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Extreme Learning Machine - Real Bases
Aluno: João Correia Costa
BreastCancer - ELM
A base de dados BreastCancer armazena informações sobre exames mamários para identificação precoce de câncer. A partir das imagens
obtidas, são extraídos 30 valores númericos que descrevem características relevantes para identificação do câncer, indicadas a seguir:
['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave
points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error',
'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst
perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal
dimension']
A proposta é: a partir da base de dados em análise, treinar um neurônio e uma ELM para identificar presença ou não de câncer, em uma
mama representada por um vetor de entrada com 30 dimensões. Para a ELM, utlizamos um modelo de aprendizado baseado na projeção
aleatória na camada intermediária, aplicando, em seguida, a pseudoinversa para minimização do erro. Para o Perceptron, aplicamos o
treinamento padrão PLA.
<seaborn.axisgrid.PairGrid at 0x1f67f28ed60>
A análise exploratória parcial dos dados demonstra que amostras Cancerígenas apresentam valores inferiores de 'worst concavity', 'worst
concave points', 'worst symmetry' e 'worst fractal dimension'. Por Tanto, essas são características relevantes na separação a ser
implementada.
Dados de Teste
Neurônios 5 10 30 50 100 300
Acurácias 0.67 +/- 0.08 0.72 +/- 0.08 0.81 +/- 0.02 0.84 +/- 0.07 0.87 +/- 0.04 0.87 +/- 0.02
Dados de Treinamento
Neurônios 5 10 30 50 100 300
Acurácias 0.66 +/- 0.08 0.72 +/- 0.09 0.82 +/- 0.08 0.85 +/- 0.06 0.89 +/- 0.03 0.93 +/- 0.01
A análise do desempenho da ELM foi baseiada na repetição do treinamento 500 vezes, calculando-se, por fim, a acurácia média e o desvio
padrão para p neurônios na camada intermediária. Conforme esperado, a medida que p aumenta, o desempenho aumenta
indefinitivamente nos dados de treinamento. Porém, um valor máximo de desempenho (0.88 +/- 0.03) nos dados de teste foi encontrado
para p=180 neurônios (Observar gráficos e tabela supramecionados). Para valores de p acima de 180 neurônios, verifica-se 'overfitting', e o
desempenho nos dados de teste cai progressivamente.
BreastCancer - Perceptron
Acurácia Treinamento: 
 0.9153768844221105 +/= 0.03560450064508073 
 
Acurácia de Teste: 
 0.87953216374269 +/= 0.04179994446858753 
O perceptron apresentou desempenho semelhante a ELM. Foi implementado treinamento 100 vezes para obter a acurácia e desvios
médios.
Statlog - ELM
A base de dados Statlog foi obtida do repositírio UCI Machine Learning Repository.
0 1 2 3 4 5 6 7 8 9 10 11 12 target target_names
0 0.854167 1.0 1.000000 0.339623 0.447489 0.0 1.0 0.290076 0.0 0.387097 0.5 1.000000 0.00 1.0 Disease
1 0.791667 0.0 0.666667 0.198113 1.000000 0.0 1.0 0.679389 0.0 0.258065 0.5 0.000000 1.00 0.0 Absence
2 0.583333 1.0 0.333333 0.283019 0.308219 0.0 0.0 0.534351 0.0 0.048387 0.0 0.000000 1.00 1.0 Disease
3 0.729167 1.0 1.000000 0.320755 0.312785 0.0 0.0 0.259542 1.0 0.032258 0.5 0.333333 1.00 0.0 Absence
4 0.937500 0.0 0.333333 0.245283 0.326484 0.0 1.0 0.381679 1.0 0.032258 0.0 0.333333 0.00 0.0 Absence
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
265 0.479167 1.0 0.666667 0.735849 0.166667 1.0 0.0 0.694656 0.0 0.080645 0.0 0.000000 1.00 0.0 Absence
266 0.312500 1.0 0.333333 0.245283 0.312785 0.0 0.0 0.778626 0.0 0.000000 0.0 0.000000 1.00 0.0 Absence
267 0.562500 0.0 0.333333 0.433962 0.383562 0.0 1.0 0.625954 0.0 0.209677 0.5 0.000000 0.00 0.0 Absence
268 0.583333 1.0 1.000000 0.433962 0.150685 0.0 0.0 0.587786 0.0 0.064516 0.5 0.000000 0.75 0.0 Absence
269 0.791667 1.0 1.000000 0.622642 0.365297 0.0 1.0 0.282443 1.0 0.241935 0.5 1.000000 0.00 1.0 Disease
270 rows × 15 columns
<seaborn.axisgrid.PairGrid at 0x17499a8e460>
A análise exploratória dos dados evidencia uma série de características que não definem uma separação dos dados. Essas características,
que não são significativamente correlacionadas a saída da rede neural implementada, poderiam ser descartadas. Porém, inserimos na rede
neural a base completa, normalizada, e obtivemos convergência.
[(0, 0.7986984126984127), (1, 0.8756825396825396), (2, 0.892068783068783), (3, 0.8968571428571428), (4, 0.90204
7619047619), (5, 0.9077089947089947), (6, 0.9253756613756614), (7, 0.9725767195767195)] 
[(0, 0.05558080409737831), (1, 0.022787305452563508), (2, 0.009595684236215036), (3, 0.010176989300066814), (4, 
0.010749936063032341), (5, 0.010606809262254182), (6, 0.01074909273537658), (7, 0.008649107780623026)] 
Dados de Teste
Neurônios 5 10 15 20 25 30 50 100
Acurácias 0.74 +/- 0.06 0.79 +/- 0.03 0.81 +/- 0.02 0.80 +/- 0.02 0.80 +/- 0.02 0.79 +/- 0.03 0.76 +/- 0.03 0.70 +/- 0.04
Dados de Treinamento
Neurônios 5 10 15 20 25 30 50 100
Acurácias 0.78 +/- 0.05 0.85 +/- 0.02 0.87 +/- 0.01 0.88 +/- 0.01 0.88 +/- 0.01 0.89 +/- 0.01 0.91 +/- 0.01 0.96 +/- 0.01
A análise do desempenho da ELM foi baseiada na repetição do treinamento 1000 vezes, calculando-se, por fim, a acurácia média e o
desvio padrão para p neurônios na camada intermediária. Conforme esperado, a medida que p aumenta, o desempenho aumenta
indefinitivamente nos dados de treinamento. Porém, um valor máximo de desempenho (0.81 +/- 0.02) nos dados de teste foi encontrado
para p=15 neurônios (Observar gráficos e tabela supramecionados). Para valores de p acima de 15 neurônios, verifica-se 'overfitting', e o
desempenho nos dados de teste cai progressivamente.
Statlog - Perceptron
Acurácia Treinamento: 
 0.8603703703703703 +/= 0.020414483724407455 
 
