コード例 #1
0
        private void button3_Click(object sender, EventArgs e)
        {
            Glass newGlass     = new Glass(-1, (double)riNum.Value, (double)naNum.Value, (double)mgNum.Value, (double)alNum.Value, (double)siNum.Value, (double)kNum.Value, (double)caNum.Value, (double)baNum.Value, (double)feNum.Value, -1);
            var   neuralOutput = network.GetOutputs(newGlass);

            output(("[BACK PROPOGATION] Siūloma tipas: "******"[K-Nearest-Neighbors] Siūloma tipas: "******"[Bayes] Siūloma tipas: " + bayes.test(newGlass)));
        }
コード例 #2
0
        private void bajes_start_Click(object sender, EventArgs e)
        {
            double       averageAccurasy = 0;
            List <Glass> glasses         = new List <Glass>();

            if (radioButton1.Checked)
            {
                glasses = dataList;
            }
            else
            {
                glasses = clearDistantValuesFromList(dataList);
            }
            int testingFileCount = glasses.Count / NUMBER_OF_SEGMENTS;

            for (int segment = 0; segment < NUMBER_OF_SEGMENTS; segment++)
            {
                richTextBox1.AppendText(String.Format("Crosscheck segment {0} \n ", segment + 1));
                int correctGuesses       = 0;
                int guessCounter         = 0;
                int falseGuesses         = 0;
                int testingDataFromIndex = segment * testingFileCount;
                int testingDataToIndex   = (segment + 1) * testingFileCount;
                var trainingData         = new List <Glass>();
                var testingData          = new List <Glass>();
                for (int i = 0; i < glasses.Count; i++)
                {
                    if (i >= testingDataFromIndex && i <= testingDataToIndex)
                    {
                        testingData.Add(glasses[i]);
                    }
                    else
                    {
                        trainingData.Add(glasses[i]);
                    }
                }
                BayesClassifier bayes = new BayesClassifier(trainingData.Count);
                bayes.trainClassifier(trainingData);
                foreach (Glass glass in testingData)
                {
                    int guess = bayes.test(glass);
                    if (guess == glass.group_type)
                    {
                        correctGuesses++;
                    }
                    else
                    {
                        falseGuesses++;
                    }
                    guessCounter++;
                }
                var    totalGlasses = glasses.Count;
                var    totalTrain   = trainingData.Count;
                var    totalTest    = testingData.Count;
                double trainProc    = Math.Round(((double)totalTrain / totalGlasses) * 100, 2);
                double testProc     = Math.Round(((double)totalTest / totalGlasses) * 100, 2);
                double accuracy     = Math.Round(((double)correctGuesses / guessCounter) * 100, 2);
                averageAccurasy += accuracy;

                richTextBox1.AppendText(string.Format("Total: {0, 3}; Training: {1, 3}({2, 4}%); Testing: {3, 3}({4, 4}%)\n", totalGlasses, totalTrain, trainProc, totalTest, testProc));
                richTextBox1.AppendText(string.Format("Correct guesses: {0, 3} ; Total guesses: {1, 3} ; Accuracy: {2, 4}%\n", correctGuesses, guessCounter, accuracy));
            }
            richTextBox1.AppendText(string.Format("Average accurasy of all segments - {0}%\n", averageAccurasy / NUMBER_OF_SEGMENTS));
        }