def generatePlot(exp_paths):
ax = plt.gca()
# ax.semilogx()
for exp_path in exp_paths:
exp = loadExperiment(exp_path)
if exp.agent == 'TDadagrad':
continue
use_ideal_h = exp._d['metaParameters'].get('use_ideal_h', False)
dashed = use_ideal_h
color = colors[exp.agent]
# load the errors and hnorm files
errors = loadResults(exp, 'errors_summary.npy')
results = loadResults(exp, 'ndh_summary.npy')
# choose the best parameters from the _errors_
best = getBestEnd(errors)
best_ndh = find(results, best)
label = exp.agent.replace('adagrad', '')
if use_ideal_h:
label += '-h*'
plotBest(best_ndh, ax, label=label, color=color, dashed=dashed)
# plt.show()
save(exp, f'norm_delta-hat')
plt.clf()
def generatePlot(exp_paths):
ax = plt.gca()
# ax.semilogx()
for exp_path in exp_paths:
exp = loadExperiment(exp_path)
rmsve = loadResults(exp, 'errors_summary.npy')
rmspbe = loadResults(exp, 'rmspbe_summary.npy')
# if exp.agent == 'TDadagrad':
# continue
# best PBE using AUC
best = getBest(rmspbe)
best_rmsve = find(rmsve, best)
use_ideal_h = exp._d['metaParameters'].get('use_ideal_h', False)
dashed = use_ideal_h
color = colors[exp.agent]
label = exp.agent.replace('adagrad', '')
if use_ideal_h:
label += '-h*'
plotBest(best_rmsve, ax, label=label, color=color, dashed=dashed)
# plt.show()
save(exp, f'rmsve_over_rmspbe', type='svg')
plt.clf()
def generatePlot(exp_paths):
ax = plt.gca()
# ax.semilogx()
for exp_path in exp_paths:
exp = loadExperiment(exp_path)
results = loadResults(exp)
use_ideal_h = exp._d['metaParameters'].get('use_ideal_h', False)
dashed = use_ideal_h
color = colors[exp.agent]
label = exp.agent.replace('adagrad', '')
if use_ideal_h:
label += '-h*'
plot(results,
ax,
label=label,
color=color,
dashed=dashed,
bestBy='end')
# plt.show()
save(exp, f'rmsve_learning-curve', type='svg')
plt.clf()
def generatePlot(exp_path):
ax = plt.gca()
# ax.semilogx()
exp = loadExperiment(exp_path)
# load the errors and hnorm files
errors = loadResults(exp, 'errors_summary.npy')
results = loadResults(exp, 'stepsize_summary.npy')
# choose the best parameters from the _errors_
best = getBestEnd(errors)
best_ss = find(results, best)
alg = exp.agent.replace('adagrad', '')
plotBest(best_ss, ax, label=['w', 'h'])
ax.set_ylim([0, 4])
print(alg)
# plt.show()
save(exp, f'stepsizes-{alg}')
plt.clf()