Update wide vs narrow code

2024-07-22 21:17:57 +08:00
parent 13f660826d
commit 9cbe8e31ea
14 changed files with 1642 additions and 0 deletions
@@ -0,0 +1,102 @@
+clc; clear; close all;
+
+N = 256;
+M = 16;
+MN = N * M;
+sigmas = [0.01];
+% noise_sigma = 0.01;
+
+filename = "./results/Signal_Model_" + string(N) + "_" + string(M) + ".mat";
+curr_metric = N;
+for TT = 1: 100
+    TT
+tau = 1e-6;
+iter_max = 25;
+epi = 0;
+[C_n, A] = get_Psi(N, M, epi);
+betas_wide = zeros(M * N, 1) + 1;
+[Lambda, Lambda_C, x] = get_sparse_vector(N, M, betas_wide, true);
+
+lg_lambdas = -3: 0.05: -1;
+lambdas = 10 .^ lg_lambdas;
+method = "cVAMPro";
+
+
+for sigma_idx = 1: length(sigmas)
+    sigma = sigmas(sigma_idx);
+    noise = get_noise(sigma, N, 1);
+    y_noise = A * x + noise;
+    
+    MSEs = zeros(length(lambdas), 1);
+    all_x_hat = zeros(length(x), length(lambdas));
+    for lambda_idx = 1: length(lambdas)
+        lambda = lambdas(lambda_idx);
+        if method == "cVAMPro"
+            [x_LASSO, x_hat_d] = cVAMPro(y_noise, A, lambda, tau, iter_max);
+        elseif method == "debiased\_LASSO"
+            [x_LASSO, sigma_w_2, threshold] = debiased_LASSO(A, y_noise, 0, sigma^2, lambda);
+        elseif method == "LASSO"
+            cvx_begin quiet
+                variable x_LASSO(MN) complex
+                minimize(lambda * norm(x_LASSO, 1) + norm(y_noise - A * x_LASSO, 2))
+            cvx_end
+        elseif method == "FISTA"
+            x_LASSO = FISTA(y_noise, A, LASSO_lambda, 1e-5);
+        elseif method == "debiased\_LASSO\_FISTA"
+            [x_LASSO, sigma_w_2, threshold] = debiased_LASSO_FISTA(A, y_noise, 0, noise_sigma^2, lambda);
+        elseif method == "BP"
+            sz = size(A);
+            N = sz(2);
+            cvx_begin quiet
+                variable x_LASSO(N) complex
+                minimize(norm(x_LASSO, 1))
+            subject to
+                A * x_LASSO == y_noise
+            cvx_end
+        end
+        MSEs(lambda_idx) = sum(real(x_LASSO - x));
+        all_x_hat(:, lambda_idx) = x_LASSO;
+    end
+    abs_MSEs = abs(MSEs);
+    [metric, idx] = min(abs_MSEs);
+    if curr_metric > metric
+        ref_lambda = lambdas(idx);
+        save(filename, "A", "C_n", "ref_lambda");
+        curr_metric = metric;
+    end
+    if curr_metric < 0.1
+        figure;
+        subplot(211);
+        semilogx(lambdas, MSEs);
+        yline(0);
+        ylim([-length(Lambda) - 0.1, 0.1]);
+        xlabel("\lambda");
+        ylabel("MSE (x\_hat - x)");
+        title("N = " + string(N) + ", M = " + string(M) + ", \sigma = " + string(sigma) + ", " + method);
+        subplot(212);
+        plot(C_n);
+        xlabel("Frequency code");
+        ylabel("Times");
+        break
+    end
+    % for i = 1: length(C_n)
+    %     fprintf("%d, ", C_n(i))
+    % end
+    % fprintf("%.15f\n", sum(MSEs));
+
+    % filename = ...
+    %     string(N) + "_" + ...
+    %     string(M) + "_" + ...
+    %     string(sigma) + "_" + ...
+    %     method + ".mat";
+    % save(filename, "lambdas", "MSEs", "x", "all_x_hat");
+    
+    % figure;
+    % semilogx(lambdas, xx);
+    % yline(0);
+    % ylim([-length(Lambda) - 0.1, 0.1]);
+    % xlabel("\lambda");
+    % ylabel("MSE (x\_hat - x)");
+    % title("N = " + string(FAR_N) + ", M = " + string(FAR_M) + ", \sigma = " + string(sigma) + ", " + method);
+end
+end