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add tests for linear regression

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Julien RICHARD 2024-10-01 14:43:44 +02:00
parent 0177ae1cd5
commit a09a72816e
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1 changed files with 25 additions and 2 deletions
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machine_learning/linear_regression.py
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@ -40,7 +40,16 @@ def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):
:param alpha : Learning rate of the model :param alpha : Learning rate of the model
:param theta : Feature vector (weight's for our model) :param theta : Feature vector (weight's for our model)
;param return : Updated Feature's, using ;param return : Updated Feature's, using
curr_features - alpha_ * gradient(w.r.t. feature) curr_features - alpha_ * gradient(w.r.t. feature)
>>> data_x = np.array([[1, 2], [1, 3], [1, 4]])
>>> data_y = np.array([[2], [2], [2]])
>>> theta = np.array([[0.0, 0.0]])
>>> alpha = 0.01
>>> len_data = len(data_x)
>>> new_theta = run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta)
>>> new_theta.round(2)
array([[0.02, 0.06]])
""" """
n = len_data n = len_data
@ -58,6 +67,13 @@ def sum_of_square_error(data_x, data_y, len_data, theta):
:param len_data : len of the dataset :param len_data : len of the dataset
:param theta : contains the feature vector :param theta : contains the feature vector
:return : sum of square error computed from given feature's :return : sum of square error computed from given feature's
>>> data_x = np.array([[1, 2], [1, 3], [1, 4]])
>>> data_y = np.array([[2], [2], [2]])
>>> theta = np.array([[0.0, 0.0]])
>>> len_data = len(data_x)
>>> sum_of_square_error(data_x, data_y, len_data, theta).round(2)
2.0
""" """
prod = np.dot(theta, data_x.transpose()) prod = np.dot(theta, data_x.transpose())
prod -= data_y.transpose() prod -= data_y.transpose()
@ -89,10 +105,17 @@ def run_linear_regression(data_x, data_y):
def mean_absolute_error(predicted_y, original_y): def mean_absolute_error(predicted_y, original_y):
"""Return sum of square error for error calculation """
Return sum of square error for error calculation
:param predicted_y : contains the output of prediction (result vector) :param predicted_y : contains the output of prediction (result vector)
:param original_y : contains values of expected outcome :param original_y : contains values of expected outcome
:return : mean absolute error computed from given feature's :return : mean absolute error computed from given feature's
>>> mean_absolute_error([3.0, 2.0, 1.0], [2.5, 2.0, 1.0])
0.16666666666666666
>>> mean_absolute_error([5.0, 6.0], [5.0, 7.0])
0.5
""" """
total = sum(abs(y - predicted_y[i]) for i, y in enumerate(original_y)) total = sum(abs(y - predicted_y[i]) for i, y in enumerate(original_y))
return total / len(original_y) return total / len(original_y)