"CHALLENGE" : Code a program which accepts infinite input errors and exceptions and loops and states the error too.

Question

Code a program which can handle infinite input errors and loops itself until the correct input/type of input.
Eg: A calculator which when given string as input, describe the error but doesn't terminate and loops until the correct type of input is provided.

Answer 1

Intresting challenge 😊
Something like this:
https://code.sololearn.com/cC6GHcx410il/?ref=app

Answer 2

while(1)
{
cin>>input;
if(input==Ok) break;
}

Answer 3

So should it terminate after getting the correct answer or just keep running even after that .

Answer 4

According to ur wish but loop even after correct input would be an interesting game!

Answer 5

using System;

class Program {

public static double RandomDouble(double min, double max) {
Random random = new Random();
return random.NextDouble() * (maximum - minimum) + minimum;
}

public static double Sigmoid(double x) {
//return 1 / (1+e^-x)
return 1 / (1 + Math.Exp(-x));
}
double input, weight1, weight2, hiddenNeuron1, hiddenNeuron2, weight3, weight4, output1;

double biasWeight1, biasWeight2, biasWeight3; //Assuming the bias value is +1.00

int trials = 0;

static void ForwardPropogate() {
trials++;
if(trials == 0) {
weight1 = RandomDouble(-1, 1);
System.Threading.Thread.Sleep(50);
weight2 = RandomDouble(-1, 1);
System.Threading.Thread.Sleep(50);
weight3 = RandomDouble(-1, 1);
System.Threading.Thread.Sleep(50);
weight4 = RandomDouble(-1, 1);
System.Threading.Thread.Sleep(50);

biasWeight1 = RandomDouble(-1, 1);
System.Threading.Thread.Sleep(50);
biasWeight2 = RandomDouble(-1, 1);
System.Threading.Thread.Sleep(50);
weight3 = RandomDouble(-1, 1);

hiddenNeuron1 = Sigmoid((input1 * weight1) * biasWeight1);
hiddenNeuron2 = Sigmoid((input2 * weight2) * biasWeight2);

output1 = Sigmoid(((hiddenNeuron1 * weight3) + (hiddenNeuron2 * weight4) * biasWeight3);

Console.WriteLine(“output1” + output1);
Console.ReadKey();
}
else {

}
}

static void BackwardPropogate() {
//This section contains calculus and gradient descent so it will not be included
}
}

//Yes, I know I should use arrays to make the code neater and shorter but without them it is easier to understand.

//This neural network is a 1 2 1 network (which means it has 1 input neuron, 2 hidden neurons and 1 output neuron. These are kept in different layers called the input layer, the hidden layer and the output layer.

//To calculate a hidden neuron, all we need to do is h1 = Sig(i1 * w1) and h2 = Sig(i2 * w2). And to calculate the output neuron, we need to do o1 = Sig((h1 * w3) + (h2 * w4))

//This method is called forward propagation and it is only half of the neural network. I just realised how hard it actually is to write code on an iPhone