# 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));