Skip to content

fix jacobian in case where target functions's output is aliased with input #106

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Mar 16, 2017
Merged

fix jacobian in case where target functions's output is aliased with input #106

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
23 changes: 11 additions & 12 deletions src/finite_difference.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -166,24 +166,23 @@ function finite_difference_jacobian!{R <: Number,

# Iterate over each dimension of the gradient separately.
if dtype == :forward
shifted_x = copy(x)
for i = 1:n
@forwardrule x[i] epsilon
oldx = x[i]
x[i] = oldx + epsilon
f_xplusdx = f(x)
x[i] = oldx
J[:, i] = (f_xplusdx - f_x) / epsilon
shifted_x[i] += epsilon
J[:, i] = (f(shifted_x) - f_x) / epsilon
shifted_x[i] = x[i]
end
elseif dtype == :central
shifted_x_plus = copy(x)
shifted_x_minus = copy(x)
for i = 1:n
@centralrule x[i] epsilon
oldx = x[i]
x[i] = oldx + epsilon
f_xplusdx = f(x)
x[i] = oldx - epsilon
f_xminusdx = f(x)
x[i] = oldx
J[:, i] = (f_xplusdx - f_xminusdx) / (epsilon + epsilon)
shifted_x_plus[i] += epsilon
shifted_x_minus[i] -= epsilon
J[:, i] = (f(shifted_x_plus) - f(shifted_x_minus)) / (epsilon + epsilon)
shifted_x_plus[i] = x[i]
shifted_x_minus[i] = x[i]
end
else
error("dtype must :forward or :central")
Expand Down
7 changes: 7 additions & 0 deletions test/derivative.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,13 @@ f4(x::Vector) = (100.0 - x[1])^2 + (50.0 - x[2])^2
@test norm(Calculus.gradient(f4, :central)([100.0, 50.0]) - [0.0, 0.0]) < 10e-4
@test norm(Calculus.gradient(f4)([100.0, 50.0]) - [0.0, 0.0]) < 10e-4

#
# jacobian()
#

@test norm(Calculus.jacobian(identity, rand(3), :forward) - eye(3)) < 10e-4
@test norm(Calculus.jacobian(identity, rand(3), :central) - eye(3)) < 10e-4

#
# second_derivative()
#
Expand Down