public CacheEntry(CacheKey key, HttpResponseMessage response)
{
Key = key;
Vary = response.Headers.Vary.Select(v => v.ToLowerInvariant()).ToList();
ResponseVaryHeaders = response.RequestMessage.Headers
.Where(h => Vary.Contains(h.Key.ToLowerInvariant()))
.ToDictionary(k => k.Key.ToLowerInvariant(), v => v.Value);
VariantId = Guid.NewGuid();
HasValidator = response.Headers.ETag != null || (response.Content != null && response.Content.Headers.LastModified != null);
CacheControl = response.Headers.CacheControl ?? new CacheControlHeaderValue();
}
public static Distribution FromDoc(Distribution existing, DocNode doc) {
// a single number means we want just a fixed value instead of a random distribution
if (doc.Type == DocNodeType.Scalar) {
var fixedValue = System.Convert.ToSingle(doc.StringValue);
if (existing != null && existing is Range) {
((Range)existing).Start = fixedValue;
((Range)existing).End = fixedValue;
return existing;
} else {
return new Range(fixedValue, fixedValue);
}
}
var first = doc[0].StringValue;
var resultType = typeof(Range);
int startingIndex = 1;
if (first == "vary") {
resultType = typeof(Vary);
} else if (first == "gaussian") {
resultType = typeof(Gaussian);
} else if (first == "binomial") {
resultType = typeof(Binomial);
} else if (first == "exponential") {
resultType = typeof(Exponential);
} else if (first == "range") {
resultType = typeof(Range);
} else {
// we haven't found a tag we know about, so assume that the first value is a number and it's a Range
startingIndex = 0;
}
// all these distributions take two floats as arguments, so let's parse those out first
float firstNum = System.Convert.ToSingle(doc[startingIndex].StringValue);
float secondNum = System.Convert.ToSingle(doc[startingIndex + 1].StringValue);
// set the numbers appropriate to the class
if (resultType == typeof(Range)) {
if (existing != null && existing is Range) {
((Range)existing).Start = firstNum;
((Range)existing).End = secondNum;
} else {
existing = new Range(firstNum, secondNum);
}
} else if (resultType == typeof(Vary)) {
if (existing != null && existing is Vary) {
((Vary)existing).Mean = firstNum;
((Vary)existing).Proportion = secondNum;
} else {
existing = new Vary(firstNum, secondNum);
}
} else if (resultType == typeof(Gaussian)) {
if (existing != null && existing is Gaussian) {
((Gaussian)existing).Mean = firstNum;
((Gaussian)existing).StdDev = secondNum;
} else {
existing = new Gaussian(firstNum, secondNum);
}
} else if (resultType == typeof(Binomial)) {
if (existing != null && existing is Binomial) {
((Binomial)existing).FlipProb = firstNum;
((Binomial)existing).Max = secondNum;
} else {
existing = new Binomial(firstNum, secondNum);
}
} else if (resultType == typeof(Exponential)) {
if (existing != null && existing is Exponential) {
((Exponential)existing).InvLambda = firstNum;
((Exponential)existing).Minimum = secondNum;
} else {
existing = new Exponential(firstNum, secondNum);
}
}
return existing;
}
