def main():
config_file = './data/configs/network.txt'
initial_weights_file = './data/configs/initial_weights.txt'
dataset_file = './data/datasets/wine.txt'
fileUtils = FileUtils(dataset_file=dataset_file, config_file=config_file)
dataset = fileUtils.getDataset()
#normalized_dataset = normalizeDataset(dataset)
neurons_per_layer = [1, 2, 1]
network = NeuralNetwork(config_file=config_file,
dataset=dataset,
initial_weights_file=initial_weights_file,
neurons_per_layer=neurons_per_layer)
network.backpropagation()
def __init__(self):
self.file_utils = FileUtils()
self.tempo_algoritmos = TempoAlgoritmos()
self._definir_o_estilo_de_plot()
self._gerar_sub_plot_de_cada_quantidade_de_numeros()
from flask import Flask, Blueprint, render_template, json, request
from utils import FileUtils
import os, time, json
bionic = Blueprint('bionic', __name__, template_folder='templates')
fu = FileUtils()
onto_names = {}
with open("static/data/ontologyDescriptions.json") as f:
ontoD = json.load(f)["results"]["bindings"]
for k in ontoD:
onto_names[k["acr"]["value"]] = k["name"]["value"]
def convert_bytes(num):
for x in ['bytes', 'KB', 'MB', 'GB', 'TB']:
if num < 1024.0:
return "%3.1f %s" % (num, x)
num /= 1024.0
def get_files_all_tsv(folder, dtype="stats"):
fileset = fu.get_reqd_fileset(folder + "tsv/", lambda x: False
if "tsv" in x else True)
ap = []
#print fileset
for k in sorted(fileset):
#t = time.ctime(os.path.getmtime(folder + k))
s = convert_bytes(os.stat(folder + "tsv/" + k).st_size)
rdf_file = folder + "rdf/" + k.split(".")[
0] + ".hdt" if dtype == "stats" else folder + "rdf/" + k.split(
# This is a sample Python script.
# Press Shift+F10 to execute it or replace it with your code.
# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
import os
import datetime
from logging import Logger
from file_processing import FileProcessor
from model_operations import Modeller
from utils import FileUtils, LoggerUtil
from sklearn.metrics import confusion_matrix, classification_report
data_path = "data"
save_model_path = "saved_models"
session_id = str(datetime.datetime.now().timestamp())
utils = FileUtils()
logger = LoggerUtil(session_id, 'files')
def prepare_dataset(dir_path, is_train=True):
processor = FileProcessor(dir_path, is_train)
processor.process_all_files()
return processor.get_dataset()
def prepare_model(dataset):
modeller = Modeller(dataset)
modeller.define_model()
modeller.train_model()
return modeller
from utils import FileUtils, HttpUtils # instantiating Objects of FileUtils file_utils = FileUtils() # instantiating Objects of HttpUtils http_utils = HttpUtils()
