public result_var errorFunc_Online()
{
result_var result;
double lostFunc1;
double lostFunc2;
double lostFunc3;
double lostFunc4;
double lostFunc;
double temp = 0;
double temp0 = 0;
Dataset_And dataset_and = new Dataset_And(1);
int epoch = 0;
lostFunc1 = Math.Abs(dataset_and.d1.output - cost_Sigmoid(dataset_and.d1.x1, dataset_and.d1.x2));
lostFunc2 = Math.Abs(dataset_and.d2.output - cost_Sigmoid(dataset_and.d2.x1, dataset_and.d2.x2));
lostFunc3 = Math.Abs(dataset_and.d3.output - cost_Sigmoid(dataset_and.d3.x1, dataset_and.d3.x2));
lostFunc4 = Math.Abs(dataset_and.d4.output - cost_Sigmoid(dataset_and.d4.x1, dataset_and.d4.x2));
lostFunc = Math.Abs(dataset_and.d1.output - cost_Sigmoid(dataset_and.d1.x1, dataset_and.d1.x2) +
dataset_and.d2.output - cost_Sigmoid(dataset_and.d2.x1, dataset_and.d2.x2) +
dataset_and.d3.output - cost_Sigmoid(dataset_and.d3.x1, dataset_and.d3.x2) +
dataset_and.d4.output - cost_Sigmoid(dataset_and.d4.x1, dataset_and.d4.x2))
;
while (lostFunc > 0.01)
{
while (lostFunc1 > 0.01)
{
bias = bias + (eta * (
(-(1 / Math.Exp((bias + weight1 * dataset_and.d1.x1 + weight2 * dataset_and.d1.x2))
* Math.Sin(dataset_and.d1.output + 1 / Math.Exp((bias + weight1 * dataset_and.d1.x1 + weight2 * dataset_and.d1.x2)) - 1)) / 2)));
weight1 = weight1 + (eta * (
(-(1 / Math.Exp(bias + weight1 * dataset_and.d1.x1 + weight2 * dataset_and.d1.x2) * dataset_and.d1.x1 * Math.Sin(dataset_and.d1.output + 1 / Math.Exp(bias + weight1 * dataset_and.d1.x1 + weight2 * dataset_and.d1.x2) - 1)) / 2)));
weight2 = weight2 + (eta * (
(-(1 / Math.Exp(bias + weight1 * dataset_and.d1.x1 + weight2 * dataset_and.d1.x2) * dataset_and.d1.x2 * Math.Sin(dataset_and.d1.output + 1 / Math.Exp(bias + weight1 * dataset_and.d1.x1 + weight2 * dataset_and.d1.x2) - 1)) / 2)));
lostFunc1 = Math.Abs(dataset_and.d1.output - cost_Sigmoid(dataset_and.d1.x1, dataset_and.d1.x2));
epoch++;
if (lostFunc1 == temp)
{
break;
}
temp = lostFunc1;
}
Console.WriteLine("1 COMPLETE");
while (lostFunc2 > 0.01)
{
bias = bias + (eta * (
(-(1 / Math.Exp((bias + weight1 * dataset_and.d2.x1 + weight2 * dataset_and.d2.x2))
* Math.Sin(dataset_and.d2.output + 1 / Math.Exp((bias + weight1 * dataset_and.d2.x1 + weight2 * dataset_and.d2.x2)) - 1)) / 2)));
weight1 = weight1 + (eta * (
(-(1 / Math.Exp(bias + weight1 * dataset_and.d2.x1 + weight2 * dataset_and.d2.x2) * dataset_and.d2.x1 * Math.Sin(dataset_and.d2.output + 1 / Math.Exp(bias + weight1 * dataset_and.d2.x1 + weight2 * dataset_and.d2.x2) - 1)) / 2)));
weight2 = weight2 + (eta * (
(-(1 / Math.Exp(bias + weight1 * dataset_and.d2.x1 + weight2 * dataset_and.d2.x2) * dataset_and.d2.x2 * Math.Sin(dataset_and.d2.output + 1 / Math.Exp(bias + weight1 * dataset_and.d2.x1 + weight2 * dataset_and.d2.x2) - 1)) / 2)));
lostFunc2 = Math.Abs(dataset_and.d2.output - cost_Sigmoid(dataset_and.d2.x1, dataset_and.d2.x2));
epoch++;
if (lostFunc2 == temp)
{
break;
}
temp = lostFunc2;
//System.Console.WriteLine(bias);
//System.Console.WriteLine(weight1);
//System.Console.WriteLine(weight2);
//System.Console.WriteLine(epoch);
//System.Console.WriteLine(lostFunc2);
}
Console.WriteLine("2 COMPLETE");
while (lostFunc3 > 0.01)
{
bias = bias + (eta * (
(-(1 / Math.Exp((bias + weight1 * dataset_and.d3.x1 + weight2 * dataset_and.d3.x2))
* Math.Sin(dataset_and.d3.output + 1 / Math.Exp((bias + weight1 * dataset_and.d3.x1 + weight2 * dataset_and.d3.x2)) - 1)) / 2)));
