updating metrics functions

src/ABMEv_metrics.jl

@@ -41,7 +41,7 @@ function findclusters(v::Vector,allextrema =true)
     return collect(h.edges...)[idx .+ 1] .+ s/2, x[idx .+ 1]
 end
 
-import Statistics.var
+import Statistics:var,mean
 # TODO: rename this to gamma diversity
 """
     var(world::Array{Agent};trait=:)
@@ -55,6 +55,15 @@ function var(world::World;trait=1)
     xarray = Float64.(get_x(world,trait))
     return var(xarray,dims=1,corrected=false)
 end
+
+"""
+function mean(world::World;trait=1)
+
+"""
+function mean(world::World;trait=1)
+    xarray = Float64.(get_x(world,trait))
+    return mean(xarray,dims=1)
+end
 """
     covgeo(world::Array{Agent,1},trait = 0)
 If trait = 0, returns the variance of the geotrait,
@@ -99,7 +108,9 @@ function get_alpha_div(world::World,trait=1)
         return mean([var(Float64.(get_geo(World(subw,space(world),parameters(world)))),corrected=false) for subw in values(g)])
     else
         # here the second mean is here when subspace is multidimensional
-        return mean([mean(var(Float64.(get_x(World(subw,space(world),parameters(world)),trait)),corrected=false)) for subw in values(g)])
+        v = [var(World(subw,space(world),parameters(world)),trait=trait) for subw in values(g)]
+        h = vcat(v...)
+        return mean(h)
     end
 end
 
@@ -114,9 +125,10 @@ function get_beta_div(world::World,trait=1)
         # need to convert to Float64, otherwise infinite variance
         sbar_i = [mean(Float64.(get_geo(World(subw,space(world),parameters(world))))) for subw in values(g)]
     else
-        sbar_i = [mean(Float64.(get_x(World(subw,space(world),parameters(world)),trait))) for subw in values(g)]
+        m = [mean(World(subw,space(world),parameters(world)),trait=trait) for subw in values(g)]
+        h=vcat(m...)
     end
-    return var(sbar_i,corrected=false)
+    return mean(var(h,dims=1,corrected=false))
 end
 
 """

test/metrics.jl

@@ -65,10 +65,10 @@ end
 
 # TODO needs to test hamming distance
 
-# testing for real space of dimension d>1
+## testing for real space of dimension d>1
 multispace = (DiscreteSegment{Int8}(1,9),RealSpace{3,Float64}(),)
 K0 = 10000;
-multia = [Agent(multispace, (rand(Int8(1):Int8(10)),tuple(randn(3)...),),ancestors=true) for i in 1:K0]
+multia = [Agent(multispace, (rand(Int8(1):Int8(10)),tuple( randn(3) ...),),ancestors=true) for i in 1:K0]
 D = (Int8(1),tuple(fill(1.,3)...),)
 mu = [1]
 NMax = 1000
@@ -78,5 +78,25 @@ a = multia[10]
 inc = get_inc(1,D[1],multispace[2])
 @test inc[1] != inc[2] # checking that we have independent increments in each direction
 @test isapprox(mean(var(multiw,trait = 2)),1.,atol=1e-2)
+@test prod([isapprox(i,0.,atol=1e-1) for i in mean(multiw,trait = 2)])
 @test isapprox(get_alpha_div(multiw,2),1.,atol = 1e-2)
 @test isapprox(get_beta_div(multiw,2),0.,atol = 1e-2)
+
+## some test to write metrics functions
+# world = multiw;trait=2
+# g = groupby(a->a[1],agents(world))
+# v = [var(World(subw,space(world),parameters(world)),trait=trait) for subw in values(g)]
+# h = vcat(v...)
+# mean(mean(h,dims=1))
+# mean(h)
+# mean([mean(var(Float64.(get_x(World(subw,space(world),parameters(world)),trait)),corrected=false)) for subw in values(g)])
+#
+# [var(Float64.(get_x(World(subw,space(world),parameters(world)),trait)),corrected=false) for subw in values(g)]
+# subw = collect(values(g))[1]
+# var(Float64.(get_x(World(subw,space(world),parameters(world)),trait)),corrected=false)
+# var(World(subw,space(world),parameters(world)),trait=trait)
+# mean(World(subw,space(world),parameters(world)),trait=1)
+#
+# m = [mean(World(subw,space(world),parameters(world)),trait=trait) for subw in values(g)]
+# h=vcat(m...)
+# var(h,dims=1,corrected=false)