Acurácia de Teste: 
 0.737037037037037 +/= 0.033131580111333485 
O perceptron apresentou desempenho inferior a ELM, 0.74 +/- 0.03 contra 0.81 +/- 0.02. Foi implementado, no Perceptron, treinamento
repetido 100 vezes para obter a acurácia e desvios médios. O desempenho das duas redes poderia ser melhorado significativamente,
utlizando uma matriz de correlação para eleminar atributos não-correlacionados com a saída.
mean
radius
mean
texture
mean
perimeter mean area
mean
smoothness
mean
compactness
mean
concavity
mean
concave
points
mean
symmetry
mean
fractal
dimension
... wotextu
count 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 ... 569.0000
mean 14.127292 19.289649 91.969033 654.889104 0.096360 0.104341 0.088799 0.048919 0.181162 0.062798 ... 25.6772
std 3.524049 4.301036 24.298981 351.914129 0.014064 0.052813 0.079720 0.038803 0.027414 0.007060 ... 6.1462
min 6.981000 9.710000 43.790000 143.500000 0.052630 0.019380 0.000000 0.000000 0.106000 0.049960 ... 12.0200
25% 11.700000 16.170000 75.170000 420.300000 0.086370 0.064920 0.029560 0.020310 0.161900 0.057700 ... 21.0800
50% 13.370000 18.840000 86.240000 551.100000 0.095870 0.092630 0.061540 0.033500 0.179200 0.061540 ... 25.4100
75% 15.780000 21.800000 104.100000 782.700000 0.105300 0.130400 0.130700 0.074000 0.195700 0.066120 ... 29.7200
max 28.110000 39.280000 188.500000 2501.000000 0.163400 0.345400 0.426800 0.201200 0.304000 0.097440 ... 49.5400
8 rows × 31 columns
mean
radius
mean
texture
mean
perimeter
mean
area
mean
smoothness
mean
compactness
mean
concavity
mean
concave
points
mean
symmetry
mean
fractal
dimension
... worstperimeter
worst
area
wor
smoothne
0 17.99 10.38 122.80 1001.0 0.11840 0.27760 0.30010 0.14710 0.2419 0.07871 ... 184.60 2019.0 0.162
1 20.57 17.77 132.90 1326.0 0.08474 0.07864 0.08690 0.07017 0.1812 0.05667 ... 158.80 1956.0 0.123
2 19.69 21.25 130.00 1203.0 0.10960 0.15990 0.19740 0.12790 0.2069 0.05999 ... 152.50 1709.0 0.144
3 11.42 20.38 77.58 386.1 0.14250 0.28390 0.24140 0.10520 0.2597 0.09744 ... 98.87 567.70.209
4 20.29 14.34 135.10 1297.0 0.10030 0.13280 0.19800 0.10430 0.1809 0.05883 ... 152.20 1575.0 0.137
... ... ... ... ... ... ... ... ... ... ... ... ... ...
564 21.56 22.39 142.00 1479.0 0.11100 0.11590 0.24390 0.13890 0.1726 0.05623 ... 166.10 2027.0 0.141
565 20.13 28.25 131.20 1261.0 0.09780 0.10340 0.14400 0.09791 0.1752 0.05533 ... 155.00 1731.0 0.116
566 16.60 28.08 108.30 858.1 0.08455 0.10230 0.09251 0.05302 0.1590 0.05648 ... 126.70 1124.0 0.113
567 20.60 29.33 140.10 1265.0 0.11780 0.27700 0.35140 0.15200 0.2397 0.07016 ... 184.60 1821.0 0.165
568 7.76 24.54 47.92 181.0 0.05263 0.04362 0.00000 0.00000 0.1587 0.05884 ... 59.16 268.6 0.089
569 rows × 32 columns
In [5]: # Bibliotecas Padrão 
import numpy as np 
import pandas as pd 
from sklearn import datasets 
import seaborn as sbo 
import warnings 
import statistics 
import random 
from copy import deepcopy 
import matplotlib.pyplot as plt 
warnings.filterwarnings('ignore') 
In [6]: # Load dataset BreastCancer 
db = datasets.load_breast_cancer() 
In [7]: df = pd.DataFrame(data=db['data'], columns=db['feature_names']) 
df['target'] = db['target'] 
df['target_names'] = df['target'].map({0 : 'Not Cancer', 1: 'Cancer'}) 
In [8]: df.describe() 
Out[8]:
In [9]: df 
Out[9]:
In [12]: # Análise Exploratória Parcial 
sbo.pairplot(df.loc[:, 'worst concavity':'target_names'], hue='target_names') 
Out[12]:
In [10]: def make_X_Y_train_test(X, Y): 
 ones = np.ones((len(X), 1)) 
 X = np.concatenate((X, ones), axis=1) 
 
 indexs = [index for index in range(len(X))] 
 indexs_train = random.sample(indexs, int(0.7*len(indexs))) 
 indexs_test = [index for index in indexs if index not in indexs_train] 
 
 X_train = np.array( [X[index] for index in indexs_train] ) 
 X_test = np.array( [X[index] for index in indexs_test] ) 
 
 Y_train = np.array( [Y[index] for index in indexs_train] ).reshape(len(indexs_train), 1) 
 Y_test = np.array( [Y[index] for index in indexs_test] ).reshape(len(indexs_test), 1) 
 
 return X_train, Y_train, X_test, Y_test 
In [11]: # Algoritimo ELM 
class ELM: 
 def __int__(self): 
 self.Z = None 
 self.W = None 
 self.X = None 
 self.ws = None 
 self.zs = None 
 
 def sigmoide(self, x): 
 return 1/(1 + np.exp(-x)) 
 
 def train(self, X, Y, p, ndimension): 
 size = len(X) 
 # Inicializando o vetor Z 
 Z = np.random.random_sample( (ndimension + 1, p) ) - 0.5 
 
 U = np.dot(X, Z) 
 H = self.sigmoide(U) 
 
 pseudo_H = np.linalg.pinv(H) 
 self.W = np.dot(pseudo_H, Y) 
 self.Z = Z 
 self.X = X 
 
 return self.W 
 
 def train_times(self, Xtrain, Ytrain, Xtest, Ytest, p, ndimension, times): 
 self.ws = [] 
 self.zs = [] 
 self.acrs_train = [] 
 self.acrs_test = [] 
 
 for i in range(times): 
 w = self.train(Xtrain, Ytrain, p, ndimension) 
 self.ws.append(w) 
 self.zs.append(self.Z) 
 
 self.acrs_train.append(self.acuracia(X_train, Y_train)) 
 self.acrs_test.append(self.acuracia(X_test, Y_test)) 
 
 def train_for_p_neuronios(self, Xtrain, Ytrain, Xtest, Ytest, ps, ndimension, times): 
 self.medias_train, self.medias_test = [], [] 
 self.desvios_train, self.desvios_test = [], [] 
 self.erros_medios_train, self.erros_medios_test = [], [] 
 self.ps = ps 
 for p in ps: 
 self.train_times(Xtrain, Ytrain, X_test, Y_test, p, ndimension, times) 
 
 self.medias_train.append(statistics.mean(self.acrs_train)) 
 self.desvios_train.append(statistics.stdev(self.acrs_train)) 
 self.erros_medios_train.append(self.erro_medio(self.acrs_train)) 
 
 self.medias_test.append(statistics.mean(self.acrs_test)) 
 self.desvios_test.append(statistics.stdev(self.acrs_test)) 
 self.erros_medios_test.append(self.erro_medio(self.acrs_test)) 
 
 
 def classify(self, X): 
 H = self.sigmoide( np.dot(X, self.Z) ) 
 Y_aprox = np.dot(H, self.W) 
 
 Y_predict = [] 
 for y in Y_aprox: 
 if y >= 0.5: 
 Y_predict.append(1) 
 else: 
 Y_predict.append(0) 
 
 return np.array(Y_predict).reshape(len(X), 1) 
 
 def acuracia(self, X, Y): 
 Y_predict = self.classify(X) 
 erros = (Y - Y_predict)**2 
 e= 0 
 for el in erros: 
 if el[0] == 1: 
 e += 1 
 
 acuracia = (len(Y) - e)/len(Y) 
 
 return acuracia 
 
 def erro_medio(self, acrs): 
 mean = statistics.mean(acrs) 
 erros = abs(np.array(acrs) - mean) 
 
 return statistics.mean(erros) 
 
