Python equivalent for the matlab A(:,1)' and A(:)
I am converting MATLAB code to Python Numpy. I am referring this doc
http://scipy.github.io/old-wiki/pages/NumPy_for_Matlab_Users
Below is the two MATLAB lines,
X = A(:,1)';
R = repmat(X(:),1,6);
Where A is two dimensional matrix
This is my converted python lines
X = A[:, 1].conj().transpose()
R = np.tile(X[:], (1,6))
I have two queries,
X = A(:,1)';- In this line, is the quotes (') refers to.conj().transpose()
- is this
X[:]right equivalent forX(:)or is itX.flatten(1)?
To be more clear, actually I am trying to understand the MATLAB code,
X = A(:,1)';- In this line, is the quotes (') refers to transpose?
X(:)- what it means in MATLAB?
python matlab numpy
asked Nov 20 '18 at 22:49
NavarasuNavarasu
2,0101822
|
show 3 more comments
I am converting MATLAB code to Python Numpy. I am referring this doc
http://scipy.github.io/old-wiki/pages/NumPy_for_Matlab_Users
Below is the two MATLAB lines,
X = A(:,1)';
R = repmat(X(:),1,6);
Where A is two dimensional matrix
This is my converted python lines
X = A[:, 1].conj().transpose()
R = np.tile(X[:], (1,6))
I have two queries,
X = A(:,1)';- In this line, is the quotes (') refers to.conj().transpose()
- is this
X[:]right equivalent forX(:)or is itX.flatten(1)?
To be more clear, actually I am trying to understand the MATLAB code,
X = A(:,1)';- In this line, is the quotes (') refers to transpose?
X(:)- what it means in MATLAB?
python matlab numpy
asked Nov 20 '18 at 22:49
NavarasuNavarasu
2,0101822
Python programmers will have an easier time answering the question if you explained what the expected output is for a sample input. They may not know matlab.
– timgeb
Nov 20 '18 at 22:51
To get the transpose, you can use np.transpose() or the .T property of a numpy array. x[:] is equal to x (no flattening going on)
– 4d11
Nov 20 '18 at 22:52
1
'is conjugate transpose in Matlab (to just transpose you use.').(:)means flatten (returns a column vector). But hereX = A(:,1)is already flattened (row vector), soX(:)just transposes that into a column vector; it is the same asX.'. See online example
– Luis Mendo
Nov 20 '18 at 23:00
1
@LuisMendo Thanks, It is helpful. You can move the above details from comments to answer section as it is the answer :)
– Navarasu
Nov 20 '18 at 23:19
4
A couple of important differences. MATLAB is always 2d (or higher);numpycan reduce dimensions to 1 (or even 0d).x[:]does nothing, except make a new view. And for a 1dndarraytranspose doesn't change anything.
– hpaulj
Nov 21 '18 at 0:52
|
show 3 more comments
I am converting MATLAB code to Python Numpy. I am referring this doc
http://scipy.github.io/old-wiki/pages/NumPy_for_Matlab_Users
Below is the two MATLAB lines,
X = A(:,1)';
R = repmat(X(:),1,6);
Where A is two dimensional matrix
This is my converted python lines
X = A[:, 1].conj().transpose()
R = np.tile(X[:], (1,6))
I have two queries,
X = A(:,1)';- In this line, is the quotes (') refers to.conj().transpose()
- is this
X[:]right equivalent forX(:)or is itX.flatten(1)?
To be more clear, actually I am trying to understand the MATLAB code,
X = A(:,1)';- In this line, is the quotes (') refers to transpose?
X(:)- what it means in MATLAB?
python matlab numpy
asked Nov 20 '18 at 22:49
NavarasuNavarasu
2,0101822
I am converting MATLAB code to Python Numpy. I am referring this doc
http://scipy.github.io/old-wiki/pages/NumPy_for_Matlab_Users
Below is the two MATLAB lines,
X = A(:,1)';
R = repmat(X(:),1,6);
Where A is two dimensional matrix
This is my converted python lines
X = A[:, 1].conj().transpose()
R = np.tile(X[:], (1,6))
I have two queries,
X = A(:,1)';- In this line, is the quotes (') refers to.conj().transpose()
- is this
X[:]right equivalent forX(:)or is itX.flatten(1)?
To be more clear, actually I am trying to understand the MATLAB code,
X = A(:,1)';- In this line, is the quotes (') refers to transpose?
