# Toy Data
#df = [ ["P1",1,2,10],["P2",3,5,26],["P3",1,1,6],["P4",6,2,20],["P5",5,3,22] ]
# [0,1,2,3]
model = baseRegressor(raw_data=df,feature_stop=265,target_column=DAT,
regressor=True,features_to_use=wt,stoppage=10000000);
model.fit(alpha=0.000000000001,target_error=1); # Best I could find for 268
"""
model = baseRegressor(raw_data=df,feature_stop=265,target_column=266,
regressor=False,features_to_use=wt);
model.fit(alpha=0.00000000001,target_error=0.1); # For Classification
"""
print model.weights; # Returns a list of random values.
l = map(abs, model.weights);
#print "Should return 2,4"
#print model.baseError_plot # this is a list
b = sorted(range(len(l)),key=lambda k: l[k]);
b.reverse()
print b;
ys = return_single_column(df,dex=DAT);
print ys[0:10];
print model.predict(df[0:10]);
wt = [12, 21, 25, 20, 11, 28, 27, 16, 26, 32, 13, 29, 2, 33, 1, 17, 30, 14]
wt_class = [0.005282546933085302,
0.00019005094290200331,
-0.3208438520005847,
0.060298374167868034,
0.47575278102153445,
-0.007341090879961012,
-0.0003345576888578404,
-0.34825960673644474,
0.0003877180341366817,
-0.28494755094203117,
2.964726478234287e-06,
-0.01292471641406875,
0.0005911449483264154,
0.008637346773164302,
0.1367495624114614,
0.004521909748139259,
-0.009062774664435601,
-0.01652866109254906]
model = baseRegressor(raw_data=training,
feature_stop=3,
target_column=4,
regressor=False,
existing_weights=wt_class,
features_to_use=wt);
predictions = model.predict(validation);
answers = return_single_column(validation,dex=266);
print "Percentage Correct: " + str(binary_cv(predictions,answers));
