#!/usr/bin/python

""" 
    This is the code to accompany the Lesson 2 (SVM) mini-project.

    Use a SVM to identify emails from the Enron corpus by their authors:    
    Sara has label 0
    Chris has label 1
"""
    
import sys
from time import time
sys.path.append("../tools/")
from email_preprocess import preprocess


### features_train and features_test are the features for the training
### and testing datasets, respectively
### labels_train and labels_test are the corresponding item labels
features_train, features_test, labels_train, labels_test = preprocess()




#########################################################
### your code goes here ###

#########################################################
from sklearn import svm

# set smaller train set
features_train = features_train[:len(features_train)/100] 
labels_train = labels_train[:len(labels_train)/100] 

# clf = svm.SVC(kernel='linear')
clf = svm.SVC(kernel='rbf', C=10000.0)
#t0 = time()
clf.fit(features_train, labels_train)
#print "training time:", round(time()-t0, 3), "s"
#t0 = time()
prediction = clf.predict(features_test)
#print "predicting time:", round(time()-t0, 3), "s"
#accuracy = clf.score(features_test, labels_test)
#print accuracy

print prediction[10]
print prediction[26]
print prediction[50]

#count = 0
#for p in prediction:
#    if p == 1:
#        count += 1

#print count
