# Standardise training and testing data
scaler_train = StandardScaler()
scaler_test = StandardScaler()
scaler_train.fit(training_data)
scaler_test.fit(testing_data)
testing_data = scaler_test.transform(testing_data)
# Convert training data to pd dataframe
columns = "BC NC LP LI".split()
training_data = pd.DataFrame(data=training_data, index=None, columns=columns)
# Replicate the training data
replicated_data1 = replicate_data(training_data, 10, 0.03)
replicated_data2 = replicate_data(training_data, 10, 0.05)
training_data = training_data.append(replicated_data1,
ignore_index=True,
sort=False)
training_data = training_data.append(replicated_data2,
ignore_index=True,
sort=False)
training_data = scaler_train.transform(training_data)
training_data = np.array(training_data)
# Calculate training and testing labels
try:
a = []
index +=1
# Standardise Test Data
for subset in subset_test_list:
subset.value = scaler_train.transform(subset.value)
# Replicate and Standardise the training data in each subset.
columns = "BC NC LP LI NIC".split()
for index, subset in enumerate(subset_train_list):
df = pd.DataFrame(data=subset.value, index=None, columns=columns)
ref = df
df = scaler_train.transform(df)
replicated_data1 = replicate_data(ref, 50, 0.03)
replicated_data1 = scaler_train.transform(replicated_data1)
df = np.append(df, replicated_data1, axis=0)
replicated_data2 = replicate_data(ref, 50, 0.05)
replicated_data2 = scaler_train.transform(replicated_data2)
df = np.append(df, replicated_data2, axis=0)
subset.value = df
# Calculate training and test labels
for index1, subset in enumerate(subset_train_list):
a = []
try:
