Python - Lesen Sie Daten aus einer numerischen Datendatei und suchen Sie die multiple Regressionslinie.
Ein Programm, das Daten aus einer durch Tabulatoren getrennten numerischen Datenreihendatei wie unten gezeigt liest und mehrere Regressionslinien erhält. Zeile 1: Zielvariable (y) 2. Zeile: Erklärungsvariable 1 (x1) Zeile 3: Erklärungsvariable 2 (x2)
MultipleRegressionAnalysis_Data1.txt
65.7 67.8 70.3 72.0 74.3 76.2
3.27 3.06 4.22 4.10 5.26 6.18
69.7 69.7 71.3 77.6 81.0 78.7
MultipleRegressionAnalysis_2.py
from matplotlib import pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
def load_data(filename):
with open(filename) as lines:
return [[float(data) for data in line.strip().split('\t')]
for line in lines]
def calc_average(data):
return sum(data) / len(data)
def calc_sum_of_square(data_list):
ave = calc_average(data_list)
return sum((d - ave)**2 for data in data_list)
def calc_sum_of_square(data_list1, data_list2):
ave1 = calc_average(data_list1)
ave2 = calc_average(data_list2)
return sum((data1 * data2 - ave1 * ave2)**2 for data1, data2 in zip(data_list1, data_list2))
if __name__ == "__main__":
data = load_data('MultipleRegressionAnalysis_Data1.txt')
s = [[calc_sum_of_square(data1, data2) for data2 in data]
for data1 in data]
det = s[1][1]*s[2][2] - s[1][2]**2
#Partielle Regressionskoeffizientenberechnung
b1 = (s[0][1]*s[2][2] - s[0][2]*s[1][2])/det
b2 = (s[0][2]*s[1][1] - s[0][1]*s[1][2])/det
b0 = calc_average(data[0]) - b1*calc_average(data[1]) - b2*calc_average(data[2])
#Anzeige mehrerer Regressionsausdrücke
print("Y = " + str(b1) + " x1 + " + str(b2) + " x2 + " + str(b0))
fig = plt.figure()
ax = Axes3D(fig)
#Plananzeige aus der erhaltenen multiplen Regressionsgleichung
x1 = np.arange(0, 10, 0.25)
x2 = np.arange(65, 80, 0.25)
X1, X2 = np.meshgrid(x1, x2)
Y = b1 * X1 + b2 * X2 + b0
ax.plot_wireframe(X1, X2, Y)
#Originaldatenanzeige
ax.scatter3D(data[1], data[2], data[0], c='red')
plt.show()
Ergebnis
> python MultipleRegressionAnalysis_2.py
Y = -17.118276566578807 x1 + 1.0700625679745137 x2 + 65.58796752824318
