"files": [],
"avg": copy.deepcopy(dmodel1),
"top": copy.deepcopy(dmodel1)
}
# create separate models for each data file
for filename in filenames:
data_file = root_data_folder + "/" + filename + ".csv"
x, y, _, _ = loader.load_dataset(data_file)
acc_train_vect[filename] = copy.deepcopy(dmodel)
acc_test_vect[filename] = copy.deepcopy(dmodel)
# print(y)
# binarize the outputs
y = loader.binarize(y)
if config["one_hot_encoding"]:
# use integer encoding
y = prep.encode(prep.adapt_input(y))
y = prep.decode_int_onehot(y)
# print(y)
# quit()
# y = prep.encode(prep.adapt_input(y))
top_acc = 0
top_model_filename = None
# session = K.get_session()
# classifiers.create_decision_tree(x, y[:,0], 20)
else:
input_file = "./data/exp_39.csv"
model_file = root_crt_model_folder + "/" + "exp_39_5_top.h5"
nvalves = config["n_valves"]
nrowskip = 0
# X1, y1 = loader.load_dataset_raw_buffer(input_file)
X1, y1, _, _ = loader.load_dataset(input_file)
# X1 = X1[120:1700]
# y1 = y1[120:1700]
# binarize the outputs
y1 = loader.binarize(y1)
s = np.shape(X1)
print(s)
nrows = s[0]
ncols = s[1]
n_bins = 20
rowskip = int(nrows / n_bins)
if use_post_rowskip:
rowskip = 1
post_rowskip = int(nrows / n_bins)