 
 
In [12]: # Inicializando os dados 
X_train, Y_train, X_test, Y_test = make_X_Y_train_test(db['data'], df['target']) 
In [15]: elm = ELM() 
elm.train_for_p_neuronios(X_train, Y_train, X_test, Y_test, [5, 10, 30, 50, 80, 100, 150, 300, 500, 700], ndime
In [20]: fig, (ax1, ax2) = plt.subplots(1, 2) 
ax1.errorbar(elm.ps, elm.medias_train, yerr = elm.erros_medios_train, ls = "None", color = (0, 0, 0.6, 0.2)) 
ax1.plot(elm.ps, elm.medias_train, marker = "h", color = "b") 
ax1.set_xlabel("P neurônios na camada escondida") 
ax1.set_ylabel("Acurácia Média") 
ax1.set_title("Acurácia média por P neurônios (Train)") 
 
ax2.errorbar(elm.ps, elm.medias_test, yerr = elm.erros_medios_test, ls = "None", color = (0.6, 0, 0, 0.2)) 
ax2.plot(elm.ps, elm.medias_test, marker = "h", color = "r") 
ax2.set_xlabel("P neurônios na camada escondida") 
ax2.set_ylabel("Acurácia Média") 
ax2.set_title("Acurácia média por P neurônios (Test)") 
 
fig.set_size_inches(10.5, 4.5, forward=True) 
fig.subplots_adjust(left=0.0, bottom=0.1, right=1.0, top=0.9, wspace=0.2, hspace=0.2) 
fig 
 
plt.show() 
In [40]: # PLA training 
class Perceptron: 
 def __init__(self): 
 self.epocas = [] 
 
 def predict(self, u): 
 if u >=0: 
 return 1 
 return 0 
 
 def acuracia(self, X, Y): 
 U = np.dot(X, self.w) 
 Y_predict = [] 
 for u in U: 
 if u >= 0: 
 Y_predict.append([1]) 
 else: 
 Y_predict.append([0]) 
 
 Y_predict = np.array(Y_predict).reshape(len(Y),1) 
 erros = 0 
 for y_predict, y in zip(Y_predict, Y): 
 if y_predict != y: 
 erros += 1 
 
 return ( len(Y)-erros )/len(Y) 
 
 
 def train(self, X, Y, eta, tol, max_epocas, ndimension): 
 N = len(X) 
 # Inicializando vetor de pesos W. 
 w = np.random.uniform(-0.5, 0.5, (ndimension+1 , 1)) 
 
 e = tol + 1.0 
 n_epocas = 0 
 while (e > tol) and (n_epocas < max_epocas): 
 e = 0 
 index = list(range(N)) 
 random.shuffle(index) 
 for i in index: 
 Y_predict = self.predict(np.dot(X[i], w)) 
 erro = ( Y[i] - Y_predict) 
 w = w + erro * eta * X[i].reshape(ndimension+1, 1) 
 e += erro[0]**2/N 
 
 self.epocas.append(e) 
 n_epocas += 1 
 self.w = w 
 return w 
 
 def train_times(self, Xtrain, Ytrain, Xtest, Ytest, eta, tol, max_epocas, ndimension, times): 
 self.ws = [] 
 acrs_train = [] 
 acrs_test = [] 
 
 for i in range(times): 
 w = self.train(Xtrain, Ytrain, eta, tol, max_epocas, ndimension) 
 self.ws.append(w) 
 
 acrs_train.append(self.acuracia(X_train, Y_train)) 
 acrs_test.append(self.acuracia(X_test, Y_test)) 
 
 self.media_train = statistics.mean(acrs_train) 
 self.desvio_train = statistics.stdev(acrs_train)self.media_test = statistics.mean(acrs_test) 
 self.desvio_test = statistics.stdev(acrs_test) 
 
 
 def view_erro(self): 
 epocas = list(range(len(self.epocas))) 
 plt.plot(epocas, self.epocas) 
 plt.xlabel("Épocas") 
 plt.ylabel("Erro") 
 plt.title("Erro Acumulado X Épocas") 
 plt.show() 
 
In [43]: # Inicializando dados no Perceptron 
pcp = Perceptron() 
pcp.train_times(X_train, Y_train, X_test, Y_test, eta=10**(-4), tol=0.01, max_epocas=800, ndimension=30, times=
pcp.view_erro() 
 
print("Acurácia Treinamento:") 
print(f"\t{pcp.media_train} +/= {pcp.desvio_train}") 
print() 
print("Acurácia de Teste:") 
print(f"\t{pcp.media_test} +/= {pcp.desvio_test}") 
In [45]: def normalize_features(X, min, max): 
 X_normalize = [] 
 for x in X: 
 x_normalize = (x-min)/(max-min) 
 X_normalize.append(x_normalize) 
 
 return X_normalize 
In [46]: with open("heart.dat") as file: 
 data = [] 
 for line in file.readlines(): 
 data.append(line.split()) 
 
data = np.array(data, 'float') 
 
min = np.min(data, axis=0) 
max = np.max(data, axis=0) 
X_normalize = normalize_features(data, min, max) 
 
X_normalize = np.delete(X_normalize, -1, 1) 
In [47]: feature_names = [col for col in '0 1 2 3 4 5 6 7 8 9 10 11 12'.split()] 
 
df = pd.DataFrame(X_normalize, columns= feature_names) 
target = np.array([X[-1] for X in data]).reshape(len(data), 1) 
 
df['target'] = target-1 
df['target_names'] = df['target'].map({0 : 'Absence', 1: 'Disease'}) 
df 
Out[47]:
In [95]: # Análise Exploratória 
sbo.pairplot(df.loc[:, '10':], hue='target_names') 
Out[95]:
In [48]: # Inicializando os dados 
X_train, Y_train, X_test, Y_test = make_X_Y_train_test(X_normalize, df['target']) 
In [49]: elm = ELM() 
elm.train_for_p_neuronios(X_train, Y_train, X_test, Y_test, [5, 10, 15, 20, 25, 30, 50, 100], ndimension=13, ti
In [51]: fig, (ax1, ax2) = plt.subplots(1, 2) 
ax1.errorbar(elm.ps, elm.medias_train, yerr = elm.erros_medios_train, ls = "None", color = (0, 0, 0.6, 0.2)) 
ax1.plot(elm.ps, elm.medias_train, marker = "h", color = "b") 
ax1.set_xlabel("P neurônios na camada escondida") 
ax1.set_ylabel("Acurácia Média") 
ax1.set_title("Acurácia média por P neurônios (Train)") 
 
ax2.errorbar(elm.ps, elm.medias_test, yerr = elm.erros_medios_test, ls = "None", color = (0.6, 0, 0, 0.2)) 
ax2.plot(elm.ps, elm.medias_test, marker = "h", color = "r") 
ax2.set_xlabel("P neurônios na camada escondida") 
ax2.set_ylabel("Acurácia Média") 
ax2.set_title("Acurácia média por P neurônios (Test)") 
 
fig.set_size_inches(10.5, 4.5, forward=True) 
fig.subplots_adjust(left=0.0, bottom=0.1, right=1.0, top=0.9, wspace=0.2, hspace=0.2) 
fig 
 
