,0.4,1,0.1,0.2,1,0.7,1]
n = 4
Full_matrix = np.zeros((n,n))
inds = np.triu_indices_from(Full_matrix, k = 0)
# use [:] to copy the list and avoid any problem if the initial list is further needed
Full_matrix[inds] = list_similarities[:]
Full_matrix[(inds[1], inds[0])] = list_similarities[:]
Results
array([[1. , 0.1, 0.6, 0.4],
[0.1, 1. , 0.1, 0.2],
[0.6, 0.1, 1. , 0.7],
[0.4, 0.2, 0.7, 1. ]])
P.S: More details about the reason I copy the list using list_similarities[:] here
answered Nov 19 '18 at 12:58
seralouk
5,66522338
I am creating a copy of the list. and this is the best way to do it. There is a huge difference betweenlist_similarities = list_similarities[:]andlist_similarities = list_similarities. This is the safiest way
– seralouk
Nov 19 '18 at 13:22
add a comment |
Its very simple using numpy.triu_indices_from.
Use this:
import numpy as np
list_similarities = [1,0.1,0.6,0.4,1,0.1,0.2,1,0.7,1]
n = 4
Full_matrix = np.zeros((n,n))
inds = np.triu_indices_from(Full_matrix, k = 0)
# use [:] to copy the list and avoid any problem if the initial list is further needed
Full_matrix[inds] = list_similarities[:]
Full_matrix[(inds[1], inds[0])] = list_similarities[:]
Results
array([[1. , 0.1, 0.6, 0.4],
[0.1, 1. , 0.1, 0.2],
[0.6, 0.1, 1. , 0.7],
[0.4, 0.2, 0.7, 1. ]])
P.S: More details about the reason I copy the list using list_similarities[:] here
answered Nov 19 '18 at 12:58
seralouk
5,66522338
I am creating a copy of the list. and this is the best way to do it. There is a huge difference betweenlist_similarities = list_similarities[:]andlist_similarities = list_similarities. This is the safiest way
– seralouk
Nov 19 '18 at 13:22
add a comment |
Its very simple using numpy.triu_indices_from.
Use this:
import numpy as np
list_similarities = [1,0.1,0.6,0.4,1,0.1,0.2,1,0.7,1]
n = 4
Full_matrix = np.zeros((n,n))
inds = np.triu_indices_from(Full_matrix, k = 0)
# use [:] to copy the list and avoid any problem if the initial list is further needed
Full_matrix[inds] = list_similarities[:]
Full_matrix[(inds[1], inds[0])] = list_similarities[:]
Results
array([[1. , 0.1, 0.6, 0.4],
[0.1, 1. , 0.1, 0.2],
[0.6, 0.1, 1. , 0.7],
[0.4, 0.2, 0.7, 1. ]])
P.S: More details about the reason I copy the list using list_similarities[:] here
answered Nov 19 '18 at 12:58
seralouk
5,66522338
Its very simple using numpy.triu_indices_from.
Use this:
import numpy as np
list_similarities = [1,0.1,0.6,0.4,1,0.1,0.2,1,0.7,1]
n = 4
Full_matrix = np.zeros((n,n))
inds = np.triu_indices_from(Full_matrix, k = 0)
# use [:] to copy the list and avoid any problem if the initial list is further needed
Full_matrix[inds] = list_similarities[:]
Full_matrix[(inds[1], inds[0])] = list_similarities[:]
Results
array([[1. , 0.1, 0.6, 0.4],
[0.1, 1. , 0.1, 0.2],
[0.6, 0.1, 1. , 0.7],
[0.4, 0.2, 0.7, 1. ]])
P.S: More details about the reason I copy the list using list_similarities[:] here
answered Nov 19 '18 at 12:58
seralouk
5,66522338
edited Nov 19 '18 at 13:23
answered Nov 19 '18 at 12:58
seralouk
5,66522338
answered Nov 19 '18 at 12:58
seralouk
5,66522338
answered Nov 19 '18 at 12:58
seralouk
5,66522338
5,66522338
I am creating a copy of the list. and this is the best way to do it. There is a huge difference betweenlist_similarities = list_similarities[:]andlist_similarities = list_similarities. This is the safiest way
– seralouk
Nov 19 '18 at 13:22
add a comment |
I am creating a copy of the list. and this is the best way to do it. There is a huge difference betweenlist_similarities = list_similarities[:]andlist_similarities = list_similarities. This is the safiest way
– seralouk
Nov 19 '18 at 13:22
I am creating a copy of the list. and this is the best way to do it. There is a huge difference between
list_similarities = list_similarities[:] and list_similarities = list_similarities. This is the safiest way– seralouk
Nov 19 '18 at 13:22
I am creating a copy of the list. and this is the best way to do it. There is a huge difference between
list_similarities = list_similarities[:] and list_similarities = list_similarities. This is the safiest way– seralouk
Nov 19 '18 at 13:22
add a comment |
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1
Possible duplicate of Numpy: convert an array to a triangular matrix
– Matthieu Brucher
Nov 19 '18 at 12:24
1
@Andrian see my answer and let me know if it helps
– seralouk
Nov 19 '18 at 13:00
@seralouk thank you for taking the time to come up with a solution! It works as expected :)
– Adrian
Nov 19 '18 at 13:08