def entrenar():
cur = conn.cursor()
cur.execute("SELECT * FROM songs")
rv = cur.fetchall()
inputsTrain = np.empty([len(rv), 36])
outputsTrain = np.empty([len(rv), 4])
def eval_fitness(genomes):
for g in genomes:
net = nn.create_feed_forward_phenotype(g)
sum_square_error = 0.0
for inputs, expected in zip(inputsTrain, outputsTrain):
output = net.serial_activate(inputs)
sum_square_error += np.mean((output - expected) ** 2)
g.fitness = -sum_square_error
for i in range(0, len(rv)):
dict = common.loadDict(os.path.join(common.load("data_dir"), rv[i]["data"]))
arr = common.featureDictToArray(dict)
inputsTrain[i, :] = arr
temp = np.zeros(4)
temp[int(rv[i]["genre"]) - 1] = 1
outputsTrain[i, :] = temp
local_dir = os.path.dirname(__file__)
config_path = os.path.join(local_dir, "neuron.config")
pop = population.Population(config_path)
reporte = NEATReporter()
pop.add_reporter(reporte)
pop.run(eval_fitness, 500)
stats = pop.statistics
winner = pop.statistics.best_genome()
pickle.dump(winner, open(os.path.join(common.load("data_dir"), "redNeuronal.p"), "w"))
visualize.plot_stats(stats, filename="app/static/results/evolucion.svg")
visualize.plot_species(stats)
visualize.draw_net(winner, filename="app/static/results/redNeuronal.svg")
return winner
from app import app
from app import ALLOWED_EXTENSIONS
from app.dbconnect import conn
from app.audio.audioClass import Audio
from app.audio import feature
from app import common
from app.train import NEAT
from flask import render_template, request, redirect
from flask import url_for, send_from_directory
from werkzeug.utils import secure_filename
import os
import threading
cur = conn.cursor()
ANALYSIS_WINDOW = 512 # Ventana de analisis de 512 muestras
HOPSIZE = 256
TEXTURE_WINDOW = 86 # Nro de ventanas de analisis
NRO_TEXTURE_WINDOWS = 2584 # 30 segundos aprox.
def daemon():
NEAT.entrenar()
d = threading.Thread(target=daemon, name='Daemon')
@app.route('/verResultados', methods=['GET'])
def verRed():
return render_template('estadistica.html')