private DataString[] CreateStringCbAdfData(int numSamples, int randomSeed = 0)
{
var random = new Random(randomSeed);
var sampleData = new DataString[numSamples];
for (int i = 0; i < numSamples; i++)
{
int numActions = random.Next(2, 5);
int[] fIndex = Enumerable.Range(1, numActions).OrderBy(ind => random.Next()).Take(numActions).ToArray();
var features = new string[numActions][];
for (int j = 0; j < numActions; j++)
{
features[j] = new string[]
{
"a_" + fIndex[j],
"b_" + fIndex[j],
"c_" + fIndex[j],
"d_" + fIndex[j]
};
}
var adf = new DataStringADF[numActions];
int labelIndex = random.Next(-1, numActions);
for (int j = 0; j < numActions; j++)
{
adf[j] = new DataStringADF {
Features = features[j]
};
if (j == labelIndex)
{
adf[j].Label = new ContextualBanditLabel
{
Cost = (float)random.NextDouble(),
Probability = (float)random.NextDouble()
};
}
}
sampleData[i] = new DataString
{
ActionDependentFeatures = adf
};
}
return(sampleData);
}
private DataString[] CreateStringCbAdfData(int numSamples, int randomSeed = 0)
{
var random = new Random(randomSeed);
var sampleData = new DataString[numSamples];
for (int i = 0; i < numSamples; i++)
{
int numActions = random.Next(2, 5);
int[] fIndex = Enumerable.Range(1, numActions).OrderBy(ind => random.Next()).Take(numActions).ToArray();
var features = new string[numActions][];
for (int j = 0; j < numActions; j++)
{
features[j] = new string[]
{
"a_" + fIndex[j],
"b_" + fIndex[j],
"c_" + fIndex[j],
"d_" + fIndex[j]
};
}
var adf = new DataStringADF[numActions];
for (int j = 0; j < numActions; j++)
{
adf[j] = new DataStringADF { Features = features[j] };
}
sampleData[i] = new DataString
{
ActionDependentFeatures = adf,
SelectedActionIndex = random.Next(-1, numActions),
Label = new ContextualBanditLabel
{
Cost = (float)random.NextDouble(),
Probability = (float)random.NextDouble()
}
};
}
return sampleData;
}
