Exemple #1
0
        /// <summary>
        /// Loads a neural network from a .ann file.
        /// </summary>
        /// <param name="path">The .ann file path.</param>
        /// <returns>The neural network.</returns>
        public NeuralNetwork LoadNeuralNetwork(string path)
        {
            var annFile = AnnFile.Open(path);
            var network = LoadNeuralNetwork(annFile);

            return(network);
        }
Exemple #2
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        public void AnnFileInputOutputTest()
        {
            var path = Environment.GetFolderPath(Environment.SpecialFolder.Desktop);

            _annFile.WriteToDisk(path);

            var annFile = AnnFile.Open(Path.Combine(path, _name));

            Assert.AreEqual(_annFile.Name, annFile.Name, "Failed to read .ann file name");
            Assert.AreEqual(_annFile.Activation, annFile.Activation, "Failed to read .ann file activation function.");

            for (var i = 0; i < annFile.InputNeurons.Count; i++)
            {
                Assert.AreEqual(_annFile.InputNeurons[i], annFile.InputNeurons[i], "Failed to read .ann file input neurons.");
            }

            for (var i = 0; i < annFile.OutputNeurons.Count; i++)
            {
                Assert.AreEqual(_annFile.OutputNeurons[i], annFile.OutputNeurons[i], "Failed to read .ann file output neurons.");
            }

            var n = annFile.AdjacencyMatrix.GetLength(0);
            var m = annFile.AdjacencyMatrix.GetLength(1);

            for (var i = 0; i < n; i++)
            {
                for (var j = 0; j < m; j++)
                {
                    Assert.AreEqual(_annFile.AdjacencyMatrix[i, j], annFile.AdjacencyMatrix[i, j], "Failed to read .ann file adjacency matrix.");
                }
            }
        }
Exemple #3
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        /// <summary>
        /// Loads a neural network from a .ann file.
        /// </summary>
        /// <param name="annFile">The .ann file/</param>
        /// <returns>The neural network.</returns>
        public NeuralNetwork LoadNeuralNetwork(AnnFile annFile)
        {
            var network = new NeuralNetwork(annFile);

            return(network);
        }