def getavgmaxmin(lblnum):
from averageretail import getcenter
delhi = getcenter('DELHI')
mumbai = getcenter('MUMBAI')
lucknow = getcenter('LUCKNOW')
t1, c1 = getavgmaxmin2(delhi, anomaliesdelhi, delhilabels, lblnum)
t2, c2 = getavgmaxmin2(mumbai, anomaliesmumbai, mumbailabels, lblnum)
t3, c3 = getavgmaxmin2(lucknow, anomalieslucknow, lucknowlabels, lblnum)
return (t1 + t2 + t3) / (c1 + c2 + c3)
#anomaliesnew = get_anomalies('data/anomaly/anomalies_extended.csv')
anomaliesdelhi = get_anomalies('data/anomaly/delhi_anomalies_new.csv')
anomalieslucknow = get_anomalies('data/anomaly/lucknow_anomalies_new.csv')
#anomalies = pd.concat([anomalies,anomaliesnew],ignore_index=True)
from averagemandi import mandipriceseries
from averagemandi import mandiarrivalseries
from averageretail import retailpriceseries
from average_export import exportseries
from rainfallmonthly import rainfallmonthly
from fuelprice import fuelpricemumbai
from cpimonthlyseries import cpimonthlyseries
from oilmonthlyseries import oilmonthlyseries
from averageretail import getcenter
retailpriceseriesdelhi = getcenter('DELHI')
retailpriceserieslucknow = getcenter('LUCKNOW')
from averagemandi import getmandi
mandipriceseriesdelhi = getmandi('Azadpur',True)
mandiarrivalseriesdelhi = getmandi('Azadpur',False)
mandipriceserieslucknow = getmandi('Devariya',True)
mandiarrivalserieslucknow = getmandi('Devariya',False)
START = CONSTANTS['STARTDATE']
END = CONSTANTS['ENDDATEOLD']
retailpriceseries = retailpriceseries[START:END]
mandipriceseries = mandipriceseries[START:END]
mandiarrivalseries = mandiarrivalseries[START:END]
retailpriceseriesdelhi = retailpriceseriesdelhi[START:END]
retailpriceserieslucknow = retailpriceserieslucknow[START:END]
def whiten_series_list(list):
for i in range(0, len(list)):
mean = list[i].mean()
list[i] -= mean
temp = pd.DataFrame()
for i in range(0, len(list)):
temp[i] = list[i]
temp = whiten(temp)
newlist = [temp[i] for i in range(0, len(list))]
return newlist
from averageretail import getcenter
retailpriceseriesmumbai = getcenter('MUMBAI')
retailpriceseriesdelhi = getcenter('DELHI')
retailpriceserieslucknow = getcenter('LUCKNOW')
retailpriceseriesbhub = getcenter('BHUBANESHWAR')
retailpriceseriespatna = getcenter('PATNA')
[retailpriceseriesdelhi, retailpriceserieslucknow,
retailpriceseriesmumbai] = whiten_series_list([
retailpriceseriesdelhi, retailpriceserieslucknow, retailpriceseriesmumbai
])
from averagemandi import getmandi
mandipriceseriesdelhi = getmandi('Azadpur', True)
mandiarrivalseriesdelhi = getmandi('Azadpur', False)
mandipriceserieslucknow = getmandi('Bahraich', True)
mandiarrivalserieslucknow = getmandi('Bahraich', False)
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from averagemandi import give_avg_series
import os
cwd = os.getcwd()
matplotlib.rcParams.update({'font.size': 26})
# from reading_timeseries import retailP, mandiP, mandiA, retailPM, mandiPM, mandiAM
# retailpriceseriesmumbai = retailP[3]
# retailpriceseriesdelhi = retailP[1]
# retailpriceserieslucknow = retailP[2]
# print retailpriceseriesmumbai
from averageretail import getcenter
retailpriceseriesmumbai = getcenter('MUMBAI')
retailpriceseriesdelhi = getcenter('DELHI')
retailpriceserieslucknow = getcenter('LUCKNOW')
retailpriceseriesbangalore = getcenter('BENGALURU')
avg_retailpriceseriesmumbai = give_avg_series(retailpriceseriesmumbai)
avg_retailpriceseriesdelhi = give_avg_series(retailpriceseriesdelhi)
avg_retailpriceserieslucknow = give_avg_series(retailpriceserieslucknow)
avg_retailpriceseriesbangalore = give_avg_series(retailpriceseriesbangalore)
from averagemandi import getmandi
mandipriceseriesdelhi = getmandi('Azadpur', True)
mandiarrivalseriesdelhi = getmandi('Azadpur', False)
mandipriceserieslucknow = getmandi('Bahraich', True)
mandiarrivalserieslucknow = getmandi('Bahraich', False)
mandipriceseriespune = getmandi('Pune', True)
