def test_auto_encoder():
iris = DataSet(name="iris")
classes = ["setosa", "versicolor", "virginica"]
iris.classes_to_numbers(classes)
inputs = np.asarray(iris.examples)
model = auto_encoder_learner(inputs, 100)
print(inputs[0])
print(model.predict(inputs[:1]))
def test_autoencoder():
iris = DataSet(name='iris')
classes = ['setosa', 'versicolor', 'virginica']
iris.classes_to_numbers(classes)
inputs = np.asarray(iris.examples)
al = AutoencoderLearner(inputs, 100)
print(inputs[0])
print(al.predict(inputs[:1]))
def test_perceptron():
iris = DataSet(name="iris")
classes = ["setosa", "versicolor", "virginica"]
iris.classes_to_numbers(classes)
perceptron = perceptron_learner(iris, learning_rate=0.01, epochs=100)
tests = [([5, 3, 1, 0.1], 0), ([5, 3.5, 1, 0], 0), ([6, 3, 4, 1.1], 1),
([6, 2, 3.5, 1], 1), ([7.5, 4, 6, 2], 2), ([7, 3, 6, 2.5], 2)]
assert grade_learner(perceptron, tests) > 1 / 2
assert err_ratio(perceptron, iris) < 0.4
def test_perceptron():
iris = DataSet(name='iris')
classes = ['setosa', 'versicolor', 'virginica']
iris.classes_to_numbers(classes)
pl = PerceptronLearner(iris, learning_rate=0.01, epochs=100)
tests = [([5, 3, 1, 0.1], 0), ([5, 3.5, 1, 0], 0), ([6, 3, 4, 1.1], 1),
([6, 2, 3.5, 1], 1), ([7.5, 4, 6, 2], 2), ([7, 3, 6, 2.5], 2)]
assert grade_learner(pl, tests) > 1 / 2
assert err_ratio(pl, iris) < 0.4
def test_perceptron():
iris = DataSet(name='iris')
classes = ['setosa', 'versicolor', 'virginica']
iris.classes_to_numbers(classes)
pl_gd = PerceptronLearner(iris,
l_rate=0.01,
epochs=100,
optimizer=stochastic_gradient_descent)
pl_adam = PerceptronLearner(iris, l_rate=0.01, epochs=100, optimizer=adam)
tests = [([5, 3, 1, 0.1], 0), ([5, 3.5, 1, 0], 0), ([6, 3, 4, 1.1], 1),
([6, 2, 3.5, 1], 1), ([7.5, 4, 6, 2], 2), ([7, 3, 6, 2.5], 2)]
assert grade_learner(pl_gd, tests) > 1 / 2
assert err_ratio(pl_gd, iris) < 0.4
assert grade_learner(pl_adam, tests) > 1 / 2
assert err_ratio(pl_adam, iris) < 0.4
def test_perceptron():
iris = DataSet(name='iris')
classes = ['setosa', 'versicolor', 'virginica']
iris.classes_to_numbers(classes)
n_samples, n_features = len(iris.examples), iris.target
X, y = (np.array([x[:n_features] for x in iris.examples]),
np.array([x[n_features] for x in iris.examples]))
pl_gd = PerceptronLearner(iris, l_rate=0.01, epochs=100, optimizer=stochastic_gradient_descent).fit(X, y)
assert grade_learner(pl_gd, iris_tests) == 1
assert err_ratio(pl_gd, iris) < 0.2
pl_adam = PerceptronLearner(iris, l_rate=0.01, epochs=100, optimizer=adam).fit(X, y)
assert grade_learner(pl_adam, iris_tests) == 1
assert err_ratio(pl_adam, iris) < 0.2
def test_neural_net():
iris = DataSet(name='iris')
classes = ['setosa', 'versicolor', 'virginica']
iris.classes_to_numbers(classes)
n_samples, n_features = len(iris.examples), iris.target
X, y = (np.array([x[:n_features] for x in iris.examples]),
np.array([x[n_features] for x in iris.examples]))
nnl_gd = NeuralNetworkLearner(iris, [4], l_rate=0.15, epochs=100, optimizer=stochastic_gradient_descent).fit(X, y)
assert grade_learner(nnl_gd, iris_tests) > 0.7
assert err_ratio(nnl_gd, iris) < 0.15
nnl_adam = NeuralNetworkLearner(iris, [4], l_rate=0.001, epochs=200, optimizer=adam).fit(X, y)
assert grade_learner(nnl_adam, iris_tests) > 0.7
assert err_ratio(nnl_adam, iris) < 0.15
def test_neural_net():
iris = DataSet(name='iris')
classes = ['setosa', 'versicolor', 'virginica']
iris.classes_to_numbers(classes)
nnl_adam = NeuralNetLearner(iris, [4], learning_rate=0.001, epochs=200, optimizer=adam)
nnl_gd = NeuralNetLearner(iris, [4], learning_rate=0.15, epochs=100, optimizer=gradient_descent)
tests = [([5.0, 3.1, 0.9, 0.1], 0),
([5.1, 3.5, 1.0, 0.0], 0),
([4.9, 3.3, 1.1, 0.1], 0),
([6.0, 3.0, 4.0, 1.1], 1),
([6.1, 2.2, 3.5, 1.0], 1),
([5.9, 2.5, 3.3, 1.1], 1),
([7.5, 4.1, 6.2, 2.3], 2),
([7.3, 4.0, 6.1, 2.4], 2),
([7.0, 3.3, 6.1, 2.5], 2)]
assert grade_learner(nnl_adam, tests) >= 1 / 3
assert grade_learner(nnl_gd, tests) >= 1 / 3
assert err_ratio(nnl_adam, iris) < 0.21
assert err_ratio(nnl_gd, iris) < 0.21
