def fit(self, data:[[float]], y:[float]) -> ([{}], [float], [float]):
logging.info("log: Linear Regression Model Fit Invoked.")
self.__validate(data, y)
self.__variables__ = len(data)
merge_data = [[None for x in range(len(y))] for x in range(len(data)+1)]
for i in range(len(merge_data)):
if i == 0:
merge_data[i] = y
else:
merge_data[i] = data[i-1]
self.__corr_mat__ = Statistics.correlation_matrix(merge_data)
self.__partial_mat__ = Statistics.partial_correlation_matrix(merge_data)
self.__stats__ = self.__stats(data, y)
m1, m2 = self.__regression_matrix(data, y)
m1i = Matrix.inverse(m1)
m1it = Matrix.transpose(m1i)
m2t = Matrix.transpose([m2])
params = Matrix.multiply(m1it, m2t)
params = Matrix.transpose(params)
params = params[0]
self.__params__ = params
self.__ycap__ = self.predicts(data)
self.__model_stats__ = self.__model_stats(y, self.__ycap__)
return (self.__stats__, self.__params__, self.__ycap__)
def test_correlation_matrix_4data(self):
x1 = [75, 83, 85, 85, 92, 97, 99]
x2 = [1.85, 1.25, 1.5, 1.75, 1.15, 1.75, 1.6]
x3 = [16, 20, 25, 27, 32, 48, 48]
x4 = [14, 21, 29, 16, 21, 47, 47]
mat = Statistics.correlation_matrix([x1, x2, x3, x4])
actual = [[
1.0, -0.15534261150561485, 0.9627972334240521, 0.8520652907262152
], [
-0.15534261150561485, 1.0, 0.10551846545168846, 0.13506331370887886
], [0.9627972334240521, 0.10551846545168846, 1.0, 0.9088795720039011],
[
0.8520652907262152, 0.13506331370887886,
0.9088795720039011, 1.0
]]
self.assertEqual(mat, actual)