diff options
| -rw-r--r-- | algorithm.tex | 2 | ||||
| -rwxr-xr-x | data/combined/graphs/plots.py | 64 | ||||
| -rwxr-xr-x | data/pair-matching/roc.py | 16 | ||||
| -rw-r--r-- | experimental.tex | 6 | ||||
| -rw-r--r-- | graphics/back.pdf | bin | 17795 -> 16183 bytes | |||
| -rw-r--r-- | graphics/face.pdf | bin | 14783 -> 13851 bytes | |||
| -rw-r--r-- | graphics/frames.pdf | bin | 10867 -> 10913 bytes | |||
| -rw-r--r-- | graphics/limbs.pdf | bin | 45439 -> 50499 bytes | |||
| -rw-r--r-- | graphics/offline-nb.pdf | bin | 17130 -> 17044 bytes | |||
| -rw-r--r-- | graphics/offline-sht.pdf | bin | 20304 -> 18712 bytes | |||
| -rw-r--r-- | graphics/online-nb.pdf | bin | 17894 -> 17633 bytes | |||
| -rw-r--r-- | graphics/online-sht.pdf | bin | 24070 -> 20829 bytes | |||
| -rw-r--r-- | graphics/roc.pdf | bin | 20430 -> 19394 bytes | |||
| -rw-r--r-- | graphics/var.pdf | bin | 16101 -> 14691 bytes | |||
| -rw-r--r-- | uniqueness.tex | 2 |
15 files changed, 54 insertions, 36 deletions
diff --git a/algorithm.tex b/algorithm.tex index fbe2dc8..38e0c39 100644 --- a/algorithm.tex +++ b/algorithm.tex @@ -27,7 +27,7 @@ In this work, we use the mixture of Gaussians model for skeleton recognition. Sk \begin{figure}[t] \centering - \includegraphics[height=4.4in, angle=90, bb=4.5in 1.5in 6.5in 7in]{graphics/ErrorMarginals} + \includegraphics{graphics/limbs.pdf} \caption{Histograms of differences between 9 skeleton measurements $x_k$ (Section~\ref{sec:experiment}) and their expectation given the class $y$.} \label{fig:error marginals} \end{figure} diff --git a/data/combined/graphs/plots.py b/data/combined/graphs/plots.py index 6f37b27..a4b01f3 100755 --- a/data/combined/graphs/plots.py +++ b/data/combined/graphs/plots.py @@ -8,15 +8,19 @@ import os import scipy import matplotlib as mpl -mpl.rcParams['font.size'] = 8 +mpl.rcParams['font.size'] = 5 mpl.rcParams['lines.linewidth'] = 0.5 -mpl.rcParams['figure.figsize'] = 6,5 -mpl.rcParams['legend.fontsize'] = 8 -mpl.rcParams['axes.linewidth'] = 0.8 +mpl.rcParams['figure.figsize'] = 2.2,2.2 +mpl.rcParams['legend.fontsize'] = 5 +mpl.rcParams['axes.linewidth'] = 0.5 +mpl.rcParams['figure.subplot.hspace'] = 0.4 +mpl.rcParams['figure.subplot.wspace'] = 0.4 +legend_width = 0.2 +#mpl.rcParams.update(params) out_dir = sys.argv[1] #limbs distribution -plt.figure() +plt.figure(figsize=(5.6,1.7)) data = np.loadtxt("../limbs-avg-zdiff/data.csv",delimiter=",") data = data[#(data[:,1] == 25) ((data != -1).all(1)) @@ -27,22 +31,25 @@ mean = data.mean(0) var = data.std(0) for i in range(len(mean)): ax = plt.subplot(2,5,i+1) - n,b,p = plt.hist(data[:,i],bins=100,normed=1,linewidth=0) - plt.plot(b,mlab.normpdf(b,mean[i],var[i])) + n,b,p = plt.hist((data[:,i]-mean[i]),bins=100,normed=1,linewidth=0,color="lightgreen") + plt.plot(b,mlab.normpdf(b,0,var[i]),"red") + plt.xticks([]) + plt.yticks([]) + plt.xlabel("$x_"+str(i+1)+"-E[x_"+str(i+1)+"|y]$") + plt.ylabel("$P(x_"+str(i+1)+"-E[x_"+str(i+1)+"|y])$") plt.savefig(os.path.join(out_dir,"limbs.pdf"),bbox_inches="tight",pad_inches=0.05) #frames distribution -fig = plt.figure(figsize=(6,4)) +plt.figure(figsize=(4.5,2)) x = np.loadtxt("frames.txt",usecols=(0,)) y = range(1,len(x)+1) width=0.8 -plt.bar(y,x/x.sum()*100,width=width) +plt.bar(y,x/x.sum()*100,width=width,linewidth=0,color="lightgreen") plt.xlim(0.8,26) plt.xticks([i+width/2. for i in range(1,len(x),5)], range(1,len(x),5)) plt.xlabel("Individual") plt.ylabel("Frame ratio [%]") plt.ylim(0,17) -ax = plt.gca() plt.savefig(os.path.join(out_dir,"frames.pdf"),bbox_inches="tight",pad_inches=0.05) l = ["3","5","10","all"] @@ -52,9 +59,10 @@ plt.figure() for i in l: x,y = np.loadtxt(i+"_nb_off.mat",unpack=True) plt.plot(100*x,100*y,label="$n_p=$ "+i) - plt.xlabel("Recall [%]") - plt.ylabel("Precision [%]") - plt.legend(loc="best") +plt.xlabel("Recall [%]") +plt.ylabel("Precision [%]") +leg =plt.legend(loc="best") +leg.get_frame().set_linewidth(legend_width) plt.axis([0,100,50,100]) plt.savefig(os.path.join(out_dir,"offline-nb.pdf"),bbox_inches="tight",pad_inches=0.05) @@ -63,9 +71,10 @@ plt.figure() for i in l: x,y = np.loadtxt(i+"_sht_off.mat",unpack=True) plt.plot(100*x,100*y,label="$n_p=$ "+i) - plt.xlabel("Recall [%]") - plt.ylabel("Precision [%]") - plt.legend(loc="lower left") +plt.xlabel("Recall [%]") +plt.ylabel("Precision [%]") +leg = plt.legend(loc="lower left") +leg.get_frame().set_linewidth(legend_width) plt.axis([0,100,50,100]) plt.savefig(os.path.join(out_dir,"offline-sht.pdf"),bbox_inches="tight",pad_inches=0.05) @@ -74,9 +83,10 @@ plt.figure() for i in l: x,y = np.loadtxt(i+"_nb_on.mat",unpack=True) plt.plot(100*x,100*y,label="$n_p=$ "+i) - plt.xlabel("Recall [%]") - plt.ylabel("Precision [%]") - plt.legend(loc="best") +plt.xlabel("Recall [%]") +plt.ylabel("Precision [%]") +leg = plt.legend(loc="best") +leg.get_frame().set_linewidth(legend_width) plt.axis([0,100,50,100]) plt.savefig(os.path.join(out_dir,"online-nb.pdf"),bbox_inches="tight",pad_inches=0.05) @@ -85,9 +95,10 @@ plt.figure() for i in l: x,y = np.loadtxt(i+"_sht_on.mat",unpack=True) plt.plot(100*x,100*y,label="$n_p=$ "+i) - plt.xlabel("Recall [%]") - plt.ylabel("Precision [%]") - plt.legend(loc="best") +plt.xlabel("Recall [%]") +plt.ylabel("Precision [%]") +leg = plt.legend(loc="best") +leg.get_frame().set_linewidth(legend_width) plt.axis([0,100,50,100]) plt.savefig(os.path.join(out_dir,"online-sht.pdf"),bbox_inches="tight",pad_inches=0.05) @@ -99,7 +110,8 @@ plt.plot(100*x,100*y,label="Skeleton") plt.plot(100*a,100*b,label="Face") plt.xlabel("Recall [%]") plt.ylabel("Precision [%]") -plt.legend(loc="best") +leg = plt.legend(loc="best") +leg.get_frame().set_linewidth(legend_width) plt.axis([0,100,50,100]) plt.savefig(os.path.join(out_dir,"face.pdf"),bbox_inches="tight",pad_inches=0.05) @@ -113,7 +125,8 @@ plt.plot(100*x,100*y,label="Train/test away") plt.plot(100*c,100*d,label="Train toward test away") plt.xlabel("Recall [%]") plt.ylabel("Precision [%]") -plt.legend(loc="best") +leg = plt.legend(loc="best") +leg.get_frame().set_linewidth(legend_width) plt.axis([0,100,50,100]) plt.savefig(os.path.join(out_dir,"back.pdf"),bbox_inches="tight",pad_inches=0.05) @@ -125,6 +138,7 @@ plt.plot(100*x,100*y,label="Reduced noise") plt.plot(100*a,100*b,label="Original noise") plt.xlabel("Recall [%]") plt.ylabel("Precision [%]") -plt.legend(loc="best") +leg = plt.legend(loc="best") +leg.get_frame().set_linewidth(legend_width) plt.axis([0,100,50,100]) plt.savefig(os.path.join(out_dir,"var.pdf"),bbox_inches="tight",pad_inches=0.05) diff --git a/data/pair-matching/roc.py b/data/pair-matching/roc.py index 19a1acf..7396ab4 100755 --- a/data/pair-matching/roc.py +++ b/data/pair-matching/roc.py @@ -6,11 +6,14 @@ import matplotlib as mpl import math from sets import ImmutableSet -mpl.rcParams['font.size'] = 8 +mpl.rcParams['font.size'] = 5 mpl.rcParams['lines.linewidth'] = 0.5 -mpl.rcParams['figure.figsize'] = 6,5 -mpl.rcParams['legend.fontsize'] = 8 -mpl.rcParams['axes.linewidth'] = 0.8 +mpl.rcParams['figure.figsize'] = 2.2,2.2 +mpl.rcParams['legend.fontsize'] = 5 +mpl.rcParams['axes.linewidth'] = 0.5 +mpl.rcParams['figure.subplot.hspace'] = 0.4 +mpl.rcParams['figure.subplot.wspace'] = 0.4 +legend_width = 0.2 def distance(a,b): return