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IncrementalLinearRegressionPythonTrain
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linearRegression.py
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()
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linearRegression.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()
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