function [idx contrib errors test_ g mg] = vq_search_mse(vq, data)
[nVec nCols] = size(vq);
- nRows = length(data);
+ nRows = rows(data);
error = zeros(1,nVec);
errors = zeros(1, nRows);
function [idx contrib errors test_ g mg] = vq_search_gain(vq, data)
[nVec nCols] = size(vq);
- nRows = length(data);
+ nRows = rows(data);
error = zeros(1,nVec);
g = zeros(nRows, nVec);
function [idx contrib errors test_ g mg] = vq_search_mag(vq, data)
[nVec nCols] = size(vq);
- nRows = length(data);
+ nRows = rows(data);
g = mg = zeros(nRows, nVec);
diff = zeros(nVec, nCols);
errors = zeros(1, nRows);
idx = error = zeros(1, nVec);
contrib = zeros(nRows, nCols);
- mg_log = [];
test_ = zeros(nVec, nCols);
weights = ones(1,nCols);
idx(f) = min_ind;
contrib(f,:) = test_(f,:) = mg(f,min_ind) * vq(min_ind,:) + g(f,min_ind);
- mg_log = [mg_log mg(f,min_ind)];
end
- %figure(2); clf; hist(mg_log);
+endfunction
+
+
+%----------------------------------------------------------------------
+% abs() search with a linear, ampl scaling, and slope term
+
+function [idx contrib errors test_ g mg sl] = vq_search_slope(vq, data)
+ [nVec nCols] = size(vq);
+ nRows = rows(data);
+
+ g = mg = sl = zeros(nRows, nVec);
+ diff = zeros(nVec, nCols);
+ errors = zeros(1, nRows);
+ idx = error = zeros(1, nVec);
+ contrib = zeros(nRows, nCols);
+ test_ = zeros(nVec, nCols);
+
+ weights = ones(1,nCols);
+
+ for f=1:nRows
+ target = data(f,:);
+ %target = 2*vq(1,:)+1;
+
+ for i=1:nVec
+ % work out gain, amp and slope for best match, 3 unknowns, 3 equations
+
+ A = [sum(vq(i,:)) nCols sum(1:nCols); ...
+ vq(i,:)*vq(i,:)' sum(vq(i,:)) (1:nCols)*vq(i,:)'; ...
+ (1:nCols)*vq(i,:)' sum(1:nCols) (1:nCols)*(1:nCols)'];
+ c = [sum(target) target*vq(i,:)' target*(1:nCols)']';
+ b = inv(A)*c;
+
+ mg(f,i) = b(1); g(f,i) = b(2); sl(f,i) = b(3);
+ diff(i,:) = target - (mg(f,i)*vq(i,:) + g(f,i) + sl(f,i)*(1:nCols));
+ diff(i,:) .* weights;
+
+ % abs in dB is MSE in linear
+
+ error(i) = mean(abs(diff(i,:)));
+
+ %printf("f: %d i: %d mg: %f g: %f sl: %f error: %f\n", f, i, mg(f,i), g(f,i), sl(f,i), error(i));
+ end
+
+ [mn min_ind] = min(error);
+ errors(f) = mn;
+ idx(f) = min_ind;
+
+ contrib(f,:) = test_(f,:) = mg(f,min_ind)*vq(min_ind,:) + g(f,min_ind) + sl(f,min_ind)*(1:nCols);
+ end
+
endfunction
endfunction
-%----------------------------------------------------------------------
-%
-% Plot histograms of SDs for comparison % % Each col of sd_per_frame
-% has the results of one test, number of cols % is number of tests. leg
-% is a col vector with one legend string for each test.
