all tests on generated data for julia 1.0

iitis · Oct 3, 2018 · 3c16dba · 3c16dba
1 parent e7aba6a
commit 3c16dba
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diff --git a/test/jkfs_select.jl b/test/jkfs_select.jl
@@ -11,71 +11,79 @@ using Random
 @everywhere import CumulantsFeatures: reduceband
 @everywhere using DatagenCopulaBased
 @everywhere using CumulantsFeatures
-
 @everywhere cut_order(x) = (x->x[3]).(x)
 
-ν = parse(Int64, ARGS[1])
-println(ν)
 
+function main(args)
+  s = ArgParseSettings("description")
+  @add_arg_table s begin
+    "--nu", "-u"
+    default = 5
+    help = "the number of degrees of freedom for the t-Student copula"
+    arg_type = Int
+  end
+  parsed_args = parse_args(s)
+  ν = parsed_args["nu"]
 
-@everywhere t = 100_000
-@everywhere n = 50
-@everywhere malf_size = 10
-data_dir = "jkfsdata_select"
-test_number = 25
-filename = "tstudent_$(ν)-t_size-$(n)_malfsize-$malf_size-t_$t.jld2"
+  println(ν)
+  @everywhere t = 100_000
+  @everywhere n = 50
+  @everywhere malf_size = 10
+  data_dir = "jkfsdata_select"
+  test_number = 25
+  filename = "tstudent_$(ν)-t_size-$(n)_malfsize-$malf_size-t_$t.jld2"
 
-data = Dict{String, Any}("variables_no" => n,
-                       "sample_number" => t,
-                       "ν" => ν,
-                       "test_number" => test_number,
-                       "malf_size" => malf_size,
-                       "data" => Dict{String, Dict{String,Any}}())
+  data = Dict{String, Any}("variables_no" => n,
+                         "sample_number" => t,
+                         "ν" => ν,
+                         "test_number" => test_number,
+                         "malf_size" => malf_size,
+                         "data" => Dict{String, Dict{String,Any}}())
 
 
-known_data_size = 0
-if isfile("$data_dir/$filename")
- data["data"] = load("$data_dir/$filename")["data"]
- known_data_size += length(data["data"])
- println("Already have $known_data_size samples \n Will generate $(test_number-known_data_size) more")
-end
+  known_data_size = 0
+  if isfile("$data_dir/$filename")
+   data["data"] = load("$data_dir/$filename")["data"]
+   known_data_size += length(data["data"])
+   println("Already have $known_data_size samples \n Will generate $(test_number-known_data_size) more")
+  end
 
-#true calculations
-println("Calculation started")
-for m=(known_data_size+1):test_number
-  @time begin
-    println(" > $m ($ν)")
-    malf = randperm(n)[1:malf_size]
-    Σ = cormatgen_rand(n)
-    samples_orig = Array(rand(MvNormal(Σ), t)')
+  #true calculations
+  println("Calculation started")
+  for m=(known_data_size+1):test_number
+    @time begin
+      println(" > $m ($ν)")
+      malf = randperm(n)[1:malf_size]
+      Σ = cormatgen_rand(n)
+      samples_orig = Array(rand(MvNormal(Σ), t)')
 
-    versions = [(x->x, "original"),
-                (x->gcop2tstudent(x, malf, ν), "malf")]
+      versions = [(x->x, "original"),
+                  (x->gcop2tstudent(x, malf, ν), "malf")]
 
-    cur_dict = Dict{String, Any}("malf" => malf,
-                                 "cor_source" => Σ)
+      cur_dict = Dict{String, Any}("malf" => malf,
+                                   "cor_source" => Σ)
 
-    data_dict = @distributed (merge) for (sampler, label)=versions
-      println(label)
-      samples = Array(sampler(samples_orig))
-      Σ_malf = SymmetricTensor(cov(samples))
-      cum = cumulants(samples, 4)
-      bands2 = cut_order(cumfsel(Σ_malf))
-      bands3 = cut_order(cumfsel(cum[2], cum[3], "hosvd"))
-      bands4 = cut_order(cumfsel(cum[2], cum[4], "hosvd"))
-      bands4n = cut_order(cumfsel(cum[2], cum[4], "norm", n-1))
-      bands4n = vcat(bands4n, setdiff(collect(1:n), bands4n))
-      Dict("cor_$label" => Σ_malf,
-           "bands_MEV_$label" => bands2,
-           "bands_JSBS_$label" => bands3,
-           "bands_JKFS_$label" => bands4,
-           "bands_JKN_$label" => bands4n)
-    end
+      data_dict = @distributed (merge) for (sampler, label)=versions
+        println(label)
+        samples = Array(sampler(samples_orig))
+        Σ_malf = SymmetricTensor(cov(samples))
+        cum = cumulants(samples, 4)
+        bands2 = cut_order(cumfsel(Σ_malf))
+        bands3 = cut_order(cumfsel(cum[2], cum[3], "hosvd"))
+        bands4 = cut_order(cumfsel(cum[2], cum[4], "hosvd"))
+        bands4n = cut_order(cumfsel(cum[2], cum[4], "norm", n-1))
+        bands4n = vcat(bands4n, setdiff(collect(1:n), bands4n))
+        Dict("cor_$label" => Σ_malf,
+             "bands_MEV_$label" => bands2,
+             "bands_JSBS_$label" => bands3,
+             "bands_JKFS_$label" => bands4,
+             "bands_JKN_$label" => bands4n)
+      end
 
-    data["data"]["$m"] = merge(cur_dict, data_dict)
-    save("$data_dir/$filename", data)
+      data["data"]["$m"] = merge(cur_dict, data_dict)
+      save("$data_dir/$filename", data)
+    end
   end
 end
 
-rmprocs(procs_id)
-exit()
+main(ARGS)
diff --git a/test/jkfsdata_select/jkfs_data_analysis.jl b/test/jkfsdata_select/jkfs_data_analysis.jl
@@ -83,8 +83,8 @@ function plotdata(plot_data, data_parts_label, ν, malf_size, var_number, repeat
     data_y = plot_data[data_label]
     data_x = collect(0:(length(data_y)-1))
     println(data_y)
-    d = replace(data_label, "bands_", "")
-    plot(data_x, data_y/repeating, p[i], label=replace(d, "_malf", ""), color = cols[i], linewidth = 0.8, markersize = 3.)
+    d = replace(data_label, "bands_"=>"")
+    plot(data_x, data_y/repeating, p[i], label=replace(d, "_malf"=>""), color = cols[i], linewidth = 0.8, markersize = 3.)
   end
   data_x = 0:malf_size
   #data_y = map(i-> theoretical(var_number, malf_size, i), data_x)