plt.show() 
In [52]: print (list(enumerate(elm.medias_train))) 
print(list(enumerate(elm.desvios_train))) 
In [93]: # Inicializando dados no Perceptron 
pcp = Perceptron() 
pcp.train_times(X_train, Y_train, X_test, Y_test, eta=10**(-4), tol=0.01, max_epocas=800, ndimension=13, times=
pcp.view_erro() 
 
print("Acurácia Treinamento:") 
print(f"\t{pcp.media_train} +/= {pcp.desvio_train}") 
print() 
print("Acurácia de Teste:") 
print(f"\t{pcp.media_test} +/= {pcp.desvio_test}") 
	Extreme Learning Machine - Real Bases
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 "<p style=\"text-align: justify\">A base de dados BreastCancer armazena informações sobre exames mamários para identificação precoce de câncer. A partir das imagens obtidas, são extraídos 30 valores númericos que descrevem características relevantes para identificação do câncer, indicadas a seguir:</p>"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "<p style=\"text-align: justify\">\n",
 "['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
 " 'mean smoothness', 'mean compactness', 'mean concavity',\n",
 " 'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
 " 'radius error', 'texture error', 'perimeter error', 'area error',\n",
 " 'smoothness error', 'compactness error', 'concavity error',\n",
 " 'concave points error', 'symmetry error',\n",
 " 'fractal dimension error', 'worst radius', 'worst texture',\n",
 " 'worst perimeter', 'worst area', 'worst smoothness',\n",
 " 'worst compactness', 'worst concavity', 'worst concave points',\n",
 " 'worst symmetry', 'worst fractal dimension']\n",
 "</p>"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "<p style=\"text-align: justify\">A proposta é: a partir da base de dados em análise, treinar um neurônio e uma ELM para identificar presença ou não de câncer, em uma mama representada por um vetor de entrada com 30 dimensões.\n",
 "Para a ELM, utlizamos um modelo de aprendizado baseado na projeção aleatória na camada intermediária, aplicando, em seguida, a pseudoinversa para minimização do erro. Para o Perceptron, aplicamos o treinamento padrão PLA.\n",
 "</p>"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 5,
 "metadata": {},
 "outputs": [],
 "source": [
 "# Bibliotecas Padrão\n",
 "import numpy as np\n",
 "import pandas as pd\n",
 "from sklearn import datasets\n",
 "import seaborn as sbo\n",
 "import warnings\n",
 "import statistics\n",
 "import random\n",
 "from copy import deepcopy\n",
 "import matplotlib.pyplot as plt\n",
 "warnings.filterwarnings('ignore')"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 6,
 "metadata": {},
 "outputs": [],
 "source": [
 "# Load dataset BreastCancer\n",
 "db = datasets.load_breast_cancer()"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 7,
 "metadata": {},
 "outputs": [],
 "source": [
 "df = pd.DataFrame(data=db['data'], columns=db['feature_names'])\n",
 "df['target'] = db['target']\n",
 "df['target_names'] = df['target'].map({0 : 'Not Cancer', 1: 'Cancer'})"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 8,
 "metadata": {},
 "outputs": [
 {
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 " <th>mean radius</th>\n",
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 " <td>14.127292</td>\n",
 " <td>19.289649</td>\n",
 " <td>91.969033</td>\n",
 " <td>654.889104</td>\n",
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 " <td>0.104341</td>\n",
 " <td>0.088799</td>\n",
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 " <td>0.062798</td>\n",
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 " <td>0.272188</td>\n",
 " <td>0.114606</td>\n",
 " <td>0.290076</td>\n",
 " <td>0.083946</td>\n",
 " <td>0.627417</td>\n",
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 " <td>0.014064</td>\n",
 " <td>0.052813</td>\n",
 " <td>0.079720</td>\n",
 " <td>0.038803</td>\n",
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 " <td>0.022832</td>\n",
 " <td>0.157336</td>\n",
 " <td>0.208624</td>\n",
 " <td>0.065732</td>\n",
 " <td>0.061867</td>\n",
 " <td>0.018061</td>\n",
 " <td>0.483918</td>\n",
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 " <th>min</th>\n",
 " <td>6.981000</td>\n",
 " <td>9.710000</td>\n",
 " <td>43.790000</td>\n",
 " <td>143.500000</td>\n",
 " <td>0.052630</td>\n",
 " <td>0.019380</td>\n",
 " <td>0.000000</td>\n",
 " <td>0.000000</td>\n",
 " <td>0.106000</td>\n",
 " <td>0.049960</td>\n",
 " <td>...</td>\n",
 " <td>12.020000</td>\n",
 " <td>50.410000</td>\n",
 " <td>185.200000</td>\n",
 " <td>0.071170</td>\n",
 " <td>0.027290</td>\n",
 " <td>0.000000</td>\n",
 " <td>0.000000</td>\n",
 " <td>0.156500</td>\n",
 " <td>0.055040</td>\n",
 " <td>0.000000</td>\n",
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 " <tr>\n",
 " <th>25%</th>\n",
 " <td>11.700000</td>\n",
 " <td>16.170000</td>\n",
 " <td>75.170000</td>\n",
 " <td>420.300000</td>\n",
 " <td>0.086370</td>\n",
 " <td>0.064920</td>\n",
 " <td>0.029560</td>\n",
 " <td>0.020310</td>\n",
 " <td>0.161900</td>\n",
 " <td>0.057700</td>\n",
 " <td>...</td>\n",
 " <td>21.080000</td>\n",
 " <td>84.110000</td>\n",
 " <td>515.300000</td>\n",
 " <td>0.116600</td>\n",
 " <td>0.147200</td>\n",
 " <td>0.114500</td>\n",
 " <td>0.064930</td>\n",
 " <td>0.250400</td>\n",
 " <td>0.071460</td>\n",
 " <td>0.000000</td>\n",
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 " <th>50%</th>\n",
 " <td>13.370000</td>\n",
 " <td>18.840000</td>\n",
 " <td>86.240000</td>\n",
 " <td>551.100000</td>\n",
 " <td>0.095870</td>\n",
 " <td>0.092630</td>\n",
 " <td>0.061540</td>\n",
 " <td>0.033500</td>\n",
 " <td>0.179200</td>\n",
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 " <td>15.780000</td>\n",
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 " <td>0.161400</td>\n",
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 " <td>0.345400</td>\n",
 " <td>0.426800</td>\n",
 " <td>0.201200</td>\n",
