Update simulator

wide vs narrow/single_point_test/simulator.m

@@ -3,9 +3,9 @@ clc; clear; close all;
 
 trail_times = 250;
 method = "cVAMPro";
-Ns = [64, 128, 256, 512, 1024];
+Ns = [128, 256, 512, 1024];
 Ms = [4, 8, 16, 32];
-sigma = 0.01;
+sigma = 0.1;
 tau = 1e-6;
 iter_max = 100;
 epi = 0;
@@ -18,11 +18,11 @@ H0_REE_maxs = zeros(length(Ns), length(Ms));
 H1_REE_means = zeros(length(Ns), length(Ms));
 H1_REE_maxs = zeros(length(Ns), length(Ms));
 
-figure;
+% figure;
 
 for N_idx = 1: length(Ns)
     for M_idx = 1: length(Ms)
-        subplot(length(Ns), length(Ms), (N_idx - 1) * length(Ms) + M_idx);
+        subplot(length(Ms), length(Ns), N_idx + (M_idx - 1) * length(Ns));
         FAR_N = Ns(N_idx);
         FAR_M = Ms(M_idx);
 
@@ -33,7 +33,7 @@ for N_idx = 1: length(Ns)
         [Lambda, Lambda_C, x] = get_sparse_vector(FAR_N, FAR_M, betas_wide, true);
 
         % get lambda
-        signal_model_filename = "./Signal_Model/Signal_Model_" + string(FAR_N) + "_" + string(FAR_M) + ".mat";
+        signal_model_filename = "./Signal_Model_05/Signal_Model_" + string(FAR_N) + "_" + string(FAR_M) + ".mat";
         if exist(signal_model_filename, "file")
             load(signal_model_filename, "A", "C_n", "ref_lambda");
         else
@@ -68,7 +68,7 @@ for N_idx = 1: length(Ns)
         % train
         LASSO_lambda = ref_lambda;
         simulate_results_filename = ...
-            "./data5/" + ...
+            "./data_05/" + ...
             "FAR" + "_" + ...
             string(FAR_N) + "_" + ...
             string(FAR_M)+ "_" + ...
@@ -99,23 +99,24 @@ for N_idx = 1: length(Ns)
         Lambda_distributes = zeros(2, length(Lambda) * trail_times);
         Lambda_C_distributes = zeros(2, length(Lambda_C) * trail_times);
         
+        adds = [];
         for t = 1:trail_times
             x_0_hat = squeeze(recovery_results(1, t, :));
             x_1_hat = squeeze(recovery_results(2, t, :)) - x;
             if anynan(x_1_hat)
                 continue;
             end
+            adds = [adds; x_1_hat];
 
-            for i = 1: length(Lambda) + length(Lambda_C)
+            for i = 1: length(Lambda_C)
-                if ismember(i, Lambda_C)
+                Lambda_C_distributes(1, i1) = x_0_hat(Lambda_C(i));
-                    Lambda_C_distributes(1, i1) = x_0_hat(i);
+                Lambda_C_distributes(2, i1) = x_1_hat(Lambda_C(i));
-                    Lambda_C_distributes(2, i1) = x_1_hat(i);
                 i1 = i1 + 1;
-                elseif ismember(i, Lambda)
-                    Lambda_distributes(1, i2) = x_0_hat(i);
-                    Lambda_distributes(2, i2) = x_1_hat(i);
-                    i2 = i2 + 1;
             end
+            for i = 1: length(Lambda)
+                Lambda_distributes(1, i2) = x_0_hat(Lambda(i));
+                Lambda_distributes(2, i2) = x_1_hat(Lambda(i));
+                i2 = i2 + 1;
             end
         end
         real_H00 = real(Lambda_C_distributes(1, 1:i1-1));
@@ -123,14 +124,20 @@ for N_idx = 1: length(Ns)
         real_H10 = real(Lambda_C_distributes(2, 1:i1-1));
         real_H11 = real(Lambda_distributes(2, 1:i2-1));
 
