def test_gradient_descent_3_parameters(self): """ Test based on Assignment 1 of the free online Stanford Machine Learning online course. """ dataset = loader.load(self.relative_to_base("datasets/ex1data2.txt"), has_ids=False, has_header=False, has_labels=True, delimiter=",") dataset.normalize_features() dataset.set_column("bias", pd.Series([1] * dataset.num_samples())) learning_rate = 1.0 iter = 50 initial_theta = pd.Series({0: 0, 1: 0, "bias": 0}) theta = optimize.gradient_descent(dataset, initial_theta, learning_rate, iterations=iter) assert_that( theta, equals_series( { 0: 110631.050279, 1: -6649.474271, "bias": 340412.659574 }, places=6))
def test_gradient_descent_2_parameters(self): """ Test based on Assignment 1 of the free online Stanford Machine Learning online course. For population = 35,000, we predict a profit of 4519.767868 For population = 70,000, we predict a profit of 45342.450129 Final cost: 4.483388 """ dataset = loader.load(self.relative_to_base("datasets/ex1data1.txt"), has_ids=False, has_header=False, has_labels=True, delimiter=",") dataset.set_column("bias", pd.Series([1] * dataset.num_samples())) learning_rate = 0.01 iter = 100 initial_theta = pd.Series({0: 0, "bias": 0}) theta = optimize.gradient_descent(dataset, initial_theta, learning_rate, iterations=iter) # assert_that(theta.tolist(), contains(-0.576556, 0.859582)) assert_that(theta, equals_series({0: 0.859582, "bias": -0.576556}, places=6))
def test_gradient_descent_2_parameters(self): """ Test based on Assignment 1 of the free online Stanford Machine Learning online course. For population = 35,000, we predict a profit of 4519.767868 For population = 70,000, we predict a profit of 45342.450129 Final cost: 4.483388 """ dataset = loader.load(self.relative_to_base("datasets/ex1data1.txt"), has_ids=False, has_header=False, has_labels=True, delimiter=",") dataset.set_column("bias", pd.Series([1] * dataset.num_samples())) learning_rate = 0.01 iter = 100 initial_theta = pd.Series({0: 0, "bias": 0}) theta = optimize.gradient_descent(dataset, initial_theta, learning_rate, iterations=iter) # assert_that(theta.tolist(), contains(-0.576556, 0.859582)) assert_that(theta, equals_series({ 0: 0.859582, "bias": -0.576556 }, places=6))
def test_gradient_descent_3_parameters(self): """ Test based on Assignment 1 of the free online Stanford Machine Learning online course. """ dataset = loader.load(self.relative_to_base("datasets/ex1data2.txt"), has_ids=False, has_header=False, has_labels=True, delimiter=",") dataset.normalize_features() dataset.set_column("bias", pd.Series([1] * dataset.num_samples())) learning_rate = 1.0 iter = 50 initial_theta = pd.Series({0: 0, 1: 0, "bias": 0}) theta = optimize.gradient_descent(dataset, initial_theta, learning_rate, iterations=iter) assert_that(theta, equals_series({0: 110631.050279, 1: -6649.474271, "bias": 340412.659574}, places=6))