def singlecore(filename, delta, popsize, generations):
"""Run serially"""
#set up the evo strategy
best_list, mut_list = [], []
evo = popstrat.Evostrategy(5000, popsize)
children = evo.iterate(evo.pop)
for i in range(generations):
for child in children:
results, conclist = run_grn(child['genome'], delta)
bestidx = results.index(max(results))
child['fitness'] = results[bestidx]
print "fitness:", child['fitness']
children = evo.iterate(children)
bestgenome = evo.pop[-1]['genome']
results, conclist = run_grn(bestgenome, delta)
filename = "best_gen_" + str(i)
graph.plot_2d(conclist, filename)
if evo.adaptive:
evo.adapt_mutation()
best_list.append(evo.pop[-1]['fitness'])
mut_list.append(evo.mut_rate)
print "best overall fitness", evo.pop[-1]['fitness']
graph.plot_2d([best_list], 'bestfit')
graph.plot_2d([mut_list], 'mutrate')
def singlecore(filename, delta, popsize, generations):
"""Run serially"""
#set up the evo strategy
best_list, mut_list = [], []
evo = popstrat.Evostrategy(5000, popsize)
children = evo.iterate(evo.pop)
for i in range(generations):
for child in children:
results, conclist = run_grn(child['genome'], delta)
bestidx = results.index(max(results))
child['fitness'] = results[bestidx]
print "fitness:", child['fitness']
children = evo.iterate(children)
bestgenome = evo.pop[-1]['genome']
results, conclist = run_grn(bestgenome, delta)
filename = "best_gen_"+str(i)
graph.plot_2d(conclist, filename)
if evo.adaptive:
evo.adapt_mutation()
best_list.append(evo.pop[-1]['fitness'])
mut_list.append(evo.mut_rate)
print "best overall fitness", evo.pop[-1]['fitness']
graph.plot_2d([best_list], 'bestfit')
graph.plot_2d([mut_list], 'mutrate')
def multicore(filename, syncsize, offset, delta, popsize, generations):
""" Uses parallel python to evaluate, PP can also be used to
distribute evaluations to machines accross the network"""
#set up the evo strategy
evo = popstrat.Evostrategy(5000, popsize)
children = evo.iterate(evo.pop)
nodes = ("*", )
job_server = pp.Server(ncpus=8, ppservers=nodes)
print "Starting pp with", job_server.get_ncpus(), "workers"
for _ in range(generations):
jobs = [(child,
job_server.submit(run_grn,
(child['genome'], delta, syncsize, offset),
(), ("cgrn as grn", "numpy", "math")))
for child in children]
for child, result in jobs:
results, conclist = result()
bestidx = results.index(max(results))
child['fitness'] = results[bestidx]
print "gen:", evo.gen_count, "fitness:", evo.pop[-1]['fitness']
children = evo.iterate(children)
if evo.adaptive:
evo.adapt_mutation()
res_file = open(filename, "a")
res_file.write(str(evo.pop[-1]) + '\n')
res_file.close()
restopname = filename + ".rest"
#plotting the best with colors
bestgenome = evo.pop[-1]['genome']
bestresult, conclist = run_grn(bestgenome, delta, syncsize, offset,
restopname)
bestidx = bestresult.index(max(bestresult))
fitness = round(max(bestresult), 1)
colors = []
for idx, result in enumerate(bestresult):
# draw the input in black
if idx == len(bestresult) - 1:
colors.append('k')
# draw the best in green
elif idx == bestidx:
colors.append('g')
# draw p_genes in red
elif result > 0:
colors.append('r')
# draw TFs in blue
else:
colors.append('b')
print "colors", colors
graphname = filename.split('/')[2]
graphname = graphname[:-4] + "-F" + str(fitness)
graph.plot_2d(conclist, graphname, colors, (0, 1))
def multicore(filename, syncsize, offset, delta, popsize, generations):
""" Uses parallel python to evaluate, PP can also be used to
distribute evaluations to machines accross the network"""
#set up the evo strategy
evo = popstrat.Evostrategy(5000, popsize)
children = evo.iterate(evo.pop)
nodes = ("*",)
job_server = pp.Server(ncpus=8, ppservers=nodes)
print "Starting pp with", job_server.get_ncpus(), "workers"
for _ in range(generations):
jobs = [(child, job_server.submit(run_grn,
(child['genome'], delta,
syncsize, offset),
(),
("cgrn as grn","numpy","math")))
for child in children]
for child, result in jobs:
results, conclist = result()
bestidx = results.index(max(results))
child['fitness'] = results[bestidx]
print "gen:", evo.gen_count, "fitness:", evo.pop[-1]['fitness']
children = evo.iterate(children)
if evo.adaptive:
evo.adapt_mutation()
res_file = open(filename,"a")
res_file.write(str(evo.pop[-1])+'\n')
res_file.close()
restopname = filename+".rest"
#plotting the best with colors
bestgenome = evo.pop[-1]['genome']
bestresult, conclist = run_grn(bestgenome, delta, syncsize, offset, restopname)
bestidx = bestresult.index(max(bestresult))
fitness = round(max(bestresult), 1)
colors = []
for idx, result in enumerate(bestresult):
# draw the input in black
if idx == len(bestresult)-1:
colors.append('k')
# draw the best in green
elif idx == bestidx:
colors.append('g')
# draw p_genes in red
elif result > 0:
colors.append('r')
# draw TFs in blue
else:
colors.append('b')
print "colors", colors
graphname = filename.split('/')[2]
graphname = graphname[:-4] + "-F" + str(fitness)
graph.plot_2d(conclist, graphname, colors, (0, 1))