# 1. contar cantidad atributos (input layer)
atts = X_train.shape[1]
# 2. contar cantidad de clases diferentes (output layer)
clss = len(np.unique(Y_train))
top = [atts, atts - 1, clss]
Y_pred = rna.fit(X_train, Y_train, X_test, Y_test, top, lr, act_f, epochs,
prt)
accuracy = rna.accuracy_score(Y_test, Y_pred)
return accuracy
dts = Datasets()
S = 2
dts.remove_data(S)
### ABALONE ###
x_abalone = dts.X_abalone
y_abalone = dts.Y_abalone
x_abalone_r = dts.X_rem_abalone
y_abalone_r = dts.Y_rem_abalone
X_train_abalone, Y_train_abalone, X_test_abalone, Y_test_abalone = separate(
0.3, x_abalone, y_abalone, S)
X_train_r_abalone, Y_train_r_abalone, X_test_r_abalone, Y_test_r_abalone = separate(
0.3, x_abalone_r, y_abalone_r, S)
print("\nAbalone original:")
print(
"NBG:",
nb_gaussiano(X_train_abalone, Y_train_abalone, X_test_abalone,
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 14 21:54:40 2020
@author: 4PF41LA_RS6
"""
from data import Datasets
import numpy as np
dts = Datasets()
#dts.remove_data(10)
dts.remove_data(1)
y_abalone = dts.Y_abalone
y_abalone_r = dts.Y_rem_abalone
print("Abalone:")
print(dts.data_info(y_abalone), dts.data_info(y_abalone_r), dts.reduce)
y_digits = dts.Y_digits
y_digits_r = dts.Y_rem_digits
print("\nDigits:")
print(dts.data_info(y_digits), dts.data_info(y_digits_r), dts.reduce)
y_cancer = dts.Y_cancer
y_cancer_r = dts.Y_rem_cancer
print("\nCancer:")
print(dts.data_info(y_cancer), dts.data_info(y_cancer_r), dts.reduce)
y_human = dts.Y_human
y_human_r = dts.Y_rem_human