 " <td>0.304000</td>\n",
 " <td>0.097440</td>\n",
 " <td>...</td>\n",
 " <td>49.540000</td>\n",
 " <td>251.200000</td>\n",
 " <td>4254.000000</td>\n",
 " <td>0.222600</td>\n",
 " <td>1.058000</td>\n",
 " <td>1.252000</td>\n",
 " <td>0.291000</td>\n",
 " <td>0.663800</td>\n",
 " <td>0.207500</td>\n",
 " <td>1.000000</td>\n",
 " </tr>\n",
 " </tbody>\n",
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 "<p>8 rows × 31 columns</p>\n",
 "</div>"
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 "text/plain": [
 " mean radius mean texture mean perimeter mean area \\\n",
 "count 569.000000 569.000000 569.000000 569.000000 \n",
 "mean 14.127292 19.289649 91.969033 654.889104 \n",
 "std 3.524049 4.301036 24.298981 351.914129 \n",
 "min 6.981000 9.710000 43.790000 143.500000 \n",
 "25% 11.700000 16.170000 75.170000 420.300000 \n",
 "50% 13.370000 18.840000 86.240000 551.100000 \n",
 "75% 15.780000 21.800000 104.100000 782.700000 \n",
 "max 28.110000 39.280000 188.500000 2501.000000 \n",
 "\n",
 " mean smoothness mean compactness mean concavity mean concave points \\\n",
 "count 569.000000 569.000000 569.000000 569.000000 \n",
 "mean 0.096360 0.104341 0.088799 0.048919 \n",
 "std 0.014064 0.052813 0.079720 0.038803 \n",
 "min 0.052630 0.019380 0.000000 0.000000 \n",
 "25% 0.086370 0.0649200.029560 0.020310 \n",
 "50% 0.095870 0.092630 0.061540 0.033500 \n",
 "75% 0.105300 0.130400 0.130700 0.074000 \n",
 "max 0.163400 0.345400 0.426800 0.201200 \n",
 "\n",
 " mean symmetry mean fractal dimension ... worst texture \\\n",
 "count 569.000000 569.000000 ... 569.000000 \n",
 "mean 0.181162 0.062798 ... 25.677223 \n",
 "std 0.027414 0.007060 ... 6.146258 \n",
 "min 0.106000 0.049960 ... 12.020000 \n",
 "25% 0.161900 0.057700 ... 21.080000 \n",
 "50% 0.179200 0.061540 ... 25.410000 \n",
 "75% 0.195700 0.066120 ... 29.720000 \n",
 "max 0.304000 0.097440 ... 49.540000 \n",
 "\n",
 " worst perimeter worst area worst smoothness worst compactness \\\n",
 "count 569.000000 569.000000 569.000000 569.000000 \n",
 "mean 107.261213 880.583128 0.132369 0.254265 \n",
 "std 33.602542 569.356993 0.022832 0.157336 \n",
 "min 50.410000 185.200000 0.071170 0.027290 \n",
 "25% 84.110000 515.300000 0.116600 0.147200 \n",
 "50% 97.660000 686.500000 0.131300 0.211900 \n",
 "75% 125.400000 1084.000000 0.146000 0.339100 \n",
 "max 251.200000 4254.000000 0.222600 1.058000 \n",
 "\n",
 " worst concavity worst concave points worst symmetry \\\n",
 "count 569.000000 569.000000 569.000000 \n",
 "mean 0.272188 0.114606 0.290076 \n",
 "std 0.208624 0.065732 0.061867 \n",
 "min 0.000000 0.000000 0.156500 \n",
 "25% 0.114500 0.064930 0.250400 \n",
 "50% 0.226700 0.099930 0.282200 \n",
 "75% 0.382900 0.161400 0.317900 \n",
 "max 1.252000 0.291000 0.663800 \n",
 "\n",
 " worst fractal dimension target \n",
 "count 569.000000 569.000000 \n",
 "mean 0.083946 0.627417 \n",
 "std 0.018061 0.483918 \n",
 "min 0.055040 0.000000 \n",
 "25% 0.071460 0.000000 \n",
 "50% 0.080040 1.000000 \n",
 "75% 0.092080 1.000000 \n",
 "max 0.207500 1.000000 \n",
 "\n",
 "[8 rows x 31 columns]"
 ]
 },
 "execution_count": 8,
 "metadata": {},
 "output_type": "execute_result"
 }
 ],
 "source": [
 "df.describe()"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 9,
 "metadata": {},
 "outputs": [
 {
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 " <tr>\n",
 " <th>0</th>\n",
 " <td>17.99</td>\n",
 " <td>10.38</td>\n",
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 " <td>1001.0</td>\n",
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 " <td>0.12380</td>\n",
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 " <td>0.2416</td>\n",
 " <td>0.1860</td>\n",
 " <td>0.2750</td>\n",
 " <td>0.08902</td>\n",
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 " <td>0.12790</td>\n",
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 " <td>0.4504</td>\n",
 " <td>0.2430</td>\n",
 " <td>0.3613</td>\n",
 " <td>0.08758</td>\n",
 " <td>0</td>\n",
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 " </tr>\n",
 " <tr>\n",
 " <th>3</th>\n",
 " <td>11.42</td>\n",
 " <td>20.38</td>\n",
 " <td>77.58</td>\n",
 " <td>386.1</td>\n",
 " <td>0.14250</td>\n",
 " <td>0.28390</td>\n",
 " <td>0.24140</td>\n",
 " <td>0.10520</td>\n",
 " <td>0.2597</td>\n",
 " <td>0.09744</td>\n",
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 " <td>0.17300</td>\n",
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 " <tr>\n",
 " <th>4</th>\n",
 " <td>20.29</td>\n",
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 " <td>135.10</td>\n",
 " <td>1297.0</td>\n",
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 " <td>152.20</td>\n",
 " <td>1575.0</td>\n",
 " <td>0.13740</td>\n",
 " <td>0.20500</td>\n",
 " <td>0.4000</td>\n",
 " <td>0.1625</td>\n",
 " <td>0.2364</td>\n",
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 " <td>...</td>\n",
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 " <td>166.10</td>\n",
 " <td>2027.0</td>\n",
 " <td>0.14100</td>\n",
 " <td>0.21130</td>\n",
 " <td>0.4107</td>\n",
 " <td>0.2216</td>\n",
 " <td>0.2060</td>\n",
 " <td>0.07115</td>\n",
 " <td>0</td>\n",
 " <td>Not Cancer</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>565</th>\n",
 " <td>20.13</td>\n",
 " <td>28.25</td>\n",
 " <td>131.20</td>\n",
 " <td>1261.0</td>\n",
 " <td>0.09780</td>\n",
 " <td>0.10340</td>\n",
 " <td>0.14400</td>\n",
 " <td>0.09791</td>\n",
 " <td>0.1752</td>\n",
 " <td>0.05533</td>\n",
 " <td>...</td>\n",
 " <td>155.00</td>\n",
 " <td>1731.0</td>\n",
 " <td>0.11660</td>\n",
 " <td>0.19220</td>\n",
 " <td>0.3215</td>\n",
 " <td>0.1628</td>\n",
 " <td>0.2572</td>\n",
 " <td>0.06637</td>\n",
 " <td>0</td>\n",
 " <td>Not Cancer</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>566</th>\n",
 " <td>16.60</td>\n",
 " <td>28.08</td>\n",
 " <td>108.30</td>\n",
 " <td>858.1</td>\n",
 " <td>0.08455</td>\n",