X(:)- what it means in MATLAB?
python matlab numpy
python matlab numpy
asked Nov 20 '18 at 22:49
NavarasuNavarasu
2,0101822
asked Nov 20 '18 at 22:49
NavarasuNavarasu
2,0101822
edited Nov 20 '18 at 22:58
Navarasu
asked Nov 20 '18 at 22:49
NavarasuNavarasu
2,0101822
asked Nov 20 '18 at 22:49
NavarasuNavarasu
2,0101822
asked Nov 20 '18 at 22:49
NavarasuNavarasu
2,0101822
2,0101822
Python programmers will have an easier time answering the question if you explained what the expected output is for a sample input. They may not know matlab.
– timgeb
Nov 20 '18 at 22:51
To get the transpose, you can use np.transpose() or the .T property of a numpy array. x[:] is equal to x (no flattening going on)
– 4d11
Nov 20 '18 at 22:52
1
'is conjugate transpose in Matlab (to just transpose you use.').(:)means flatten (returns a column vector). But hereX = A(:,1)is already flattened (row vector), soX(:)just transposes that into a column vector; it is the same asX.'. See online example
– Luis Mendo
Nov 20 '18 at 23:00
1
@LuisMendo Thanks, It is helpful. You can move the above details from comments to answer section as it is the answer :)
– Navarasu
Nov 20 '18 at 23:19
4
A couple of important differences. MATLAB is always 2d (or higher);numpycan reduce dimensions to 1 (or even 0d).x[:]does nothing, except make a new view. And for a 1dndarraytranspose doesn't change anything.
– hpaulj
Nov 21 '18 at 0:52
|
show 3 more comments
Python programmers will have an easier time answering the question if you explained what the expected output is for a sample input. They may not know matlab.
– timgeb
Nov 20 '18 at 22:51
To get the transpose, you can use np.transpose() or the .T property of a numpy array. x[:] is equal to x (no flattening going on)
– 4d11
Nov 20 '18 at 22:52
1
'is conjugate transpose in Matlab (to just transpose you use.').(:)means flatten (returns a column vector). But hereX = A(:,1)is already flattened (row vector), soX(:)just transposes that into a column vector; it is the same asX.'. See online example
– Luis Mendo
Nov 20 '18 at 23:00
1
@LuisMendo Thanks, It is helpful. You can move the above details from comments to answer section as it is the answer :)
– Navarasu
Nov 20 '18 at 23:19
4
A couple of important differences. MATLAB is always 2d (or higher);numpycan reduce dimensions to 1 (or even 0d).x[:]does nothing, except make a new view. And for a 1dndarraytranspose doesn't change anything.
– hpaulj
Nov 21 '18 at 0:52
Python programmers will have an easier time answering the question if you explained what the expected output is for a sample input. They may not know matlab.
– timgeb
Nov 20 '18 at 22:51
Python programmers will have an easier time answering the question if you explained what the expected output is for a sample input. They may not know matlab.
– timgeb
Nov 20 '18 at 22:51
To get the transpose, you can use np.transpose() or the .T property of a numpy array. x[:] is equal to x (no flattening going on)
– 4d11
Nov 20 '18 at 22:52
To get the transpose, you can use np.transpose() or the .T property of a numpy array. x[:] is equal to x (no flattening going on)
– 4d11
Nov 20 '18 at 22:52
1
1
' is conjugate transpose in Matlab (to just transpose you use .'). (:) means flatten (returns a column vector). But here X = A(:,1) is already flattened (row vector), so X(:) just transposes that into a column vector; it is the same as X.'. See online example– Luis Mendo
Nov 20 '18 at 23:00
' is conjugate transpose in Matlab (to just transpose you use .'). (:) means flatten (returns a column vector). But here X = A(:,1) is already flattened (row vector), so X(:) just transposes that into a column vector; it is the same as X.'. See online example– Luis Mendo
Nov 20 '18 at 23:00
1
1
@LuisMendo Thanks, It is helpful. You can move the above details from comments to answer section as it is the answer :)
– Navarasu
Nov 20 '18 at 23:19
@LuisMendo Thanks, It is helpful. You can move the above details from comments to answer section as it is the answer :)
– Navarasu
Nov 20 '18 at 23:19
4
4
A couple of important differences. MATLAB is always 2d (or higher);
numpy can reduce dimensions to 1 (or even 0d). x[:] does nothing, except make a new view. And for a 1d ndarray transpose doesn't change anything.– hpaulj
Nov 21 '18 at 0:52
A couple of important differences. MATLAB is always 2d (or higher);
numpy can reduce dimensions to 1 (or even 0d). x[:] does nothing, except make a new view. And for a 1d ndarray transpose doesn't change anything.– hpaulj
Nov 21 '18 at 0:52
|
show 3 more comments
1 Answer
1
active
oldest
votes
Let's define an example A:
>> A = [1 2 3; 4 5 6];
' is conjugate transpose. To just transpose use .'.
>> A(:,1)
ans =
1
4
>> X = A(:,1)'
X =
1 4
(:) means reshape (flatten) into a column vector. Here X = A(:,1)' is already flattened, namely it is a row vector, so X(:) just transposes that into a column vector; it is the same as X.':
>> X(:)
ans =
1
4
>> X.'
ans =
1
4
answered Nov 20 '18 at 23:37
Luis MendoLuis Mendo
93.8k1157125
add a comment |
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1 Answer
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oldest
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oldest
votes
Let's define an example A:
>> A = [1 2 3; 4 5 6];
' is conjugate transpose. To just transpose use .'.
>> A(:,1)
ans =
1
4
>> X = A(:,1)'
X =
1 4
(:) means reshape (flatten) into a column vector. Here X = A(:,1)' is already flattened, namely it is a row vector, so X(:) just transposes that into a column vector; it is the same as X.':
>> X(:)
ans =
1
4
>> X.'
ans =
1
4
answered Nov 20 '18 at 23:37
Luis MendoLuis Mendo
93.8k1157125
add a comment |
Let's define an example A:
>> A = [1 2 3; 4 5 6];
' is conjugate transpose. To just transpose use .'.
>> A(:,1)
ans =
1
4
>> X = A(:,1)'
X =
1 4
(:) means reshape (flatten) into a column vector. Here X = A(:,1)' is already flattened, namely it is a row vector, so X(:) just transposes that into a column vector; it is the same as X.':
>> X(:)
ans =
1
4
>> X.'
ans =
1
4
answered Nov 20 '18 at 23:37
Luis MendoLuis Mendo
93.8k1157125
add a comment |
Let's define an example A:
>> A = [1 2 3; 4 5 6];
' is conjugate transpose. To just transpose use .'.
>> A(:,1)
ans =
1
4
>> X = A(:,1)'
X =
1 4
(:) means reshape (flatten) into a column vector. Here X = A(:,1)' is already flattened, namely it is a row vector, so X(:) just transposes that into a column vector; it is the same as X.':
>> X(:)
ans =
1
4
>> X.'
ans =
1
4
answered Nov 20 '18 at 23:37
Luis MendoLuis Mendo
93.8k1157125
Let's define an example A:
>> A = [1 2 3; 4 5 6];
' is conjugate transpose. To just transpose use .'.
>> A(:,1)
ans =
1
4
>> X = A(:,1)'
X =
1 4
(:) means reshape (flatten) into a column vector. Here X = A(:,1)' is already flattened, namely it is a row vector, so X(:) just transposes that into a column vector; it is the same as X.':
>> X(:)
ans =
1
4
>> X.'
ans =
1
4
answered Nov 20 '18 at 23:37
Luis MendoLuis Mendo
93.8k1157125
answered Nov 20 '18 at 23:37
Luis MendoLuis Mendo
93.8k1157125
answered Nov 20 '18 at 23:37
Luis MendoLuis Mendo
93.8k1157125
answered Nov 20 '18 at 23:37
Luis MendoLuis Mendo
93.8k1157125
93.8k1157125
add a comment |
add a comment |
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Python programmers will have an easier time answering the question if you explained what the expected output is for a sample input. They may not know matlab.
– timgeb
Nov 20 '18 at 22:51
To get the transpose, you can use np.transpose() or the .T property of a numpy array. x[:] is equal to x (no flattening going on)
– 4d11
Nov 20 '18 at 22:52
1
'is conjugate transpose in Matlab (to just transpose you use.').(:)means flatten (returns a column vector). But hereX = A(:,1)is already flattened (row vector), soX(:)just transposes that into a column vector; it is the same asX.'. See online example– Luis Mendo
Nov 20 '18 at 23:00
1
@LuisMendo Thanks, It is helpful. You can move the above details from comments to answer section as it is the answer :)
– Navarasu
Nov 20 '18 at 23:19
4
A couple of important differences. MATLAB is always 2d (or higher);
numpycan reduce dimensions to 1 (or even 0d).x[:]does nothing, except make a new view. And for a 1dndarraytranspose doesn't change anything.– hpaulj
Nov 21 '18 at 0:52