mock_solved.py
6c9541759d07master

mock_solved.py
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			"""
			EE-311
			======

			Lab 2: NumPy, Matplotlib and Scikit-learn (linear regression example)
			---------------------------------------------------------------------

			created by François Marelli on 03.03.2021
			"""


			def sum_bigger(array, thresh):
			"""
			Sum all elements of an array that are strictly bigger than a given threshold

			Parameters:
			array: ndarray (float), the array we want to process
			thresh: float, the threshold

			Returns:
			result: float, the sum of all elements in the input array that are strictly bigger than the threshold
			"""

			mask = array > thresh

			selected = array[mask]

			return selected.sum()


			def predict(model, data_X):
			"""
			Use a trained model to predict label values at specific given points

			Parameters:
			model: sklearn.linear_model.LinearRegression, the linear regression model object (already trained)
			data_X: ndarray of shape (N, 1) with N the number of samples, containing the points at which we want to predict labels

			Returns:
			data_out: ndarray of shape (N, ) containing the predicted values
			"""

			return model.predict(data_X)