Adding Training Strategies to dae_solvers.jl

src/dae_solve.jl

@@ -82,6 +82,43 @@ function generate_loss(strategy::GridTraining, phi, f, autodiff::Bool, tspan, p,
     return loss
 end
 
+function generate_loss(strategy::StochasticTraining, phi, f, autodiff::Bool, tspan, p, differential_vars::AbstractVector)
+    autodiff && throw(ArgumentError("autodiff not supported for StochasticTraining."))
+    function loss(θ, _)
+        ts = adapt(parameterless_type(θ),
+            [(tspan[2] - tspan[1]) * rand() + tspan[1] for i in 1:(strategy.points)])
+        sum(abs2, inner_loss(phi, f, autodiff, ts, θ, p, differential_vars))
+    end
+    return loss
+end
+
+
+function generate_loss(strategy::WeightedIntervalTraining, phi, f, autodiff::Bool, tspan, p,differential_vars::AbstractVector)
+autodiff && throw(ArgumentError("autodiff not supported for WeightedIntervalTraining."))
+minT = tspan[1]
+maxT = tspan[2]
+
+weights = strategy.weights ./ sum(strategy.weights)
+
+N = length(weights)
+points = strategy.points
+
+difference = (maxT - minT) / N
+
+data = Float64[]
+for (index, item) in enumerate(weights)
+    temp_data = rand(1, trunc(Int, points * item)) .* difference .+ minT .+
+                ((index - 1) * difference)
+    data = append!(data, temp_data)
+end
+
+ts = data
+function loss(θ, _)
+    sum(abs2, inner_loss(phi, f, autodiff, ts, θ, p, differential_vars))
+end
+return loss
+end
+
 function DiffEqBase.__solve(prob::DiffEqBase.AbstractDAEProblem,
         alg::NNDAE,
         args...;