Optim using gradient Error: "no method matching" - julia

Question

I’m trying to optimize a function using one of the algorithms that require a gradient. Basically I’m trying to learn how to optimize a function using a gradient in Julia. I’m fairly confident that my gradient is specified correctly. I know this because the similarly defined Matlab function for the gradient gives me the same values as in Julia for some test values of the arguments. Also, the Matlab version using fminunc with the gradient seems to optimize the function fine.
However when I run the Julia script, I seem to get the following error:
julia> include("ex2b.jl")
ERROR: `g!` has no method matching g!(::Array{Float64,1}, ::Array{Float64,1})
while loading ...\ex2b.jl, in ex
pression starting on line 64
I'm running Julia 0.3.2 on a windows 7 32bit machine. Here is the code (basically a translation of some Matlab to Julia):
using Optim
function mapFeature(X1, X2)
degrees = 5
out = ones(size(X1)[1])
for i in range(1, degrees+1)
for j in range(0, i+1)
term = reshape( (X1.^(i-j) .* X2.^(j)), size(X1.^(i-j))[1], 1)
out = hcat(out, term)
end
end
return out
end
function sigmoid(z)
return 1 ./ (1 + exp(-z))
end
function costFunc_logistic(theta, X, y, lam)
m = length(y)
regularization = sum(theta[2:end].^2) * lam / (2 * m)
return sum( (-y .* log(sigmoid(X * theta)) - (1 - y) .* log(1 - sigmoid(X * theta))) ) ./ m + regularization
end
function costFunc_logistic_gradient!(theta, X, y, lam, m)
grad= X' * ( sigmoid(X * theta) .- y ) ./ m
grad[2:end] = grad[2:end] + theta[2:end] .* lam / m
return grad
end
data = readcsv("ex2data2.txt")
X = mapFeature(data[:,1], data[:,2])
m, n = size(data)
y = data[:, end]
theta = zeros(size(X)[2])
lam = 1.0
f(theta::Array) = costFunc_logistic(theta, X, y, lam)
g!(theta::Array) = costFunc_logistic_gradient!(theta, X, y, lam, m)
optimize(f, g!, theta, method = :l_bfgs)
And here is some of the data:
0.051267,0.69956,1
-0.092742,0.68494,1
-0.21371,0.69225,1
-0.375,0.50219,1
-0.51325,0.46564,1
-0.52477,0.2098,1
-0.39804,0.034357,1
-0.30588,-0.19225,1
0.016705,-0.40424,1
0.13191,-0.51389,1
0.38537,-0.56506,1
0.52938,-0.5212,1
0.63882,-0.24342,1
0.73675,-0.18494,1
0.54666,0.48757,1
0.322,0.5826,1
0.16647,0.53874,1
-0.046659,0.81652,1
-0.17339,0.69956,1
-0.47869,0.63377,1
-0.60541,0.59722,1
-0.62846,0.33406,1
-0.59389,0.005117,1
-0.42108,-0.27266,1
-0.11578,-0.39693,1
0.20104,-0.60161,1
0.46601,-0.53582,1
0.67339,-0.53582,1
-0.13882,0.54605,1
-0.29435,0.77997,1
-0.26555,0.96272,1
-0.16187,0.8019,1
-0.17339,0.64839,1
-0.28283,0.47295,1
-0.36348,0.31213,1
-0.30012,0.027047,1
-0.23675,-0.21418,1
-0.06394,-0.18494,1
0.062788,-0.16301,1
0.22984,-0.41155,1
0.2932,-0.2288,1
0.48329,-0.18494,1
0.64459,-0.14108,1
0.46025,0.012427,1
0.6273,0.15863,1
0.57546,0.26827,1
0.72523,0.44371,1
0.22408,0.52412,1
0.44297,0.67032,1
0.322,0.69225,1
0.13767,0.57529,1
-0.0063364,0.39985,1
-0.092742,0.55336,1
-0.20795,0.35599,1
-0.20795,0.17325,1
-0.43836,0.21711,1
-0.21947,-0.016813,1
-0.13882,-0.27266,1
0.18376,0.93348,0
0.22408,0.77997,0
Let me know if you guys need additional details. Btw, this relates to a coursera machine learning course if curious.

Answer 1

The gradient should not be a function to compute the gradient,
but a function to store it
(hence the exclamation mark in the function name, and the second argument in the error message).
The following seems to work.
function g!(theta::Array, storage::Array)
storage[:] = costFunc_logistic_gradient!(theta, X, y, lam, m)
end
optimize(f, g!, theta, method = :l_bfgs)

Answer 2

The same using closures and currying (version for those who got used to a function that returns the cost and gradient):
function cost_gradient(θ, X, y, λ)
m = length(y);
return (θ::Array) -> begin
h = sigmoid(X * θ); #(m,n+1)*(n+1,1) -> (m,1)
J = (1 / m) * sum(-y .* log(h) .- (1 - y) .* log(1 - h)) + λ / (2 * m) * sum(θ[2:end] .^ 2);
end, (θ::Array, storage::Array) -> begin
h = sigmoid(X * θ); #(m,n+1)*(n+1,1) -> (m,1)
storage[:] = (1 / m) * (X' * (h .- y)) + (λ / m) * [0; θ[2:end]];
end
end
Then, somewhere in the code:
initialθ = zeros(n,1);
f, g! = cost_gradient(initialθ, X, y, λ);
res = optimize(f, g!, initialθ, method = :cg, iterations = your_iterations);
θ = res.minimum;