-
-function compare_hist(atitle, sdpf, leg)
- [nRows nCols] = size(sd);
- for c=1:nCols
- [yy, xx] = hist(sd(:, c));
- if c == 2, hold on; end;
- l = char(cellstr(leg)(c));
- plot(xx, yy, leg(c));
- end
- if nCols > 1, hold off; end;
- title(atitle)
-end
-
-
-function plot_sd_results(title_str, fg, offset, sd)
- figure(fg); clf;
- samples = offset+[1 4 7];
- bits = log2([64 128 256]);
- errorbar(bits-0.1, mean(sd(:,samples)), std(sd(:, samples),[]),'b+-; MSE search;');
- hold on;
- samples = offset+[2 5 8];
- errorbar(bits+0.0, mean(sd(:,samples)), std(sd(:,samples),[]),'g+-;Gain search;');
- samples = offset+[3 6 9];
- errorbar(bits+0.1, mean(sd(:,samples)), std(sd(:,samples),[]),'r+-;Mag search;');
- hold off;
- xlabel('VQ size (bits)')
- ylabel('mean SD (dB)');
- title(title_str);
-endfunction
-
-
-function plot_sd_results2(title_str, fg, sd)
- figure(fg); clf;
- samples = 9+[3 6 9];
- bits = log2([64 128 256]);
- errorbar(bits-0.1, mean(sd(:,samples)), std(sd(:, samples),[]),'b+-; MSE train;');
- hold on;
- samples = 18+[3 6 9];
- errorbar(bits+0.0, mean(sd(:,samples)), std(sd(:,samples),[]),'g+-;Gain train;');
- samples = 27+[3 6 9];
- errorbar(bits+0.1, mean(sd(:,samples)), std(sd(:,samples),[]),'r+-;Mag train;');
- hold off;
- xlabel('VQ size (bits)')
- ylabel('mean SD (dB)');
- title(title_str);
-endfunction
-
-
function search_func = get_search_func(short_name)
if strcmp(short_name, "mse")
search_func = 'vq_search_mse';
if strcmp(short_name, "mag")
search_func = 'vq_search_mag';
end
+ if strcmp(short_name, "slope")
+ search_func = 'vq_search_slope';
+ end
end
test_func = get_search_func(test_func_short);
asd = run_test(vq, sim_in.testvec, Nvec, test_func);
sd = [sd asd];
- ades = sprintf("Nvec: %3d train: %-4s test: %-4s",
- Nvec, sim_in.train_func_short, test_func_short);
- des = [des; ades];
- printf(" %s SD: %3.2f\n", ades, mean(asd));
+ ades.Nvec = Nvec;
+ ades.train_func_short = sim_in.train_func_short;
+ ades.test_func_short = test_func_short;
+ ades_str = sprintf("Nvec: %3d train: %-5s test: %-5s",
+ ades.Nvec, ades.train_func_short, ades.test_func_short);
+ des = [des ades];
+ printf(" %s SD: %3.2f\n", ades_str, mean(asd));
end
endfunction
-function sd = long_tests(quick_check=0)
+% Search for test that matchs train/test and any Nvec and constructs points for error bar plot
+
+function [y x leg] = search_tests(sd, desc, train, test)
+ nTests = length(desc)
+ x = []; y = []; leg = [];
+ for i=1:nTests
+ if strcmp(desc(i).train_func_short, train) && strcmp(desc(i).test_func_short, test)
+ printf("i: %2d Nvec: %3d train: %5s test: %5s\n", i, desc(i).Nvec, desc(i).train_func_short, desc(i).test_func_short);
+ x = [x; log2(desc(i).Nvec)];
+ y = [y; mean(sd(:,i)) std(sd(:,i))];
+ end
+ end
+endfunction
+
+
+function plot_sd_results(fg, title_str, sd, desc, train_list, test_list)
+ figure(fg); clf;
+
+ nlines = 0; inc = -0.1;
+ for i=1:length(cellstr(train_list))
+ train_func_short = char(cellstr(train_list)(i));
+ for j=1:length(cellstr(test_list))
+ test_func_short = char(cellstr(test_list)(j));
+ [y x] = search_tests(sd, desc, train_func_short, test_func_short);
+ leg = sprintf("o-%d;train: %5s test: %5s;", nlines, train_func_short, test_func_short)
+ if nlines, hold on; end;
+ x += inc; inc += 0.1; % separate x coords a bit to make errors bars legible
+ errorbar(x, y(:,1), y(:,2), leg);
+ nlines++;
+ end
+ if nlines>1, hold off; end;
+ end
+
+ xlabel('VQ size (bits)')
+ ylabel('mean SD (dB)');
+ title(title_str);
+endfunction
+
+
+% Search for test that matches all fields for histogram plots
+
+function testNum = search_tests_Nvec(sd, desc, train, test, Nvec)
+ nTests = length(desc);
+ testNum = 0;
+ for i=1:nTests
+ if strcmp(desc(i).train_func_short, train) && strcmp(desc(i).test_func_short, test) && desc(i).Nvec == Nvec
+ printf("i: %2d Nvec: %3d train: %5s test: %5s\n", i, desc(i).Nvec, desc(i).train_func_short, desc(i).test_func_short);
+ testNum = i;
+ end
+ end
+endfunction
+
+%----------------------------------------------------------------------
+%
+% Plot histograms of SDs for comparison. Each col of sd_per_frame
+% has the results of one test, number of cols % is number of tests. leg
+% is a col vector with one legend string for each test.
