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対数軸を持つ確率密度関数に最適な線をプロットしようとしています。Y 軸 (PDF) は 10^-12 から 10^-28 で、X 軸は 10^10 から 10^20 です。polyfit を試してみましたが、うまくいきませんでした。何か案は？添付は私のコードです。

ありがとう、ケビン

clc;
clear all;

load Aug2005_basin_variables.mat

% Initialize

j_len = length(W_SH);
prob_dens_all = zeros(j_len,30);
ii = 1 : j_len;
count(1:30) = 0;
bin(1:30) = 0;

for i = 1 : 30
    bin(i) = 10^(11 + (0.3*i));
end


% Bin the Watts

for i = 1 : j_len
    if((log10(W_SH(i)) >= 11) && (log10(W_SH(i)) < 11.3))
        count(1) = count(1) + 1;
    end
    if((log10(W_SH(i)) >= 11.3) && (log10(W_SH(i)) < 11.6))
        count(2) = count(2) + 1;
    end
    if((log10(W_SH(i)) >= 11.6) && (log10(W_SH(i)) < 11.9))
        count(3) = count(3) + 1;
    end
    if((log10(W_SH(i)) >= 11.9) && (log10(W_SH(i)) < 12.2))
        count(4) = count(4) + 1;
    end
    if((log10(W_SH(i)) >= 12.2) && (log10(W_SH(i)) < 12.5))
        count(5) = count(5) + 1;
    end
    if((log10(W_SH(i)) >= 12.5) && (log10(W_SH(i)) < 12.8))
        count(6) = count(6) + 1;
    end
    if((log10(W_SH(i)) >= 12.8) && (log10(W_SH(i)) < 13.1))
        count(7) = count(7) + 1;
    end
    if((log10(W_SH(i)) >= 13.1) && (log10(W_SH(i)) < 13.4))
        count(8) = count(8) + 1;
    end
    if((log10(W_SH(i)) >= 13.4) && (log10(W_SH(i)) < 13.7))
        count(9) = count(9) + 1;
    end
    if((log10(W_SH(i)) >= 13.7) && (log10(W_SH(i)) < 14.0))
        count(10) = count(10) + 1;
    end
    if((log10(W_SH(i)) >= 14.0) && (log10(W_SH(i)) < 14.3))
        count(11) = count(11) + 1;
    end
    if((log10(W_SH(i)) >= 14.3) && (log10(W_SH(i)) < 14.6))
        count(12) = count(12) + 1;
    end
    if((log10(W_SH(i)) >= 14.6) && (log10(W_SH(i)) < 14.9))
        count(13) = count(13) + 1;
    end
    if((log10(W_SH(i)) >= 14.9) && (log10(W_SH(i)) < 15.2))
        count(14) = count(14) + 1;
    end
    if((log10(W_SH(i)) >= 15.2) && (log10(W_SH(i)) < 15.5))
        count(15) = count(15) + 1;
    end
    if((log10(W_SH(i)) >= 15.5) && (log10(W_SH(i)) < 15.8))
        count(16) = count(16) + 1;
    end
    if((log10(W_SH(i)) >= 15.8) && (log10(W_SH(i)) < 16.1))
        count(17) = count(17) + 1;
    end
    if((log10(W_SH(i)) >= 16.1) && (log10(W_SH(i)) < 16.4))
        count(18) = count(18) + 1;
    end
    if((log10(W_SH(i)) >= 16.4) && (log10(W_SH(i)) < 16.7))
        count(19) = count(19) + 1;
    end
    if((log10(W_SH(i)) >= 16.7) && (log10(W_SH(i)) < 17.0))
        count(20) = count(20) + 1;
    end
    if((log10(W_SH(i)) >= 17.3) && (log10(W_SH(i)) < 17.6))
        count(21) = count(21) + 1;
    end
    if((log10(W_SH(i)) >= 17.6) && (log10(W_SH(i)) < 17.9))
        count(22) = count(22) + 1;
    end
    if((log10(W_SH(i)) >= 17.9) && (log10(W_SH(i)) < 18.2))
        count(23) = count(23) + 1;
    end
    if((log10(W_SH(i)) >= 18.2) && (log10(W_SH(i)) < 18.5))
        count(24) = count(24) + 1;
    end
    if((log10(W_SH(i)) >= 18.5) && (log10(W_SH(i)) < 18.8))
        count(25) = count(25) + 1;
    end
    if((log10(W_SH(i)) >= 18.8) && (log10(W_SH(i)) < 19.1))
        count(26) = count(26) + 1;
    end
    if((log10(W_SH(i)) >= 19.1) && (log10(W_SH(i)) < 19.4))
        count(27) = count(27) + 1;
    end
    if((log10(W_SH(i)) >= 19.4) && (log10(W_SH(i)) < 19.7))
        count(28) = count(28) + 1;
    end
    if((log10(W_SH(i)) >= 19.7) && (log10(W_SH(i)) < 20.0))
        count(29) = count(29) + 1;
    end
    if((log10(W_SH(i)) >= 20.0) && (log10(W_SH(i)) < 20.3))
        count(30) = count(30) + 1;
    end
end


for i=1:30
    prob(i) = count(i)/sum(count);
    prob_dens(i) = prob(i)/bin(i);
end

% Check
sum(prob_dens.*bin);
prob_dens_all(i,:) = prob_dens(:);

%end

prob_dens_mean = zeros(1,30);


for i = 1 : 30
  prob_dens_mean(1,i) = mean(prob_dens_all(:,i));
  %prob_dens_std(1,i) = std(prob_dens_all(:,i));
end

% Plot

best_fit = polyfit(bin,log10(prob_dens_mean),11)

h = figure;
loglog(bin,prob_dens_mean,'ro','MarkerSize',10)
hold on;
plot(best_fit,'b')
t = title('Event Power Distribution, SHem, August 2005');
set(t, 'FontWeight', 'bold', 'FontSize', 12)
set(gca, 'FontWeight', 'bold', 'FontSize', 12)
xlabel('Event Power (W)');
ylabel('Probability Density');
print -dpng SHem_Wattage_PDF_AUG2005.png
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1 に答える 1

0

私はあなたのデータを持っていませんが、ランダムな正規分布のランダムデータを使用した例を次に示します

x=randn(1000,1)+5; % create some data, keep numbers positive by adding 5
[n,xb]=hist(x); % Create the histogram
n = n/sum(n); % convert counts to a pdf
p=polyfit(log(xb), log(n), 3); % Do a 3rd order fit
loglog(xb,n, '*-', xb, exp(polyval(p, log(xb))), 'r')
grid on
legend('PDF', 'Fit', 0)
于 2014-01-27T16:02:59.903 に答える