def basic_stats_and_plots_tree():
basename = sys.argv[1]
filename = os.path.join(basename, "depth_2/TP.dat")
tp = np.genfromtxt(filename)
N = len(tp)
sd = random_walks.mu_sigma(tp)[0]
logN_sd = sd * np.log(len(tp))
sqrtN_sd = sd * (N**0.5)
cbrtN_sd = sd * (N**(1.0/3))
g = random_walks.mean_gini_coeff(tp)
cv = random_walks.mu_sigma_cv(tp)[0]
mu, sigma = rw_experiment_with_tp(tp)
prop_unique_v_expl = []
space = "tree"
op = "subtree"
size = 2
prop_unique_v_expl.append("%s %d %s %f %f %f %f %f %f %f %f" % (
space, size, op, sd, cv, logN_sd, sqrtN_sd, cbrtN_sd, g, mu, sigma))
filename = os.path.join(basename, "space_%s/size_%d_prop_unique_v_expl.dat" % (space, size))
open(filename, "w").write("\n".join(prop_unique_v_expl))
barchart(basename, space, size, "cbrt(N) SD", [cbrtN_sd], [op])
def ga_hc_experiment(path_results):
"""Run some hill-climbs on variations of a GA space. Report
performance."""
uniformify_vals = [
0.1, 0.5, .75, 0.9, 1.0, 1.0 / 0.9, 1.0 / .75, 2.0, 10.0
]
noise_vals = [0, 1, 10, 100, 1000]
results = OrderedDict()
ga_length = 10
tp_path = os.path.join(path_results, "ga_length_10", "TP.dat")
try:
ga_tp = np.genfromtxt(tp_path)
except:
ga_tp, _ = random_walks.generate_ga_tm(ga_length, pmut=1.0 / ga_length)
np.savetxt(tp_path, ga_tp)
fit_path = os.path.join(path_results, "ga_length_10", "fitness_vals.dat")
try:
ga_fit = np.genfromtxt(fit_path)
except:
ga_fit = random_walks.onemax_fitvals(ga_length)
np.savetxt(fit_path, ga_fit)
# just get mu(sigma()), don't bother with sigma(sigma())
mu_sigma_vals = [
random_walks.mu_sigma(random_walks.uniformify(ga_tp,
uniformify_val))[0]
for uniformify_val in uniformify_vals
]
reps = 30
steps = 50
for rep_name, tp, fitvals in [["ga", ga_tp, ga_fit]]:
for noise_val in noise_vals:
tmp_fit = random_walks.permute_vals(fitvals, noise_val)
for uniformify_val in uniformify_vals:
for rep in range(reps):
tp_tmp = random_walks.uniformify(tp, uniformify_val)
samples, fit_samples, best = random_walks.hillclimb(
tp_tmp, tmp_fit, steps, rw=False)
x = best
results[rep_name, uniformify_val, noise_val, rep] = x
return results, mu_sigma_vals
def ga_hc_experiment(path_results):
"""Run some hill-climbs on variations of a GA space. Report
performance."""
uniformify_vals = [0.1, 0.5, .75, 0.9, 1.0, 1.0/0.9, 1.0/.75, 2.0, 10.0]
noise_vals = [0, 1, 10, 100, 1000]
results = OrderedDict()
ga_length = 10
tp_path = os.path.join(path_results, "ga_length_10", "TP.dat")
try:
ga_tp = np.genfromtxt(tp_path)
except:
ga_tp, _ = random_walks.generate_ga_tm(ga_length, pmut=1.0/ga_length)
np.savetxt(tp_path, ga_tp)
fit_path = os.path.join(path_results, "ga_length_10", "fitness_vals.dat")
try:
ga_fit = np.genfromtxt(fit_path)
except:
ga_fit = random_walks.onemax_fitvals(ga_length)
np.savetxt(fit_path, ga_fit)
# just get mu(sigma()), don't bother with sigma(sigma())
mu_sigma_vals = [random_walks.mu_sigma(random_walks.uniformify(ga_tp, uniformify_val))[0]
for uniformify_val in uniformify_vals]
reps = 30
steps = 50
for rep_name, tp, fitvals in [["ga", ga_tp, ga_fit]]:
for noise_val in noise_vals:
tmp_fit = random_walks.permute_vals(fitvals, noise_val)
for uniformify_val in uniformify_vals:
for rep in range(reps):
tp_tmp = random_walks.uniformify(tp, uniformify_val)
samples, fit_samples, best = random_walks.hillclimb(tp_tmp, tmp_fit,
steps, rw=False)
x = best
results[rep_name, uniformify_val, noise_val, rep] = x
return results, mu_sigma_vals