#! /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="$n_p=$ "+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)
plt.cla()
for i in l:
    x,y = np.loadtxt(i+"_sht_off.mat",unpack=True)
    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("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="$n_p=$ "+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)
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")
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")