Code fragments of linearRegression.py

• import pickle
• from random import randrange
• import matplotlib
• import matplotlib.pyplot as plt
• import seaborn as sns
• sns.set()
• import numpy as np
• for iter in range(1):
• x = []
• y = []
• file = open('stationID.txt', 'r')
• id = file.readline()
• file.close()
• file = open('stationID.txt', 'w')
• with open ('input1X.txt', 'rb') as fp:
• x1 = pickle.load(fp)
• with open('input1Y.txt', 'rb') as fp:
• y1 = pickle.load(fp)
• with open('input2X.txt', 'rb') as fp:
• x2 = pickle.load(fp)
• with open('input2Y.txt', 'rb') as fp:
• y2 = pickle.load(fp)
• if id is "1":
• x = x1
• y = y1
• file.write('2')
• if id is "2":
• x = x2
• y = y2
• file.write('1')
• file.close()
• plt.scatter(x1[:, np.newaxis], y1[:], c='blue')
• plt.scatter(x2[:, np.newaxis], y2[:], c='blue')
• filename = 'model.sav'
• loaded_model = pickle.load(open(filename, 'rb'))
• plt.scatter(x[:, np.newaxis], y[:], c='red')
• #visit Station 1 first
• for _ in range(25):
• loaded_model.partial_fit(x[:, np.newaxis], y[:])
• pickle.dump(loaded_model, open(filename, 'wb'))
• xfit = np.linspace(0, 10, 1000)
• yfitIncr1 = loaded_model.predict(xfit[:, np.newaxis])
• plt.plot(xfit, yfitIncr1)
• plt.savefig(str(iter)+'.png')
• #plt.savefig('images/final.png')
• plt.close()

Graph