Rewrite some paper

4 files changed, 9 insertions, 12 deletions

diff --git a/README.md b/README.md
index fbbd86d..029e1a0 100644
--- a/README.md
+++ b/README.md
@@ -35,3 +35,4 @@ optional arguments:
   -A, --mAP             Display Mean Average Precision
   -P PCA, --PCA PCA     Perform pca with PCA eigenvectors
   ```
+
diff --git a/evaluate.py b/evaluate.py
index 4c1264e..0f8fe48 100755
--- a/evaluate.py
+++ b/evaluate.py
@@ -158,10 +158,8 @@ def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam
             for i in range(probe_label.shape[0]):
                 for j in range(11):
                     max_level_precision[i][j] = np.max(precision[i][np.where(recall[i]>=(j/10))])
-                    #print(mAP[i])
             for i in range(probe_label.shape[0]):
                 mAP[i] = sum(max_level_precision[i])/11
-                #mAP[i] = sum(precision[i])/args.neighbors
             print('mAP:',np.mean(mAP))
                     
     return target_pred
diff --git a/opt.py b/opt.py
index ee63cc0..29acea4 100755
--- a/opt.py
+++ b/opt.py
@@ -87,7 +87,6 @@ def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam
                                MemorySave = False, Minibatch = 2000)
     else:
         if args.mahalanobis:
-            # metric = 'jaccard' is also valid
             cov_inv = np.linalg.inv(np.cov(gallery_data.T))
             distances = np.zeros((probe_data.shape[0], gallery_data.shape[0]))
             for i in range(int(probe_data.shape[0]/10)):
@@ -118,7 +117,7 @@ def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam
                   probe_label[probe_idx] == gallery_label[row[n]]):
                     n += 1
                 nneighbors[probe_idx][q] = gallery_label[row[n]]
-                nnshowrank[probe_idx][q] = showfiles_train[row[n]] #
+                nnshowrank[probe_idx][q] = showfiles_train[row[n]] 
                 q += 1
                 n += 1
 
@@ -160,10 +159,8 @@ def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam
             for i in range(probe_label.shape[0]):
                 for j in range(11):
                     max_level_precision[i][j] = np.max(precision[i][np.where(recall[i]>=(j/10))])
-                    #print(mAP[i])
             for i in range(probe_label.shape[0]):
                 mAP[i] = sum(max_level_precision[i])/11
-                #mAP[i] = sum(precision[i])/args.neighbors
             print('mAP:',np.mean(mAP))
             return np.mean(mAP)
         
@@ -177,7 +174,6 @@ def eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
         labs = labels[train_idx].reshape((labels[train_idx].shape[0],1))
         tt = np.hstack((train_idx, cam)) 
         train, test, train_label, test_label = train_test_split(tt, labs, test_size=0.3, random_state=0)
-        #to make it smaller we do a double split
         del labs
         del cam
         train_data = feature_vectors[train[:,0]]
diff --git a/report2/paper.md b/report2/paper.md
index 7099df8..6358445 100755
--- a/report2/paper.md
+++ b/report2/paper.md
@@ -115,7 +115,7 @@ original distance ranking compared to square euclidiaen metrics. Results can
 be observed using the `-m|--mahalanobis` when running evalution with the 
 repository complimenting this paper.
 
-COMMENT ON VARIANCE AND MAHALANOBIS RESULTS
+**COMMENT ON VARIANCE AND MAHALANOBIS RESULTS**
 
 \begin{figure}
 \begin{center}
@@ -166,15 +166,17 @@ through Jaccardian metric as:
 $$ d_J(p,g_i)=1-\frac{\sum\limits_{j=1}^N min(V_{p,g_j},V_{g_i,g_j})}{\sum\limits_{j=1}^N max(V_{p,g_j},V_{g_i,g_j})} $$
 
 It is then possible to perform a local query expansion using the g\textsubscript{i} neighbors of
-defined as $V_p=\frac{1}{|N(p,k_2)|}\sum\limits_{g_i\in N(p,k_2)}V_{g_i}$. We refer to $k_2$ since
-we limit the size of the nighbors to prevent noise from the $k_2$ neighbors. The dimension k of the *$R^*$* 
-set will instead be defined as $k_1$:$R^*(g_i,k_1)$.
+defined as:
+$$ V_p=\frac{1}{|N(p,k_2)|}\sum\limits_{g_i\in N(p,k_2)}V_{g_i} $$. 
+We refer to $k_2$ since we limit the size of the nighbors to prevent noise 
+from the $k_2$ neighbors. The dimension k of the *$R^*$* set will instead 
+be defined as $k_1$: $R^*(g_i,k_1)$.
 
 The distances obtained are then mixed, obtaining a final distance $d^*(p,g_i)$ that is used to obtain the
 improved ranklist: $d^*(p,g_i)=(1-\lambda)d_J(p,g_i)+\lambda d(p,g_i)$.
 
 The aim is to learn optimal values for $k_1,k_2$ and $\lambda$ in the training set that improve top1 identification accuracy.
-This is done through a simple **GRADIENT DESCENT** algorithm followed by exhaustive search to estimate 
+This is done through a simple multi-direction search algorithm followed by exhaustive search to estimate 
 $k_{1_{opt}}$ and $k_{2_{opt}}$ for eleven values of $\lambda$ from zero(only Jaccard distance) to one(only original distance)
 in steps of 0.1. The results obtained through this approach suggest: $k_{1_{opt}}=9, k_{2_{opt}}=3, 0.1\leq\lambda_{opt}\leq 0.3$.