Code fragments of linearRegression.py

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undefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedlinearRegression.py import pickle None 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') 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()