def __init__(self, regressand_vector_matrix, regressor_matrix):
self.regressand_vector_matrix = regressand_vector_matrix
self.regressor_matrix = regressor_matrix
f = linregression.add_to_matrix_for_intercept
# Add intercept for improved performance of the model
self.regressor_matrix_for_intecept = f(regressor_matrix)
self.coeficents = linregression.coeficents_estimation(
regressand_vector_matrix, regressor_matrix)
self.mean = statchar.aritmetic_mean(regressand_vector_matrix.matrix[0])
self.SStot = statchar.SS_with_mean(regressand_vector_matrix.matrix[0],
self.mean)
fi = []
for element in regressor_matrix.matrix:
fi.append(LinearRegression.predict(self,
matrices.Matrix([element])))
self.SSreg = statchar.SS_with_mean(fi, self.mean)
self.SSres = statchar.SS_abstract(fi,
regressand_vector_matrix.matrix[0])
self.R_squared = self.SSreg/self.SStot
n = len(regressand_vector_matrix.matrix[0])
p = len(regressor_matrix.matrix[0])
A = n - 1
B = n - p - 1
self.adjusted_R_squared = 1-(1-self.R_squared)*(A/B)
def test_results(self):
matrix_1 = matrices.Matrix([[1], [2], [3], [4], [5]])
matrix_2 = matrices.Matrix([[1, 2, 1.3, 3.75, 2.25]])
result = linregression.coeficents_estimation(matrix_2, matrix_1)
self.assertTrue(0.78 < result[0][0] and result[0][0] < 0.79)
self.assertTrue(0.42 < result[0][1] and result[0][1] < 0.43)