weight1 = weight1 + (eta * (
(-(1 / Math.Exp(bias + weight1 * dataset_and.d3.x1 + weight2 * dataset_and.d3.x2) * dataset_and.d3.x1 * Math.Sin(dataset_and.d3.output + 1 / Math.Exp(bias + weight1 * dataset_and.d3.x1 + weight2 * dataset_and.d3.x2) - 1)) / 2)));
weight2 = weight2 + (eta * (
(-(1 / Math.Exp(bias + weight1 * dataset_and.d3.x1 + weight2 * dataset_and.d3.x2) * dataset_and.d3.x2 * Math.Sin(dataset_and.d3.output + 1 / Math.Exp(bias + weight1 * dataset_and.d3.x1 + weight2 * dataset_and.d3.x2) - 1)) / 2)));
lostFunc3 = Math.Abs(dataset_and.d3.output - cost_Sigmoid(dataset_and.d3.x1, dataset_and.d3.x2));
//System.Console.WriteLine(bias);
//System.Console.WriteLine(weight1);
//System.Console.WriteLine(weight2);
//System.Console.WriteLine(epoch);
epoch++;
if (lostFunc3 == temp)
{
break;
}
temp = lostFunc3;
}
Console.WriteLine("3 COMPLETE");
while (lostFunc4 > 0.01)
{
bias = bias + (eta * (
(-(1 / Math.Exp((bias + weight1 * dataset_and.d4.x1 + weight2 * dataset_and.d4.x2))
* Math.Sin(dataset_and.d4.output + 1 / Math.Exp((bias + weight1 * dataset_and.d4.x1 + weight2 * dataset_and.d4.x2)) - 1)) / 2)));
weight1 = weight1 + (eta * (
(-(1 / Math.Exp(bias + weight1 * dataset_and.d4.x1 + weight2 * dataset_and.d4.x2) * dataset_and.d4.x1 * Math.Sin(dataset_and.d4.output + 1 / Math.Exp(bias + weight1 * dataset_and.d4.x1 + weight2 * dataset_and.d4.x2) - 1)) / 2)));
weight2 = weight2 + (eta * (
(-(1 / Math.Exp(bias + weight1 * dataset_and.d4.x1 + weight2 * dataset_and.d4.x2) * dataset_and.d4.x2 * Math.Sin(dataset_and.d4.output + 1 / Math.Exp(bias + weight1 * dataset_and.d4.x1 + weight2 * dataset_and.d4.x2) - 1)) / 2)));
lostFunc4 = Math.Abs(dataset_and.d4.output - cost_Sigmoid(dataset_and.d4.x1, dataset_and.d4.x2));
//System.Console.WriteLine(bias);
//System.Console.WriteLine(weight1);
//System.Console.WriteLine(weight2);
//System.Console.WriteLine(epoch);
epoch++;
if (lostFunc4 == temp)
{
break;
}
temp = lostFunc4;
}
Console.WriteLine("4 COMPLETE");
lostFunc = Math.Abs(dataset_and.d1.output - cost_Sigmoid(dataset_and.d1.x1, dataset_and.d1.x2) +
dataset_and.d2.output - cost_Sigmoid(dataset_and.d2.x1, dataset_and.d2.x2) +
dataset_and.d3.output - cost_Sigmoid(dataset_and.d3.x1, dataset_and.d3.x2) +
dataset_and.d4.output - cost_Sigmoid(dataset_and.d4.x1, dataset_and.d4.x2))
;
epoch++;
if (lostFunc == temp0)
{
break;
}
temp0 = lostFunc;
}
Console.WriteLine("ALL Complete");
result.bias = bias;
result.w0 = weight1;
result.w1 = weight2;
Console.WriteLine("W0 : {0} | W1 : {1} | W2 : {2} | Error : {3} | Epoch : {4} ", bias, weight1, weight2, lostFunc, epoch);
return(result);
}
public result_var errorFunc_Batch()
{
result_var result;
double lostFunc;
Dataset_And dataset_and = new Dataset_And(1);
double temp0 = 0;
int epoch = 0;
lostFunc = Math.Abs(dataset_and.d1.output - cost_Sigmoid(dataset_and.d1.x1, dataset_and.d1.x2) +
dataset_and.d2.output - cost_Sigmoid(dataset_and.d2.x1, dataset_and.d2.x2) +
dataset_and.d3.output - cost_Sigmoid(dataset_and.d3.x1, dataset_and.d3.x2) +
dataset_and.d4.output - cost_Sigmoid(dataset_and.d4.x1, dataset_and.d4.x2))
;
while (lostFunc > 0.01)
{
bias = bias + (eta * (
(-(1 / Math.Exp((bias + weight1 * dataset_and.d1.x1 + weight2 * dataset_and.d1.x2))
* Math.Sin(dataset_and.d1.output + 1 / Math.Exp((bias + weight1 * dataset_and.d1.x1 + weight2 * dataset_and.d1.x2)) - 1)) / 2) +
(-(1 / Math.Exp((bias + weight1 * dataset_and.d2.x1 + weight2 * dataset_and.d2.x2))
