def test_result(self):
a_matrix_1 = matrices.Matrix([[1, 2, 3], [4, 5, 6]])
a_matrix_2 = matrices.Matrix([[7, 8], [9, 10], [11, 12]])
bad_matrix = matrices.Matrix([[1, 1, 7], [2, 3, 4]])
result_list = [[58, 64], [139, 154]]
result_matrix = matrices.multiplication(a_matrix_1, a_matrix_2)
with self.assertRaises(matrices.NonValidMatricesForMultiplicationError):
matrices.multiplication(a_matrix_1, bad_matrix)
self.assertEqual(result_list, result_matrix.matrix)
def coeficents_estimation(regressand_vector_matrix_raw, regressor_matrix):
"""
Estimates the optimal coeficents in the linear regression model
using the method of least squares.
-
For more information:
http://en.wikipedia.org/wiki/
Linear_least_squares_%28mathematics%29#Computation
-
"""
regressand_matrix = regressand_vector_matrix_raw.transposed()
regressor_matrix = add_to_matrix_for_intercept(regressor_matrix)
transposed = regressor_matrix.transposed()
D = matrices.multiplication(transposed, regressor_matrix)
C = D.find_inverse()
F = matrices.multiplication(C, transposed)
K = matrices.multiplication(F, regressand_matrix)
return K.transpose().matrix
