else:
data_files, add_to_name, data_config = common_code()
algo_name = algo_core+algo_type+"MultiStep"+str(multistep) +"Window"+\
str(window_size)+anomalyScore_func+anomalyScore_type+add_to_name
model = predictionLstmStepAhead(input_shape, multistep)
result_files = use_whole_data(data_files,
input_shape,
train_nStepPrediction_based_models_new,
model,
nStepAhead=multistep,
nb_epoch=nb_epoch,
anomaly_score=anomalyScore_type)
print(algo_name)
write_result(algorithm_name=algo_name,
data_files=result_files,
results_path=cwd + '/results')
store_param(window_size, nb_epoch, input_shape, algo_core, algo_type,
algo_name, model, normalized_input, anomalyScore_func,
anomalyScore_type, multistep)
#for i in range(len(df)):
# a.append(al.anomalyProbability(df.value.values[i],df.anomaly_score.values[i],df.timestamp.values[i]))
# 1- Params of model
# 2- Params of training
# 3- Get model
# 4- Get type of training
# 5- Train and get result
# 6- Write output and params
from utility import read_data, train_prediction_based_models, use_whole_data, write_result, common_code, store_param, common_code_normalized
from models import predictionCnn
import os
cwd = os.getcwd()
window_size = 10
nb_epoch = 1
nb_features = 1
input_shape = (window_size, nb_features)
model = predictionCnn(input_shape)
data_files, add_to_name, data_config = common_code_normalized()
result_files = data_files
result_files = use_whole_data(data_files,
input_shape,
train_prediction_based_models,
model,
nb_epoch=nb_epoch)
algo_type = "predictionCnnOneEpoch"
algo_name = algo_type + add_to_name
print(algo_name)
write_result(algorithm_name=algo_name,
data_files=result_files,
results_path=cwd + '/results')
store_param(window_size, nb_epoch, input_shape, algo_type, algo_name, model,
data_config)