From acb955cb8757d7ee32807a2a8a1275d805feb431 Mon Sep 17 00:00:00 2001 From: Anamaria Miranda Date: Wed, 6 Nov 2024 14:57:19 +0100 Subject: [PATCH] Added test to linear regression #9943 --- machine_learning/linear_regression.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/machine_learning/linear_regression.py b/machine_learning/linear_regression.py index 839a5366d..1d11e5a9c 100644 --- a/machine_learning/linear_regression.py +++ b/machine_learning/linear_regression.py @@ -41,6 +41,14 @@ def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta): :param theta : Feature vector (weight's for our model) ;param return : Updated Feature's, using curr_features - alpha_ * gradient(w.r.t. feature) + >>> import numpy as np + >>> data_x = np.array([[1, 2], [3, 4]]) + >>> data_y = np.array([5, 6]) + >>> len_data = len(data_x) + >>> alpha = 0.01 + >>> theta = np.array([0.1, 0.2]) + >>> run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta) + array([0.196, 0.343]) """ n = len_data @@ -58,6 +66,12 @@ def sum_of_square_error(data_x, data_y, len_data, theta): :param len_data : len of the dataset :param theta : contains the feature vector :return : sum of square error computed from given feature's + + Example: + >>> vc_x = np.array([[1.1], [2.1], [3.1]]) + >>> vc_y = np.array([1.2, 2.2, 3.2]) + >>> round(sum_of_square_error(vc_x, vc_y, 3, np.array([1])),3) + np.float64(0.005) """ prod = np.dot(theta, data_x.transpose()) prod -= data_y.transpose() @@ -93,6 +107,11 @@ def mean_absolute_error(predicted_y, original_y): :param predicted_y : contains the output of prediction (result vector) :param original_y : contains values of expected outcome :return : mean absolute error computed from given feature's + + >>> predicted_y = [3, -0.5, 2, 7] + >>> original_y = [2.5, 0.0, 2, 8] + >>> mean_absolute_error(predicted_y, original_y) + 0.5 """ total = sum(abs(y - predicted_y[i]) for i, y in enumerate(original_y)) return total / len(original_y) @@ -114,4 +133,7 @@ def main(): if __name__ == "__main__": + import doctest + + doctest.testmod() main()