Exemple #1
0
        /// <summary>
        /// Trains the ANN that has been previously been loaded using "LoadANN()"
        /// </summary>
        /// <param name="?"></param>
        /// <returns></returns>
        public bool TrainAnn(TrainOptions options, int numTrainingSets, float[] inputs, float[] outputs, ref int terminatedAfterIter)
        {
            int numInputLayers  = inputs.Count() / numTrainingSets;
            int numOutputLayers = 1;

            Console.WriteLine("Starting Training with theses parameters: " +
                              " Number of Training Datasets: " + numTrainingSets +
                              " Number of Input Layers: " + numInputLayers +
                              " Number of Output Layers: " + numOutputLayers);

            Console.WriteLine("Inputs:");
            for (int i = 0; i < inputs.Count(); i++)
            {
                if (i == 16)
                {
                    Console.WriteLine();
                }
                Console.Write(inputs[i] + " ");
            }
            Console.WriteLine("Outputs:");
            for (int i = 0; i < outputs.Count(); i++)
            {
                if (i == 1)
                {
                    Console.WriteLine();
                }
                Console.Write(outputs[i] + " ");
            }

            int res = klu_trainAnn(options, numTrainingSets, inputs, numInputLayers, outputs, numOutputLayers, out terminatedAfterIter);

            Console.WriteLine("Training finished after iteration #: " + terminatedAfterIter);

            return(res == 1);
        }
Exemple #2
0
 private static extern int klu_trainAnn(
     [In, MarshalAs(UnmanagedType.LPStruct)] TrainOptions options,
     int numTrainingSets,
     [In, MarshalAs(UnmanagedType.LPArray)] float[] inputs,
     int numInputNeurons,
     [In, MarshalAs(UnmanagedType.LPArray)] float[] outputs,
     int numOutputNeurons,
     out int terminatedAfterIter
     );
Exemple #3
0
        /// <summary>
        /// Trains the ANN that has been previously been loaded using "LoadANN()"
        /// </summary>
        /// <param name="?"></param>
        /// <returns></returns>
        public bool TrainAnn(TrainOptions options, int numTrainingSets, float[] inputs, float[] outputs, ref int terminatedAfterIter)
        {
            int numInputLayers = inputs.Count() / numTrainingSets;
            int numOutputLayers = 1;
            Console.WriteLine("Starting Training with theses parameters: " +
                " Number of Training Datasets: " + numTrainingSets +
                " Number of Input Layers: " + numInputLayers +
                " Number of Output Layers: " + numOutputLayers);

            Console.WriteLine("Inputs:");
            for (int i = 0; i < inputs.Count(); i++)
            {
                if (i == 16)
                {
                    Console.WriteLine();
                }
                Console.Write(inputs[i] + " ");
            }
            Console.WriteLine("Outputs:");
            for (int i = 0; i < outputs.Count(); i++)
            {
                if (i == 1)
                {
                    Console.WriteLine();
                }
                Console.Write(outputs[i] + " ");
            }

            int res = klu_trainAnn(options, numTrainingSets, inputs, numInputLayers, outputs, numOutputLayers, out terminatedAfterIter);

            Console.WriteLine("Training finished after iteration #: " + terminatedAfterIter);

            return (res == 1);
        }