在用哈希进行检索时，常会用到precision recall曲线对其性能进行定量评价。precision recall的定义在信息检索评价指标中已做了详细说明，这里再记录一下precision recall的具体实现。

precision recall曲线matlab一般使用的都是下面的版本：

function[recall, precision, rate] =recall_precision(Wtrue, Dhat)

%

% Input:

% Wtrue = true neighbors [Ntest * Ndataset], can be a full matrix NxN

% Dhat = estimated distances

%

% Output:

%

% exp. # of good pairs inside hamming ball of radius <= (n-1)

% precision(n) = --------------------------------------------------------------

% exp. # of total pairs inside hamming ball of radius <= (n-1)

%

% exp. # of good pairs inside hamming ball of radius <= (n-1)

% recall(n) = --------------------------------------------------------------

% exp. # of total good pairs

max_hamm = max(Dhat(:))

hamm_thresh = min(3,max_hamm);

[Ntest, Ntrain] = size(Wtrue);

total_good_pairs = sum(Wtrue(:));

% find pairs with similar codes

precision = zeros(max_hamm,1);

recall = zeros(max_hamm,1);

rate = zeros(max_hamm,1);

for n = 1:length(precision)

j = (Dhat<=((n-1)+0.00001));

%exp. # of good pairs that have exactly the same code

retrieved_good_pairs = sum(Wtrue(j));

% exp. # of total pairs that have exactly the same code

retrieved_pairs = sum(j(:));

precision(n) = retrieved_good_pairs/retrieved_pairs;

recall(n)= retrieved_good_pairs/total_good_pairs;

rate(n) = retrieved_pairs / (Ntest*Ntrain);

end

% The standard measures for IR are recall and precision. Assuming that:

%

% * RET is the set of all items the system has retrieved for a specific inquiry;

% * REL is the set of relevant items for a specific inquiry;

% * RETREL is the set of the retrieved relevant items

%

% then precision and recall measures are obtained as follows:

%

% precision = RETREL / RET

% recall = RETREL / REL

% if nargout == 0 || nargin > 3

% if isempty(fig);

% fig = figure;

% end

% figure(fig)

%

% subplot(311)

% plot(0:hamm_thresh-1, precision(1:hamm_thresh), varargin{:})

% hold on

% xlabel('hamming radius')

% ylabel('precision')

%

% subplot(312)

% plot(0:hamm_thresh-1, recall(1:hamm_thresh), varargin{:})

% hold on

% xlabel('hamming radius');

% ylabel('recall');

%

% subplot(313);

% plot(recall, precision, varargin{:});

% hold on;

% axis([0 1 0 1]);

% xlabel('recall');

% ylabel('precision');

%

% drawnow;

% end

function[score, recall] =evaluation(Wtrue, Dhat, fig, varargin)

%

% Input:

% Wtrue = true neighbors [Ntest * Ndataset], can be a full matrix NxN

% Dhat = estimated distances

% The next inputs are optional:

% fig = figure handle

% options = just like in the plot command

%

% Output:

%

% exp. # of good pairs inside hamming ball of radius <= (n-1)

% score(n) = --------------------------------------------------------------

% exp. # of total pairs inside hamming ball of radius <= (n-1)

%

% exp. # of good pairs inside hamming ball of radius <= (n-1)

% recall(n) = --------------------------------------------------------------

% exp. # of total good pairs

[Ntest, Ntrain] = size(Wtrue);

total_good_pairs = sum(Wtrue(:));

% find pairs with similar codes

score = zeros(20,1);

for n = 1:length(score)

j = find(Dhat<=((n-1)+0.00001));

%exp. # of good pairs that have exactly the same code

retrieved_good_pairs = sum(Wtrue(j));

% exp. # of total pairs that have exactly the same code

retrieved_pairs = length(j);

score(n) = retrieved_good_pairs/retrieved_pairs;

recall(n)= retrieved_good_pairs/total_good_pairs;

end

% The standard measures for IR are recall and precision. Assuming that:

%

% * RET is the set of all items the system has retrieved for a specific inquiry;

% * REL is the set of relevant items for a specific inquiry;

% * RETREL is the set of the retrieved relevant items

%

% then precision and recall measures are obtained as follows:

%

% precision = RETREL / RET

% recall = RETREL / REL

if nargout == 0 || nargin > 3

if isempty(fig);

fig = figure;

end

figure(fig)

subplot(211)

plot(0:length(score)-1, score, varargin{:})

hold on

xlabel('hamming radium')

ylabel('percent correct (precision)')

title('percentage of good neighbors inside the hamm ball')

subplot(212)

plot(recall, score, varargin{:})

hold on

axis([0 1 0 1])

xlabel('recall')

ylabel('percent correct (precision)')

drawnow

end

不能看出，上面的score就是前面的precision,在追溯到08年，也就是谱哈希SH发表的那年，同样可以在SH中有画precision recall的曲线，跟第二个一样。考证这些，无非就是想说在自己画PR曲线时，就用这些牛提供的比较靠谱，自己写出来的不一定对。

好了，再对画precision recall输入的参数做些梳理。画precision recall曲线时，用到的groundtruth是原欧式空间中查询样本的近邻，所以在计算Wtrue时，可以采用下面的方法计算：

%center, then normalize data

X = X - ones(size(X,1),1)*mean(X);

for i = 1:size(X,1)

X(i,:) = X(i,:) / norm(X(i,:));

end

rp = randperm(size(X,1));

trIdx = rp(1:trN);

testIdx = rp(trN+1:trN+testN);

Xtr = X(trIdx,:);

Xtst = X(testIdx,:);

D_tst = distMat(Xtst,Xtr);

D_tr = distMat(Xtr);

Dball = sort(D_tr,2);

Dball = mean(Dball(:,50));

WTT = D_tst < Dball;

上面第一步先对数据进行中心化，然后进行归一化。之后挑选出训练样本和测试样本(查询样本)，然后计算Wture。Dhat就是计算查询样本与database之间的汉明距离，可以通过下面方法计算：

%get Hamming distance between queries and database

B1 = compactbit(H);

B2 = compactbit(H_query);

Dhamm = hammingDist(B2,B1);

H是database中的编码，进行压缩以十进制数进行表示，同理H_query即为查询样本的编码。将上面都计算出来后，便可以得到precision和recall,plot一下就可以了。

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