def trial(in_sample, out_sample):
target_function = random_target_function()
training_set = random_set(in_sample, target_function)
weight, iterations = stochastic_logistic_regression(training_set)
testing_set = random_set(out_sample, target_function)
out_of_sample_error = error(cross_entrophy_error, testing_set, weight)
return weight, iterations, out_of_sample_error
def test_two(in_sample, out_sample):
target_function = random_target_function()
training_set = random_set(in_sample, target_function)
weight = linear_percepton(training_set.z, training_set.y)
in_error = weight_error(weight, training_set.z, training_set.y)
testing_set = random_set(out_sample, target_function)
out_error = weight_error(weight, testing_set.z, testing_set.y)
return in_error, out_error
def test_two(in_sample, out_sample):
target_function = random_target_function()
training_set = random_set(in_sample, target_function)
weight = linear_percepton(training_set.z, training_set.y)
in_error = weight_error(weight, training_set.z, training_set.y)
testing_set = random_set(out_sample, target_function)
out_error = weight_error(weight, testing_set.z, testing_set.y)
return in_error, out_error
def trial(in_sample, out_sample):
target_function = random_target_function()
training_set = random_set(in_sample, target_function)
initial_weight = np.zeros(len(training_set.x[0]))
weight, iterations = pla(training_set.z, training_set.y, initial_weight, True)
testing_set = random_set(out_sample, target_function)
out_error = weight_error(weight, testing_set.z, testing_set.y)
return out_error, iterations
def trial(in_sample, out_sample):
target_function = random_target_function()
training_set = random_set(in_sample, target_function)
initial_weight = np.zeros(len(training_set.x[0]))
weight, iterations = pla(training_set.z, training_set.y, initial_weight,
True)
testing_set = random_set(out_sample, target_function)
out_error = weight_error(weight, testing_set.z, testing_set.y)
return out_error, iterations
def myTrial(in_sample, out_sample):
target_function = random_target_function()
#w0 es agregado por default
training_set = random_set(in_sample, target_function)
initial_weight = np.zeros(len(training_set.x[0]))
weight, iterations = myOwnPlaImplementation(training_set.z, training_set.y,
initial_weight)
testing_set = random_set(out_sample, target_function)
out_error = weight_error(weight, testing_set.z, testing_set.y)
return out_error, iterations
def test_four(in_sample, out_sample):
training_set = random_set(in_sample, moved_circle)
noisy_indices = np.random.choice(in_sample, size=round(0.1 * in_sample), replace=False)
training_set.y[noisy_indices] *= -1
weight = linear_percepton(training_set.z, training_set.y)
in_error_no_transform = weight_error(weight, training_set.z, training_set.y)
training_set.z = second_order(training_set.z)
weight = linear_percepton(training_set.z, training_set.y)
in_error_transform = weight_error(weight, training_set.z, training_set.y)
testing_set = random_set(out_sample, moved_circle, second_order)
noisy_indices = np.random.choice(out_sample, size=round(0.1 * out_sample), replace=False)
testing_set.y[noisy_indices] *= -1
out_error_transform = weight_error(weight, testing_set.z, testing_set.y)
return in_error_no_transform, weight, out_error_transform
def trial(in_sample, out_of_sample):
target_function = random_target_function()
training_set = random_set(in_sample, target_function)
pla_weight = pla(training_set.z, training_set.y)
svm_weight = svm(training_set.z, training_set.y)
testing_set = random_set(out_of_sample, target_function)
pla_error, svm_error = [ weight_error(weight, testing_set.z, testing_set.y)
for weight in
[ pla_weight, svm_weight] ]
def helper(x):
if x > 0:
return 0
else:
return 1
difference = pla_error - svm_error
svm_better = helper(difference)
total_support_vectors = sum([ 1 for x in svm_weight if x >= 10*-3 ])
return svm_better, total_support_vectors
def test_four(in_sample, out_sample):
training_set = random_set(in_sample, moved_circle)
noisy_indices = np.random.choice(in_sample,
size=round(0.1 * in_sample),
replace=False)
training_set.y[noisy_indices] *= -1
weight = linear_percepton(training_set.z, training_set.y)
in_error_no_transform = weight_error(weight, training_set.z,
training_set.y)
training_set.z = second_order(training_set.z)
weight = linear_percepton(training_set.z, training_set.y)
in_error_transform = weight_error(weight, training_set.z, training_set.y)
testing_set = random_set(out_sample, moved_circle, second_order)
noisy_indices = np.random.choice(out_sample,
size=round(0.1 * out_sample),
replace=False)
testing_set.y[noisy_indices] *= -1
out_error_transform = weight_error(weight, testing_set.z, testing_set.y)
return in_error_no_transform, weight, out_error_transform
def trial(in_sample, out_of_sample):
target_function = random_target_function()
training_set = random_set(in_sample, target_function)
pla_weight = pla(training_set.z, training_set.y)
svm_weight = svm(training_set.z, training_set.y)
testing_set = random_set(out_of_sample, target_function)
pla_error, svm_error = [
weight_error(weight, testing_set.z, testing_set.y)
for weight in [pla_weight, svm_weight]
]
def helper(x):
if x > 0:
return 0
else:
return 1
difference = pla_error - svm_error
svm_better = helper(difference)
total_support_vectors = sum([1 for x in svm_weight if x >= 10 * -3])
return svm_better, total_support_vectors
def generate_data():
return random_set(100, f), random_set(100, f)
def test_three(in_sample):
target_function = random_target_function()
training_set = random_set(in_sample, target_function)
return pla(training_set.z, training_set.y, return_iterations=True)[1]
def generate_data():
return random_set(100, f), random_set(100, f)
def test_three(in_sample):
target_function = random_target_function()
training_set = random_set(in_sample, target_function)
return pla(training_set.z, training_set.y, return_iterations=True)[1]