karpathy

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

jensdebruijn

# Python implementation of the Nadeau and Bengio correction of dependent Student's t-test
# using the equation stated in https://www.cs.waikato.ac.nz/~eibe/pubs/bouckaert_and_frank.pdf

from scipy.stats import t
from math import sqrt
from statistics import stdev

def corrected_dependent_ttest(data1, data2, n_training_samples, n_test_samples, alpha):
    n = len(data1)
    differences = [(data1[i]-data2[i]) for i in range(n)]