SilvaEmerson

"""
Stratified K-Fold implementation
"""

def stratified_k_fold(arr, k=None, class_ratio=.5):
    if k == None:
        return None

    number_elements_fold = len(arr) // k
    folds = [[]] * k 

SilvaEmerson

import time
import random

comparison_num = 0

class Quick(object):
    def particao(self, a, ini, fim):
        global comparison_num
        comparison_num += 1
        pivo = a[fim-1]

SilvaEmerson

SilvaEmerson

from inspect import signature


def get_params_len(fn): return len(signature(fn).parameters)


def curry(fn):
    fn_params_len = get_params_len(fn)
    temp_params = []

SilvaEmerson

#include <iostream>
#include <vector>

using namespace std;

vector<int> numbers;

typedef struct node {
    struct node *left;
    struct node *rigth;

SilvaEmerson

SilvaEmerson

public class Main {
    public static void main(String[] args){
        Process processo1 = new Process("Processo1");
        Process processo2 = processo1.fork("Processo2");
        System.out.println("Processo 1 UID: " + processo1.getOwner());
        System.out.println("Processo 2 UID: " + processo2.getOwner());

        System.out.println("Data de criação do Processo 1: " + processo1.getCreationDateTime());
        System.out.println("Data de criação do Processo 2: " + processo1.getCreationDateTime());

SilvaEmerson

SilvaEmerson

SilvaEmerson

import tensorflow as tf
import numpy as np

X_train, X_test = np.load('X_train.npy'), np.load('X_test.npy')
Y_train, Y_test = np.load('Y_train.npy'), np.load('Y_test.npy')

Y_train, Y_test = tf.keras.utils.to_categorical(Y_train, num_classes=4), tf.keras.utils.to_categorical(Y_test, num_classes=4)

features_num = len(X_train[0])
l1 = 50