brano's comments

2 files changed, 613 insertions, 600 deletions

diff --git a/data/combined/graphs/face.csv b/data/combined/graphs/face.csv
index 799f4ed..2f7ff26 100644
--- a/data/combined/graphs/face.csv
+++ b/data/combined/graphs/face.csv
@@ -36,270 +36,492 @@
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-0,1
-0,1
-0,1
-0,1
-0,1
-0,1
-0,1
-0,1
-0,1
-0,1
-0,1
-0,1
-0,1
-0,1
-0,1
-0,1
+0.000618811881188,1.0
+0.000618811881188,1.0
+0.000618811881188,1.0
+0.000618811881188,1.0
+0.000618811881188,1.0
+0.000618811881188,1.0
+0.000618811881188,1.0
+0.000618811881188,1.0
+0.000618811881188,1.0
+0.000618811881188,1.0
+0.000618811881188,1.0
+0.000618811881188,1.0
+0.000618811881188,1.0
+0.000618811881188,1.0
 0,1
 0,1
 0,1
diff --git a/data/combined/graphs/plots.py b/data/combined/graphs/plots.py
index 5fd3c2c..7b3bb3e 100755
--- a/data/combined/graphs/plots.py
+++ b/data/combined/graphs/plots.py
@@ -20,34 +20,45 @@ l = ["3","5","10","all"]
 
 #10-fold, naive
 plt.cla()
-ax = plt.subplot(121)
+#ax = plt.subplot(121)
 plt.axis([0,100,50,100])
-ax.set_aspect(2)
+#ax.set_aspect(2)
 for i in l:
     x,y = np.loadtxt(i+"_nb_off.mat",unpack=True)
-    plt.plot(100*x,100*y,label="$n=$ "+i,linewidth=0.8)
+    plt.plot(100*x,100*y,label="$n_p=$ "+i,linewidth=0.8)
     plt.xlabel("Recall [%]")
     plt.ylabel("Precision [%]")
     plt.legend(loc="best")
+plt.savefig("offline-nb.pdf")
 #10-fold, SHT
-ax = plt.subplot(122)
-plt.axis([0,100,50,100])
-ax.set_aspect(2)
+#ax = plt.subplot(122)
+#plt.axis([0,100,50,100])
+#ax.set_aspect(2)
+plt.cla()
 for i in l:
     x,y = np.loadtxt(i+"_sht_off.mat",unpack=True)
-    plt.plot(100*x,100*y,label="$n=$ "+i,linewidth=0.8)
+    plt.plot(100*x,100*y,label="$n_p=$ "+i,linewidth=0.8)
     plt.xlabel("Recall [%]")
     plt.ylabel("Precision [%]")
     plt.legend(loc="best")
 
 plt.axis([0,100,50,100])
-plt.savefig("10fold.pdf")
+plt.savefig("offline-sht.pdf")
 
+#online,NB
+plt.cla()
+for i in l:
+    x,y = np.loadtxt(i+"_nb_on.mat",unpack=True)
+    plt.plot(100*x,100*y,label="$n_p=$ "+i,linewidth=0.8,markersize=4)
+    plt.xlabel("Recall [%]")
+    plt.ylabel("Precision [%]")
+    plt.legend(loc="best")
+plt.savefig("online-nb.pdf")
 #online,SHT
 plt.cla()
 for i in l:
     x,y = np.loadtxt(i+"_sht_on.mat",unpack=True)
-    plt.plot(100*x,100*y,label="$n=$ "+i,linewidth=0.8,markersize=4)
+    plt.plot(100*x,100*y,label="$n_p=$ "+i,linewidth=0.8,markersize=4)
     plt.xlabel("Recall [%]")
     plt.ylabel("Precision [%]")
     plt.legend(loc="best")
@@ -69,8 +80,10 @@ plt.savefig("face.pdf")
 plt.cla()
 x,y = np.loadtxt("back_all_sht_on.mat",unpack=True)
 a,b = np.loadtxt("all_sht_on.mat",unpack=True)
-plt.plot(100*x,100*y,linewidth=0.8,label="Away")
-plt.plot(100*a,100*b,linewidth=0.8,label="Toward")
+c,d = np.loadtxt("front_back_all_sht.mat",unpack=True)
+plt.plot(100*a,100*b,linewidth=0.8,label="Train/test toward")
+plt.plot(100*x,100*y,linewidth=0.8,label="Train/test away")
+plt.plot(100*c,100*d,linewidth=0.8,label="Train toward test away")
 plt.xlabel("Recall [%]")
 plt.ylabel("Precision [%]")
 plt.legend(loc="best")