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fix: return the sum of squared errors #15272

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fix: return the sum of squared errors #15272

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13 changes: 5 additions & 8 deletions machine_learning/linear_regression.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,25 +67,22 @@ def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):
return theta


def sum_of_square_error(data_x, data_y, len_data, theta):
def sum_of_square_error(data_x, data_y, 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

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)
>>> round(sum_of_square_error(vc_x, vc_y, np.array([1])), 3)
np.float64(0.03)
"""
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
return np.sum(np.square(prod))


def run_linear_regression(data_x, data_y):
Expand All @@ -104,7 +101,7 @@ def run_linear_regression(data_x, data_y):

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)
error = sum_of_square_error(data_x, data_y, theta)
print(f"At Iteration {i + 1} - Error is {error:.5f}")

return theta
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