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)
