ggcr

#include <iostream>
#include <vector>
#include <cassert>
#include <algorithm>

/*
O(n^2) in avg and worst.
*/

// Implementation using std::find_if

ggcr

#include <iostream>
#include <vector>
#include <cassert>
#include <algorithm>

/*
O(n^2) in avg and worst.
Can be done easily at space complexity O(n).
*/

ggcr

#include <iostream>
#include <vector>
#include <cassert>
#include <algorithm>

// Implementation

void print_vector(std::vector<int> array, std::string title) {
    cout << title << ": " << "[";
    for(int i : array)

ggcr

import torch
import numpy as np
from PIL import Image
from torchvision import transforms
from sklearn.datasets import fetch_olivetti_faces

faces, _ = fetch_olivetti_faces(data_home='../data/', return_X_y=True, shuffle=True, random_state=np.random.RandomState(0))
faces = torch.from_numpy(faces)

n = faces.shape[0]

ggcr

import numpy as np

x = np.array([-0.5, -0.43, -0.23, 0.12, 0.40, 0.71, 1]).reshape(-1, 1)
y = np.array([-2, -0.2, 0.1, 0.83, 1.4, 0.98, 2.2]).reshape(-1, 1)

# Add a column of ones to the input data for the intercept term
X = np.concatenate((np.ones_like(x), x), axis=1)

xxT = X.T.dot(X)
inv = np.linalg.inv(xxT)