protected override List <List <double> > GenerateValues()
{
List <List <double> > data = new List <List <double> >();
List <double> oneVariableTestData = SequenceGenerator.GenerateSteps(-0.05m, 6.05m, 0.02m).Select(v => (double)v).ToList();
List <List <double> > testData = new List <List <double> >()
{
oneVariableTestData, oneVariableTestData
};
var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList <IEnumerable <double> >();
var rand = new MersenneTwister((uint)Seed);
for (int i = 0; i < AllowedInputVariables.Count(); i++)
{
data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), 30, 0.1, 5.9).ToList());
data[i].AddRange(combinations[i]);
}
double x1, x2;
List <double> results = new List <double>();
for (int i = 0; i < data[0].Count; i++)
{
x1 = data[0][i];
x2 = data[1][i];
results.Add(6 * Math.Sin(x1) * Math.Cos(x2));
}
data.Add(results);
return(data);
}
protected override List <List <double> > GenerateValues()
{
List <List <double> > data = new List <List <double> >();
List <double> evenlySpacedSequence = SequenceGenerator.GenerateSteps(-5, 5, 0.4m).Select(v => (double)v).ToList();
List <List <double> > trainingData = new List <List <double> >()
{
evenlySpacedSequence, evenlySpacedSequence
};
var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(trainingData).ToList();
var rand = new MersenneTwister((uint)Seed);
for (int i = 0; i < AllowedInputVariables.Count(); i++)
{
data.Add(combinations[i].ToList());
data[i].AddRange(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), 1000, -5, 5).ToList());
}
double x, y;
List <double> results = new List <double>();
for (int i = 0; i < data[0].Count; i++)
{
x = data[0][i];
y = data[1][i];
results.Add(1 / (1 + Math.Pow(x, -4)) + 1 / (1 + Math.Pow(y, -4)));
}
data.Add(results);
return(data);
}
protected override List <List <double> > GenerateValues()
{
List <List <double> > data = new List <List <double> >();
List <double> oneVariableTestData = SequenceGenerator.GenerateSteps(-3, 3, 0.1m).Select(v => (double)v).ToList();
List <List <double> > testData = new List <List <double> >()
{
oneVariableTestData, oneVariableTestData
};
var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList();
var rand = new MersenneTwister((uint)Seed);
for (int i = 0; i < AllowedInputVariables.Count(); i++)
{
data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), 20, -3, 3).ToList());
data[i].AddRange(combinations[i]);
}
double x, y;
List <double> results = new List <double>();
for (int i = 0; i < data[0].Count; i++)
{
x = data[0][i];
y = data[1][i];
results.Add(Math.Pow(x, 4) - Math.Pow(x, 3) + Math.Pow(y, 2) / 2 - y);
}
data.Add(results);
return(data);
}
protected override List <List <double> > GenerateValues()
{
List <List <double> > data = new List <List <double> >();
List <double> oneVariableTestData = SequenceGenerator.GenerateSteps(-3, 3, 0.01m).Select(v => (double)v).ToList();
List <List <double> > testData = new List <List <double> >()
{
oneVariableTestData, oneVariableTestData
};
var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList();
for (int i = 0; i < AllowedInputVariables.Count(); i++)
{
data.Add(ValueGenerator.GenerateUniformDistributedValues(20, -3, 3).ToList());
data[i].AddRange(combinations[i]);
}
double x, y;
List <double> results = new List <double>();
for (int i = 0; i < data[0].Count; i++)
{
x = data[0][i];
y = data[1][i];
results.Add(6 * Math.Sin(x) * Math.Cos(y));
}
data.Add(results);
return(data);
}
protected override List <List <double> > GenerateValues()
{
List <List <double> > data = new List <List <double> >();
List <double> oneVariableTestData = SequenceGenerator.GenerateSteps(-0.25m, 6.35m, 0.2m).Select(v => (double)v).ToList();
List <List <double> > testData = new List <List <double> >()
{
oneVariableTestData, oneVariableTestData
};
var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList <IEnumerable <double> >();
for (int i = 0; i < AllowedInputVariables.Count(); i++)
{
data.Add(ValueGenerator.GenerateUniformDistributedValues(50, 0.05, 6.05).ToList());
data[i].AddRange(combinations[i]);
}
double x1, x2;
List <double> results = new List <double>();
for (int i = 0; i < data[0].Count; i++)
{
x1 = data[0][i];
x2 = data[1][i];
results.Add((Math.Pow(x1 - 3, 4) + Math.Pow(x2 - 3, 3) - x2 + 3) / (Math.Pow(x2 - 2, 4) + 10));
}
data.Add(results);
return(data);
}
protected override List <List <double> > GenerateValues()
{
List <List <double> > data = new List <List <double> >();
int n = 300;
var rand = new MersenneTwister((uint)Seed);
data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), n, 0.05, 2).ToList());
data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), n, 1, 2).ToList());
data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), n, 0.05, 2).ToList());
List <List <double> > testData = new List <List <double> >()
{
SequenceGenerator.GenerateSteps(-0.05m, 2.05m, 0.15m).Select(v => (double)v).ToList(),
SequenceGenerator.GenerateSteps(0.95m, 2.05m, 0.1m).Select(v => (double)v).ToList(),
SequenceGenerator.GenerateSteps(-0.05m, 2.05m, 0.15m).Select(v => (double)v).ToList()
};
var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList <IEnumerable <double> >();
for (int i = 0; i < AllowedInputVariables.Count(); i++)
{
data[i].AddRange(combinations[i]);
}
double x1, x2, x3;
List <double> results = new List <double>();
for (int i = 0; i < data[0].Count; i++)
{
x1 = data[0][i];
x2 = data[1][i];
x3 = data[2][i];
results.Add(30 * ((x1 - 1) * (x3 - 1)) / (Math.Pow(x2, 2) * (x1 - 10)));
}
data.Add(results);
return(data);
}
protected override List <List <double> > GenerateValues()
{
List <List <double> > data = new List <List <double> >();
List <List <double> > trainingData = new List <List <double> >()
{
SequenceGenerator.GenerateSteps(0.05m, 10, 0.1m).Select(v => (double)v).ToList(),
SequenceGenerator.GenerateSteps(0.05m, 10.05m, 2).Select(v => (double)v).ToList()
};
List <List <double> > testData = new List <List <double> >()
{
SequenceGenerator.GenerateSteps(-0.5m, 10.5m, 0.05m).Select(v => (double)v).ToList(),
SequenceGenerator.GenerateSteps(-0.5m, 10.5m, 0.5m).Select(v => (double)v).ToList()
};
var trainingComb = ValueGenerator.GenerateAllCombinationsOfValuesInLists(trainingData).ToList <IEnumerable <double> >();
var testComb = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList <IEnumerable <double> >();
for (int i = 0; i < AllowedInputVariables.Count(); i++)
{
data.Add(trainingComb[i].ToList());
data[i].AddRange(testComb[i]);
}
double x1, x2;
List <double> results = new List <double>();
for (int i = 0; i < data[0].Count; i++)
{
x1 = data[0][i];
x2 = data[1][i];
results.Add(Math.Exp(-x1) * Math.Pow(x1, 3) * Math.Cos(x1) * Math.Sin(x1) * (Math.Cos(x1) * Math.Pow(Math.Sin(x1), 2) - 1) * (x2 - 5));
}
data.Add(results);
return(data);
}