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#! /usr/bin/python
import numpy as np
import matplotlib.pyplot as plt
#frames distribution
plt.cla()
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.xlim(0.8,25)
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(0,10)
plt.savefig("frames.pdf")
l = ["3","5","10","all"]
#10-fold, naive
plt.cla()
ax = plt.subplot(121)
plt.axis([0,100,50,100])
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.xlabel("Recall [%]")
plt.ylabel("Precision [%]")
plt.legend(loc="best")
#10-fold, SHT
ax = plt.subplot(122)
plt.axis([0,100,50,100])
ax.set_aspect(2)
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.xlabel("Recall [%]")
plt.ylabel("Precision [%]")
plt.legend(loc="best")
plt.axis([0,100,50,100])
plt.savefig("10fold.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.xlabel("Recall [%]")
plt.ylabel("Precision [%]")
plt.legend(loc="best")
plt.savefig("online-sht.pdf")
#face
plt.cla()
x,y = np.loadtxt("all_nb_off.mat",unpack=True)
a,b = np.loadtxt("face.csv",delimiter=",", unpack=True)
plt.plot(100*x,100*y,linewidth=0.8,label="Skeleton")
plt.plot(100*a,100*b,linewidth=0.8,label="Face")
plt.xlabel("Recall [%]")
plt.ylabel("Precision [%]")
plt.legend(loc="best")
plt.savefig("face.pdf")
#back
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")
plt.xlabel("Recall [%]")
plt.ylabel("Precision [%]")
plt.legend(loc="best")
plt.savefig("back.pdf")
#variance-reduction
plt.cla()
x,y = np.loadtxt("half-var-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="Reduced noise")
plt.plot(100*a,100*b,linewidth=0.8,label="Original noise")
plt.xlabel("Recall [%]")
plt.ylabel("Precision [%]")
plt.legend(loc="best")
plt.savefig("var.pdf")
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