Adriano Avelar EdsonAvelar

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EdsonAvelar / NeuralNetwork.lua

Created August 21, 2020 16:14 — forked from cassiozen/NeuralNetwork.lua

Lua Neural Network

	ACTIVATION_RESPONSE = 1




	NeuralNetwork = {

	transfer = function( x) return 1 / (1 + math.exp(-x / ACTIVATION_RESPONSE)) end --This is the Transfer function (in this case a sigmoid)

	}

EdsonAvelar / random_forest.py

Created May 22, 2019 00:34

	from sklearn.ensemble import RandomForestClassifier
	clf = RandomForestClassifier()
	clf.fit(X_train, y_train)
	y_pred = clf.predict(X_test)


	y_pred_probability = clf.predict_proba(X_test)[::,1]
	fpr, tpr, _ = metrics.roc_curve(y_test, y_pred_probability)
	auc = metrics.roc_auc_score(y_test, y_pred_probability)
	plt.plot(fpr,tpr,label="RandomForest, auc="+str(auc))

EdsonAvelar / decision_logistic.py

Created May 22, 2019 00:29

	from sklearn.tree import DecisionTreeClassifier
	clf = DecisionTreeClassifier()
	clf.fit(X_train, y_train)
	y_pred = clf.predict(X_test)


	y_pred_probability = clf.predict_proba(X_test)[::,1]
	fpr, tpr, _ = metrics.roc_curve(y_test, y_pred_probability)
	auc = metrics.roc_auc_score(y_test, y_pred_probability)
	plt.plot(fpr,tpr,label="DecisionTree, auc="+str(auc))

EdsonAvelar / auc_plot.py

Created May 22, 2019 00:22

	# Accuracy
	print("Accuracy", metrics.accuracy_score(y_test, y_pred))

	#AUC Curve
	y_pred_probability = clf.predict_proba(X_test)[::,1]
	fpr, tpr, _ = metrics.roc_curve(y_test, y_pred_probability)
	auc = metrics.roc_auc_score(y_test, y_pred_probability)
	plt.plot(fpr,tpr,label="data 1, auc="+str(auc))
	plt.legend(loc=4)
	plt.show()

EdsonAvelar / auc_model.py

Created May 22, 2019 00:11

	# Carregando Breast Cancer Dataset
	breast_cancer = load_breast_cancer()
	X = breast_cancer.data
	y = breast_cancer.target

	# Separando o Dataset
	X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.33, random_state=44)

	# Criando um modelo
	clf = LogisticRegression(penalty='l2', C=0.1)

EdsonAvelar / auc.py

Last active May 22, 2019 00:19

	from sklearn import metrics # Metricas para calcular accuracy score
	from sklearn.linear_model import LogisticRegression # Modelo utilizado
	from sklearn.model_selection import train_test_split # Separa dados de treinamento e teste
	from sklearn.datasets import load_breast_cancer # Carrega o dataset Breast Cancer
	import matplotlib.pyplot as plt # Plotagem de gráficos