-        % figure;
+        if 1 == 0
-        % subplot(2, 2, 1); histfit(real_H00); xlabel("x\_hat"); ylabel("times"); title("H_0 (not in support set)")
+            figure;
-        % subplot(2, 2, 2); histfit(real_H01); xlabel("x\_hat"); ylabel("times"); title("H_0 (in support set)")
+            subplot(2, 2, 1); histfit(real_H00); xlabel("x\_hat"); ylabel("times"); title("H_0 (not in support set)")
-        % subplot(2, 2, 3); histfit(real_H10); xlabel("x\_hat"); ylabel("times"); title("H_1 (not in support set)")
+            subplot(2, 2, 2); histfit(real_H01); xlabel("x\_hat"); ylabel("times"); title("H_0 (in support set)")
-        % subplot(2, 2, 4); histfit(real_H11); xlabel("x\_hat"); ylabel("times"); title("H_1 (in support set)")
+            subplot(2, 2, 3); histfit(real_H10); xlabel("x\_hat"); ylabel("times"); title("H_1 (not in support set)")
-        % sgtitle("N = " + string(FAR_N) + ", M = " + string(FAR_M));
+            subplot(2, 2, 4); histfit(real_H11); xlabel("x\_hat"); ylabel("times"); title("H_1 (in support set)")
+            sgtitle("N = " + string(FAR_N) + ", M = " + string(FAR_M));
+        end
 
-        histfit(real_H11); xlabel("x\_hat"); ylabel("times"); title("N = " + string(FAR_N) + ", M = " + string(FAR_M));
+        % histfit(real_H00);
+        % histfit(real_H01);
+        % histfit(real_H10);
+        histfit(real_H11);
+        xlabel("x\_hat"); ylabel("times"); title("N = " + string(FAR_N) + ", M = " + string(FAR_M));
 
         means = [ ...
                      mean(Lambda_C_distributes(1, 1:i1-1)), ...
@@ -163,34 +170,33 @@ for N_idx = 1: length(Ns)
         fprintf("Test Complete \n\n");
     end
 end
-sgtitle("H_1 (in support set)");
+% sgtitle("H_0 (not in support set)")
-
+% sgtitle("H_0 (in support set)")
-figure;
+% sgtitle("H_1 (not in support set)")
-
+sgtitle("H_1 (in support set)")
-subplot(221);
-h = heatmap(Ns, Ms, H0_REE_means');
-h.XLabel = "N";
-h.YLabel = "M";
-h.Title = "H_0, mean(REE)";
-
-subplot(222);
-h = heatmap(Ns, Ms, H0_REE_maxs');
-h.XLabel = "N";
-h.YLabel = "M";
-h.Title = "H_0, max(REE)";
-
-subplot(223);
-h = heatmap(Ns, Ms, H1_REE_means');
-h.XLabel = "N";
-h.YLabel = "M";
-h.Title = "H_1, mean(REE)";
-
-subplot(224);
-h = heatmap(Ns, Ms, H1_REE_maxs');
-h.XLabel = "N";
-h.YLabel = "M";
-h.Title = "H_1, max(REE)";
-
-
 
+% figure;
+% subplot(221);
+% h = heatmap(Ns, Ms, H0_REE_means');
+% h.XLabel = "N";
+% h.YLabel = "M";
+% h.Title = "H_0, mean(REE)";
+% 
+% subplot(222);
+% h = heatmap(Ns, Ms, H0_REE_maxs');
+% h.XLabel = "N";
+% h.YLabel = "M";
+% h.Title = "H_0, max(REE)";
+% 
+% subplot(223);
+% h = heatmap(Ns, Ms, H1_REE_means');
+% h.XLabel = "N";
+% h.YLabel = "M";
+% h.Title = "H_1, mean(REE)";
+% 
+% subplot(224);
+% h = heatmap(Ns, Ms, H1_REE_maxs');
+% h.XLabel = "N";
+% h.YLabel = "M";
+% h.Title = "H_1, max(REE)";