# CC_MAE_RMSE.py for demoing (pearsonr) Correlation Coefficient (CC),
# Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE).
# D. Parson, September 2024.

from scipy.stats import pearsonr, spearmanr
from random import Random
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from math import sqrt, log2

def CC(sequence1, sequence2):
    # Correlation Coefficient (CC) without numpy types
    cc, err = pearsonr(sequence1, sequence2)
    cc = round(float(cc), 2)
    return cc

def SR(sequence1, sequence2):
    # Correlation Coefficient (CC) without numpy types
    cc, err = spearmanr(sequence1, sequence2)
    cc = round(float(cc), 2)
    return cc

def MAE(sequence1, sequence2):
    # Mean absolute error without any numpy types
    return round(float(mean_absolute_error(sequence1, sequence2)),2)

def RMSE(sequence1, sequence2):
    # Root mean squared error without any numpy types
    return round(sqrt(float(mean_squared_error(sequence1, sequence2))),2)

lin1 = list(range(0,50,5))

lin2 = list(range(0,20,2))

sqr2 = [element ** 2 + 5 for element in lin1]

pow2 = [2 ** element + 7 for element in lin1]

sqrt2 = [sqrt(element) for element in sqr2]

invpow2 = [log2(element) for element in pow2]