#print AvgRuntimeVND
np.savetxt("Runtime_Tabu.txt", np.array(RuntimeTabu))
np.savetxt("Runtime_VND.txt", np.array(RuntimeVND))
#plotting Runtimes of both algorithms on instance#6
plt.plot(range(1, 21), RuntimeTabu[5], 'ro', label='Tabu')
plt.plot(range(1, 21), RuntimeVND[5], 'bo', label='VND')
plt.xlabel('Iteration')
plt.xlim(1, 20)
plt.ylabel('Runtime (s)')
plt.ylim(0, 40)
plt.title('Runtimes Comparison of Tabu Search and VND')
plt.legend()
plt.show()
#Generating Scatter Plot
#help(plot_scatter.plot_scatter_plot)
plot_scatter.plot_scatter_plot(np.array(AvgRuntimeTabu),
np.array(AvgRuntimeVND),
labels=["Tabu", "VND"],
title='Tabu VS VND',
save='scatter.png')
plt.show()
#Generating Boxplot
#help(plt.boxplot)
plt.figure()
plt.boxplot((RuntimeTabu, RuntimeVND))
plt.show()
import matplotlib.pyplot as plt
import numpy as np
from plot_scatter import plot_scatter_plot
from pylab import *
data_sa = [.1246404793, .2860261318, .0802291212, .0753011860, .0662239493, 3.0800689215, 1.5275161933,
.7680770744, .1372658342, 1.6589732123, .6470984249, 1.5993877497, 9.9692374581, 30.0060543784]
data_hc = [.0659917897, .0711625110, .0735541013, .0686513972, .0650763787, .4528222578, .1932201330,
.1268824969, .1493043956, 1.4135952366, .3481941039, .5358545771, 13.2071397053, 30.0059365538]
labels = ['Simulated annealing', 'Hill Climbing']
plt.boxplot([data_sa, data_hc])
plt.xticks([1, 2], labels)
plt.ylabel("Runtime in sec")
plt.yscale('log')
plt.grid()
#plt.show()
savefig('box_plot.png')
plot_scatter_plot(np.array(data_sa), np.array(data_hc), labels, save="scatter_plot.png", max_val=30)
satisfiable = False
print("\nRuntimes, each columns show timetaken on one of iterations and row shows datasets.\n"
"Eg. row 0 column 3 show time taken to solve first dataset on 4th iteration\n")
print(time_matrix1)
print(time_matrix2)
tm1_avg = np.mean(time_matrix1, axis=1)
tm2_avg = np.mean(time_matrix2, axis=1)
print(tm1_avg)
print(tm2_avg)
plt.hist(time_matrix1[0], bins=40, normed=True, cumulative=True, histtype='step', color='b', label='WalkSAT')
plt.hist(time_matrix2[0], bins=40, normed=True, cumulative=True, histtype='step', color='r', label='TabuSearch')
plt.title("WalkSat vs TabuSearch")
plt.xlabel("TimeTaken in Secs")
plt.ylabel("Probability")
plt.legend()
plt.savefig('sls_runtime.png')
ps.plot_scatter_plot(tm1_avg, tm2_avg,labels=["WalkSat", "Tabu"], title="WalkSat vs TabuSearch",
save="ps.png", debug=False, min_val=0, max_val=0.6, grey_factor=1,
linefactors=None, user_fontsize=20, dpi=200)
data = [tm1_avg, tm2_avg]
fig = plt.figure()
ax = fig.add_subplot(111)
plt.ylabel("Runtime Ratio")
ax.boxplot(data)
ax.set_xticklabels(["WalkSat", "Tabu"])
plt.savefig("boxplot.png")
