refactor: use SurrogatesBase and clean up code

lib/SurrogatesSVM/src/SurrogatesSVM.jl

@@ -1,79 +1,68 @@
 module SurrogatesSVM
 
-import Surrogates: AbstractSurrogate, add_point!
-export SVMSurrogate
-
+using SurrogatesBase
 using LIBSVM
 
-mutable struct SVMSurrogate{X, Y, M, L, U} <: AbstractSurrogate
+export SVMSurrogate, update!
+
+mutable struct SVMSurrogate{X, Y, M, L, U} <: AbstractDeterministicSurrogate
     x::X
     y::Y
     model::M
     lb::L
     ub::U
 end
 
-function SVMSurrogate(x, y, lb::Number, ub::Number)
-    xn = reshape(x, length(x), 1)
-    model = LIBSVM.fit!(SVC(), xn, y)
-    SVMSurrogate(xn, y, model, lb, ub)
-end
+"""
+    SVMSurrogate(x, y, lb, ub)
 
-function (svmsurr::SVMSurrogate)(val::Number)
-    return LIBSVM.predict(svmsurr.model, [val])
-end
+Builds a SVM Surrogate using [LIBSVM](https://github.com/JuliaML/LIBSVM.jl).
 
-"""
-SVMSurrogate(x,y,lb,ub)
+## Arguments
 
-Builds SVM surrogate.
+  - `x`: Input data points.
+  - `y`: Output data points.
+  - `lb`: Lower bound of input data points.
+  - `ub`: Upper bound of output data points.
 """
 function SVMSurrogate(x, y, lb, ub)
-    X = Array{Float64, 2}(undef, length(x), length(x[1]))
-    for j in 1:length(x)
-        X[j, :] = vec(collect(x[j]))
+    X = Array{Float64, 2}(undef, length(x), length(first(x)))
+    if length(lb) == 1
+        for j in eachindex(x)
+            X[j, 1] = x[j]
+        end
+    else
+        for j in eachindex(x)
+            X[j, :] = x[j]
+        end
     end
     model = LIBSVM.fit!(SVC(), X, y)
     SVMSurrogate(x, y, model, lb, ub)
 end
 
 function (svmsurr::SVMSurrogate)(val)
     n = length(val)
-    return LIBSVM.predict(svmsurr.model, reshape(collect(val), 1, n))[1]
+    return LIBSVM.predict(svmsurr.model, reshape(val, 1, n))[1]
 end
 
-function add_point!(svmsurr::SVMSurrogate, x_new, y_new)
+"""
+    update!(svmsurr::SVMSurrogate, x_new, y_new)
+
+## Arguments
+
+  - `svmsurr`: Surrogate of type [`SVMSurrogate`](@ref).
+  - `x_new`: Vector of new data points to be added to the training set of SVMSurrogate.
+  - `y_new`: Vector of new output points to be added to the training set of SVMSurrogate.
+"""
+function update!(svmsurr::SVMSurrogate, x_new, y_new)
+    svmsurr.x = vcat(svmsurr.x, x_new)
+    svmsurr.y = vcat(svmsurr.y, y_new)
     if length(svmsurr.lb) == 1
-        #1D
-        svmsurr.x = vcat(svmsurr.x, x_new)
-        svmsurr.y = vcat(svmsurr.y, y_new)
-        svmsurr.model = LIBSVM.fit!(SVC(), reshape(svmsurr.x, length(svmsurr.x), 1),
-            svmsurr.y)
+        svmsurr.model = LIBSVM.fit!(
+            SVC(), reshape(svmsurr.x, length(svmsurr.x), 1), svmsurr.y)
     else
-        n_previous = length(svmsurr.x)
-        a = vcat(svmsurr.x, x_new)
-        n_after = length(a)
-        dim_new = n_after - n_previous
-        n = length(svmsurr.x)
-        d = length(svmsurr.x[1])
-        tot_dim = n + dim_new
-        X = Array{Float64, 2}(undef, tot_dim, d)
-        for j in 1:n
-            X[j, :] = vec(collect(svmsurr.x[j]))
-        end
-        if dim_new == 1
-            X[n + 1, :] = vec(collect(x_new))
-        else
-            i = 1
-            for j in (n + 1):tot_dim
-                X[j, :] = vec(collect(x_new[i]))
-                i = i + 1
-            end
-        end
-        svmsurr.x = vcat(svmsurr.x, x_new)
-        svmsurr.y = vcat(svmsurr.y, y_new)
-        svmsurr.model = LIBSVM.fit!(SVC(), X, svmsurr.y)
+        svmsurr.model = LIBSVM.fit!(SVC(), transpose(reduce(hcat, svmsurr.x)), svmsurr.y)
     end
-    nothing
 end
+
 end # module