Python weight_error Examples

Example #1

File: hw2.py Project: zhiyanfoo/caltech-machine-learning

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

Example #2

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

Example #3

File: hw2.py Project: zhiyanfoo/caltech-machine-learning

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

Example #4

File: hw6.py Project: zhiyanfoo/caltech-machine-learning

def test1(training_data, testing_data):
    training_set = DataML(training_data, transform)
    weight = linear_percepton(training_set.z, training_set.y)
    testing_set = DataML(testing_data, transform)
    in_error, out_error = [ weight_error(weight, data_set.z, data_set.y)
            for data_set in [training_set, testing_set] ]
    return in_error, out_error

Example #5

File: hw1.py Project: zhiyanfoo/caltech-machine-learning

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

Example #6

def best_model(model_weights, testing_set):
    errors = [
        weight_error(weight, testing_set.z[:, :len(weight)], testing_set.y)
        for weight in model_weights
    ]
    return len(
        model_weights[np.argmin(errors)]
    ) - 1, errors  # return k value that yields least error. see k_values

Example #7

File: HW1-7.py Project: gabrielcamr9/LearningFromData-Solutions

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

Example #8

def trial(training_data, testing_data, a):
    training_set = DataML(training_data, transform)
    weights = minimize_error_aug(training_set.z, training_set.y, a)
    in_error, out_error = [
        weight_error(weights, tset.z, tset.y)
        for tset in [training_set,
                     DataML(testing_data, transform)]
    ]
    return in_error, out_error

Example #9

def test1(training_data, testing_data):
    training_set = DataML(training_data, transform)
    weight = linear_percepton(training_set.z, training_set.y)
    testing_set = DataML(testing_data, transform)
    in_error, out_error = [
        weight_error(weight, data_set.z, data_set.y)
        for data_set in [training_set, testing_set]
    ]
    return in_error, out_error

Example #10

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

Example #11

File: HW1-7.py Project: gabrielcamr9/LearningFromData-Solutions

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

Example #12

File: hw7.py Project: zhiyanfoo/caltech-machine-learning

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

Example #13

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

Example #14

def train_test(training_set, testing_set, learn, learn_args=[]):
    weight = learn(training_set.z, training_set.y, *learn_args)
    in_sample_error = weight_error(weight, training_set.z, training_set.y)
    out_of_sample_error = weight_error(weight, testing_set.z, testing_set.y)
    return [in_sample_error, out_of_sample_error]

Example #15

File: hw7.py Project: zhiyanfoo/caltech-machine-learning

def best_model(model_weights, testing_set):
    errors =  [ weight_error(
                    weight, testing_set.z[:,:len(weight)], testing_set.y)
        for weight in model_weights ]
    return len(model_weights[np.argmin(errors)]) - 1, errors # return k value that yields least error. see k_values

Example #16

File: hw9.py Project: zhiyanfoo/caltech-machine-learning

def train_test(training_set, testing_set, learn, learn_args=[]):
    weight = learn(training_set.z, training_set.y, *learn_args)
    in_sample_error = weight_error(weight, training_set.z, training_set.y)
    out_of_sample_error = weight_error(weight, testing_set.z, testing_set.y)
    return [ in_sample_error, out_of_sample_error ]

Example #17

File: hw6.py Project: zhiyanfoo/caltech-machine-learning

def trial(training_data, testing_data, a):
    training_set = DataML(training_data, transform)
    weights = minimize_error_aug(training_set.z, training_set.y, a)
    in_error, out_error = [ weight_error(weights, tset.z, tset.y)
        for tset in [training_set, DataML(testing_data, transform)] ]
    return in_error, out_error