X = df.drop(['Grade'], axis=1)
Y = df.Grade
#Using Built in train test split function in sklearn
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)
# RBF and kmeans clustering by class
#Number of prototypes
#prototypes = int(input("Number of seed points:"))
prototypes = 12
#Finding cluster centers
df_cluster = X_train
df_cluster[
'Grade'] = Y_train #Reproduce original data but only with training values
rbfk_net = RBFKMeans(
n_clusters=prototypes) #Chose number of clusters that you want
rbfk_net.train(df_cluster, epsilon=1e-5)
center = pd.DataFrame(rbfk_net.centers)
# Turn the centers into prototypes values needed
X_prototypes = center.iloc[:, 0:-1]
Y_prototypes = center.iloc[:,
-1] #Y_prototypes is the last column of center since 'Grade' is the last feature added to center.
#Train GRNN
GRNNet = GRNN(std=0.1)
GRNNet.train(X_prototypes, Y_prototypes)
# Cross validataion
score = cross_val_score(GRNNet, X_train, Y_train, scoring='r2', cv=5)
print("")
