你的要求有点不清楚,但仔细阅读这些文字和你的评论,我认为这就是你想要的:
edges = [0.2 0.4 0.6 1];
A(:,1) = (0.05:0.05:0.80).';
A(:,2) = [ones(7,1); [0.6; 0.6; 0.4]; zeros(6,1)];
[~,A_bins] = histc(A(:,1),edges);
[A_bin_vals,~,A_idx] = unique(A_bins);
A_avg = accumarray(A_idx,A(:,2),[numel(A_bin_vals) 1],@mean) ;
B(:,1) = (0.15:0.1:0.95).';
B(:,2) = [ones(4,1); [0.8; 0.8; 0.2]; zeros(2,1)];
[~,B_bins] = histc(B(:,1),edges);
[B_bin_vals,~,B_idx] = unique(B_bins);
B_avg = accumarray(B_idx,B(:,2),[numel(B_bin_vals) 1],@mean) ;
plot(0:3,A_avg,'-.r*'),hold on,plot(0:3,B_avg,'-.b*'),hold off;
xticks(0:3);
xticklabels({'0.2' '0.4' '0.6' '0.8'});
输出:
实际上,为了实现这一点,我使用
histc
. 然后,使用
accumarray function
,我应用了
mean function
到按分组的第二列的所有值
bin
.