* Math.Sin(dataset_and.d2.output + 1 / Math.Exp((bias + weight1 * dataset_and.d2.x1 + weight2 * dataset_and.d2.x2)) - 1)) / 2) +
(-(1 / Math.Exp((bias + weight1 * dataset_and.d3.x1 + weight2 * dataset_and.d3.x2))
* Math.Sin(dataset_and.d3.output + 1 / Math.Exp((bias + weight1 * dataset_and.d3.x1 + weight2 * dataset_and.d3.x2)) - 1)) / 2) +
(-(1 / Math.Exp((bias + weight1 * dataset_and.d4.x1 + weight2 * dataset_and.d4.x2))
* Math.Sin(dataset_and.d4.output + 1 / Math.Exp((bias + weight1 * dataset_and.d4.x1 + weight2 * dataset_and.d4.x2)) - 1)) / 2)
));
weight1 = weight1 + (eta * (
(-(1 / Math.Exp(bias + weight1 * dataset_and.d1.x1 + weight2 * dataset_and.d1.x2) * dataset_and.d1.x1 * Math.Sin(dataset_and.d1.output + 1 / Math.Exp(bias + weight1 * dataset_and.d1.x1 + weight2 * dataset_and.d1.x2) - 1)) / 2) +
(-(1 / Math.Exp(bias + weight1 * dataset_and.d2.x1 + weight2 * dataset_and.d2.x2) * dataset_and.d2.x1 * Math.Sin(dataset_and.d2.output + 1 / Math.Exp(bias + weight1 * dataset_and.d2.x1 + weight2 * dataset_and.d2.x2) - 1)) / 2) +
(-(1 / Math.Exp(bias + weight1 * dataset_and.d3.x1 + weight2 * dataset_and.d3.x2) * dataset_and.d3.x1 * Math.Sin(dataset_and.d3.output + 1 / Math.Exp(bias + weight1 * dataset_and.d3.x1 + weight2 * dataset_and.d3.x2) - 1)) / 2) +
(-(1 / Math.Exp(bias + weight1 * dataset_and.d4.x1 + weight2 * dataset_and.d4.x2) * dataset_and.d4.x1 * Math.Sin(dataset_and.d4.output + 1 / Math.Exp(bias + weight1 * dataset_and.d4.x1 + weight2 * dataset_and.d4.x2) - 1)) / 2)));
weight2 = weight2 + (eta * (
(-(1 / Math.Exp(bias + weight1 * dataset_and.d1.x1 + weight2 * dataset_and.d1.x2) * dataset_and.d1.x2 * Math.Sin(dataset_and.d1.output + 1 / Math.Exp(bias + weight1 * dataset_and.d1.x1 + weight2 * dataset_and.d1.x2) - 1)) / 2) +
(-(1 / Math.Exp(bias + weight1 * dataset_and.d2.x1 + weight2 * dataset_and.d2.x2) * dataset_and.d2.x2 * Math.Sin(dataset_and.d2.output + 1 / Math.Exp(bias + weight1 * dataset_and.d2.x1 + weight2 * dataset_and.d2.x2) - 1)) / 2) +
(-(1 / Math.Exp(bias + weight1 * dataset_and.d3.x1 + weight2 * dataset_and.d3.x2) * dataset_and.d3.x2 * Math.Sin(dataset_and.d3.output + 1 / Math.Exp(bias + weight1 * dataset_and.d3.x1 + weight2 * dataset_and.d3.x2) - 1)) / 2) +
(-(1 / Math.Exp(bias + weight1 * dataset_and.d4.x1 + weight2 * dataset_and.d4.x2) * dataset_and.d4.x2 * Math.Sin(dataset_and.d4.output + 1 / Math.Exp(bias + weight1 * dataset_and.d4.x1 + weight2 * dataset_and.d4.x2) - 1)) / 2)));
lostFunc = Math.Abs(dataset_and.d1.output - cost_Sigmoid(dataset_and.d1.x1, dataset_and.d1.x2) +
dataset_and.d2.output - cost_Sigmoid(dataset_and.d2.x1, dataset_and.d2.x2) +
dataset_and.d3.output - cost_Sigmoid(dataset_and.d3.x1, dataset_and.d3.x2) +
dataset_and.d4.output - cost_Sigmoid(dataset_and.d4.x1, dataset_and.d4.x2));
epoch++;
Console.WriteLine(lostFunc);
if (lostFunc == temp0)
{
break;
}
temp0 = lostFunc;
//System.Console.WriteLine(bias);
//System.Console.WriteLine(weight1);
//System.Console.WriteLine(weight2);
//System.Console.WriteLine("Lost : {0}", lostFunc);
//System.Console.WriteLine("Net : {0}==1", net(bias, weight1, weight2, dataset_and.d1.x1, dataset_and.d1.x2));
//System.Console.WriteLine(epoch);
}
Console.WriteLine("W0 : {0} | W1 : {1} | W2 : {2} | Error : {3} | Epoch : {4} ", bias, weight1, weight2, lostFunc, epoch);
result.bias = bias;
result.w0 = weight1;
result.w1 = weight2;
return(result);
}
