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utils.py
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utils.py
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import numpy as np
import pickle
from os.path import dirname, join, makedirs
import time, os, pickle
from tqdm import tqdm
def dump_pkl(obj, path):
with open(path, "wb") as f:
pickle.dump(obj, f)
return 1
def open_pkl(path):
with open(path, "rb") as f:
obj = pickle.load(f)
return obj
def norm_one(X_train):
return (X_train - X_train.min()) / (X_train.max() - X_train.min())
def normalize(X_train, a, b):
"""Normalize the data between a and b
Parameters
----------
X_train : numpy array
Input data
a : float
Lower bound
b : float
Upper bound
Returns
-------
numpy array
Normalized data
"""
X_train = (a - b) * norm_one(X_train) + b
return X_train