else:
return X, y, timey
if __name__ == "__main__":
q_set = [0.025, 0.16, 0.5, 0.84, 0.975]
for q in q_set:
cross_training(qnn,
pipeline_small_exclude,
1,
q=q,
layers=1,
neurons=32,
dropout=0.05,
noise_in=0.0,
noise_out=0.,
l1_hidden=0.0,
l2_hidden=0,
l1_out=0.,
l2_out=0.,
batch_size=100,
lr=0.01,
epochs=5000,
n_segments=5,
n_members_segment=3,
patience=25,
verbose=0,
name="qnn_ex_pca_tanh",
activation="tanh")
return X, y, timey, y_persistance
else:
return X, y, timey
if __name__ == "__main__":
q_set = [0.025, 0.16, 0.5, 0.84, 0.975]
cross_training(mqnn,
pipeline,
1,
q=q_set,
layers=1,
neurons=96,
dropout=0.1,
noise_in=0.0,
noise_out=0.,
l1_hidden=0.0,
l2_hidden=0,
l1_out=0.,
l2_out=0.,
batch_size=100,
lr=0.01,
epochs=5000,
n_segments=5,
n_members_segment=1,
patience=25,
verbose=0,
name="mqnn",
activation="tanh")
timey = oni.index[lead_time + time_lag + shift:]
if return_persistance:
y_persistance = yorg[time_lag:-lead_time - shift]
return X, y, timey, y_persistance
else:
return X, y, timey
if __name__ == "__main__":
cross_training(ipnn,
pipeline_small,
10,
layers=1,
neurons=32,
dropout=0.2,
noise_in=0.0,
noise_out=0.,
l1_hidden=[0.002, 0.15, 'log'],
l2_hidden=0.,
l1_out=0.,
l2_out=0.,
batch_size=100,
lr=0.01,
epochs=5000,
n_segments=5,
n_members_segment=1,
patience=25,
verbose=0,
name="ipnn_new")
from ninolearn.learn.fit import cross_training
from mlr import mlr, pipeline, pipeline_noise
if __name__=="__main__":
cross_training(mlr, pipeline_noise, 50, alpha=[0.,0.001], name='mlr_review_noise')
return X, y, timey, y_persistance
else:
return X, y, timey
if __name__ == "__main__":
cross_training(DEM,
pipeline_small,
1,
layers=1,
neurons=32,
dropout=0.05,
noise_in=0.0,
noise_sigma=0.,
noise_mu=0.,
l1_hidden=0.0,
l2_hidden=0.,
l1_mu=0,
l2_mu=0.,
l1_sigma=0,
l2_sigma=0.0,
lr=0.01,
batch_size=100,
epochs=5000,
n_segments=5,
n_members_segment=3,
patience=25,
verbose=0,
pdf=None,
name="mlp")
if return_persistance:
y_persistance = yorg[time_lag:-lead_time - shift]
return X, y, timey, y_persistance
else:
return X, y, timey
if __name__ == "__main__":
cross_training(DEM,
pipeline_small,
200,
layers=1,
dropout=[0.1, 0.5],
noise_in=[0.1, 0.5],
noise_sigma=[0.1, 0.5],
noise_mu=[0.1, 0.5],
l1_hidden=[0.0, 0.02],
l2_hidden=[0., 0.02],
l1_mu=[0.0, 0.02],
l2_mu=[0.0, 0.02],
l1_sigma=[0.0, 0.02],
l2_sigma=[0.0, 0.02],
lr=[0.0001, 0.01],
batch_size=100,
epochs=500,
n_segments=5,
n_members_segment=1,
patience=30,
verbose=0,
pdf="normal",
name="dem_small")
from ninolearn.learn.fit import cross_training
from mlr import mlr, pipeline
if __name__ == "__main__":
cross_training(mlr, pipeline, 50, alpha=[0., 0.001], name='mlr')
np.save(join(infodir,'Xorg'), Xorg)
# arange the feature array
X = Xorg[:-lead_time-shift,:]
X = include_time_lag(X, n_lags=n_lags, step=step)
# arange label
yorg = oni.values
y = yorg[lead_time + n_lags*step + shift:]
# get the time axis of the label
timey = oni.index[lead_time + n_lags*step + shift:]
if return_persistance:
y_persistance = yorg[n_lags*step: - lead_time - shift]
return X, y, timey, y_persistance
else:
return X, y, timey
if __name__=="__main__":
cross_training(DEM, pipeline, 1, lead_times,
layers=1, neurons = 32, dropout=0.05, noise_in=0.0, noise_sigma=0.,
noise_mu=0., l1_hidden=0.0, l2_hidden=0.,
l1_mu=0, l2_mu=0., l1_sigma=0,
l2_sigma=0.0, lr=0.01, batch_size=100,
epochs=5000, n_segments=5, n_members_segment=3, patience=25,
activation='tanh',
verbose=0, pdf="normal", name="gdnn_ex_pca")
print("\n \nStep 2 finished, continue to step 3!")