//установление заголовков для отчета по отключения нейронов в xlsx файл
public static ExcelWorksheet setNeuronsWorkBookHeaders(ExcelWorksheet QualityWorkSheet, Record[] rangeNeurons)
{
for (int header = 0; header < rangeNeurons.Length; header++)
{
QualityWorkSheet.Cells[1, header + 2].Value = rangeNeurons[header].numberLayer.ToString() + ":" + rangeNeurons[header].numberNeuron.ToString();
}
QualityWorkSheet.Cells[1, rangeNeurons.Length + 2].Value = "Число отключенных нейронов";
QualityWorkSheet.Cells[1, rangeNeurons.Length + 3].Value = "ЧОН/Нейронов всего";
QualityWorkSheet.Cells[1, rangeNeurons.Length + 4].Value = "Соотношение нейронов";
return QualityWorkSheet;
}
//установление заголовков для отчета по связям нейронов в xls файл
public static ExcelWorksheet setWeightsWorkBookHeaders(ExcelWorksheet QualityWorkSheet, Record[] rangeNeurons)
{
int step = 1;
for (int header = 0; header < rangeNeurons.Length; header++)
{
QualityWorkSheet.Cells[1, step + 1].Value = rangeNeurons[header].numberLayer.ToString() + ":" + rangeNeurons[header].numberNeuron.ToString() + "|Вх.";
QualityWorkSheet.Cells[1, step + 2].Value = rangeNeurons[header].numberLayer.ToString() + ":" + rangeNeurons[header].numberNeuron.ToString() + "|Исх.";
step += 2;
}
return QualityWorkSheet;
}
/// <summary>
/// Computes output value of neuron.
/// </summary>
///
/// <param name="input">Input vector.</param>
///
/// <returns>Returns neuron's output value.</returns>
///
/// <remarks><para>The output value of activation neuron is equal to value
/// of nueron's activation function, which parameter is weighted sum
/// of its inputs plus threshold value. The output value is also stored
/// in <see cref="Neuron.Output">Output</see> property.</para>
///
/// <para><note>The method may be called safely from multiple threads to compute neuron's
/// output value for the specified input values. However, the value of
/// <see cref="Neuron.Output"/> property in multi-threaded environment is not predictable,
/// since it may hold neuron's output computed from any of the caller threads. Multi-threaded
/// access to the method is useful in those cases when it is required to improve performance
/// by utilizing several threads and the computation is based on the immediate return value
/// of the method, but not on neuron's output property.</note></para>
/// </remarks>
///
/// <exception cref="ArgumentException">Wrong length of the input vector, which is not
/// equal to the <see cref="Neuron.InputsCount">expected value</see>.</exception>
///
public override double Compute( double[] input )
{
this.relationsValues = new List<Record>();
// check for corrent input vector
if ( input.Length != inputsCount )
throw new ArgumentException( "Wrong length of the input vector." );
// initial sum value
double sum = 0.0;
// compute weighted sum of inputs
for ( int i = 0; i < weights.Length; i++ )
{
sum += weights[i] * input[i];
Record elem = new Record();
elem.numberWeight = i;
elem.value = weights[i] * input[i];
this.relationsValues.Add(elem);
}
sum += threshold;
// local variable to avoid mutlithreaded conflicts
double output = function.Function( sum );
// assign output property as well (works correctly for single threaded usage)
this.output = output;
return output;
}
