In [1]:

    

import csv
f = open('exampledata.txt', 'r')
fcsv = csv.reader(f)

d = []
try:
    while True:
        d.append(fcsv.next())
except:
    pass
f.close()
print len(d)


    

    




97

In [2]:

    

import numpy as np
from sklearn import linear_model


    

In [3]:

    

for i in range(len(d)):
    for j in range(2):
        d[i][j] = float(d[i][j])

x = []
y = []
for i in range(len(d)):
    x.append(d[i][1:])
    y.append(d[i][0])


    

In [11]:

    

clf = linear_model.SGDRegressor(alpha=0.0, loss='squared_loss',n_iter=1000, eta0=0.001, learning_rate='constant' )
sgd = clf.fit(x,y, coef_init=[0], intercept_init=[0])
print [sgd.intercept_, sgd.coef_]


    

    




[array([ 4.67074621]), array([ 0.70734623])]

In [ ]: