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Fast way of computing covariance matrix of nonstationary kernel in Python
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$begingroup$
Suppose I have symmetric positive definite covariance function $k:mathbb{R}timesmathbb{R}rightarrow mathbb{R}$ that is non-stationary (i.e. $k(x,y) neq g(|x-y|)$ for any function $g$). Is there a fast way in Python given data $X = (x_1,ldots,x_n$) to calculate its covariance matrix $(k(x_i,x_j))_{i,j}$?
If the covariance function is stationary then we can compute the whole matrix at once using numpy's matrix operations and avoid slow Python loops - e.g. in this.
Currently my implementation is:
dim = len(X)
kern_mat = np.zeros((dim,dim))
for i in range(dim):
for j in range(i+1):
kern_mat[i,j] = kern(X[i],X[j])
kern_mat[j,i] = kern_mat[i,j]
Any help with speedups or otherwise is appreciated!
machine-learning python statistics numpy gaussian
edited 9 hours ago
ebrahimi
73321021
asked 14 hours ago
Timothy HedgeworthTimothy Hedgeworth
1062
New contributor
Timothy Hedgeworth is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Suppose I have symmetric positive definite covariance function $k:mathbb{R}timesmathbb{R}rightarrow mathbb{R}$ that is non-stationary (i.e. $k(x,y) neq g(|x-y|)$ for any function $g$). Is there a fast way in Python given data $X = (x_1,ldots,x_n$) to calculate its covariance matrix $(k(x_i,x_j))_{i,j}$?
If the covariance function is stationary then we can compute the whole matrix at once using numpy's matrix operations and avoid slow Python loops - e.g. in this.
Currently my implementation is:
dim = len(X)
kern_mat = np.zeros((dim,dim))
for i in range(dim):
for j in range(i+1):
kern_mat[i,j] = kern(X[i],X[j])
kern_mat[j,i] = kern_mat[i,j]
Any help with speedups or otherwise is appreciated!
machine-learning python statistics numpy gaussian
edited 9 hours ago
ebrahimi
73321021
asked 14 hours ago
Timothy HedgeworthTimothy Hedgeworth
1062
New contributor
Timothy Hedgeworth is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Suppose I have symmetric positive definite covariance function $k:mathbb{R}timesmathbb{R}rightarrow mathbb{R}$ that is non-stationary (i.e. $k(x,y) neq g(|x-y|)$ for any function $g$). Is there a fast way in Python given data $X = (x_1,ldots,x_n$) to calculate its covariance matrix $(k(x_i,x_j))_{i,j}$?
If the covariance function is stationary then we can compute the whole matrix at once using numpy's matrix operations and avoid slow Python loops - e.g. in this.
Currently my implementation is:
dim = len(X)
kern_mat = np.zeros((dim,dim))
for i in range(dim):
for j in range(i+1):
kern_mat[i,j] = kern(X[i],X[j])
kern_mat[j,i] = kern_mat[i,j]
Any help with speedups or otherwise is appreciated!
machine-learning python statistics numpy gaussian
edited 9 hours ago
ebrahimi
73321021
asked 14 hours ago
Timothy HedgeworthTimothy Hedgeworth
1062
New contributor
Timothy Hedgeworth is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Suppose I have symmetric positive definite covariance function $k:mathbb{R}timesmathbb{R}rightarrow mathbb{R}$ that is non-stationary (i.e. $k(x,y) neq g(|x-y|)$ for any function $g$). Is there a fast way in Python given data $X = (x_1,ldots,x_n$) to calculate its covariance matrix $(k(x_i,x_j))_{i,j}$?
If the covariance function is stationary then we can compute the whole matrix at once using numpy's matrix operations and avoid slow Python loops - e.g. in this.
Currently my implementation is:
dim = len(X)
kern_mat = np.zeros((dim,dim))
for i in range(dim):
for j in range(i+1):
kern_mat[i,j] = kern(X[i],X[j])
kern_mat[j,i] = kern_mat[i,j]
Any help with speedups or otherwise is appreciated!
machine-learning python statistics numpy gaussian
machine-learning python statistics numpy gaussian
edited 9 hours ago
ebrahimi
73321021
asked 14 hours ago
Timothy HedgeworthTimothy Hedgeworth
1062
New contributor
Timothy Hedgeworth is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 9 hours ago
ebrahimi
73321021
asked 14 hours ago
Timothy HedgeworthTimothy Hedgeworth
1062
New contributor
Timothy Hedgeworth is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 9 hours ago
ebrahimi
73321021
edited 9 hours ago
ebrahimi
73321021
edited 9 hours ago
ebrahimi
73321021
73321021
asked 14 hours ago
Timothy HedgeworthTimothy Hedgeworth
1062
New contributor
Timothy Hedgeworth is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 14 hours ago
Timothy HedgeworthTimothy Hedgeworth
1062
asked 14 hours ago
Timothy HedgeworthTimothy Hedgeworth
1062
1062
New contributor
Timothy Hedgeworth is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Timothy Hedgeworth is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Timothy Hedgeworth is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
1 Answer
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$begingroup$
I still would apply numpy's covaranice function using numpy.apply_along_axis
import numpy as np
x = np.array([[0, 2], [1, 1], [2, 0]]).T
np.apply_along_axis(func1d=np.cov, arr=x, axis=0)
answered 13 hours ago
Brian SpieringBrian Spiering
3,7881028
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
I still would apply numpy's covaranice function using numpy.apply_along_axis
import numpy as np
x = np.array([[0, 2], [1, 1], [2, 0]]).T
np.apply_along_axis(func1d=np.cov, arr=x, axis=0)
answered 13 hours ago
Brian SpieringBrian Spiering
3,7881028
$endgroup$
add a comment |
$begingroup$
I still would apply numpy's covaranice function using numpy.apply_along_axis
import numpy as np
x = np.array([[0, 2], [1, 1], [2, 0]]).T
np.apply_along_axis(func1d=np.cov, arr=x, axis=0)
answered 13 hours ago
Brian SpieringBrian Spiering
3,7881028
$endgroup$
add a comment |
$begingroup$
I still would apply numpy's covaranice function using numpy.apply_along_axis
import numpy as np
x = np.array([[0, 2], [1, 1], [2, 0]]).T
np.apply_along_axis(func1d=np.cov, arr=x, axis=0)
answered 13 hours ago
Brian SpieringBrian Spiering
3,7881028
$endgroup$
I still would apply numpy's covaranice function using numpy.apply_along_axis
import numpy as np
x = np.array([[0, 2], [1, 1], [2, 0]]).T
np.apply_along_axis(func1d=np.cov, arr=x, axis=0)
answered 13 hours ago
Brian SpieringBrian Spiering
3,7881028
answered 13 hours ago
Brian SpieringBrian Spiering
3,7881028
answered 13 hours ago
Brian SpieringBrian Spiering
3,7881028
answered 13 hours ago
Brian SpieringBrian Spiering
3,7881028
3,7881028
add a comment |
add a comment |
Timothy Hedgeworth is a new contributor. Be nice, and check out our Code of Conduct.
Timothy Hedgeworth is a new contributor. Be nice, and check out our Code of Conduct.
Timothy Hedgeworth is a new contributor. Be nice, and check out our Code of Conduct.
Timothy Hedgeworth is a new contributor. Be nice, and check out our Code of Conduct.
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