#for i in range(len(train)):
# x.append(train[i])
# o.append(train[i][3])
#
print 'test infinite'
p = csv.writer(open('preparedData.csv',"wb"))# write data for validating the model
for i in train:
p.writerow(i)
q = csv.writer(open('testData.csv',"wb"))# write data for validating the model
for i in x:
q.writerow(i)
print len(x),'x len'
da.delCollumn(x,3)
print len(x[0]),'x'
print len(train[0]),'train'
#sys.exit('')
#train data ready
#___________________________________________________________________________
#from sklearn.svm import SVR
#from sklearn import linear_model
#wfrom sklearn.tree import DecisionTreeRegressor
#clf= SVR(kernel= 'rbf', C = 1e3)
#clf = DecisionTreeRegressor(max_depth = 36)
#clf = linear_model.LinearRegression()
import csv
import math
import numpy as np
import six
import main as da
train= csv.reader(open(r'preparedData.csv'))
train= [[i for i in y ] for y in train]
train= [[float(i) for i in y ] for y in train]
actualValue = []
for i in range(1806350,len(train),10):
actualValue.append(train[i][3])
print len(train[0])
da.delCollumn(train,5)
da.delCollumn(train,4)
da.delCollumn(train,3)
print len(actualValue)
x = csv.reader(open(r'testData.csv'))
x = [[i for i in y ] for y in x]
x = [[float(i) for i in y ] for y in x]
o = da.getCollumn(x,3)
da.delCollumn(x,3)
print "data uploaded"
#from sklearn.tree import DecisionTreeRegressor
#clf = DecisionTreeRegressor(max_depth = 38)
#16:18=45,14=48,22=0.075,24=0,067,26=0.06,28=0.056,32=0.053,34=0,05,40=0.049
# x.append(train[i])
# o.append(train[i][3])
#
print 'test infinite'
p = csv.writer(open('preparedData.csv',
"wb")) # write data for validating the model
for i in train:
p.writerow(i)
q = csv.writer(open('testData.csv',
"wb")) # write data for validating the model
for i in x:
q.writerow(i)
print len(x), 'x len'
da.delCollumn(x, 3)
print len(x[0]), 'x'
print len(train[0]), 'train'
#sys.exit('')
#train data ready
#___________________________________________________________________________
#from sklearn.svm import SVR
#from sklearn import linear_model
#wfrom sklearn.tree import DecisionTreeRegressor
#clf= SVR(kernel= 'rbf', C = 1e3)
#clf = DecisionTreeRegressor(max_depth = 36)
#clf = linear_model.LinearRegression()
