Gustavo de Rosa gugarosa

## penalized_objective.py
import numpy as np

from opytimizer import Opytimizer
from opytimizer.functions import ConstrainedFunction
from opytimizer.optimizers.swarm import PSO
from opytimizer.spaces import SearchSpace

# Defines a customized objective function
def spfunc(x):
    # Just a mock-up objective to emulate the problem

## compare_sets_pokemons.py
import numpy as np
import pandas as pd
from pokemontcgsdk import Card, Set

# Sets to be searched
SETS = ['g1', 'sm1', 'sm2', 'sm4', 'sm6', 'sm7',
        'sm11', 'sm12', 'swsh1', 'swsh2', 'swsh3', 'swsh4']

# Instantiates an array of cards from all sets
cards_sets = np.zeros((894, len(SETS)))

## host_scripting.sh
#!/bin/bash

# User to enter the host
USER=username

# Script to run in host
# In this case, we will be dumping the process my memory usage order
SCRIPT="ps aux --sort -%mem"

# List of hosts

## plot_3d_function.m
% Function to be plotted
z = @(x,y) (sin(x) * sqrt(x)) * (sin(y) * sqrt(y));

% Plots the function
h = fsurf(z,[0 10]);

% Defines some attributes on its positioning and brightness
camlight(110,70)
brighten(0.7)
h.EdgeColor = 'none';

## optimize.py
from opytimizer import Opytimizer
from opytimizer.core.function import Function
from opytimizer.optimizers.abc import ABC
from opytimizer.spaces.search import SearchSpace
from sklearn.manifold import TSNE

import word2vec

# Loading word2vec word embeddings
w2v = word2vec.load_word_vectors()

## simulated_annealing.py
from math import pi, sin

import numpy as np


def function(x):
    """Fitness function.

    Args:
        x (float): Input value for fitness function.

## hill_climbing.py
from math import pi, sin

import numpy as np


def function(x):
    """Fitness function.

    Args:
        x (float): Input value for fitness function.

## aco.py
import numpy as np

import utils as u


class ACO:
    """An Ant-Colony Optimization implementation.

    """

## mnist_mosaic.m
% Check this repository to create your own MNIST Matlab's matrix
% https://github.com/daniel-e/mnist_octave/

% Loading MNIST matrix file
load('mnist.mat');

% Creating an empty array of 100 samples
A = zeros(28, 28, 1, 100, 'uint8');

% Looping through 100 random samples

## youtube_channel_scrapper.py
import time

import requests
from bs4 import BeautifulSoup

# Youtube channel URL
url = 'https://www.youtube.com/channel/UCTCykZFeSbgMuL2ZzhSyVzg'

# Sleeping time
sleep_time = 5
	import numpy as np

	from opytimizer import Opytimizer
	from opytimizer.functions import ConstrainedFunction
	from opytimizer.optimizers.swarm import PSO
	from opytimizer.spaces import SearchSpace

	# Defines a customized objective function
	def spfunc(x):
	# Just a mock-up objective to emulate the problem
	import numpy as np
	import pandas as pd
	from pokemontcgsdk import Card, Set

	# Sets to be searched
	SETS = ['g1', 'sm1', 'sm2', 'sm4', 'sm6', 'sm7',
	'sm11', 'sm12', 'swsh1', 'swsh2', 'swsh3', 'swsh4']

	# Instantiates an array of cards from all sets
	cards_sets = np.zeros((894, len(SETS)))
	#!/bin/bash

	# User to enter the host
	USER=username

	# Script to run in host
	# In this case, we will be dumping the process my memory usage order
	SCRIPT="ps aux --sort -%mem"

	# List of hosts
	% Function to be plotted
	z = @(x,y) (sin(x) * sqrt(x)) * (sin(y) * sqrt(y));

	% Plots the function
	h = fsurf(z,[0 10]);

	% Defines some attributes on its positioning and brightness
	camlight(110,70)
	brighten(0.7)
	h.EdgeColor = 'none';
	from opytimizer import Opytimizer
	from opytimizer.core.function import Function
	from opytimizer.optimizers.abc import ABC
	from opytimizer.spaces.search import SearchSpace
	from sklearn.manifold import TSNE

	import word2vec

	# Loading word2vec word embeddings
	w2v = word2vec.load_word_vectors()
	from math import pi, sin

	import numpy as np


	def function(x):
	"""Fitness function.

	Args:
	x (float): Input value for fitness function.
	import numpy as np

	import utils as u


	class ACO:
	"""An Ant-Colony Optimization implementation.

	"""
	% Check this repository to create your own MNIST Matlab's matrix
	% https://github.com/daniel-e/mnist_octave/

	% Loading MNIST matrix file
	load('mnist.mat');

	% Creating an empty array of 100 samples
	A = zeros(28, 28, 1, 100, 'uint8');

	% Looping through 100 random samples
	import time

	import requests
	from bs4 import BeautifulSoup

	# Youtube channel URL
	url = 'https://www.youtube.com/channel/UCTCykZFeSbgMuL2ZzhSyVzg'

	# Sleeping time
	sleep_time = 5