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Modified Linear Regression to work on OLS, fixes #8847 #11311
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Original file line number | Diff line number | Diff line change | ||||
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@@ -31,85 +31,32 @@ def collect_dataset(): | |||||
return dataset | ||||||
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def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta): | ||||||
"""Run steep gradient descent and updates the Feature vector accordingly_ | ||||||
:param data_x : contains the dataset | ||||||
:param data_y : contains the output associated with each data-entry | ||||||
:param len_data : length of the data_ | ||||||
:param alpha : Learning rate of the model | ||||||
:param theta : Feature vector (weight's for our model) | ||||||
;param return : Updated Feature's, using | ||||||
curr_features - alpha_ * gradient(w.r.t. feature) | ||||||
""" | ||||||
n = len_data | ||||||
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prod = np.dot(theta, data_x.transpose()) | ||||||
prod -= data_y.transpose() | ||||||
sum_grad = np.dot(prod, data_x) | ||||||
theta = theta - (alpha / n) * sum_grad | ||||||
return theta | ||||||
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def sum_of_square_error(data_x, data_y, len_data, theta): | ||||||
"""Return sum of square error for error calculation | ||||||
:param data_x : contains our dataset | ||||||
:param data_y : contains the output (result vector) | ||||||
:param len_data : len of the dataset | ||||||
:param theta : contains the feature vector | ||||||
:return : sum of square error computed from given feature's | ||||||
""" | ||||||
prod = np.dot(theta, data_x.transpose()) | ||||||
prod -= data_y.transpose() | ||||||
sum_elem = np.sum(np.square(prod)) | ||||||
error = sum_elem / (2 * len_data) | ||||||
return error | ||||||
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def run_linear_regression(data_x, data_y): | ||||||
"""Implement Linear regression over the dataset | ||||||
:param data_x : contains our dataset | ||||||
:param data_y : contains the output (result vector) | ||||||
def run_linear_regression_ols(data_x, data_y): | ||||||
"""Implement Linear regression using OLS over the dataset | ||||||
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Suggested change
Slight rewording for clarity |
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:param data_x : contains our dataset | ||||||
:param data_y : contains the output (result vector) | ||||||
:return : feature for line of best fit (Feature vector) | ||||||
""" | ||||||
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. The OLS regression function needs doctests—make sure you verify the outputs of your tests with a calculator that can do linear regression (e.g., Wolfram Alpha) |
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iterations = 100000 | ||||||
alpha = 0.0001550 | ||||||
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no_features = data_x.shape[1] | ||||||
len_data = data_x.shape[0] - 1 | ||||||
# Add a column of ones to data_x for the bias term | ||||||
data_x = np.c_[np.ones(data_x.shape[0]), data_x].astype(float) | ||||||
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theta = np.zeros((1, no_features)) | ||||||
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for i in range(iterations): | ||||||
theta = run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta) | ||||||
error = sum_of_square_error(data_x, data_y, len_data, theta) | ||||||
print(f"At Iteration {i + 1} - Error is {error:.5f}") | ||||||
# Use NumPy's built-in function to solve the linear regression problem | ||||||
theta = np.linalg.inv(data_x.T.dot(data_x)).dot(data_x.T).dot(data_y) | ||||||
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Suggested change
Instead of using |
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return theta | ||||||
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def mean_absolute_error(predicted_y, original_y): | ||||||
"""Return sum of square error for error calculation | ||||||
: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 | ||||||
""" | ||||||
total = sum(abs(y - predicted_y[i]) for i, y in enumerate(original_y)) | ||||||
return total / len(original_y) | ||||||
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def main(): | ||||||
"""Driver function""" | ||||||
data = collect_dataset() | ||||||
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len_data = data.shape[0] | ||||||
data_x = np.c_[np.ones(len_data), data[:, :-1]].astype(float) | ||||||
data_x = data[:, :-1].astype(float) | ||||||
data_y = data[:, -1].astype(float) | ||||||
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theta = run_linear_regression(data_x, data_y) | ||||||
len_result = theta.shape[1] | ||||||
print("Resultant Feature vector : ") | ||||||
for i in range(len_result): | ||||||
print(f"{theta[0, i]:.5f}") | ||||||
theta = run_linear_regression_ols(data_x, data_y) | ||||||
print("Resultant Feature vector (weights): ") | ||||||
theta_list = theta.tolist()[0] | ||||||
for i in range(len(theta_list)): | ||||||
print(f"{theta_list[i]:.5f}") | ||||||
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if __name__ == "__main__": | ||||||
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