public static ValueType[] CropValuesEnd <ValueType>(ValueType[] array, ValueType crop_value)
{
int index_last_non_value = -1;
for (int index = 0; index < array.Length; index++)
{
if (!array[index].Equals(crop_value))
{
index_last_non_value = index;
}
}
return(ToolsCollection.Select(array, 0, index_last_non_value + 1));
}
public static int[] CropZerosEnd(int[] array)
{
int index_last_non_zero = -1;
for (int index = 0; index < array.Length; index++)
{
if (array[index] != 0)
{
index_last_non_zero = index;
}
}
return(ToolsCollection.Select(array, 0, index_last_non_zero + 1));
}
public float[] back_propagate(float[] out_error)
{
//only use after forward signal has been processed
//store local copy of the output error
d_output_error = ToolsCollection.Select(out_error, 0, d_output_size); //will remove bias of next layer
//calculate and store activation error
for (int o = 0; o < d_output_size; o++)
{
activation_error[o] = d_output_function_derivative.Compute((float)d_activation[o]) * d_output_error[o];
}
//calculate and store the input error
ToolsCollection.SetValue(input_error, 0);
for (int i = 0; i < d_input_size; i++)
{
for (int o = 0; o < d_output_size; o++)
{
input_error[i] += activation_error[o] * d_weights[o, i];
}
}
//and return signal to be processed by previous layers
return(input_error);
}
public void TestCreateMedium()
{
PriceSet price_set = new PriceSet(ToolsPrice.DefaultSymbolGBPUSD, ToolsCollection.Select(ToolsPrice.GetPriceList(ToolsPrice.DefaultSymbolGBPUSD), 0, 40));
List <IIndicator> indicators_features = new List <IIndicator>();
indicators_features.Add(new IndicatorRunningAverage(0));
indicators_features.Add(new IndicatorRunningAverage(1));
List <IIndicator> indicators_labels = new List <IIndicator>();
indicators_labels.Add(new IndicatorMagicProfit(0));
indicators_labels.Add(new IndicatorMagicProfit(1));
MarketModelSimulation market = new MarketModelSimulation(1000, price_set);
IDataSet <double, double> dataset = ToolsTradingDataSet.CreateDataSet(market, new IndicatorFusion(indicators_features), new IndicatorFusion(indicators_labels));
Assert.AreEqual(39, dataset.InstanceCount);
Assert.AreEqual(2, dataset.FeatureCount);
Assert.AreEqual(4, dataset.LabelCount);
}
public override LabelType GetLabel(DomainType[] instance_features)
{
return(model.GetLabel(ToolsCollection.Select(instance_features, selected_feature_indexes)));
}