gabrieldernbach

from torchvision.datasets import MNIST
import numpy as np

def data(train):
    mnist = MNIST(root='.', download=True, train=train)
    X = mnist.data.numpy().reshape(-1, 784) / 255
    y = mnist.targets.numpy()
    return X, y
  

gabrieldernbach

import numpy as np
from scipy.stats import chi2_contingency

# example data taken from
# https://en.wikipedia.org/wiki/Chi-squared_test#Example_chi-squared_test_for_categorical_data
X = np.array([
    [90, 60, 104, 95],
    [30, 50, 51, 20],
    [30, 40, 45, 35],
])

gabrieldernbach

fswatch ~/repo/ -o | xargs -I {} rsync -avzhe ssh ~/repo/ server:/home/user/repo/ --delete --exclude "venv/*"

gabrieldernbach

import cProfile
import pstats
import io

def profile(file_path=None, breakpoint_after_call=False):
    """decorator for runtime profiling of functions or class methods

    prints the profiling statistics sorted by
        cumulative time,
        total time,