normalizer.py

import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler, StandardScaler


def decimalNormalize(x):
  return (np.array(x) / 10**np.ceil(np.log10(max(x))))

def decimalNormalizeOver(x,maximum):
    X = (np.array(x) / 10 ** np.ceil(np.log10(maximum)))
    X[X > 1.0] = 1.0  # limit top
    return X


def decimalDenormalize (x,maximum):
  return (np.array(x)*(10**np.ceil(np.log10(maximum))))

def minMaxNormalize(x):
    scaler = MinMaxScaler(feature_range=(-1, 1))
    scaler.fit(x)
    return scaler.transform(x), scaler

def minMaxNormalizeOver(x, scaler):
    X = scaler.transform(x)
    X[X>1.0] = 1.0 # limit top
    X[X<-1.0] = -1.0 #limit bottom
    return X


def minMaxDenormalize(x, scaler):
    return scaler.inverse_transform(x)


def zNormalize(x):
    scaler = StandardScaler()
    scaler.fit(x)
    return scaler.transform(x), scaler

def zNormalizeOver(x, scaler):
    return scaler.transform(x)

def zDenormalize(x, scaler):
    return scaler.inverse_transform(x)