+
+function compare_hist(fg, atitle, sd, desc)
+ figure(fg); clf;
+ [nRows nCols] = size(sd);
+ for c=1:nCols
+ [yy, xx] = hist(sd(:, c));
+ if c == 2, hold on; end;
+ leg = sprintf("o-%d;train: %5s test: %5s;", c-1, desc(c).train_func_short, desc(c).test_func_short);
+ plot(xx, yy, leg);
+ end
+ if nCols > 1, hold off; end;
+ title(atitle)
+end
+
+%----------------------------------------------------------------------
+% Run a bunch of long tests in parallel and plot results
+
+function [sd desc] = long_tests(quick_check=0)
num_cores = 4;
K = 10;
- load surf_train_120; load surf_all;
load surf_train_120_hpf150; load surf_all_hpf150;
if quick_check
NtrainVec = 1000;
NtestVec = 100;
else
- NtrainVec = length(surf_train_120);
- NtestVec = length(surf_all);
+ NtrainVec = length(surf_train_120_hpf150);
+ NtestVec = length(surf_all_hpf150);
end
- trainvec = surf_train_120(1:NtrainVec,1:K);
- testvec = surf_all(1:NtestVec,1:K);
- trainvec_hpf150 = surf_train_120_hpf150(1:NtrainVec,1:K);
- testvec_hpf150 = surf_all_hpf150(1:NtestVec,1:K);
+ trainvec = surf_train_120_hpf150(1:NtrainVec,1:K);
+ testvec = surf_all_hpf150(1:NtestVec,1:K);
+
+ % build up a big array of tests to run ------------------------
sim_in.trainvec = trainvec; sim_in.testvec = testvec;
sim_in.Nvec = 64;
sim_in.train_func_short = "mse";
- sim_in.tests = ["mse"; "gain"; "mag"];
+ sim_in.tests = ["mse"; "gain"; "mag"; "slope"];
+
+ % Test1: mse training, 64, 128, 256
+
sim_in_vec(1:3) = sim_in;
sim_in_vec(2).Nvec = 128; sim_in_vec(3).Nvec = 256;
- [sd desc] = train_vq_and_run_tests(sim_in(1));
-
- desc
+ test_list = sim_in_vec;
-#{
- sd = pararrayfun(num_cores, @three_tests, sim_in_vec);
- plot_sd_results("MSE Training", 1, 0, sd)
+ % Test2: gain training, 64, 128, 256
- for i=1:3, sim_in_vec(i).trainvec = trainvec_hpf150; sim_in_vec(i).testvec = testvec_hpf150; end;
- sd = [sd pararrayfun(num_cores, @three_tests, sim_in_vec)];
- plot_sd_results("MSE training 150Hz HPF", 2, 9, sd)
+ for i=1:3, sim_in_vec(i).train_func_short = "gain"; end;
+ test_list = [test_list sim_in_vec];
- for i=1:3, sim_in_vec(i).train_func = 'vq_search_gain'; end;
- sd = [sd pararrayfun(num_cores, @three_tests, sim_in_vec)];
- plot_sd_results("Gain training 150Hz HPF", 3, 18, sd)
+ % Test3: mag training, 64, 128, 256
- for i=1:3, sim_in_vec(i).train_func = 'vq_search_mse'; end;
- sd = [sd pararrayfun(num_cores, @three_tests, sim_in_vec)];
- plot_sd_results("Mag training 150Hz HPF", 4, 27, sd)
+ for i=1:3, sim_in_vec(i).train_func_short = "mag"; end;
+ test_list = [test_list sim_in_vec];
+
+ % Test4: slope training, 64, 128, 256
+
+ for i=1:3, sim_in_vec(i).train_func_short = "slope"; end;
+ test_list = [test_list sim_in_vec];
+
+ % run test list in parallel
