def custo_reglog(theta, X, y):
theta = np.matrix(theta)
X = np.matrix(X)
y = np.matrix(y)
grad0 = np.multiply(-y, np.log(sigmoide(X * theta.T)))
grad1 = np.multiply((1 - y), np.log(1 - sigmoide(X * theta.T)))
return np.sum(grad0 - grad1) / (len(X))
def gd_reglog(theta, _lambda, X, y):
m = y.size
h = sigmoide(X.dot(theta.reshape(-1, 1)))
reg = (_lambda / m) * np.r_[[[0]], theta[1:].reshape(-1, 1)]
grad = (1 / m) * X.T.dot(h - y) + reg
return grad.flatten()
def cost_function_reg(theta, _lambda, X, y):
m = y.size
theta = theta.T
reg = (_lambda / (2 * m)) * np.square(theta[1:]).sum()
gx = sigmoide(X.dot(theta))
grad0 = np.log(gx).T.dot(y)
grad = np.log(1 - gx).T.dot(1 - y)
J = -(1 / m) * (grad0 + grad) + reg
return (J[0])
def gd_reglog(theta, X, y):
theta = np.matrix(theta)
X = np.matrix(X)
y = np.matrix(y)
parametros = int(theta.ravel().shape[1])
grad = np.zeros(parametros)
erro = sigmoide(X * theta.T) - y
for i in range(parametros):
term = np.multiply(erro, X[:, i])
grad[i] = np.sum(term) / len(X)
return grad
def predizer(theta, X):
probabilidade = sigmoide(X * theta.T)
return [1 if x >= 0.5 else 0 for x in probabilidade]
from Parte2.normalizacao import normalizar_caracteristica
if __name__ == '__main__':
warnings.filterwarnings("ignore")
print("=== Parte 3 ===")
print("== Visualizacao dos dados ==")
data, _, _ = plot_ex2data1.importarDados()
plot_ex2data1.plot(data)
print("=== Done ===")
print("== Sigmoide ==")
print("> sig 0")
s = sigmoide(0)
print(s)
print("> sig pos")
s = sigmoide(1000000)
print(s)
print("> sig neg")
s = sigmoide(-9999999999)
print(s)
print("> sig array")
s = sigmoide(np.array([11,2222222,-3,444,-55]))
print(s)
print("=== Done ===")
print("== Custo J ==")
_, X, y = plot_ex2data1.importarDados()
theta = np.array([0, 0, 0], ndmin=2)