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##Finding the average output##

Let's figure out what your average output should be. Your neural network has 3 inputs in the first layer, 2 nodes in the second layer, and one output. Each weight is randomized to a value from 0..1, so call it 0.5 on average.

The inputs you use in the program are: 1, 0, 1. On each layer, you also have a "bias" input of -1. So with average weights of 0.5, the the input layer will do the following:

inputs = 1, 0, 1, -1
output = sigmoid(1*0.5 + 0*0.5 + 1*0.5 - 1*0.5) = sigmoid(0.5) = 0.62

The second layer has two nodes, each with average input 0.62. It will do:

inputs = 0.62, 0.62, -1
output = sigmoid(0.62*0.5 + 0.62*0.5 - 1*0.5) = sigmoid(0.12) = 0.53

So your average output should be 0.53. I modified your program to sum the outputs and found that this was close (actual was 0.528). Now as for the percentage of time the output is above 0.5, that depends on the distribution of the output, and I don't know that it is easy to compute by hand. But your program shows through experimentation that the answer is roughly 60% of the time.

##Random weights##

I think that your choice of random weights from the range 0..1 is the source of your confusion. If you were to choose random weights in the range -1..1 like this:

weights[j][i] = Math.random()*2 - 1.0;

then your output would be 0.5 on average and the percentage of outputs greater than 0.5 would be 50% (I modified your program to verify this). Perhaps that is what you were expecting.

As far as your neural net code goes, it appears to be correct as far as I can tell.

Finding the average output

Let's figure out what your average output should be. Your neural network has 3 inputs in the first layer, 2 nodes in the second layer, and one output. Each weight is randomized to a value from 0..1, so call it 0.5 on average.

The inputs you use in the program are: 1, 0, 1. On each layer, you also have a "bias" input of -1. So with average weights of 0.5, the the input layer will do the following:

inputs = 1, 0, 1, -1
output = sigmoid(1*0.5 + 0*0.5 + 1*0.5 - 1*0.5) = sigmoid(0.5) = 0.62

The second layer has two nodes, each with average input 0.62. It will do:

inputs = 0.62, 0.62, -1
output = sigmoid(0.62*0.5 + 0.62*0.5 - 1*0.5) = sigmoid(0.12) = 0.53

So your average output should be 0.53. I modified your program to sum the outputs and found that this was close (actual was 0.528). Now as for the percentage of time the output is above 0.5, that depends on the distribution of the output, and I don't know that it is easy to compute by hand. But your program shows through experimentation that the answer is roughly 60% of the time.

Random weights

I think that your choice of random weights from the range 0..1 is the source of your confusion. If you were to choose random weights in the range -1..1 like this:

weights[j][i] = Math.random()*2 - 1.0;

then your output would be 0.5 on average and the percentage of outputs greater than 0.5 would be 50% (I modified your program to verify this). Perhaps that is what you were expecting.

As far as your neural net code goes, it appears to be correct as far as I can tell.