def main():
data = dataset_block(load_cancer_dataset(), withOnes=False)
reg_const = optimize_regularization(data)
data_train, data_test = split(data)
theta = logistic.train(data_train, l=reg_const)
err = logistic.average_error(data_test, theta)
print("average error:%6.2f\n" % err)
print("regularization constant used: %6.2f\n" % reg_const)
def optimize_regularization(data):
reg_best, err_best = 0, 10
data_train, data_test = split(data)
for d in range(-40, 10):
reg_current = 2 ** d
theta = logistic.train(data_train, l=reg_current)
err_current = logistic.average_error(data_test, theta)
if err_best > err_current:
reg_best, err_best = reg_current, err_current
return reg_best
def main():
data = dataset_block(load_chips_dataset(), withOnes=False)
reg_const, p = optimize_regularization(data)
data_train, data_test = split(data)
theta = logistic.train(data_train, l=reg_const)
err = logistic.average_error(data_test, theta)
print("average error:%6.2f\n" % err)
print("regularization constant used: %16.10f\n" % reg_const)
print(" == 2 ** ", p)
def optimize_regularization(data):
rc_best, err_best = 0, 1000
data_train, data_test = split(data)
p_best = 0
for p in range(-40, 10):
# print_time()
print("2 ** ", p)
rc = 2**p
theta = logistic.train(data_train, l=rc)
err = logistic.average_error(data_test, theta)
if err_best > err:
rc_best, err_best = rc, err
p_best = p
return rc_best, p_best
def optimize_regularization(data):
rc_best, err_best = 0, 1000
data_train, data_test = split(data)
p_best = 0
for p in range(-40, 10):
# print_time()
print("2 ** ", p)
rc = 2 ** p
theta = logistic.train(data_train, l=rc)
err = logistic.average_error(data_test, theta)
if err_best > err:
rc_best, err_best = rc, err
p_best = p
return rc_best, p_best