# Plot #
"""pca = PCA(n_components=2)
pca.fit(X)
X = pca.transform(X)
S = [(np.array([1]+X[i].tolist()),Y[i]) for i in xrange(len(X))]
print S"""
########
########
# Aprender vector theta #
rho = 0.5
nu = 0.5
l = 1
theta1 = MLPLearning.back_propagation_batch(S,rho,units_by_layer,factivation,850,50)
theta2 = MLPLearning.back_propagation_online(S,rho,units_by_layer,factivation,850,50)
theta3 = MLPLearning.back_propagation_batch_momentum(S,rho,nu,units_by_layer,factivation,850,50)
theta4 = MLPLearning.back_propagation_batch_buffer(S,rho,l,units_by_layer,factivation,850,50)
theta5 = MLPLearning.back_propagation_online_buffer(S,rho,l,units_by_layer,factivation,850,50)
theta6 = MLPLearning.back_propagation_online_momentum(S,rho,nu,units_by_layer,factivation,850,50)
theta7,fitness = MLPLearning.evolutional(S,units_by_layer,factivation,200,500,-2,2,1.1,0.9)
##############################
# Clasificacion #
logging.info("Clase con theta1: (Backprop batch): "+str(Decision.classify(units_by_layer,[1.0,-6.3,1.0],theta1,factivation)))
logging.info("Clase con theta2: (Backprop online): "+str(Decision.classify(units_by_layer,[1.0,-6.3,1.0],theta2,factivation)))
logging.info("Clase con theta3: (Backprop batch con momentum): "+str(Decision.classify(units_by_layer,[1.0,-6.3,1.0],theta3,factivation)))
logging.info("Clase con theta4: (Backprop batch con amortiguamiento): "+str(Decision.classify(units_by_layer,[1.0,-6.3,1.0],theta4,factivation)))
logging.info("Clase con theta5: (Backprop online con amortiguamiento): "+str(Decision.classify(units_by_layer,[1.0,-6.3,1.0],theta5,factivation)))
logging.info("Clase con theta6: (Backprop online con momentum): "+str(Decision.classify(units_by_layer,[1.0,-6.3,1.0],theta6,factivation)))
def classify(units_by_layer, xk, theta, factivation):
phi, s = MLPLearning.forward_propagation(units_by_layer, xk, theta,
factivation)
return s[-1].index(max(s[-1]))
# Plot #
"""pca = PCA(n_components=2)
pca.fit(X)
X = pca.transform(X)
S = [(np.array([1]+X[i].tolist()),Y[i]) for i in xrange(len(X))]
print S"""
########
########
# Aprender vector theta #
rho = 0.5
nu = 0.5
l = 1
theta1 = MLPLearning.back_propagation_batch(S, rho, units_by_layer,
factivation, 850, 50)
theta2 = MLPLearning.back_propagation_online(S, rho, units_by_layer,
factivation, 850, 50)
theta3 = MLPLearning.back_propagation_batch_momentum(
S, rho, nu, units_by_layer, factivation, 850, 50)
theta4 = MLPLearning.back_propagation_batch_buffer(S, rho, l,
units_by_layer,
factivation, 850, 50)
theta5 = MLPLearning.back_propagation_online_buffer(
S, rho, l, units_by_layer, factivation, 850, 50)
theta6 = MLPLearning.back_propagation_online_momentum(
S, rho, nu, units_by_layer, factivation, 850, 50)
theta7, fitness = MLPLearning.evolutional(S, units_by_layer, factivation,
200, 500, -2, 2, 1.1, 0.9)
##############################
def regression(units_by_layer, xk, theta, factivation):
phi, s = MLPLearning.forward_propagation(units_by_layer, xk, theta,
factivation)
return s[-1]
def get_output_vector(units_by_layer, xk, theta, factivation):
phi, s = MLPLearning.forward_propagation(units_by_layer, xk, theta,
factivation)
return s[len(units_by_layer) - 1]
def get_output_vector(units_by_layer,xk,theta,factivation): phi,s = MLPLearning.forward_propagation(units_by_layer,xk,theta,factivation) return s[len(units_by_layer)-1]
def classify(units_by_layer,xk,theta,factivation): phi,s = MLPLearning.forward_propagation(units_by_layer,xk,theta,factivation) return s[-1].index(max(s[-1]))
def regression(units_by_layer,xk,theta,factivation): phi,s = MLPLearning.forward_propagation(units_by_layer,xk,theta,factivation) return s[-1]