 " <td>0.10230</td>\n",
 " <td>0.09251</td>\n",
 " <td>0.05302</td>\n",
 " <td>0.1590</td>\n",
 " <td>0.05648</td>\n",
 " <td>...</td>\n",
 " <td>126.70</td>\n",
 " <td>1124.0</td>\n",
 " <td>0.11390</td>\n",
 " <td>0.30940</td>\n",
 " <td>0.3403</td>\n",
 " <td>0.1418</td>\n",
 " <td>0.2218</td>\n",
 " <td>0.07820</td>\n",
 " <td>0</td>\n",
 " <td>Not Cancer</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>567</th>\n",
 " <td>20.60</td>\n",
 " <td>29.33</td>\n",
 " <td>140.10</td>\n",
 " <td>1265.0</td>\n",
 " <td>0.11780</td>\n",
 " <td>0.27700</td>\n",
 " <td>0.35140</td>\n",
 " <td>0.15200</td>\n",
 " <td>0.2397</td>\n",
 " <td>0.07016</td>\n",
 " <td>...</td>\n",
 " <td>184.60</td>\n",
 " <td>1821.0</td>\n",
 " <td>0.16500</td>\n",
 " <td>0.86810</td>\n",
 " <td>0.9387</td>\n",
 " <td>0.2650</td>\n",
 " <td>0.4087</td>\n",
 " <td>0.12400</td>\n",
 " <td>0</td>\n",
 " <td>Not Cancer</td>\n",
 " </tr>\n",
 " <tr>\n",
 " <th>568</th>\n",
 " <td>7.76</td>\n",
 " <td>24.54</td>\n",
 " <td>47.92</td>\n",
 " <td>181.0</td>\n",
 " <td>0.05263</td>\n",
 " <td>0.04362</td>\n",
 " <td>0.00000</td>\n",
 " <td>0.00000</td>\n",
 " <td>0.1587</td>\n",
 " <td>0.05884</td>\n",
 " <td>...</td>\n",
 " <td>59.16</td>\n",
 " <td>268.6</td>\n",
 " <td>0.08996</td>\n",
 " <td>0.06444</td>\n",
 " <td>0.0000</td>\n",
 " <td>0.0000</td>\n",
 " <td>0.2871</td>\n",
 " <td>0.07039</td>\n",
 " <td>1</td>\n",
 " <td>Cancer</td>\n",
 " </tr>\n",
 " </tbody>\n",
 "</table>\n",
 "<p>569 rows × 32 columns</p>\n",
 "</div>"
 ],
 "text/plain": [
 " mean radius mean texture mean perimeter mean area mean smoothness \\\n",
 "0 17.99 10.38 122.80 1001.0 0.11840 \n",
 "1 20.57 17.77 132.90 1326.0 0.08474 \n",
 "2 19.69 21.25 130.00 1203.0 0.10960 \n",
 "3 11.42 20.38 77.58 386.1 0.14250 \n",
 "4 20.29 14.34 135.10 1297.0 0.10030 \n",
 ".. ... ... ... ... ... \n",
 "564 21.56 22.39 142.00 1479.0 0.11100 \n",
 "565 20.13 28.25 131.20 1261.0 0.09780 \n",
 "566 16.60 28.08 108.30 858.1 0.08455 \n",
 "567 20.60 29.33 140.10 1265.0 0.11780 \n",
 "568 7.76 24.54 47.92 181.0 0.05263 \n",
 "\n",
 " mean compactness mean concavity mean concave points mean symmetry \\\n",
 "0 0.27760 0.30010 0.14710 0.2419 \n",
 "1 0.07864 0.08690 0.07017 0.1812 \n",
 "2 0.15990 0.19740 0.12790 0.2069 \n",
 "3 0.28390 0.24140 0.10520 0.2597 \n",
 "4 0.13280 0.19800 0.10430 0.1809 \n",
 ".. ... ... ... ... \n",
 "564 0.11590 0.24390 0.13890 0.1726 \n",
 "565 0.10340 0.14400 0.09791 0.1752 \n",
 "566 0.10230 0.09251 0.05302 0.1590 \n",
 "567 0.27700 0.35140 0.15200 0.2397 \n",
 "568 0.04362 0.00000 0.00000 0.1587 \n",
 "\n",
 " mean fractal dimension ... worst perimeter worst area \\\n",
 "0 0.07871 ... 184.60 2019.0 \n",
 "1 0.05667 ... 158.80 1956.0 \n",
 "2 0.05999 ...152.50 1709.0 \n",
 "3 0.09744 ... 98.87 567.7 \n",
 "4 0.05883 ... 152.20 1575.0 \n",
 ".. ... ... ... ... \n",
 "564 0.05623 ... 166.10 2027.0 \n",
 "565 0.05533 ... 155.00 1731.0 \n",
 "566 0.05648 ... 126.70 1124.0 \n",
 "567 0.07016 ... 184.60 1821.0 \n",
 "568 0.05884 ... 59.16 268.6 \n",
 "\n",
 " worst smoothness worst compactness worst concavity \\\n",
 "0 0.16220 0.66560 0.7119 \n",
 "1 0.12380 0.18660 0.2416 \n",
 "2 0.14440 0.42450 0.4504 \n",
 "3 0.20980 0.86630 0.6869 \n",
 "4 0.13740 0.20500 0.4000 \n",
 ".. ... ... ... \n",
 "564 0.14100 0.21130 0.4107 \n",
 "565 0.11660 0.19220 0.3215 \n",
 "566 0.11390 0.30940 0.3403 \n",
 "567 0.16500 0.86810 0.9387 \n",
 "568 0.08996 0.06444 0.0000 \n",
 "\n",
 " worst concave points worst symmetry worst fractal dimension target \\\n",
 "0 0.2654 0.4601 0.11890 0 \n",
 "1 0.1860 0.2750 0.08902 0 \n",
 "2 0.2430 0.3613 0.08758 0 \n",
 "3 0.2575 0.6638 0.17300 0 \n",
 "4 0.1625 0.2364 0.07678 0 \n",
 ".. ... ... ... ... \n",
 "564 0.2216 0.2060 0.07115 0 \n",
 "565 0.1628 0.2572 0.06637 0 \n",
 "566 0.1418 0.2218 0.07820 0 \n",
 "567 0.2650 0.4087 0.12400 0 \n",
 "568 0.0000 0.2871 0.07039 1 \n",
 "\n",
 " target_names \n",
 "0 Not Cancer \n",
 "1 Not Cancer \n",
 "2 Not Cancer \n",
 "3 Not Cancer \n",
 "4 Not Cancer \n",
 ".. ... \n",
 "564 Not Cancer \n",
 "565 Not Cancer \n",
 "566 Not Cancer \n",
 "567 Not Cancer \n",
 "568 Cancer \n",
 "\n",
 "[569 rows x 32 columns]"
 ]
 },
 "execution_count": 9,
 "metadata": {},
 "output_type": "execute_result"
 }
 ],
 "source": [
 "df"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 12,
 "metadata": {},
 "outputs": [
 {
 "data": {
 "text/plain": [
 "<seaborn.axisgrid.PairGrid at 0x1f67f28ed60>"
 ]
 },
 "execution_count": 12,
 "metadata": {},
 "output_type": "execute_result"
 },
 {
 "data": {
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\n","text/plain": [
 "<Figure size 991.625x900 with 30 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 }
 ],
 "source": [
 "# Análise Exploratória Parcial\n",
 "sbo.pairplot(df.loc[:, 'worst concavity':'target_names'], hue='target_names')"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "<p style=\"text-align: justify\">A análise exploratória parcial dos dados demonstra que amostras <i>Cancerígenas</i> apresentam valores inferiores de 'worst concavity', 'worst concave points', 'worst symmetry' e 'worst fractal dimension'. Por Tanto, essas são características relevantes na separação a ser implementada.</p>"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 10,
 "metadata": {},
 "outputs": [],
 "source": [
 "def make_X_Y_train_test(X, Y):\n",
 " ones = np.ones((len(X), 1))\n",
 " X = np.concatenate((X, ones), axis=1)\n",
 "\n",
 " indexs = [index for index in range(len(X))]\n",
 " indexs_train = random.sample(indexs, int(0.7*len(indexs)))\n",
 " indexs_test = [index for index in indexs if index not in indexs_train]\n",
 "\n",
 " X_train = np.array( [X[index] for index in indexs_train] )\n",
 " X_test = np.array( [X[index] for index in indexs_test] )\n",
 "\n",
