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Add support of missing values for L2Loss #223

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Add support of missing values for L2Loss #223

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22 changes: 11 additions & 11 deletions src/cost_functions.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -50,14 +50,14 @@ function (f::L2Loss)(sol::DiffEqBase.AbstractNoTimeSolution)

if weight == nothing
@inbounds for i in 1:length(sol)
sumsq += (data[i] - sol[i])^2
sumsq += (coalesce(data[i] - sol[i], 0))^2
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use zero of the same type as elements

end
else
@inbounds for i in 1:length(sol)
if typeof(weight) <: Real
sumsq = sumsq + ((data[i] - sol[i])^2) * weight
sumsq = sumsq + ((coalesce(data[i] - sol[i], 0))^2) * weight
else
sumsq = sumsq + ((data[i] - sol[i])^2) * weight[i]
sumsq = sumsq + ((coalesce(data[i] - sol[i], 0))^2) * weight[i]
end
end
end
Expand All @@ -83,16 +83,16 @@ function (f::L2Loss)(sol::SciMLBase.AbstractSciMLSolution)
if weight == nothing
@inbounds for i in 1:length(sol)
for j in 1:length(sol[i])
sumsq += (data[j, i] - sol[j, i])^2
sumsq += (coalesce(data[j, i] - sol[j, i], 0))^2
end
if diff_weight != nothing && i != 1
for j in 1:length(sol[i])
if typeof(diff_weight) <: Real
sumsq += diff_weight *
((data[j, i] - data[j, i - 1] - sol[j, i] + sol[j, i - 1])^2)
((coalesce(data[j, i] - data[j, i - 1] - sol[j, i] + sol[j, i - 1], 0))^2)
else
sumsq += diff_weight[j, i] *
((data[j, i] - data[j, i - 1] - sol[j, i] + sol[j, i - 1])^2)
((coalesce(data[j, i] - data[j, i - 1] - sol[j, i] + sol[j, i - 1], 0))^2)
end
end
end
Expand All @@ -101,21 +101,21 @@ function (f::L2Loss)(sol::SciMLBase.AbstractSciMLSolution)
@inbounds for i in 1:length(sol)
if typeof(weight) <: Real
for j in 1:length(sol[i])
sumsq = sumsq + ((data[j, i] - sol[j, i])^2) * weight
sumsq = sumsq + ((coalesce(data[j, i] - sol[j, i], 0))^2) * weight
end
else
for j in 1:length(sol[i])
sumsq = sumsq + ((data[j, i] - sol[j, i])^2) * weight[j, i]
sumsq = sumsq + ((coalesce(data[j, i] - sol[j, i], 0))^2) * weight[j, i]
end
end
if diff_weight != nothing && i != 1
for j in 1:length(sol[i])
if typeof(diff_weight) <: Real
sumsq += diff_weight *
((data[j, i] - data[j, i - 1] - sol[j, i] + sol[j, i - 1])^2)
((coalesce(data[j, i] - data[j, i - 1] - sol[j, i] + sol[j, i - 1], 0))^2)
else
sumsq += diff_weight[j, i] *
((data[j, i] - data[j, i - 1] - sol[j, i] + sol[j, i - 1])^2)
((coalesce(data[j, i] - data[j, i - 1] - sol[j, i] + sol[j, i - 1], 0))^2)
end
end
end
Expand Down Expand Up @@ -267,7 +267,7 @@ end
function l2lossgradient!(grad, sol, data, sensitivities, num_p)
fill!(grad, 0.0)
data_x_size = size(data, 1)
my_grad = @. 2 * (data - sol)
my_grad = @. 2 * coalesce.(data - sol, 0)
u0len = length(data[1])
K = size(my_grad, 2)
for k in 1:K, i in 1:num_p, j in 1:data_x_size
Expand Down