def load_rbns_from_erb(directory, remember):
rbn_distribution, name = glob_load(directory + '*distribution')[0]
scatterplot = ["x y"]
for accuracy, rbn in rbn_distribution:
cc = measure_computational_capability(rbn, 100, remember)
scatterplot.append("{} {}".format(cc, accuracy))
distribution = {}
distplot = ["\\myboxplot{"]
for accuracy, rbn in rbn_distribution:
ic = rbn.input_connectivity
if ic not in distribution:
distribution[ic] = []
distribution[ic].append(accuracy)
for i, l in enumerate(sorted(distribution.keys())):
distplot.append("% L: {}".format(i))
distplot.append("\\addplot[boxplot]")
distplot.append("table[row sep=\\\\, y index=0] {")
distplot.append("data")
for fitness in distribution[l]:
distplot.append("{} \\\\".format(fitness))
distplot.append("};")
distplot.append("}}{{{}}}".format(10.0 / max(distribution.keys())))
return '\n'.join(scatterplot), '\n'.join(distplot)
def load_rbns_from_erb(directory, remember):
rbn_distribution, name = glob_load(directory + '*distribution')[0]
scatterplot = ["x y"]
for accuracy, rbn in rbn_distribution:
cc = measure_computational_capability(rbn, 100, remember)
scatterplot.append("{} {}".format(cc, accuracy))
distribution = {}
distplot = ["\\myboxplot{"]
for accuracy, rbn in rbn_distribution:
ic = rbn.input_connectivity
if ic not in distribution:
distribution[ic] = []
distribution[ic].append(accuracy)
for i, l in enumerate(sorted(distribution.keys())):
distplot.append("% L: {}".format(i))
distplot.append("\\addplot[boxplot]")
distplot.append("table[row sep=\\\\, y index=0] {")
distplot.append("data")
for fitness in distribution[l]:
distplot.append("{} \\\\".format(fitness))
distplot.append("};")
distplot.append("}}{{{}}}".format(10.0 / max(distribution.keys())))
return '\n'.join(scatterplot), '\n'.join(distplot)
#}},
#] coordinates {{}};""".format(l, i, median, upperq, lowerq, upperw, lowerw)
#
# print boxplot
#
# print "}}{{{}}}".format(10.0 / max(distribution.keys()))
if __name__ == '__main__':
#filename = "pickle_dumps/distribution-100-5-3/combined-distribution"
#computational_power_scatter()
#distribution_to_plot()
#erb()
rbn, _ = glob_load('pickle_dumps/70input-2/*-reservoir')[0]
ccs = [measure_computational_capability(rbn, 100, 3) for _ in range(20)]
print ccs, np.median(ccs), np.mean(ccs)
#postfix = default_input('Postfix?', '-3-1')
#remember = int(postfix.split("-")[1]) - 1
#scatter, box = load_rbns_from_erb('pickle_dumps/c-distribution-100' + postfix + '/', remember)
#print scatter
#sc = open('pickle_dumps/c-distribution-100' + postfix +
# '/computational-power-100' + postfix + '.dat', 'w')
#sc.write(scatter)
#sc.close()
#bx = open('pickle_dumps/c-distribution-100' + postfix +
# lower whisker={}
#}},
#] coordinates {{}};""".format(l, i, median, upperq, lowerq, upperw, lowerw)
#
# print boxplot
#
# print "}}{{{}}}".format(10.0 / max(distribution.keys()))
if __name__ == '__main__':
#filename = "pickle_dumps/distribution-100-5-3/combined-distribution"
#computational_power_scatter()
#distribution_to_plot()
#erb()
rbn, _ = glob_load('pickle_dumps/70input-2/*-reservoir')[0]
ccs = [measure_computational_capability(rbn, 100, 3) for _ in range(20)]
print ccs, np.median(ccs), np.mean(ccs)
#postfix = default_input('Postfix?', '-3-1')
#remember = int(postfix.split("-")[1]) - 1
#scatter, box = load_rbns_from_erb('pickle_dumps/c-distribution-100' + postfix + '/', remember)
#print scatter
#sc = open('pickle_dumps/c-distribution-100' + postfix +
# '/computational-power-100' + postfix + '.dat', 'w')
#sc.write(scatter)
#sc.close()
#bx = open('pickle_dumps/c-distribution-100' + postfix +
