How do I combine the weights of two predictor in a regression model with GRNN?

Question

I am trying to build an algorithm that uses GRNN for regression, a model based on the formula:

My csv files are looks like:

Train.csv:                               Test.csv:
Number1  Number2  NumberT                Number1  Number2
2        4        3                      5        7
4        6        5
6        8        7
8        10       9

Predictors are Number1 and Number2. My Target is NumberT. It is pretty easy to predict the output with only 1 predictor Number1. But when multiple features comes in, I can't figure it out.

To solve this problem, I have stored all the features and outputs automatically:

inputsTrain = [[2,4,6,8],[4,6,8,10]]
outputsTrain = [3,5,7,9]
inputsTest = [[5],[7]]

I've used inputsTrain and outputsTrain to find the weights by activation function. (I assumed σ=1). To calculate the weights, I found all the distances between inputsTest and inputsTrain.

for test input 5: euc_distances = [[9, 1, 1, 9], [1, 1, 9, 25]]
for test input 7: euc_distances = [[25, 9, 1, 1], [9, 1, 1, 9]]

After inserting these distances into the activation function, I have stored all the weights in a list called hiddenLayers.

weights = [[[0, 0.6, 0.6, 0], [0.6, 0.6, 0, 0]], [[0, 0, 0.6, 0.6], [0, 0.6, 0.6, 0]]]

But now I don't know what to do with these weights. It was easy when I got 1 predictor, I could just multiply weights with corresponding outputsTrain elements and then do numerator/denominator. But when it comes to multiple predictors, I can't find what to do after this point. Any help would be appreciated.

Answer 1

Alright seems like I had to apply the same method for each feature. But the steps I followed were wrong. I thought there was something more complicated. Anyway, if someone gets stuck just like me, here is the solution:

your test inputs are a1=5 and a2=7

For each test input find the euclidean distance **SEPERATELY**

d11 = 9    d21 = 9 
d12 = 1    d22 = 1
d13 = 1    d23 = 1
d14 = 9    d24 = 9

Now, we need to find the weights, we will do the same thing.(values can be soo close to 0)

w11 = 0      w21 = 0
w12 = 0.6    w22 = 0.6
w13 = 0.6    w23 = 0.6
w14 = 0      w24 = 0

After finding weights, calculate numerator and dominator for each one.

Num1 = w11*3 + w12*5 + w13*7 + w14*9 = 7.2
Num2 = w21*3 + w22*5 + w23*7 + w24*9 = 7.2

Denom1 = w11 + w12 + w13 + w14 = 1.2
Denom2 = w21 + w22 + w23 + w24 = 1.2

Output is

Num1 + Num2 / Denom1 + Denom2 = 14.4/2.4 = **6**