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69 lines
2.3 KiB
Python
69 lines
2.3 KiB
Python
# Import folder where sorting algorithms
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import sys
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import unittest
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import numpy as np
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# For importing from different folders
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# OBS: This is supposed to be done with automated testing,
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# hence relative to folder we want to import from
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sys.path.append("ML/algorithms/linearregression")
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# If run from local:
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# sys.path.append('../../ML/algorithms/linearregression')
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from linear_regression_gradient_descent import LinearRegression
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class TestLinearRegression_GradientDescent(unittest.TestCase):
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def setUp(self):
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# test cases we want to run
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self.linearReg = LinearRegression()
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self.X1 = np.array([[0, 1, 2]])
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self.y1 = np.array([[1, 2, 3]])
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self.W1_correct = np.array([[1, 1]]).T
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self.X2 = np.array([[0, 1]])
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self.y2 = np.array([[1, 0]])
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self.W2_correct = np.array([[1, -1]]).T
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self.X3 = np.array([[1, 2, 3], [1, 2, 4]])
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self.y3 = np.array([[5, 10, 18]])
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self.W3_correct = np.array([[0, 2, 3]]).T
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self.X4 = np.array([[0, 0]])
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self.y4 = np.array([[0, 0]])
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self.W4_correct = np.array([[0, 0]]).T
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self.X5 = np.array([[0, 1, 2, 3, 4, 5]])
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self.y5 = np.array([[0, 0.99, 2.01, 2.99, 4.01, 4.99]])
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self.W5_correct = np.array([[0, 1]]).T
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def test_perfectpositiveslope(self):
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W = self.linearReg.main(self.X1, self.y1)
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boolean_array = np.isclose(W, self.W1_correct, atol=0.1)
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self.assertTrue(boolean_array.all())
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def test_perfectnegativeslope(self):
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W = self.linearReg.main(self.X2, self.y2)
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boolean_array = np.isclose(W, self.W2_correct, atol=0.1)
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self.assertTrue(boolean_array.all())
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def test_multipledimension(self):
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W = self.linearReg.main(self.X3, self.y3)
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boolean_array = np.isclose(W, self.W3_correct, atol=0.1)
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self.assertTrue(boolean_array.all())
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def test_zeros(self):
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W = self.linearReg.main(self.X4, self.y4)
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boolean_array = np.isclose(W, self.W4_correct, atol=0.1)
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self.assertTrue(boolean_array.all())
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def test_noisydata(self):
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W = self.linearReg.main(self.X5, self.y5)
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boolean_array = np.isclose(W, self.W5_correct, atol=0.1)
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self.assertTrue(boolean_array.all())
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if __name__ == "__main__":
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print("Running Linear Regression Normal Equation tests:")
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unittest.main()
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