Examples of INetworkValues

Examples of zdenekdrahos.AI.FeedForward.INetworkValues

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        trainingSet = sets[0];
        testingSet = sets[1];
    }

    private void trainNetwork() {
        INetworkValues values;
        double[] inputPattern, targetPattern;
        for (int patternIndex = 0; patternIndex < trainingSet.size(); patternIndex++) {
            inputPattern = trainingSet.inputs.get(patternIndex);
            targetPattern = trainingSet.targets.get(patternIndex);
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Examples of zdenekdrahos.AI.FeedForward.INetworkValues

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    @Override
    public void simulate(double[] inputPattern) {
        if (network == null) {
            throw new NullPointerException("Network");
        }
        INetworkValues values = feedForward.buildNetwork(network, inputPattern);
        lastSimulation = values.getLayerValues(indexOfOutputLayer);
    }
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Examples of zdenekdrahos.AI.FeedForward.INetworkValues

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        network = NetworkTestUtil.getNetwork();
    }

    @Test
    public void testTrainNetwork() {
        INetworkValues values = feedForwardNetwork();
        double[][][] expectedWeights = {
            {
                {0.106478, -0.1},
                {-0.105682, 0.2},
                {0.284352, -0.15},
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Examples of zdenekdrahos.AI.FeedForward.INetworkValues

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        System.out.println();
        System.out.println("Prediction");
       
        IFeedForward feedForward = new FeedForward();
        INetworkValues values;
        double excepted, real;
        for (int i = 0; i < input.length; i++) {
            values = feedForward.buildNetwork(network, input[i]);
            for (int j = 0; j < output[i].length; j++) {
                excepted = output[i][j];
                real = values.getNeuronValue(network.getOutputLayerIndex(), j);
                System.out.printf("Expected %f , but result %f\n", excepted, real);
            }           
        }
       
    }
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