+
+ [sd desc] = pararrayfun(num_cores, @train_vq_and_run_tests, test_list);
+
+ % Plot results -----------------------------------------------
+
+ fg = 1;
+ plot_sd_results(fg++, "MSE Training", sd, desc, "mse", ["mse"; "gain"; "mag"; "slope"]);
+ plot_sd_results(fg++, "Gain Training", sd, desc, "gain", ["mse"; "gain"; "mag"; "slope"]);
+ plot_sd_results(fg++, "Mag Training", sd, desc, "mag", ["mse"; "gain"; "mag"; "slope"]);
+ plot_sd_results(fg++, "Slope Training", sd, desc, "slope", ["mse"; "gain"; "mag"; "slope"]);
+ plot_sd_results(fg++, "Slope Searching", sd, desc, ["mse"; "gain"; "mag"; "slope"], "slope");
+
+ % histogram of results from Nvec=64, all training methods, slope search
+
+ testnum = search_tests_Nvec(sd, desc, "mse", "slope", 64);
+ hist_sd = sd(:,testnum); hist_desc = desc(testnum);
+ testnum = search_tests_Nvec(sd, desc, "gain", "slope", 64);
+ hist_sd = [hist_sd sd(:,testnum)]; hist_desc = [hist_desc desc(testnum)];
+ testnum = search_tests_Nvec(sd, desc, "mag", "slope", 64);
+ hist_sd = [hist_sd sd(:,testnum)]; hist_desc = [hist_desc desc(testnum)];
+ testnum = search_tests_Nvec(sd, desc, "slope", "slope", 64);
+ hist_sd = [hist_sd sd(:,testnum)]; hist_desc = [hist_desc desc(testnum)];
+ compare_hist(fg, "Histogram of SDs Nvec=64", hist_sd, hist_desc);
- plot_sd_results2("Mag search 150Hz HPF", 5, sd)
-
- figure(6); clf; compare_hist("Mag search Nvec=64", sd(:,3), sd(:,12), sd(:,21));
-#}
endfunction
endfunction
+function test_training_slope
+ K = 40; NtrainVec = 5; Nvec = 1;
+
+ % Given a vector x, create a set of training data y = m*x + c + sl, where:
+ % x is a vector
+ % c is a constant offset
+ % m is a magnitude multiplier on all elements of x
+ % sl is a linear slope vector
+
+ load surf_all;
+ prototype = surf_all(73,:);
+ trainvec = zeros(NtrainVec,K);
+ for v=1:NtrainVec
+ trainvec(v,:) = 2*prototype + 1 + v*(1:K);
+ end
+ figure(1); clf; plot(trainvec');
+
+ [idx vq] = kmeans2(trainvec, Nvec,
+ "start", "first",
+ "search_func", "vq_search_slope");
+
+
+ [idx contrib errors test_ g mg sl] = vq_search_slope(prototype, trainvec)
+
+ % todo: how to auto test? Need to solve same euqations?
+#{
+ tol = 0.001; ok = 0;
+ for i=1:Nvec
+ diff = vq(i,:) - [1 1 1];
+ if std(diff) < tol, ok++; end;
+ diff = vq(i,:) - [1 0 -1];
+ if std(diff) < tol, ok++; end;
+ end
+ if ok == 2, printf("gain: OK\n"); end;
+#}
+endfunction
+
% ---------------------------------------------------------
format; more off;
% we want same results on every run
%short_detailed_test('vq_search_mag', 'vq_search_mag');
-sd = long_tests(quick_check=1);
-%test_training_mag
+[sd desc] = long_tests(quick_check=0);
+%test_training_slope
projects / freetel-svn-tracking.git / commitdiff