progress towards partial planning

main.jl

@@ -18,7 +18,7 @@ tdfast = tempdecay(215, 25, 0.08)
 
 
 ### LOAD IN DATA
-obj = "/Users/jayant/phd/tempaware/models/" * "M5"
+obj = "/Users/jayant/phd/tempaware/models/" * "M3"
 contours = clean_contour.(contour.(JSON.parse(open(obj * "contours.json"))))
 cdata = contourdata(contours, 20, 5) # contour data
 @time vd = voxdata(obj * "_voxels.csv", cdata)
@@ -31,26 +31,9 @@ c = cost_f(rl)
 
 # local search loop
 k = 150 # TODO k is hardcoded in function below (uses global variable)
-function best_neighbor!(rl::Vector{Int}, current_cost::Number)
-    costs = Dict()
-    for i in 1:1:length(rl)-1
-        for j in i+1:min(i+k,length(rl)) # TODO length rl is constant
-            if valid_swap(rl, i, j, cdata)
-                swap!(rl, i, j)
-                costs[i,j] = cost_f(rl) # TODO cost function global vairable being used here
-                swap!(rl, i, j)
-            end
-        end
-    end
-    v, (i,j) = findmin(costs)
-    if abs(v - current_cost) < current_cost/1e6
-        return 0
-    elseif v < current_cost
-        swap!(rl, i, j)
-        return v
-    end
-    return 0
-end
+layer_range = [(1,1), (15, 25)]
+crange = contour_range(layer_range, cdata)
+construct_neighbour_searcher(k, cost_f, crange)
 
 
 function local_search!(rl::Vector{Int}, max_iter::Int)
@@ -71,6 +54,8 @@ end
 rl = random_rollout(cdata)
 cost_f(rl)
 max_iterations = 50
+rl =Vector(1:length(rl))
+cost_f(rl)
 @time local_search!(rl, max_iterations)
 
 # run many times

utils.jl

@@ -605,7 +605,7 @@ end
 
 function plot(rl::Vector{Int}, cdata::contourdata)
     n = length(rl)
-    cols = range(HSV(720,1,1), stop=HSV(-720,1,1), length=n)
+    cols = range(HSV(0,1,1), stop=HSV(-360,1,1), length=n)
     traces = Vector{GenericTrace}()
     layout = Layout(
         yaxis=attr(scaleanchor="x", scaleratio=1)
@@ -622,4 +622,46 @@ function plot(rl::Vector{Int}, cdata::contourdata)
             ))
     end
     plot(traces, layout)
+end
+
+
+function contour_range(layer_ranges::Vector{Tuple{Int, Int}}, cdata::contourdata)
+    contour_ranges = Vector{Tuple{Int,Int}}()
+    for lr in layer_ranges
+        push!(contour_ranges, (cdata.layers[lr[1]][1], cdata.layers[lr[2]][end]))
+    end
+    return contour_ranges
+end
+
+
+function construct_neighbour_searcher(k::Int, cost_func::Function, search_ranges::Vector{Tuple{Int, Int}})
+    # k -> max swpa dist
+    # cost_func
+    # search_ranges allowed ranges of swaps
+
+    a = quote
+        function best_neighbor!(rl::Vector{Int}, current_cost::Number)
+            costs = Dict()
+            for (s,e) in $search_ranges
+                for i in s:e-1
+                    for j in i+1:e
+                        if valid_swap(rl, i, j, cdata)
+                            swap!(rl, i, j)
+                            costs[i,j] = $cost_func(rl)
+                            swap!(rl, i, j)
+                        end
+                    end
+                end
+            end
+            v, (i,j) = findmin(costs)
+            if abs(v - current_cost) < current_cost/1e6
+                return 0
+            elseif v < current_cost
+                swap!(rl, i, j)
+                return v
+            end
+            return 0
+        end
+    end
+    return eval(a) # create best_neighbour function to use below
 end
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