basic model

from sklearn.ensemble import RandomForestClassifier

#assume data is already loaded as xtrain, ytrain and xdev, ydev


clf = RandomForestClassifier()
clf.fit(xtrain,ytrain)
closed_out = clf.predict(xtrain)
open_out = clf.predict(xdev)

#calculate error -- note: he actually has a script for this and you should use his style but this will work while you're just figuring shit out


#closed loop
num_wrong = 0.

for i in xrange(len(ytrain)):
    if closed_out[i]!=ytrain[i]:
        num_wrong+=1.

print("open loop error: " + str(num_wrong/len(ytrain)))


#open loop
num_wrong = 0.

for i in xrange(len(ydev)):
    if open_out[i]!=ydev[i]:
        num_wrong+=1.

print("open loop error: " + str(num_wrong/len(ydev)))