def plot_num_diff_vs_n(self, dim, matrix, scale):
param = IterGrid({'dim':dim, 'matrix':matrix,'scale':scale})
plot_bw = dpplot.plot_bw()
for p in param:
data,nnodes = zip(*[(np.mean(r['num_diff'])/r['nnodes'], r['nnodes'])
for r in self.results if r['matrix']==p['matrix']
and r['scale']==p['scale']
and r['dim']==p['dim']])
line_label = p['matrix']+(' (Scaled)' if p['scale'] else ' (Unscaled)')
style = ('-' if p['matrix']=='Adjacency' else '--')
color = ('b' if p['matrix']=='Adjacency' else 'r')
marker = 's' if p['scale'] else 'd'
markersize = 10
plot.plot(nnodes, data,
linestyle=style,marker=marker,
markersize=markersize, linewidth=2, color=color,label=line_label)
#plot.plot(nnodes, data, label=line_label)
plot.legend(loc='best')
plot.ylabel("Percent Error")
plot.xlabel(r'$n$ - Number of vertices')
plot.show()
def plot_num_diff_vs_n_ari(self, dim, matrix, scale):
param = IterGrid({'dim':dim, 'matrix':matrix,'scale':scale})
plot_bw = dpplot.plot_bw()
for p in param:
data,nnodes = zip(*[(np.mean(r['rand_idx'])/r['nnodes'], r['nnodes'])
for r in self.results if r['matrix']==p['matrix']
and r['scale']==p['scale']
and r['dim']==p['dim']])
line_label = p['matrix']+(' (Scaled)' if p['scale'] else ' (Unscaled)') + ' dim.: '+repr(p['dim'])
#plot.plot(nnodes, data, label=line_label)
plot.legend(loc='best')
plot.ylabel("Percent Error")
plot.xlabel(r'$n$ - Number of vertices')
plot.show()
def plot_num_diff_vs_d(self, nnodes):
if nnodes not in self.nnodes:
print "Woahh: we didn't do that number of nodes"
return
if not self.results:
return "Get some results first ... run_monte_carlo()"
plot_bw = dpplot.plot_bw()
results = [r for r in self.results if r['nnodes']==nnodes]
params = IterGrid({'matrix':self.matrix.keys(), 'scale':self.scale})
for p in params:
data,dim = zip(*[(np.mean(r['num_diff']), r['dim'])
for r in results if r['matrix']==p['matrix'] and r['scale']==p['scale'] ])
line_label = p['matrix']+(' (Scaled)' if p['scale'] else ' (Unscaled)')
#legend.append(p['matrix']+"; Scale:"+repr(p['scale']))
plot_bw.plot(dim, data, label=line_label)
plot.legend(loc='best')
plot.ylabel("Mean Number Errors")
plot.xlabel("Embedding Dimension")
plot.show()
