#!/usr/bin/env python3

# -*- coding: utf-8 -*-

"""

Created on Wed Oct 11 21:55:15 2017

@author: austin

"""

import pandas as pd

import numpy as np

# cd /media/austin/56E4F88BE4F86E9D2/Learn/Karunya/7_sem/

df = pd.read_csv('input.csv')

df=df.dropna()

features = list(df)

features =['Latitude',

'Radiation']

features.remove('Radiation')

X = df.as_matrix(features).astype(np.float)

y = df.as_matrix(['Radiation']).astype(np.float)

from sklearn import preprocessing

scaler = preprocessing.StandardScaler()

X = scaler.fit_transform(X) #features made unit varient

from sklearn import ensemble

clf= ensemble.RandomForestRegressor()

scores = cross_val_score(clf, X,y,cv=5)

print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))

'''

Accuracy: 0.46 (+/- 0.22)

np.argwhere(np.isnan(X))

df1=preprocessing.normalize(df)

from wordsegment import segment

wo = segment("how are you")

from nltk.corpus import words

english_vocab = set(w.lower() for w in words.words())

'''