void drawCelsiusFaringate2()
        {
            var ira = new IRINA_NN(new int[] { 1, 6, 9, 3, 1 }, 0.2);

            var input = new List <double[]>
            {
                new double[] { 0.040 },
                new double[] { 0.035 },
                new double[] { 0.037 },
                new double[] { 0.048 },
                new double[] { -0.012 },
                new double[] { -0.040 },
                new double[] { 0.057 },
                new double[] { 0.089 },
                new double[] { -0.005 },
                new double[] { 0.100 },
                new double[] { -0.042 }
            };

            var target = new List <double[]>
            {
                new double[] { 0.104 },
                new double[] { 0.095 },
                new double[] { 0.0986 },
                new double[] { 0.1184 },
                new double[] { 0.0104 },
                new double[] { -0.040 },
                new double[] { 0.1346 },
                new double[] { 0.1922 },
                new double[] { 0.023 },
                new double[] { 0.212 },
                new double[] { -0.0436 }
            };

            var samplesCount = input.Count;

            var random = new Random();

            for (int epoch = 0; epoch < count; ++epoch)
            {
                var index = random.Next(0, samplesCount);

                var inputItem  = input[index];
                var targetItem = target[index];

                ira.Train(inputItem, targetItem);

                var error = IRINA_NN.MSE(ira.Predict(inputItem), inputItem);

                if (epoch % even == 0)
                {
                    SeriesCollection[0].Values.Add(error);
                }
            }

            var result = ira.Predict(new double[] { 0.67 })[0];

            result = 0d;
        }
        void drawXor()
        {
            var ira = new IRINA_NN(new int[] { 2, 4, 4, 1 }, 0.2);

            var e1 = ira.Predict(input: new double[] { 0.99, 0.99 })[0];
            var e2 = ira.Predict(input: new double[] { 0.99, 0.01 })[0];
            var e3 = ira.Predict(input: new double[] { 0.01, 0.99 })[0];
            var e4 = ira.Predict(input: new double[] { 0.01, 0.01 })[0];

            var error = (e1 + e2 + e3 + e4) / 4d;

            for (int epoch = 0; epoch < count; ++epoch)
            {
                ira.Train(
                    input: new double[] { 0.01, 0.01 },
                    target: new double[] { 0.01 }
                    );

                e1 = IRINA_NN.MSE(ira.Predict(new double[] { 0.01, 0.01 }), new double[] { 0.01 });

                ira.Train(
                    input: new double[] { 0.99, 0.01 },
                    target: new double[] { 0.99 }
                    );

                e2 = IRINA_NN.MSE(ira.Predict(new double[] { 0.99, 0.01 }), new double[] { 0.99 });

                ira.Train(
                    input: new double[] { 0.01, 0.99 },
                    target: new double[] { 0.99 }
                    );

                e3 = IRINA_NN.MSE(ira.Predict(new double[] { 0.01, 0.99 }), new double[] { 0.99 });

                ira.Train(
                    input: new double[] { 0.99, 0.99 },
                    target: new double[] { 0.01 }
                    );

                e4 = IRINA_NN.MSE(ira.Predict(new double[] { 0.99, 0.99 }), new double[] { 0.01 });

                error = (e1 + e2 + e3 + e4) / 4d;

                //lbOutput.Items.Add($"Ошибка эпохи { epoch }: { error }");

                if (epoch % even == 0)
                {
                    SeriesCollection[0].Values.Add(error);
                }
            }

            e2 = ira.Predict(input: new double[] { 0.99, 0.01 })[0];
            e1 = ira.Predict(input: new double[] { 0.99, 0.99 })[0];
            e4 = ira.Predict(input: new double[] { 0.01, 0.01 })[0];
            e3 = ira.Predict(input: new double[] { 0.01, 0.99 })[0];
            e1 = e2 = e3 = e4;
        }
        void drawCelsiusFaringate()
        {
            var ira = new IRINA_NN(new int[] { 8, 16, 32, 16, 8 }, 0.2);

            for (int epoch = 0; epoch < count; ++epoch)
            {
                ira.Train(                 // 40 - 104
                    input: new double[] { 0.01, 0.01, 0.99, 0.01, 0.99, 0.01, 0.01, 0.01 },
                    target: new double[] { 0.01, 0.99, 0.99, 0.01, 0.99, 0.01, 0.01, 0.01 }
                    );

