utils.py

import numpy as np
import os
from sklearn.preprocessing import LabelEncoder

def create_directory(directory_path):
    
    if not os.path.isdir(directory_path):
        os.mkdir(directory_path)

def load_data(file_name):
    
    folder_path = "/home/hadi/datasets/UCRArchive_2018/"
    folder_path += (file_name + "/")

    train_path = folder_path + file_name + "_TRAIN.tsv"
    test_path = folder_path + file_name + "_TEST.tsv"

    if (os.path.exists(test_path) <= 0):
        print("File not found")
        return None, None, None, None

    train = np.loadtxt(train_path, dtype=np.float64)
    test = np.loadtxt(test_path, dtype=np.float64)

    ytrain = train[:, 0]
    ytest = test[:, 0]

    xtrain = np.delete(train, 0, axis=1)
    xtest = np.delete(test, 0, axis=1)

    return xtrain, ytrain, xtest, ytest

def znormalisation(x):

    stds = np.std(x,axis=1,keepdims=True)
    if len(stds[stds == 0.0]) > 0:
        stds[stds == 0.0] = 1.0
        return (x - x.mean(axis=1, keepdims=True)) / stds
    return (x - x.mean(axis=1, keepdims=True)) / (x.std(axis=1, keepdims=True))

def encode_labels(y):
    
    labenc = LabelEncoder()
    
    return labenc.fit_transform(y)