def compare_pdfs_train():
"""
Affiche et compare les pdfs des différents training sets.
"""
from options import MultiOptions
opt = MultiOptions()
opt.opdict['stations'] = ['IJEN']
opt.opdict['channels'] = ['Z']
opt.opdict['Types'] = ['Tremor', 'VulkanikB', '?']
opt.opdict['train_file'] = '%s/train_10' % (opt.opdict['libdir'])
opt.opdict[
'label_filename'] = '%s/Ijen_reclass_all.csv' % opt.opdict['libdir']
train = read_binary_file(opt.opdict['train_file'])
nb_tir = len(train)
for sta in opt.opdict['stations']:
for comp in opt.opdict['channels']:
opt.x, opt.y = opt.features_onesta(sta, comp)
X = opt.x
Y = opt.y
c = ['r', 'b', 'g']
lines = ['-', '--', '-.', ':', '-', '--', '-.', ':', '*', 'v']
features = opt.opdict['feat_list']
for feat in features:
print feat
opt.opdict['feat_list'] = [feat]
fig = plt.figure()
fig.set_facecolor('white')
for tir in range(nb_tir):
tr = map(int, train[tir])
opt.x = X.reindex(index=tr, columns=[feat])
opt.y = Y.reindex(index=tr)
opt.classname2number()
opt.compute_pdfs()
g = opt.gaussians
for it, t in enumerate(opt.types):
plt.plot(g[feat]['vec'],
g[feat][t],
ls=lines[tir],
color=c[it])
plt.title(feat)
plt.legend(opt.types)
plt.show()
def compare_pdfs_train():
"""
Affiche et compare les pdfs des différents training sets.
"""
from options import MultiOptions
opt = MultiOptions()
opt.opdict['stations'] = ['IJEN']
opt.opdict['channels'] = ['Z']
opt.opdict['Types'] = ['Tremor','VulkanikB','?']
opt.opdict['train_file'] = '%s/train_10'%(opt.opdict['libdir'])
opt.opdict['label_filename'] = '%s/Ijen_reclass_all.csv'%opt.opdict['libdir']
train = opt.read_binary_file(opt.opdict['train_file'])
nb_tir = len(train)
for sta in opt.opdict['stations']:
for comp in opt.opdict['channels']:
opt.x, opt.y = opt.features_onesta(sta,comp)
X = opt.x
Y = opt.y
c = ['r','b','g']
lines = ['-','--','-.',':','-','--','-.',':','*','v']
features = opt.opdict['feat_list']
for feat in features:
print feat
opt.opdict['feat_list'] = [feat]
fig = plt.figure()
fig.set_facecolor('white')
for tir in range(nb_tir):
tr = map(int,train[tir])
opt.x = X.reindex(index=tr,columns=[feat])
opt.y = Y.reindex(index=tr)
opt.classname2number()
opt.compute_pdfs()
g = opt.gaussians
for it,t in enumerate(opt.types):
plt.plot(g[feat]['vec'],g[feat][t],ls=lines[tir],color=c[it])
plt.title(feat)
plt.legend(opt.types)
plt.show()
