pedrohbtp

from flask import Flask
import flask
import json
from flask import Response
app = Flask(__name__)

@app.route('/test',methods=['GET'])
def test():
    ''' 
    GET: Receives the request in /test route and returns a response containing {"response": "test"}

pedrohbtp

import torch
import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        # Defining 3 linear layers but NOT the way they should be connected

pedrohbtp

import torch.optim as optim
import torch.nn as nn
# instantiate your network that should be defined by you
net = Net()
# create your optimizer
optimizer = optim.SGD(net.parameters(), lr=0.01)
# define your criterion for optimization
criterion = nn.MSELoss()
# dat_set comes from somewhere
for data in data_set:

pedrohbtp

import torch
import pandas as pd
from torch.utils.data import Dataset, DataLoader

class ExampleDataset(Dataset):
    """Example Dataset"""

    def __init__(self, csv_file):
        """ 
        csv_file (string): Path to the csv file containing data.

pedrohbtp

import gym
from gym import spaces

class MyEnv(gym.Env):
    """Custom Environment"""
    metadata = {'render.modes': ['human']}
    
    def __init__(self):
        super(Snake, self).__init__()
        # necessary internal env id

pedrohbtp

# Author: Pedro Borges
import heapq
import sys

def knapsack(val, wt, total_w):
    elements = transform_to_list_dict(val, wt)
    open_list = []
    closed_list = []
    # set to mark the nodes already seen
    generated_nodes = set()

pedrohbtp

import heapq
def dijkstra(cost_matrix, destination_node):
    open_list = []
    closed_list = set()
    number_of_nodes = len(cost_matrix)
    # (cum_cost, (previous_node, current_node))
    heapq.heappush(open_list, (0, (0, 0)))
    while open_list:
        current_node = heapq.heappop(open_list)
        # if the current node not already expanded