viniciusarruda

import os
from huggingface_hub import InferenceClient
from typing import List, Literal, TypedDict, Callable


Role = Literal["system", "user", "assistant"]


class Message(TypedDict):
    role: Role

viniciusarruda

viniciusarruda

import numpy as np # version 1.16.1 and python 3.6.7

def nms_cpu_kernel(dets, scores, iou_threshold):

    x1 = dets[:, 0]
    y1 = dets[:, 1]
    x2 = dets[:, 2]
    y2 = dets[:, 3]

    areas = (x2 - x1) * (y2 - y1)

viniciusarruda

# Author: Vinicius Arruda
# viniciusarruda.github.io
# Source code modified from: https://medium.com/@stepanulyanin/implementing-grad-cam-in-pytorch-ea0937c31e82

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils import data
from torchvision import transforms
from torchvision import datasets

viniciusarruda

EPOCH   FITNESS         CHROMOSOME
0         86       dktzmkeqixkmpzskjqjn
1         85       enstfgzpbwrcxoosnfbh
2         78       jetvbgqcbpceqtnnctdm
3         66       cpujjdaldsjhuuoraejn
4         55       enurcichvsoepthmckeh
5         44       enstfggohqnewnoncmkk
6         43       bosqbgigbwrculrqcmkm
7         38       enstfggohqnevqsqcmcj
8         38       enstfggohqnevqsqcmcj

viniciusarruda

EPOCH    FITNESS                     CHROMOSOME
0        10279        [8, 6, 12, 1, 11, 9, 2, 0, 7, 5, 10, 3, 4]
1         9972        [7, 0, 2, 9, 3, 5, 11, 6, 8, 1, 4, 12, 10]
2         9972        [7, 0, 2, 9, 3, 5, 11, 6, 8, 1, 4, 12, 10]
3         9759        [9, 2, 12, 11, 1, 8, 6, 4, 10, 5, 3, 7, 0]
4         9509        [8, 6, 1, 11, 4, 10, 5, 9, 7, 0, 2, 3, 12]
5         9309        [7, 0, 2, 4, 9, 3, 5, 10, 12, 11, 1, 8, 6]
6         9161        [0, 7, 3, 2, 9, 5, 10, 11, 6, 1, 8, 12, 4]
7         9161        [0, 7, 3, 2, 9, 5, 10, 11, 6, 1, 8, 12, 4]
8         9161        [0, 7, 3, 2, 9, 5, 10, 11, 6, 1, 8, 12, 4]

viniciusarruda

city_distance = [
 [ 0, 2451, 713, 1018, 1631, 1374, 2408, 213, 2571, 875, 1420, 2145, 1972], # New York
 [2451, 0, 1745, 1524, 831, 1240, 959, 2596, 403, 1589, 1374, 357, 579], # Los Angeles
 [ 713, 1745, 0, 355, 920, 803, 1737, 851, 1858, 262, 940, 1453, 1260], # Chicago
 [1018, 1524, 355, 0, 700, 862, 1395, 1123, 1584, 466, 1056, 1280, 987], # Minneapolis
 [1631, 831, 920, 700, 0, 663, 1021, 1769, 949, 796, 879, 586, 371], # Denver
 [1374, 1240, 803, 862, 663, 0, 1681, 1551, 1765, 547, 225, 887, 999], # Dallas
 [2408, 959, 1737, 1395, 1021, 1681, 0, 2493, 678, 1724, 1891, 1114, 701], # Seattle
 [ 213, 2596, 851, 1123, 1769, 1551, 2493, 0, 2699, 1038, 1605, 2300, 2099], # Boston
 [2571, 403, 1858, 1584, 949, 1765, 678, 2699, 0, 1744, 1645, 653, 600], # San Francisco

viniciusarruda

def genetic_algorithm(n):
    population = init_population(n)
    for _ in xrange(epochs):
        fn = fitness(population)  
        parents = selection(n, fn)
        offspring = crossover(parents)
        offspring = mutation(offspring)
        population = parents + offspring
    return best(population)

viniciusarruda

# -*- coding: utf-8 -*-

#
# Author:     Vinicius Ferraco Arruda                   
# Email:      viniciusferracoarruda@gmail.com            
# Website:    viniciusarruda.github.io                   
#

import argparse
from PIL import Image

viniciusarruda

from PIL import Image
import os
import numpy as np

def _square(img):
	w, h = img.size
	s = min(h, w)
	sh = (h - s)/2.0
	sw = (w - s)/2.0
	cropped_img = img.crop((sw, sh, sw+s, sh+s))