def train_rls(): X_train, Y_train, X_test, Y_test = load_housing() cb = Callback(X_test, Y_test) learner = GreedyRLS(X_train, Y_train, 13, callbackfun = cb) #Test set predictions P_test = learner.predict(X_test) print("test error %f" %sqerror(Y_test, P_test)) print("Selected features " +str(learner.selected))
def train_rls(): X_train, Y_train, X_test, Y_test = load_housing() #we select 5 features learner = GreedyRLS(X_train, Y_train, 5) #Test set predictions P_test = learner.predict(X_test) print("test error %f" %sqerror(Y_test, P_test)) print("Selected features " +str(learner.selected))
def train_rls(): X_train, Y_train, X_test, Y_test = load_housing() cb = Callback(X_test, Y_test) learner = GreedyRLS(X_train, Y_train, 13, callbackfun=cb) #Test set predictions P_test = learner.predict(X_test) print("test error %f" % sqerror(Y_test, P_test)) print("Selected features " + str(learner.selected))
def train_rls(): #Trains RLS with default parameters (regparam=1.0, kernel='LinearKernel') X_train, Y_train, X_test, Y_test = load_housing() learner = GreedyRLS(X_train, Y_train, 5) #This is how we make predictions P_test = learner.predict(X_test) #We can separate the predictor from learner predictor = learner.predictor #And do the same predictions P_test = predictor.predict(X_test) #Let's get the coefficients of the predictor w = predictor.W b = predictor.b print("number of coefficients %d" %len(w)) print("w-coefficients " +str(w)) print("bias term %f" %b)
def train_rls(): #Trains RLS with default parameters (regparam=1.0, kernel='LinearKernel') X_train, Y_train, X_test, Y_test = load_housing() learner = GreedyRLS(X_train, Y_train, 5) #This is how we make predictions P_test = learner.predict(X_test) #We can separate the predictor from learner predictor = learner.predictor #And do the same predictions P_test = predictor.predict(X_test) #Let's get the coefficients of the predictor w = predictor.W b = predictor.b print("number of coefficients %d" % len(w)) print("w-coefficients " + str(w)) print("bias term %f" % b)