Perceptron Training Issues - neural-network

Question

I am trying to build a simple perceptron. Right now I am just trying to find the weight values for the training data set to make sure it is working, but it will not converge no matter how many epochs I use.
here's the code:
`
var inputs = [];
inputs[0] = [15, 30, 1];
inputs[1] = [45, 90, -1];
inputs[2] = [64, 120, -1];
inputs[3] = [21, 40, 1];
inputs[4] = [9, 18, 1];
var weights = [0,0];
var epochs = 100;
var lRate = 0.1;
var bias = 12;
function weighted_sum(inputs, weights)
{
var products = [];
var weightedSum = 0;
for (var i = 0; i < weights.length; i++)
{
products.push(inputs[i]*weights[i]);
}
for (var i = 0; i < products.length; i++)
{
weightedSum += products[i];
}
return weightedSum;
}
function activate(weightedSum)
{
if (weightedSum >= 0)
{
return 1;
}
else
{
return -1;
}
}
function adjust(error, weights, input)
{
if (error !== 0)
{
for (var i = 0; i < weights.length; i++)
{
weights[i] += lRate * error * input[i];
}
bias += lRate * error;
}
}
function train(inputs, weights, epochs)
{
for (var n = 0; n < epochs; n++)
{
for (var i = 0; i < inputs.length; i++)
{
var set = inputs[i];
var weightedSum = weighted_sum(set, weights);
var result = activate(weightedSum);
var target = set[2];
var error = target - result;
adjust(error, weights, set);
console.log("result: "+ result + " target: " + target + " error: " + error + " weights: " + weights);
}
for (i = inputs.length; i > 0; i--)
{
var set = inputs[i];
var weightedSum = weighted_sum(set, weights);
var result = activate(weightedSum);
var target = set[2];
var error = target - result;
adjust(error, weights, set);
console.log("result: "+ result + " target: " + target + " error: " + error + " weights: " + weights);
}
}
}
train(inputs, weights, epochs);
console.log(weights);
`
Any help is appreciated, with my problem, or just general improvements. Thanks!