73 lines
1.8 KiB
Matlab
Executable File
73 lines
1.8 KiB
Matlab
Executable File
function P_fa = PD_simu(threshold)
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global N method trail_times
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% Define measurement matrix
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B = dftmtx(N);
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% Define sparse vector z
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[Lambda, z] = get_sparse_vector(N, 1);
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Lambda_C = setdiff(1:N, Lambda);
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results = zeros(trail_times, N);
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% Calcuate test statistics
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P2 = [];
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if exist("PD_recovery_0_debiasedLASSO_500.mat")
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load("PD_recovery_1_debiasedLASSO_500.mat", "results");
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else
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for T = 1: trail_times
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% y = Bz + n
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n = randn(N, 1) * 0.1;
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y_noise = n;
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% z_hat = CS(B, y)
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z_hat = recovery(B, y_noise);
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% Calculate test statistic
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results(T, :) = z_hat;
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end
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end
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% Distribute of H_0 and H_1
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H_0_distribute = zeros(1, length(Lambda_C));
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H_1_distribute = zeros(1, length(Lambda));
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i1 = 1;
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i2 = 1;
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Ts = [];
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for t = 1: trail_times
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z_hat = results(t, :);
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T = 0;
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for i = 1: length(z)
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if ismember(i, Lambda_C)
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H_0_distribute(i1) = z_hat(i);
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i1 = i1 + 1;
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elseif ismember(i, Lambda)
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T = T + abs(z_hat(i));
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H_1_distribute(i2) = z_hat(i);
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i2 = i2 + 1;
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end
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end
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Ts = [Ts T];
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end
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% Draw
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figure(2)
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subplot(2, 1, 1);
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title("Freq histogram of H_0");
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histfit(real(H_0_distribute));
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subplot(2, 1, 2);
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title("Freq histogram of H_1");
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histfit(real(H_1_distribute));
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H_0_mean = mean(H_0_distribute);
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H_0_std = std(H_0_distribute);
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H_1_mean = mean(H_1_distribute);
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H_1_std = std(H_1_distribute);
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fprintf("H_0: mu = %f, std = %f\n", H_0_mean, H_0_std);
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fprintf("H_1: mu = %f, std = %f\n", H_1_mean, H_1_std);
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% P_fa = normcdf(threshold, H_0_mean, H_0_std);
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end |