Esempio n. 1
0
        private static float[] GenerateWeights(SportSettings settings, List <Result> trainingCases, Func <Result, bool> expectedResultGetter,
                                               float acceptableError, float alpha, int maxIterations, float initialBias, out float bias)
        {
            float[] weights     = new float[3]; // Age, Height, IMC
            int     testCount   = trainingCases.Count;
            float   currentBias = initialBias;
            float   totalError  = float.MaxValue;

            int elapsedIterations = 0;

            // To avoid an infinite loop, either a loop count hard-cap or acceptable error threshold will be reached.
            while (elapsedIterations < maxIterations && totalError > acceptableError)
            {
                for (int i = 0; i < testCount; ++i)
                {
                    Result testCase       = trainingCases[i];
                    bool   expectedResult = expectedResultGetter(testCase);
                    bool   output         = GetResult(GetCriteriaResults(settings, testCase), weights, currentBias);
                    int    delta          = (expectedResult ? 1 : 0) - (output ? 1 : 0);

                    weights = UpdateWeights(weights, alpha, delta, settings, testCase);

                    currentBias += alpha * delta;
                }

                totalError = GetError(settings, trainingCases, expectedResultGetter, weights, currentBias);
                elapsedIterations++;
            }
            bias = currentBias;
            return(weights);
        }
Esempio n. 2
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 /// <summary>
 /// Obtains an array that describes what sets of criteria a data set has "cleared", and which they haven't.
 /// </summary>
 public static bool[] GetCriteriaResults(SportSettings settings, IEvaluatable toEvaluate)
 {
     return(new bool[]
     {
         (settings.MinAge <= toEvaluate.Edad && toEvaluate.Edad <= settings.MaxAge),
         (settings.MinHeight <= toEvaluate.Estatura && toEvaluate.Estatura <= settings.MaxHeight),
         (settings.MinIMC <= toEvaluate.IMC && toEvaluate.IMC <= settings.MaxIMC)
     });
 }
Esempio n. 3
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        public static float GetError(SportSettings settings, List <Result> trainingCases, Func <Result, bool> expectedResultGetter, float[] currentWeights, float bias)
        {
            float errorSum = 0;
            int   size     = trainingCases.Count;

            for (int i = 0; i < size; ++i)
            {
                Result testCase       = trainingCases[i];
                bool   expectedResult = expectedResultGetter(testCase);
                bool   output         = GetResult(GetCriteriaResults(settings, testCase), currentWeights, bias);
                errorSum += Math.Abs((expectedResult ? 1 : 0) - (output ? 1 : 0));
            }
            return(errorSum / ((float)size));
        }
Esempio n. 4
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        public static bool[] ExecutePerceptron(int maxIterations, float acceptableError, float alpha, float initialBias,
                                               SportSettings settings, List <Result> trainingCases, Func <Result, bool> expectedResultGetter, List <InputData> toEvaluate, TextBox resultsTextBox)
        {
            float[] weights        = GenerateWeights(settings, trainingCases, expectedResultGetter, acceptableError, alpha, maxIterations, initialBias, out float bias);
            float   weightsError   = GetError(settings, trainingCases, expectedResultGetter, weights, bias);
            int     toEvaluateSize = toEvaluate.Count;

            bool[] results = new bool[toEvaluateSize];

            for (int i = 0; i < toEvaluateSize; i++)
            {
                InputData next = toEvaluate[i];
                results[i] = GetResult(GetCriteriaResults(settings, next), weights, bias);
            }

            resultsTextBox.Text += $"La influencia de cada una de las características físicas para determinar la aptitud fue la siguiente:\nEdad: {weights[0]*100}% | Estatura: {weights[1]*100}% | IMC: {weights[2]*100}%\n";
            resultsTextBox.Text += $"El error final para estos pesos fue de {weightsError * 100}%.\n\n";

            return(results);
        }
Esempio n. 5
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        public static float[] UpdateWeights(float[] currentWeights, float alpha, float delta, SportSettings settings, IEvaluatable toEvaluate)
        {
            bool[]  criteriaResults = GetCriteriaResults(settings, toEvaluate);
            float[] result          = new float[3]; // Age, Height, IMC
            int     size            = result.Length;

            for (int i = 0; i < size; i++)
            {
                result[i] = currentWeights[i] + (alpha * delta * (criteriaResults[i] ? 1 : 0));
            }
            return(result);
        }