                var e1 = IRINA_NN.MSE(ira.Predict(new double[] { 0.01, 0.01, 0.99, 0.01, 0.99, 0.01, 0.01, 0.01 }), new double[] { 0.01, 0.99, 0.99, 0.01, 0.99, 0.01, 0.01, 0.01 });

                ira.Train(                 // 60 - 140
                    input: new double[] { 0.01, 0.01, 0.99, 0.99, 0.99, 0.99, 0.01, 0.01 },
                    target: new double[] { 0.99, 0.01, 0.01, 0.01, 0.01, 0.99, 0.99, 0.01 }
                    );

                var e2 = IRINA_NN.MSE(ira.Predict(new double[] { 0.01, 0.01, 0.99, 0.99, 0.99, 0.99, 0.01, 0.01 }), new double[] { 0.99, 0.01, 0.01, 0.01, 0.01, 0.99, 0.99, 0.01 });

                ira.Train(                 // 15 - 59
                    input: new double[] { 0.01, 0.01, 0.01, 0.01, 0.99, 0.99, 0.99, 0.99 },
                    target: new double[] { 0.01, 0.01, 0.99, 0.99, 0.99, 0.01, 0.99, 0.99 }
                    );

                var e3 = IRINA_NN.MSE(ira.Predict(new double[] { 0.01, 0.01, 0.01, 0.01, 0.99, 0.99, 0.99, 0.99 }), new double[] { 0.01, 0.01, 0.99, 0.99, 0.99, 0.01, 0.99, 0.99 });

                ira.Train(                 // 25 - 77
                    input: new double[] { 0.01, 0.01, 0.01, 0.99, 0.99, 0.01, 0.01, 0.99 },
                    target: new double[] { 0.01, 0.99, 0.01, 0.01, 0.99, 0.99, 0.01, 0.99 }
                    );

                var e4 = IRINA_NN.MSE(ira.Predict(new double[] { 0.01, 0.01, 0.01, 0.99, 0.99, 0.01, 0.01, 0.99 }), new double[] { 0.01, 0.99, 0.01, 0.01, 0.99, 0.99, 0.01, 0.99 });

                var error = (e1 + e2 + e3 + e4) / 4d;

                if (epoch % even == 0)
                {
                    SeriesCollection[0].Values.Add(error);
                }
            }

            var predicted = ira.Predict(new double[] { 0.01, 0.01, 0.01, 0.99, 0.99, 0.01, 0.01, 0.99 });
            var target    = new double[] { 0.01, 0.99, 0.01, 0.01, 0.99, 0.99, 0.01, 0.99 };

            var err = IRINA_NN.MSE(predicted, target);

            predicted = ira.Predict(new double[] { 0.01, 0.01, 0.99, 0.01, 0.01, 0.01, 0.99, 0.99 });
            target    = new double[] { 0.01, 0.99, 0.01, 0.99, 0.99, 0.99, 0.99, 0.99 };

            err = IRINA_NN.MSE(predicted, target);
        }
        void drawIris()
        {
            var ira = new IRINA_NN(new int[] { 4, 16, 64, 24, 3 }, 0.02);

            IrisDataReader irisDataReader = new IrisDataReader(@"D:\NeuralNetworks\DataSets\Iris Plants Database\bezdekIris.data");

            var rand = new Random();

            for (int epoch = 0; epoch < count; ++epoch)
            {
                var irisData = irisDataReader.Read(rand.Next(0, 150));
                var(input, target) = irisToNN(irisData);

                ira.Train(input, target);

                var predict = ira.Predict(input);

                var error = IRINA_NN.MSE(predict, target);

                if (epoch % even == 0)
                {
                    SeriesCollection[0].Values.Add(error);
                }
            }

            var data = irisDataReader.Read(25);

            var(inputRes, targetRes) = irisToNN(data);

            var predictRes = ira.Predict(inputRes);

            var errorRes = IRINA_NN.MSE(predictRes, targetRes);

            data = irisDataReader.Read(72);
            (inputRes, targetRes) = irisToNN(data);

            predictRes = ira.Predict(inputRes);

            errorRes = IRINA_NN.MSE(predictRes, targetRes);

            data = irisDataReader.Read(119);
            (inputRes, targetRes) = irisToNN(data);

            predictRes = ira.Predict(inputRes);

            errorRes = IRINA_NN.MSE(predictRes, targetRes);
        }