gabrieldernbach

# leverage scores l_[i] = tr(X @ (X^T X)^-1 @ X)
# indication of self-sensitivity or self-influence of i-th sample.
import numpy as np

n = 2048 # samples
d = 256 # dimensions
X = np.random.randn(n, d) # design matrix

# navie computation, high memory footprint (quadtratic in n^2) 
l_naive = np.trace(X @ np.linalg.inv(X) @ X)

gabrieldernbach

from dataclasses import dataclass, replace
from joblib import Parallel, delayed
from typing import Optional, Callable, List, Iterable

@dataclass(frozen=True)
class Either:
    value: Optional = None
    exception: Optional = None
        
def forward_exceptions(fun: Callable):

gabrieldernbach

FROM ubuntu:20.04
ARG DEBIAN_FRONTEND=noninteractive

RUN apt-get update; apt-get upgrade -y
RUN apt-get install -y emacs vim r-base r-base-dev libcurl4-openssl-dev
ARG DOWNLOAD_STATIC_LIBV8=1
RUN R -e 'install.packages("rstan")'

gabrieldernbach

import torch.nn as nn

class Residual(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.layer = nn.Sequential(
            nn.Conv2d(dim, dim, 7, 1, 3, groups=dim),
            nn.BachNorm2d(dim),
            nn.Conv2d(dim, dim*4, 1),
            nn.ReLU(),

gabrieldernbach

import functools
from joblib import Parallel, delayed

def compose2(f, g):
    return lambda x: g(f(x))

def compose(*fs):
    return functools.reduce(compose2, fs)

def pipe(x, *fs):

gabrieldernbach

import functools
import queue
from random import randint
from random import seed
from random import uniform
from typing import NamedTuple


class Item(NamedTuple):
    id: int

gabrieldernbach

import numpy
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt

from torchvision.datasets import MNIST
from torch.utils.data import DataLoader
import torchvision.transforms as T
from einops import rearrange

gabrieldernbach

import numpy as np
import matplotlib.pyplot as plt
from sklearn.kernel_approximation import Nystroem
from sklearn.cluster import MiniBatchKMeans

# dot in the middle
X = np.random.randn(100, 2)
# circle around
Y = X  / np.sqrt((X**2).mean(1, keepdims=True)) * 8
Y = Y + np.random.randn(100, 2)

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],
])