 " Y_train = np.array( [Y[index] for index in indexs_train] ).reshape(len(indexs_train), 1)\n",
 " Y_test = np.array( [Y[index] for index in indexs_test] ).reshape(len(indexs_test), 1)\n",
 " \n",
 " return X_train, Y_train, X_test, Y_test"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 11,
 "metadata": {},
 "outputs": [],
 "source": [
 "# Algoritimo ELM\n",
 "class ELM:\n",
 " def __int__(self):\n",
 " self.Z = None\n",
 " self.W = None\n",
 " self.X = None\n",
 " self.ws = None\n",
 " self.zs = None\n",
 " \n",
 " def sigmoide(self, x):\n",
 " return 1/(1 + np.exp(-x))\n",
 " \n",
 " def train(self, X, Y, p, ndimension):\n",
 " size = len(X)\n",
 " # Inicializando o vetor Z\n",
 " Z = np.random.random_sample( (ndimension + 1, p) ) - 0.5\n",
 " \n",
 " U = np.dot(X, Z)\n",
 " H = self.sigmoide(U)\n",
 "\n",
 " pseudo_H = np.linalg.pinv(H)\n",
 " self.W = np.dot(pseudo_H, Y)\n",
 " self.Z = Z\n",
 " self.X = X\n",
 " \n",
 " return self.W\n",
 " \n",
 " def train_times(self, Xtrain, Ytrain, Xtest, Ytest, p, ndimension, times):\n",
 " self.ws = []\n",
 " self.zs = []\n",
 " self.acrs_train = []\n",
 " self.acrs_test = []\n",
 " \n",
 " for i in range(times):\n",
 " w = self.train(Xtrain, Ytrain, p, ndimension)\n",
 " self.ws.append(w)\n",
 " self.zs.append(self.Z)\n",
 " \n",
 " self.acrs_train.append(self.acuracia(X_train, Y_train))\n",
 " self.acrs_test.append(self.acuracia(X_test, Y_test))\n",
 " \n",
 " def train_for_p_neuronios(self, Xtrain, Ytrain, Xtest, Ytest, ps, ndimension, times):\n",
 " self.medias_train, self.medias_test = [], []\n",
 " self.desvios_train, self.desvios_test = [], []\n",
 " self.erros_medios_train, self.erros_medios_test = [], []\n",
 " self.ps = ps\n",
 " for p in ps:\n",
 " self.train_times(Xtrain, Ytrain, X_test, Y_test, p, ndimension, times)\n",
 " \n",
 " self.medias_train.append(statistics.mean(self.acrs_train))\n",
 " self.desvios_train.append(statistics.stdev(self.acrs_train))\n",
 " self.erros_medios_train.append(self.erro_medio(self.acrs_train))\n",
 " \n",
 " self.medias_test.append(statistics.mean(self.acrs_test))\n",
 " self.desvios_test.append(statistics.stdev(self.acrs_test))\n",
 " self.erros_medios_test.append(self.erro_medio(self.acrs_test))\n",
 " \n",
 " \n",
 " def classify(self, X):\n",
 " H = self.sigmoide( np.dot(X, self.Z) )\n",
 " Y_aprox = np.dot(H, self.W)\n",
 " \n",
 " Y_predict = []\n",
 " for y in Y_aprox:\n",
 " if y >= 0.5:\n",
 " Y_predict.append(1)\n",
 " else:\n",
 " Y_predict.append(0)\n",
 " \n",
 " return np.array(Y_predict).reshape(len(X), 1)\n",
 " \n",
 " def acuracia(self, X, Y):\n",
 " Y_predict = self.classify(X)\n",
 " erros = (Y - Y_predict)**2\n",
 " e= 0\n",
 " for el in erros:\n",
 " if el[0] == 1:\n",
 " e += 1\n",
 "\n",
 " acuracia = (len(Y) - e)/len(Y)\n",
 " \n",
 " return acuracia\n",
 " \n",
 " def erro_medio(self, acrs):\n",
 " mean = statistics.mean(acrs)\n",
 " erros = abs(np.array(acrs) - mean)\n",
 " \n",
 " return statistics.mean(erros)\n",
 " \n",
 " \n",
 " "
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 12,
 "metadata": {},
 "outputs": [],
 "source": [
 "# Inicializando os dados\n",
 "X_train, Y_train, X_test, Y_test = make_X_Y_train_test(db['data'], df['target'])"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 15,
 "metadata": {},
 "outputs": [],
 "source": [
 "elm = ELM()\n",
 "elm.train_for_p_neuronios(X_train, Y_train, X_test, Y_test, [5, 10, 30, 50, 80, 100, 150, 300, 500, 700], ndimension=30, times=500)"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 20,
 "metadata": {},
 "outputs": [
 {
 "data": {
 "image/png": 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\n","text/plain": [
 "<Figure size 756x324 with 2 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 }
 ],
 "source": [
 "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
 "ax1.errorbar(elm.ps, elm.medias_train, yerr = elm.erros_medios_train, ls = \"None\", color = (0, 0, 0.6, 0.2))\n",
 "ax1.plot(elm.ps, elm.medias_train, marker = \"h\", color = \"b\")\n",
 "ax1.set_xlabel(\"P neurônios na camada escondida\")\n",
 "ax1.set_ylabel(\"Acurácia Média\")\n",
 "ax1.set_title(\"Acurácia média por P neurônios (Train)\")\n",
 "\n",
 "ax2.errorbar(elm.ps, elm.medias_test, yerr = elm.erros_medios_test, ls = \"None\", color = (0.6, 0, 0, 0.2))\n",
 "ax2.plot(elm.ps, elm.medias_test, marker = \"h\", color = \"r\")\n",
 "ax2.set_xlabel(\"P neurônios na camada escondida\")\n",
 "ax2.set_ylabel(\"Acurácia Média\")\n",
 "ax2.set_title(\"Acurácia média por P neurônios (Test)\")\n",
 "\n",
 "fig.set_size_inches(10.5, 4.5, forward=True)\n",
 "fig.subplots_adjust(left=0.0, bottom=0.1, right=1.0, top=0.9, wspace=0.2, hspace=0.2)\n",
 "fig\n",
 "\n",
 "plt.show()"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "<body>\n",
 "<table class=\"table\">\n",
 " <thead class=\"thead-dark\">\n",
 " <tr>\n",
 " <th colspan=\"6\" style=\"text-align: center\">\n",
 " Dados de Teste\n",
 " </th>\n",
 " </tr>\n",
 " <tr>\n",
 " <th scope=\"col\">Neurônios</th>\n",
 " <th scope=\"col\">5</th>\n",
 " <th scope=\"col\">10</th>\n",
 " <th scope=\"col\">30</th>\n",
 " <th scope=\"col\">50</th>\n",
 " <th scope=\"col\">100</th>\n",
 " <th scope=\"col\">300</th>\n",
 " </tr>\n",
 " </thead>\n",
 " <tbody>\n",
 " <tr>\n",
 " <th scope=\"row\">Acurácias</th>\n",
 " <td>0.67 +/- 0.08</td>\n",
 " <td>0.72 +/- 0.08</td>\n",
 " <td>0.81 +/- 0.02</td>\n",
 " <td>0.84 +/- 0.07</td>\n",
 " <td>0.87 +/- 0.04</td>\n",
 " <td>0.87 +/- 0.02</td>\n",
 " </tr>\n",
 "</table>\n",
 " \n",
 " <table class=\"table\">\n",
 " <thead class=\"thead-dark\">\n",
 " <tr>\n",
 " <th colspan=\"6\" style=\"text-align: center\">\n",
 " Dados de Treinamento\n",
 " </th>\n",
 " </tr>\n",
 " <tr>\n",
 " <th scope=\"col\">Neurônios</th>\n",
 " <th scope=\"col\">5</th>\n",
 " <th scope=\"col\">10</th>\n",
 " <th scope=\"col\">30</th>\n",