math.sqrt(np.square(a-b).sum()) @@ -43,6 +46,7 @@ def gen_pairs(var,sk_data): return result if __name__ == "__main__": + plt.figure(figsize=(3,2.2)) ap = np.loadtxt("associatepredict.txt",delimiter=",") indices = [i for i in range(ap.shape[0]) if ap[i,1]<0.1] ap_false = ap[:,1][indices] @@ -74,9 +78,9 @@ if __name__ == "__main__": true_pos = np.array(true_pos) true_pos = true_pos[indices] plt.plot(false_pos,true_pos,label="$\sigma$ = "+str(s)) - plt.legend(loc="lower right") + leg = plt.legend(loc="lower right") + leg.get_frame().set_linewidth(legend_width) plt.savefig("roc.pdf",bbox_inches="tight",pad_inches=0.05) - plt.show() diff --git a/experimental.tex b/experimental.tex index b96893a..8bd7b9d 100644 --- a/experimental.tex +++ b/experimental.tex @@ -100,7 +100,7 @@ the skeleton in a contiguous way. This allows us to define the concept of a \begin{figure}[t] \begin{center} - \includegraphics[width=0.60\textwidth]{graphics/frames.pdf} + \includegraphics[]{graphics/frames.pdf} \end{center} \vspace{-1.5\baselineskip} \caption{Distribution of the frame ratio of each individual in the @@ -134,11 +134,11 @@ the algorithm. \begin{figure*}[t] \begin{center} \subfloat[Mixture of Gaussians]{ - \includegraphics[width=0.49\textwidth]{graphics/offline-nb.pdf} + \includegraphics[]{graphics/offline-nb.pdf} \label{fig:offline:nb} } \subfloat[Sequential Hypothesis Learning]{ - \includegraphics[width=0.49\textwidth]{graphics/offline-sht.pdf} + \includegraphics[]{graphics/offline-sht.pdf} \label{fig:offline:sht} } \caption{Precision-recall curve for the mixture of Gaussians model diff --git a/graphics/back.pdf b/graphics/back.pdf Binary files differindex ebda274..0702bf9 100644 --- a/graphics/back.pdf +++ b/graphics/back.pdf diff --git a/graphics/face.pdf b/graphics/face.pdf Binary files differindex c92b8c2..2663b70 100644 --- a/graphics/face.pdf +++ b/graphics/face.pdf diff --git a/graphics/frames.pdf b/graphics/frames.pdf Binary files differindex 819e23b..cb12743 100644 --- a/graphics/frames.pdf +++ b/graphics/frames.pdf diff --git a/graphics/limbs.pdf b/graphics/limbs.pdf Binary files differindex fe92ce2..58336eb 100644 --- a/graphics/limbs.pdf +++ b/graphics/limbs.pdf diff --git a/graphics/offline-nb.pdf b/graphics/offline-nb.pdf Binary files differindex cf1a43b..95e89a7 100644 --- a/graphics/offline-nb.pdf +++ b/graphics/offline-nb.pdf diff --git a/graphics/offline-sht.pdf b/graphics/offline-sht.pdf Binary files differindex 9ef1a03..4cadc70 100644 --- a/graphics/offline-sht.pdf +++ b/graphics/offline-sht.pdf diff --git a/graphics/online-nb.pdf b/graphics/online-nb.pdf Binary files differindex 3cdc3cb..febf14e 100644 --- a/graphics/online-nb.pdf +++ b/graphics/online-nb.pdf diff --git a/graphics/online-sht.pdf b/graphics/online-sht.pdf Binary files differindex 067fbd2..fcd418c 100644 --- a/graphics/online-sht.pdf +++ b/graphics/online-sht.pdf diff --git a/graphics/roc.pdf b/graphics/roc.pdf Binary files differindex 2d4a42b..8e25ab7 100644 --- a/graphics/roc.pdf +++ b/graphics/roc.pdf diff --git a/graphics/var.pdf b/graphics/var.pdf Binary files differindex d51105c..13a94af 100644 --- a/graphics/var.pdf +++ b/graphics/var.pdf diff --git a/uniqueness.tex b/uniqueness.tex index f034a21..84ec9ce 100644 --- a/uniqueness.tex +++ b/uniqueness.tex @@ -69,7 +69,7 @@ output of the algorithm for the threshold $\delta$ is defined as: \begin{figure}[t] \begin{center} - \includegraphics[width=0.6\columnwidth]{graphics/roc.pdf} + \includegraphics[]{graphics/roc.pdf} \end{center} \vspace{-1.5\baselineskip} \caption{ROC curve for several standard deviations of the noise and |