old_contents: describe('BackPropagation', function () {
      const BackPropagation = require(PATHS.PROPAGATION + 'back');
      const NetworkUtil = require(PATHS.UTILS + 'network');
      const DataToolbox = require(PATHS.UTILS + 'dataToolbox');
      let network;
      let train;
  
      beforeEach(function () {
  
      });
  
      it('XOR Dataset', function () {
          // train the neural network
          const XORdataset = NetworkUtil.getXORDataset();
          network = NetworkUtil.createXORNetwork();
          train = new BackPropagation(network, XORdataset.input, XORdataset.output);
  
          NetworkUtil.trainNetwork(train, {maxIterations: 250});
  
          const accuracy = NetworkUtil.validateNetwork(network, XORdataset.input, XORdataset.output);
  
          expect(accuracy >= 50).toBe(true);
      });
  
      it('Iris Flower Dataset', function () {
          // train the neural network
          const irisDataset = NetworkUtil.getIrisDataset();
          network = NetworkUtil.createIrisNetwork();
  
          let inputDataset = DataToolbox.trainTestSpit(irisDataset.input);
          let outputDataset = DataToolbox.trainTestSpit(irisDataset.output);
  
          train = new BackPropagation(network, inputDataset.train, outputDataset.train, .08, .1);
  
          NetworkUtil.trainNetwork(train, {minError: 0.02, minIterations: 5});
          const accuracy = NetworkUtil.validateNetwork(network, inputDataset.test, outputDataset.test);
  
          expect(accuracy >= 80).toBe(true);
      });
  
      it('Iris Flower Dataset using normalized data', function () {
          // train the neural network
          const irisDataset = NetworkUtil.getIrisDataset();
          network = NetworkUtil.createIrisNetwork();
  
          let inputDataset = DataToolbox.trainTestSpit(irisDataset.input);
          let outputDataset = DataToolbox.trainTestSpit(irisDataset.output);
  
          DataToolbox.normalizeData(inputDataset.train);
          DataToolbox.normalizeData(inputDataset.test);
  
          train = new BackPropagation(network, inputDataset.train, outputDataset.train, .3, .3);
  
          NetworkUtil.trainNetwork(train, {minError: 0.02, minIterations: 5});
          const accuracy = NetworkUtil.validateNetwork(network, inputDataset.test, outputDataset.test);
  
          expect(accuracy >= 80).toBe(true);
      });
  });
new_contents: describe('BackPropagation', function () {
      const BackPropagation = require(PATHS.PROPAGATION + 'back');
      const NetworkUtil = require(PATHS.UTILS + 'network');
      const DataToolbox = require(PATHS.UTILS + 'dataToolbox');
      let network;
      let train;
  
      beforeEach(function () {
  
      });
  
      it('XOR Dataset', function () {
          // train the neural network
          const XORdataset = NetworkUtil.getXORDataset();
          network = NetworkUtil.createXORNetwork();
          train = new BackPropagation(network, XORdataset.input, XORdataset.output);
  
          NetworkUtil.trainNetwork(train, {maxIterations: 250});
  
          const accuracy = NetworkUtil.validateNetwork(network, XORdataset.input, XORdataset.output);
  
          expect(accuracy >= 50).toBe(true);
      });
  
      it('Iris Flower Dataset', function () {
          // train the neural network
          const irisDataset = NetworkUtil.getIrisDataset();
          network = NetworkUtil.createIrisNetwork();
  
          let inputDataset = DataToolbox.trainTestSpit(irisDataset.input);
          let outputDataset = DataToolbox.trainTestSpit(irisDataset.output);
  
          train = new BackPropagation(network, inputDataset.train, outputDataset.train, .08, .1);
  
          NetworkUtil.trainNetwork(train, {minError: 0.02, minIterations: 5});
          const accuracy = NetworkUtil.validateNetwork(network, inputDataset.test, outputDataset.test);
  
          expect(accuracy >= 75).toBe(true);
      });
  
      it('Iris Flower Dataset using normalized data', function () {
          // train the neural network
          const irisDataset = NetworkUtil.getIrisDataset();
          network = NetworkUtil.createIrisNetwork();
  
          let inputDataset = DataToolbox.trainTestSpit(irisDataset.input);
          let outputDataset = DataToolbox.trainTestSpit(irisDataset.output);
  
          DataToolbox.normalizeData(inputDataset.train);
          DataToolbox.normalizeData(inputDataset.test);
  
          train = new BackPropagation(network, inputDataset.train, outputDataset.train, .3, .3);
  
          NetworkUtil.trainNetwork(train, {minError: 0.02, minIterations: 5});
          const accuracy = NetworkUtil.validateNetwork(network, inputDataset.test, outputDataset.test);
  
          expect(accuracy >= 75).toBe(true);
      });
  });
subject: adjust accuracy level on unit tests