 " <th scope=\"col\">50</th>\n",
 " <th scope=\"col\">100</th>\n",
 " <th scope=\"col\">300</th>\n",
 " </tr>\n",
 " </thead>\n",
 " <tbody>\n",
 " <tr>\n",
 " <th scope=\"row\">Acurácias</th>\n",
 " <td>0.66 +/- 0.08</td>\n",
 " <td>0.72 +/- 0.09</td>\n",
 " <td>0.82 +/- 0.08</td>\n",
 " <td>0.85 +/- 0.06</td>\n",
 " <td>0.89 +/- 0.03</td>\n",
 " <td>0.93 +/- 0.01</td>\n",
 " </tr>\n",
 "</table>\n",
 "</body>"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "<p style=\"text-align: justify\">\n",
 " A análise do desempenho da ELM foi baseiada na repetição do treinamento 500 vezes, calculando-se, por fim, a acurácia média e o desvio padrão para p neurônios na camada intermediária. Conforme esperado, a medida que p aumenta, o desempenho aumenta indefinitivamente nos dados de treinamento. Porém, um valor máximo de desempenho (0.88 +/- 0.03) nos dados de teste foi encontrado para p=180 neurônios (Observar gráficos e tabela supramecionados). Para valores de p acima de 180 neurônios, verifica-se 'overfitting', e o desempenho nos dados de teste cai progressivamente.\n",
 "</p>"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "<h5>BreastCancer - Perceptron</h5>"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 40,
 "metadata": {},
 "outputs": [],
 "source": [
 "# PLA training\n",
 "class Perceptron:\n",
 " def __init__(self):\n",
 " self.epocas = []\n",
 " \n",
 " def predict(self, u):\n",
 " if u >=0:\n",
 " return 1\n",
 " return 0\n",
 " \n",
 " def acuracia(self, X, Y):\n",
 " U = np.dot(X, self.w)\n",
 " Y_predict = []\n",
 " for u in U:\n",
 " if u >= 0:\n",
 " Y_predict.append([1])\n",
 " else:\n",
 " Y_predict.append([0]) \n",
 " \n",
 " Y_predict = np.array(Y_predict).reshape(len(Y),1)\n",
 " erros = 0\n",
 " for y_predict, y in zip(Y_predict, Y):\n",
 " if y_predict != y:\n",
 " erros += 1\n",
 " \n",
 " return ( len(Y)-erros )/len(Y)\n",
 " \n",
 " \n",
 " def train(self, X, Y, eta, tol, max_epocas, ndimension):\n",
 " N = len(X)\n",
 " # Inicializando vetor de pesos W.\n",
 " w = np.random.uniform(-0.5, 0.5, (ndimension+1 , 1))\n",
 "\n",
 " e = tol + 1.0\n",
 " n_epocas = 0\n",
 " while (e > tol) and (n_epocas < max_epocas):\n",
 " e = 0\n",
 " index = list(range(N))\n",
 " random.shuffle(index)\n",
 " for i in index:\n",
 " Y_predict = self.predict(np.dot(X[i], w))\n",
 " erro = ( Y[i] - Y_predict)\n",
 " w = w + erro * eta * X[i].reshape(ndimension+1, 1)\n",
 " e += erro[0]**2/N\n",
 " \n",
 " self.epocas.append(e)\n",
 " n_epocas += 1\n",
 " self.w = w\n",
 " return w\n",
 " \n",
 " def train_times(self, Xtrain, Ytrain, Xtest, Ytest, eta, tol, max_epocas, ndimension, times):\n",
 " self.ws = []\n",
 " acrs_train = []\n",
 " acrs_test = []\n",
 " \n",
 " for i in range(times):\n",
 " w = self.train(Xtrain, Ytrain, eta, tol, max_epocas, ndimension)\n",
 " self.ws.append(w)\n",
 " \n",
 " acrs_train.append(self.acuracia(X_train, Y_train))\n",
 " acrs_test.append(self.acuracia(X_test, Y_test))\n",
 " \n",
 " self.media_train = statistics.mean(acrs_train)\n",
 " self.desvio_train = statistics.stdev(acrs_train)\n",
 " self.media_test = statistics.mean(acrs_test)\n",
 " self.desvio_test = statistics.stdev(acrs_test)\n",
 " \n",
 " \n",
 " def view_erro(self):\n",
 " epocas = list(range(len(self.epocas)))\n",
 " plt.plot(epocas, self.epocas)\n",
 " plt.xlabel(\"Épocas\")\n",
 " plt.ylabel(\"Erro\")\n",
 " plt.title(\"Erro Acumulado X Épocas\")\n",
 " plt.show()\n",
 " "
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 43,
 "metadata": {},
 "outputs": [
 {
 "data": {
 "image/png": 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\n","text/plain": [
 "<Figure size 432x288 with 1 Axes>"
 ]
 },
 "metadata": {
 "needs_background": "light"
 },
 "output_type": "display_data"
 },
 {
 "name": "stdout",
 "output_type": "stream",
 "text": [
 "Acurácia Treinamento:\n",
 "\t0.9153768844221105 +/= 0.03560450064508073\n",
 "\n",
 "Acurácia de Teste:\n",
 "\t0.87953216374269 +/= 0.04179994446858753\n"
 ]
 }
 ],
 "source": [
 "# Inicializando dados no Perceptron\n",
 "pcp = Perceptron()\n",
 "pcp.train_times(X_train, Y_train, X_test, Y_test, eta=10**(-4), tol=0.01, max_epocas=800, ndimension=30, times=100)\n",
 "pcp.view_erro()\n",
 "\n",
 "print(\"Acurácia Treinamento:\")\n",
 "print(f\"\\t{pcp.media_train} +/= {pcp.desvio_train}\")\n",
 "print()\n",
 "print(\"Acurácia de Teste:\")\n",
 "print(f\"\\t{pcp.media_test} +/= {pcp.desvio_test}\")\n",
 "\n"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "<p style=\"text-align: justify\">\n",
 " O perceptron apresentou desempenho semelhante a ELM. Foi implementado treinamento 100 vezes para obter a acurácia e desvios médios.\n",
 "</p>"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "<h5>Statlog - ELM</h5>"
 ]
 },
 {
 "cell_type": "markdown",
 "metadata": {},
 "source": [
 "<p style=\"text-align: justify\">\n",
 " A base de dados Statlog foi obtida do repositírio <i>UCI Machine Learning Repository.</i>\n",
 "</p>"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 45,
 "metadata": {},
 "outputs": [],
 "source": [
 "def normalize_features(X, min, max):\n",
 " X_normalize = []\n",
 " for x in X:\n",
 " x_normalize = (x-min)/(max-min)\n",
 " X_normalize.append(x_normalize)\n",
 " \n",
 " return X_normalize"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 46,
 "metadata": {},
 "outputs": [],
 "source": [
 "with open(\"heart.dat\") as file:\n",
 " data = []\n",
 " for line in file.readlines():\n",
 " data.append(line.split())\n",
 "\n",
 "data = np.array(data, 'float')\n",
 "\n",
 "min = np.min(data, axis=0)\n",
 "max = np.max(data, axis=0)\n",
 "X_normalize = normalize_features(data, min, max)\n",
 "\n",
 "X_normalize = np.delete(X_normalize, -1, 1)"
 ]
 },
 {
 "cell_type": "code",
 "execution_count": 47,
 "metadata": {},
 "outputs": [
 {
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