Examples of NetworkBuilder

Examples of com.axemblr.provisionr.api.network.NetworkBuilder

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        return result.createSoftware();
    }

    @VisibleForTesting
    Network apply(Network network) {
        NetworkBuilder result = network.toBuilder();
        for (int port : ports) {
            result.addRules(Rule.builder().anySource().tcp().port(port).createRule());
        }
        return result.createNetwork();
    }
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Examples of com.greentea.relaxation.jnmf.constructing.NetworkBuilder

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      Assert.assertEquals(imageLength, strImages[1].length());
      Assert.assertEquals(imageLength, strImages[2].length());

      int n = imageLength;

      NetworkBuilder builder = new NetworkBuilder();
      builder.setActivationFunctionClass(MyThresholdFunction.class);

      BufferedImage target = readImageFromFile(new File(TARGET_PATH + "target.bmp"));
      String strTarget = toBitString(target);
      HopfieldAlgorithm alg = new HopfieldAlgorithm(builder, strTarget, imageWidth);
      builder.setController(alg);
      builder.setDataSource(alg);

      // �������� 1-�� ����
      for (int i = 0; i < n; ++i)
      {
         for (int j = 0; j < n; ++j)
         {
            double w = 0;

            if (i != j)
            {
               for (int k = 0; k < strImages.length; ++k)
               {
                  w += toInt(strImages[k].charAt(i)) * toInt(strImages[k].charAt(j));
               }
            }

            //System.out.println(w);
            builder.addSynapseGenerative(0, i, w, 1, j);
         }
      }

      // �������� �������� ������
      for (int i = 0; i < n; ++i)
      {
         builder.addSynapseGenerative(1, i, 1, 0, i);
      }

      network = builder.getNetwork();
   }
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Examples of com.greentea.relaxation.jnmf.constructing.NetworkBuilder

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   private boolean disableNewClassesCreation;

   public Art2mAlgorithm()
   {
      super(new NetworkBuilder());
   }
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Examples of com.greentea.relaxation.jnmf.constructing.NetworkBuilder

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   private BackPropagation backPropagation;

   public LogisticRegressionAlgorithm()
   {
      super(new NetworkBuilder());
      backPropagation = new BackPropagation(builder.getNetwork());
   }
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Examples of com.greentea.relaxation.jnmf.constructing.NetworkBuilder

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      }
   };

   public CasCorAlgorithm()
   {
      super(new NetworkBuilder());

      casCorMath = new CasCorMathematics(builder.getNetwork());

      backPropagation = new BackPropagation(builder.getNetwork());
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Examples of com.greentea.relaxation.jnmf.constructing.NetworkBuilder

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   private BackPropagation backPropagation;

   public MultilayerPerceptronAlgorithm()
   {
      super(new NetworkBuilder());
      backPropagation = new BackPropagation(builder.getNetwork());
   }
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Examples of com.greentea.relaxation.jnmf.constructing.NetworkBuilder

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{
   public static final String ALGORITHM_NAME = "PNN";

   public PNNAlgorithm()
   {
      super(new NetworkBuilder());
   }
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Examples of org.apache.provisionr.api.network.NetworkBuilder

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        return result.createSoftware();
    }

    @VisibleForTesting
    Network apply(Network network) {
        NetworkBuilder result = network.toBuilder();
        for (int port : ports) {
            result.addRules(Rule.builder().anySource().tcp().port(port).createRule());
        }
        return result.createNetwork();
    }
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Examples of zdenekdrahos.AI.NeuralNetwork.Builder.NetworkBuilder

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            {0.1, 0.01, 0.15, 0.21, -0.50}
        }
    };

    public static INeuralNetwork getNetwork() {
        INetworkBuilder builder = new NetworkBuilder();
        INeuralNetwork network = builder.build(topology, activations);
        network.setWeights(weights);
        return network;
    }
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Examples of zdenekdrahos.AI.NeuralNetwork.Builder.NetworkBuilder

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        }

        int[] topology = {1, 2, 1};
        Activations[] activations = {Activations.LIN, Activations.SIG, Activations.LIN};

        INetworkBuilder networkBuilder = new NetworkBuilder();
        INeuralNetwork network = networkBuilder.build(topology, activations);

        TrainingInput in = new TrainingInput();
        in.iterationsCount = 2000;
        in.learningRate = 0.1;
        in.momentum = 0;
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