/// <summary>
/// Generate a random training set.
/// </summary>
/// <param name="seed">The seed value to use, the same seed value will always produce
/// the same results.</param>
/// <param name="count">How many training items to generate.</param>
/// <param name="inputCount">How many input numbers.</param>
/// <param name="idealCount">How many ideal numbers.</param>
/// <param name="min">The minimum random number.</param>
/// <param name="max">The maximum random number.</param>
/// <returns>The random training set.</returns>
public static BasicMLDataSet Generate(long seed,
int count, int inputCount,
int idealCount, double min, double max)
{
var rand =
new LinearCongruentialGenerator(seed);
var result = new BasicMLDataSet();
for (int i = 0; i < count; i++)
{
var inputData = new BasicMLData(inputCount);
for (int j = 0; j < inputCount; j++)
{
inputData[j] = rand.Range(min, max);
}
var idealData = new BasicMLData(idealCount);
for (int j = 0; j < idealCount; j++)
{
idealData[j] = rand.Range(min, max);
}
var pair = new BasicMLDataPair(inputData,
idealData);
result.Add(pair);
}
return result;
}
public ConsistentRandomizer(double min, double max, int seed)
{
this._xffd6352b2e5d70e3 = max;
this._xd088075e67f6ea91 = min;
this._x5f33b0fc94247cc0 = seed;
this._xc25f3ba15c9fba69 = new LinearCongruentialGenerator((long) seed);
}
/// <summary>
/// Generate random training into a training set.
/// </summary>
/// <param name="training">The training set to generate into.</param>
/// <param name="seed">The seed to use.</param>
/// <param name="count">How much data to generate.</param>
/// <param name="min">The low random value.</param>
/// <param name="max">The high random value.</param>
public static void Generate(IMLDataSetAddable training,
long seed,
int count,
double min, double max)
{
var rand
= new LinearCongruentialGenerator(seed);
int inputCount = training.InputSize;
int idealCount = training.IdealSize;
for (int i = 0; i < count; i++)
{
var inputData = new BasicMLData(inputCount);
for (int j = 0; j < inputCount; j++)
{
inputData[j] = rand.Range(min, max);
}
var idealData = new BasicMLData(idealCount);
for (int j = 0; j < idealCount; j++)
{
idealData[j] = rand.Range(min, max);
}
var pair = new BasicMLDataPair(inputData,
idealData);
training.Add(pair);
}
}
/// <summary>
/// Construct a range randomizer.
/// </summary>
/// <param name="seed">The seed for the random number generator.</param>
/// <param name="min">The minimum random value.</param>
/// <param name="max">The maximum random value.</param>
public ConsistentRandomizer(int seed, double min,
double max)
{
this.rand = new LinearCongruentialGenerator(seed);
this.Max = max;
this.Min = min;
}
public static BasicMLDataSet Generate(long seed, int count, int inputCount, int idealCount, double min, double max)
{
IMLData data;
int num2;
IMLData data2;
BasicMLDataPair pair;
LinearCongruentialGenerator generator = new LinearCongruentialGenerator(seed);
BasicMLDataSet set = new BasicMLDataSet();
int num = 0;
goto Label_0018;
Label_000C:
set.Add(pair);
Label_0014:
num++;
Label_0018:
if (num < count)
{
data = new BasicMLData(inputCount);
num2 = 0;
while (num2 < inputCount)
{
data.Data[num2] = generator.Range(min, max);
num2++;
}
data2 = new BasicMLData(idealCount);
if (((uint) count) < 0)
{
goto Label_000C;
}
if ((((uint) idealCount) & 0) != 0)
{
goto Label_0014;
}
int num3 = 0;
Label_002D:
if (num3 < idealCount)
{
data2[num3] = generator.Range(min, max);
if ((((uint) idealCount) - ((uint) max)) <= uint.MaxValue)
{
num3++;
goto Label_002D;
}
}
else
{
goto Label_0032;
}
goto Label_00C6;
}
if (0 == 0)
{
return set;
}
Label_0032:
pair = new BasicMLDataPair(data, data2);
Label_00C6:
if ((((uint) num2) & 0) == 0)
{
goto Label_000C;
}
goto Label_0018;
}
public static void Generate(IMLDataSet training, long seed, int count, double min, double max)
{
int num;
int idealSize;
int num3;
IMLData data;
int num4;
IMLData data2;
int num5;
LinearCongruentialGenerator generator = new LinearCongruentialGenerator(seed);
goto Label_0111;
Label_0023:
if (num3 < count)
{
data = new BasicMLData(num);
num4 = 0;
while (true)
{
if (num4 < num)
{
data[num4] = generator.Range(min, max);
}
else
{
data2 = new BasicMLData(idealSize);
num5 = 0;
goto Label_004D;
}
num4++;
}
}
if ((((uint) num) - ((uint) num)) >= 0)
{
if ((((uint) num4) - ((uint) num3)) >= 0)
{
if ((((uint) seed) + ((uint) seed)) >= 0)
{
return;
}
goto Label_0111;
}
goto Label_00F5;
}
Label_0047:
num5++;
Label_004D:
if (num5 >= idealSize)
{
if ((((uint) max) + ((uint) num3)) <= uint.MaxValue)
{
BasicMLDataPair inputData = new BasicMLDataPair(data, data2);
training.Add(inputData);
num3++;
}
}
else
{
data2[num5] = generator.Range(min, max);
if (((uint) num5) >= 0)
{
goto Label_0047;
}
}
goto Label_0023;
Label_00F5:
num3 = 0;
goto Label_0023;
Label_0111:
num = training.InputSize;
idealSize = training.IdealSize;
goto Label_00F5;
}
