tuelwer

import numpy as np
from scipy.linalg import circulant

def solve_circulant(A,b):
    return np.fft.ifft(np.fft.fft(b)/np.fft.fft(A[:,0]))

n = 10000
a = np.random.randn(1,n)
A = circulant(a)
b = np.random.rand(n)

tuelwer

\documentclass{article}
\usepackage{algorithm}
\usepackage{minted}

\begin{document}
\begin{algorithm}
\vspace{0.2em}
\begin{minted}[xleftmargin=1em, numbersep=6pt, linenos, breaklines]{python}
def fib(n): 
    """This function calculates the n-th Fibonacci number."""

tuelwer

import argparse

parser = argparse.ArgumentParser()
parser.add_argument('arg1')  
parser.add_argument('--arg2', default=0.1, type=float)
args = parser.parse_args()

print(args.arg1)
print(args.arg2)

tuelwer

def check_triangle_inequality(D):
    """ Returns true iff the matrix D fulfills
    the triangle inequaltiy.
    """
    n = len(D)
    valid = True
    for i in range(n):
        for j in range(i, n):
            for k in range(n):
                if k == j or k == i:

tuelwer

import numpy as np
import matplotlib.pyplot as plt
import matplotlib

# ugly hack to embed fonts
matplotlib.rc("pdf", fonttype=42)

edgecolor = "black"

bar_scale = 0.8

tuelwer

import pickle

r = [1, 2, 3]
pickle.dump(r, open('r.file', mode='wb'))
r = pickle.load(open('r.file', mode='rb'))

tuelwer

import torch
import torch.optim as optim
import matplotlib.pyplot as plt


# 2d Rosenbrock function
def f(x):
    return (1 - x[0])**2 + 100 * (x[1] - x[0]**2)**2

tuelwer

class GMM(torch.nn.Module):
    def __init__(self, n, d=2, k=2):
        super(GMM, self).__init__()
        self.d = d
        self.k = k
        self.n = n
        
        self.covs = torch.eye(self.d).view(-1, self.d, self.d).repeat(self.k,1,1)
        self.mus = torch.zeros(n, k)
        self.member = torch.zeros(n, k)