def print_longterm_activity():
path = os.path.join(
SG_SIM_PATH, "isgt-env-replace-3-of-7",
"output_esn_run_0_bc-data_no_clean.txt")
genome = best_genes_esn(path)
options = load_prediction.get_options()
options.num_predictions = 1 * 24
options.bc_data = True
mc = load_prediction_esn.ESNModelCreator()
model = mc.get_model(options)
model.dataset = mc.get_dataset(options)
model.cleaning_disabled = True
model.train_and_predict_func = _esn_feedback_with_hook
model.day = options.num_predictions
(target, predictions) = test_genome_store_states(genome, model)
plt.figure()
plot_target_predictions(target, predictions)
plt.figure()
for act in activities:
plt.plot(act)
plt.axvline(x=1342)
plt.show()
import ipdb
import numpy as np
import Oger, mdp
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.gridspec as gridspec
import pandas as pd
import sg.models.esn as esn
import sg.utils
from sg.data.sintef.create_full_temp_data import data as read_temperatures
import sg.data.sintef.userloads as ul
import sg.data.bchydro as bc
import sg.models.load_prediction as load_prediction
options = load_prediction.get_options()
dataset = load_prediction.BCHydroDataset(options, dt(hours=672))
# [len_data, res_size, leak, input, bias, spectral,
# seed, ridge, tmp_sm, load_sm]
genome = [672, 194, 0.9507914597451542, 0.23017393420143673, 0.18145624723908402, 1.1091372652108626, 53380, 1.4880952380952382e-07]
l = sg.utils.Enum('hindsight', 'size', 'leak', 'in_scale',
'bias_scale', 'spectral', 'seed', 'ridge')#,
#'t_smooth', 'l_smooth', 't_zscore', 'l_zscore')
# A bit of work is needed to normalize an array that contains NaNs.
prediction_steps = 24
train_iter = dataset.train_data_iterator()
test_iter = dataset.test_data_iterator()
