alberto-santini

from sklearn.datasets import fetch_openml
from sklearn.pipeline import Pipeline, make_pipeline
from sklearn.compose import ColumnTransformer, make_column_selector
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder, FunctionTransformer
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split

from mlxtend.regressor import StackingRegressor

alberto-santini

Download and burn the Arch ISO

I get the ISO, which is about 530MB large, and burn it on the USB key.

$ lsblk
NAME                MAJ:MIN RM   SIZE RO TYPE  MOUNTPOINT
sda                   8:0    1   3.8G  0 disk  
└─sda1                8:1    1   3.8G  0 part  

alberto-santini

#include <ext/stdio_filebuf.h>  // For stdio_filebuf
#include <unistd.h>             // For pipe, close
#include <cstdlib>              // For _Exit
#include <memory>               // For unique_ptr
#include <sys/types.h>          // For pid_t
#include <sys/wait.h>           // For waitpid
#include <stdio.h>              // For perror
#include <iostream>
#include <cassert>
#include <string>

alberto-santini

def flatten(a):
    return sum(a, [])

def local_minima(a):
    if len(a) < 2:
        return list(range(len(a)))
    
    lmin = True
    curr = list()
    local_minima = list()

alberto-santini

#include <iostream>
#include <string>
#include <typeinfo>
#include <cxxabi.h>

std::string classname(const std::type_info& ti) {
    int status;
    return abi::__cxa_demangle(ti.name(), 0, 0, &status);
}

alberto-santini

#include <algorithm>
#include <chrono>
#include <functional>
#include <future>
#include <iomanip>
#include <iostream>
#include <random>

#include "pcg_random.hpp" // Random number generator