Dense(units=nodes,
kernel_initializer='uniform',
activation='relu',
input_dim=inputnum))
model.add(
Dense(units=nodes, kernel_initializer='uniform', activation='relu'))
model.add(Dense(units=1, kernel_initializer='uniform'))
# compile the model
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse'])
return model
# load data files
df_train = load_Training_data(file)
df_test = load_test_data(file)
# filter dataframe by ID
if unit != 0:
df_train = df_train[df_train['UNIT_ID'] == unit]
df_test = df_test[df_test['UNIT_ID'] == unit]
print('result filtered for ID', unit)
# create the training and testing sets from the dataframes
training_set = df_train.iloc[:, 2:].values
test_set = df_test.iloc[:, 2:].values
# scaling
scaler = MinMaxScaler((-1, 1))
training_scaled = scaler.fit_transform(training_set)
for i in range(len(test)):
d = pred[i] - test[i]
score = 0
if d < 0:
score = (math.exp(-(d / 10))) - 1
result = result + score
elif d > 0:
score = (math.exp(d / 13)) - 1
result = result + score
return result
# Importing the training dataframe
dataset_train = load_Training_data(file)
# filter dataframe by ID
if unit != 0:
dataset_train = dataset_train[dataset_train['UNIT_ID'] == unit]
# construct the dataset as numpy array
training_set = dataset_train.iloc[:, 2:].values
# Feature Scaling
sc = MinMaxScaler(feature_range=(-1, 1))
training_set_scaled = sc.fit_transform(training_set[:, 0:])
#restore original values of RUL
