Generalized Watts-Strogatz Graph Experiments Output Visualization

In [3]:

    

#!/usr/bin/python
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from stats import parse_results, get_percentage, get_avg_per_seed, draw_pie, draw_bars, draw_bars_comparison, draw_avgs


    

In [4]:

    

pr, eigen, bet = parse_results('test_genws.txt')


    

In [5]:

    

draw_pie(get_percentage(pr))


    

In [6]:

    

draw_bars_comparison('Avg adopters per seeds', 'Avg adopters', np.array(get_avg_per_seed(pr)+[(0, np.mean(pr[:,1]))]))


    

In [7]:

    

draw_pie(get_percentage(eigen))


    

In [8]:

    

draw_bars_comparison('Avg adopters per seeds', 'Avg adopters', np.array(get_avg_per_seed(eigen)+[(0, np.mean(eigen[:,1]))]))


    

In [9]:

    

draw_pie(get_percentage(bet))


    

In [10]:

    

draw_bars_comparison('Avg adopters per seeds', 'Avg adopters', np.array(get_avg_per_seed(bet)+[(0, np.mean(bet[:,1]))]))


    

In [11]:

    

draw_bars(np.sort(pr.view('i8,i8'), order=['f0'], axis=0).view(np.int),
         np.sort(eigen.view('i8,i8'), order=['f0'], axis=0).view(np.int),
         np.sort(bet.view('i8,i8'), order=['f0'], axis=0).view(np.int))


    

In [12]:

    

pr_mean = np.mean(pr[:,1])
pr_mean_seed = np.mean(pr[:,0])
print 'Avg Seed:',pr_mean_seed, 'Avg adopters:', pr_mean


    

    




Avg Seed: 97.85 Avg adopters: 175.92

In [13]:

    

eigen_mean = np.mean(eigen[:,1])
eigen_mean_seed = np.mean(eigen[:,0])
print 'Avg Seed:',eigen_mean_seed, 'Avg adopters:',eigen_mean


    

    




Avg Seed: 97.9 Avg adopters: 211.67

In [14]:

    

bet_mean = np.mean(bet[:,1])
bet_mean_seed = np.mean(bet[:,0])
print 'Avg Seed:',bet_mean_seed, 'Avg adopters:',bet_mean


    

    




Avg Seed: 98.0 Avg adopters: 299.76

In [15]:

    

draw_avgs([pr_mean, eigen_mean, bet_mean])