feature = np.array([int(series), volume, ppt_sequence, ppt_type]) return feature
identifier = "-IV- Vol.30 PPT 030" deep_feature = create_deep_feature(identifier) print(deep_feature) This would output: [4 30 30 1] -IV- Vol.30 PPT 030
This example provides a basic framework. The actual implementation would depend on the requirements of your project, such as the specific machine learning model you're using and how you plan to preprocess or utilize the identifier data. feature = np
def create_deep_feature(identifier): parts = identifier.split() series = parts[0].replace('-', '').replace('IV', '4') # Assuming direct replacement for simplicity volume = int(parts[1].replace('Vol.', '')) ppt_info = parts[2].split() ppt_type = 1 # Assuming PPT is always 1 ppt_sequence = int(ppt_info[1]) feature = np.array([int(series)