public abstract BigFloat Divide(
BigFloat dividend,
BigFloat divisor);
protected abstract BigFloat Subtract(
BigFloat minuend,
BigFloat subtrahend);
/**
* Creates a new instance of <code>Location</code> with the given parameters.
*
* @param latitude
* the latitude, in degrees, of this location. North latitude is positive, south negative.
* @param longitude
* the longitude, in degrees, of this location. East longitude is positive, east negative.
*/
public Location(double latitude, double longitude)
{
this.latitude = new BigFloat(latitude);
this.longitude = new BigFloat(longitude);
}
protected override BigFloat Truncate(BigFloat value)
{
return(value.Truncate());
}
protected abstract BigFloat Multiply(
BigFloat multiplicand,
BigFloat multiplier);
protected abstract BigFloat Add(
BigFloat leftAddend,
BigFloat rightAddend);
public static int[,] Bayes(Tokenizer train, Tokenizer test)
{
/*
* Olasılık hesaplanırken sayılar çarpılarak çok fazla küçüldüğünü için (1/15000)^3 gibi
* double ın 10^-324 lük sınırına sığmadı bunu aşmak içinde bigFloat kütüphanesini kullandık
* https://github.com/Osinko/BigFloat
*/
int classCount = train.DocumentClassIndex[train.DocumentClassIndex.Count - 1] + 1;
BigFloat[] ratio = new BigFloat[classCount];//her classa ait döküman sayısının tüm döküman sayısına oranı
BigFloat[] prob = new BigFloat[classCount];
int[] count = new int[classCount];
Dictionary <string, int>[] classWordFreq = new Dictionary <string, int> [classCount];
int[] classWordFreqSum = new int[classCount];
int[,] result = new int[test.DocumentWordFreq.Count, 2];
for (int i = 0; i < classCount; i++)//atama işlemleri
{
ratio[i] = new BigFloat(0);
prob[i] = new BigFloat(0);
}
for (int i = 0; i < train.DocumentClassIndex.Count; i++)
{
ratio[train.DocumentClassIndex[i]]++;
}
for (int i = 0; i < classCount; i++)
{
classWordFreq[i] = new Dictionary <string, int>();
ratio[i] /= train.DocumentClassIndex.Count;
}
for (int j = 0; j < train.DocumentWordFreq.Count; j++)// her bir classın kelime frekansını bulur
{
foreach (KeyValuePair <string, int> pair in train.DocumentWordFreq[j])
{
if (classWordFreq[train.DocumentClassIndex[j]].ContainsKey(pair.Key))
{
classWordFreq[train.DocumentClassIndex[j]][pair.Key] += pair.Value;
}
else
{
classWordFreq[train.DocumentClassIndex[j]].Add(pair.Key, pair.Value);
}
classWordFreqSum[train.DocumentClassIndex[j]] += pair.Value;//Her bir classın içindeki toplam kelime sayısı
}
}
int counter = 0;
int freq = 0;
foreach (Dictionary <string, int> document in test.DocumentWordFreq)
{
for (int i = 0; i < classCount; i++)//her döküman için olasılık önce orana eşitlenir
{
prob[i] = ratio[i];
}
//ardından mn bayes formülüne göre oran kelimelerin olasılıkları ile çarpılır
//ve dökümanın her classa ait olma olasılığı bulunur
foreach (KeyValuePair <string, int> pair in document)
{
for (int i = 0; i < classCount; i++)
{
if ((classWordFreq[i].ContainsKey(pair.Key)))
{
freq = classWordFreq[i][pair.Key];
}
else
{
freq = 0;
}
prob[i] *= new BigFloat(new BigFloat(freq + 1).Divide(new BigFloat(classWordFreqSum[i] + train.corpus.Count)).Pow(pair.Value));
}
}
BigFloat max = new BigFloat(-1);
int maxclass = -1;
for (int i = 0; i < classCount; i++)//en büyük oran seçilir.Birbirine eşit oldukları durumda ilk bulduğunu seçer
{
if (prob[i] > max)
{
max = prob[i];
maxclass = i;
}
}
result[counter, 0] = maxclass;
result[counter, 1] = test.DocumentClassIndex[counter];
counter++;
}
return(result);
}
protected override BigFloat PostDecrement(ref BigFloat value)
{
return(value--);
}
//Turns a JArray deserialized object into an AlgoValue.
private static AlgoValue ParseJsonArray(ParserRuleContext context, dynamic deserialized)
{
//Enumerate over values.
List <AlgoValue> list = new List <AlgoValue>();
foreach (JToken token in ((JArray)deserialized).Children())
{
switch (token.Type)
{
//Same parsing as below, but for a list.
case JTokenType.Integer:
list.Add(new AlgoValue()
{
Type = AlgoValueType.Integer,
Value = BigInteger.Parse(token.ToString())
});
break;
case JTokenType.String:
list.Add(new AlgoValue()
{
Type = AlgoValueType.String,
Value = token.ToString()
});
break;
case JTokenType.Array:
list.Add(ParseJsonArray(context, (JArray)token));
break;
case JTokenType.Boolean:
list.Add(new AlgoValue()
{
Type = AlgoValueType.Boolean,
Value = bool.Parse(token.ToString())
});
break;
case JTokenType.Float:
list.Add(new AlgoValue()
{
Type = AlgoValueType.Float,
Value = BigFloat.Parse(token.ToString())
});
break;
case JTokenType.Null:
list.Add(AlgoValue.Null);
break;
case JTokenType.Object:
list.Add(ParseJsonObject(context, (JObject)token));
break;
}
}
return(new AlgoValue()
{
Type = AlgoValueType.List,
Value = list
});
}
//Turns a JObject deserialized object into an AlgoValue.
private static AlgoValue ParseJsonObject(ParserRuleContext context, dynamic deserialized)
{
//Enumerate over properties.
AlgoObject obj = new AlgoObject();
foreach (JProperty token in ((JObject)deserialized).Properties())
{
switch (token.Value.Type)
{
//JSON Integer Representation
case JTokenType.Integer:
obj.ObjectScopes.AddVariable(token.Name, new AlgoValue()
{
Type = AlgoValueType.Integer,
Value = BigInteger.Parse(token.Value.ToString())
});
break;
//JSON String Representation
case JTokenType.String:
obj.ObjectScopes.AddVariable(token.Name, new AlgoValue()
{
Type = AlgoValueType.String,
Value = token.Value.ToString()
});
break;
//JSON Array Representation
case JTokenType.Array:
obj.ObjectScopes.AddVariable(token.Name, ParseJsonArray(context, (JArray)token.Value));
break;
//JSON Boolean Representation
case JTokenType.Boolean:
obj.ObjectScopes.AddVariable(token.Name, new AlgoValue()
{
Type = AlgoValueType.Boolean,
Value = bool.Parse(token.Value.ToString())
});
break;
//JSON Float Representation
case JTokenType.Float:
obj.ObjectScopes.AddVariable(token.Name, new AlgoValue()
{
Type = AlgoValueType.Float,
Value = BigFloat.Parse(token.Value.ToString())
});
break;
//JSON Null Representation
case JTokenType.Null:
obj.ObjectScopes.AddVariable(token.Name, AlgoValue.Null);
break;
//JSON Object Representation
case JTokenType.Object:
obj.ObjectScopes.AddVariable(token.Name, ParseJsonObject(context, (JObject)token.Value));
break;
default:
Error.Fatal(context, "Invalid type '" + token.Value.Type.ToString() + "' to parse from JSON.");
return(null);
}
}
//Return the finished object.
return(new AlgoValue()
{
Type = AlgoValueType.Object,
Value = obj
});
}
public async Task <SendTransactionOperationResult> SendTransaction(Keys keys, string from, string to, BigFloat amount, BigFloat fee, BigFloat gasLimit = null, BigFloat storageLimit = null, JObject param = null)
{
gasLimit = gasLimit ?? 200;
storageLimit = storageLimit ?? 0;
JObject head = await GetHeader();
JObject account = await GetAccountForBlock(head["hash"].ToString(), from);
int counter = int.Parse(account["counter"].ToString());
JArray operations = new JArray();
JToken managerKey = await GetManagerKey(from);
string gas = gasLimit.ToString();
string storage = storageLimit.ToString();
if (keys != null && managerKey["key"] == null)
{
JObject revealOp = new JObject();
operations.AddFirst(revealOp);
revealOp["kind"] = "reveal";
revealOp["fee"] = "0";
revealOp["public_key"] = keys.DecryptPublicKey();
revealOp["source"] = from;
revealOp["storage_limit"] = storage;
revealOp["gas_limit"] = gas;
revealOp["counter"] = (++counter).ToString();
}
JObject transaction = new JObject();
operations.Add(transaction);
transaction["kind"] = Operations.Transaction;
transaction["source"] = from;
transaction["fee"] = fee.ToString();
transaction["counter"] = (++counter).ToString();
transaction["gas_limit"] = gas;
transaction["storage_limit"] = storage;
transaction["amount"] = new BigFloat(amount.ToMicroTez().ToString(6)).Round().ToString(); // Convert to microtez, truncate at 6 digits, round up
transaction["destination"] = to;
if (param != null)
{
transaction["parameters"] = param;
}
else
{
JObject parameters = new JObject();
transaction["parameters"] = parameters;
parameters["prim"] = "Unit";
parameters["args"] = new JArray(); // No args for this contract.
}
List <OperationResult> sendResults = await SendOperations(operations, keys, head);
return(sendResults.LastOrDefault() as SendTransactionOperationResult);
}
protected override BigFloat Subtract(
BigFloat minuend,
BigFloat subtrahend)
{
return(minuend - subtrahend);
}
protected abstract double NaturalLog(BigFloat value);
static bool Near(BigFloat a, BigFloat b, BigFloat c, BigFloat d, BigFloat tol)
{
a -= c;
b -= d;
return ((a * a + b * b) * (a * a + b * b) < tol);
}
public abstract BigFloat AbsoluteValue(BigFloat value);
protected override BigFloat Add(BigFloat leftAddend, BigFloat rightAddend)
{
return(BigFloat.Add(leftAddend, rightAddend));
}
/// <summary>
/// Transfer funds from one wallet to another.
/// </summary>
/// <param name="from">From where to transfer the funds.</param>
/// <param name="to">To where the funds should be transferred.</param>
/// <param name="amount">The amount to transfer.</param>
/// <param name="fee">The fee to transfer.</param>
/// <param name="gasLimit">The gas limit to transfer.</param>
/// <param name="storageLimit">The storage limit to transfer.</param>
/// <returns>The result of the transfer operation.</returns>
public async Task <OperationResult> Transfer(string from, string to, BigFloat amount, BigFloat fee, BigFloat gasLimit = null, BigFloat storageLimit = null)
{
return(await new Rpc(Provider).SendTransaction(Keys, from, to, amount, fee, gasLimit, storageLimit));
}
protected override BigFloat PowerTo(BigFloat @base, int exponent)
{
return(BigFloat.PowerTo(@base, exponent));
}
protected abstract BigFloat PowerTo(
BigFloat @base,
int exponent);
public override BigFloat AbsoluteValue(BigFloat value)
{
return(+value);
}
public void Can_print_format()
{
var culture = new CultureInfo("en-US", false)
{
NumberFormat = { NumberDecimalDigits = 3 }
};
Thread.CurrentThread.CurrentCulture = culture;
Thread.CurrentThread.CurrentUICulture = culture;
new BigFloat("NaN").ToString().Should().Be("NaN");
new BigFloat("Inf").ToString().Should().Be("∞");
new BigFloat("Inf").ToString("^!").Should().Be("+∞");
new BigFloat("-Inf").ToString().Should().Be("-∞");
new BigFloat("5").ToString("b2d5").Should().Be("0.101E+3");
new BigFloat("0.001").ToString().Should().Be("0.1E-2");
new BigFloat("-5.123").ToString("d3").Should().Be("-0.512E+1");
new BigFloat("5").ToString("d5u").Should().Be("0.50000E+1");
new BigFloat("0.001").ToString().Should().Be("0.1E-2");
new BigFloat("-0.001").ToString().Should().Be("-0.1E-2");
new BigFloat("-0.001").ToString("E2").Should().Be("-0.1E-02");
new BigFloat("-0.001").ToString("E2").Should().Be("-0.1E-02");
new BigFloat("-0.001").ToString("e3^__").Should().Be("-0.1e002");
new BigFloat("-0.001").ToString("@3^_!").Should().Be("-0.1@-002");
new BigFloat("-0.001").ToString("E3^_;").Should().Be("-0.1E-002");
new BigFloat("-0.1").ToString("E^___").Should().Be("-0.1E0");
new BigFloat("-0.1").ToString("E^__!").Should().Be("-0.1E+0");
new BigFloat("-0.1").ToString("E^__;").Should().Be("-0.1E+0");
new BigFloat("-0.1").ToString("E^__+").Should().Be("-0.1E+0");
new BigFloat("-0.1").ToString("E^__-").Should().Be("-0.1E-0");
new BigFloat("-1").ToString("E2^!").Should().Be("-0.1E+01");
new BigFloat("-1").ToString("E1^;").Should().Be("-0.1E+1");
new BigFloat("-1").ToString("E0^_").Should().Be("-0.1E1");
new BigFloat("-1").ToString("E").Should().Be("-0.1E+1");
new BigFloat("1").ToString("b16e").Should().Be("0.1@+1");
new BigFloat("1").ToString("b16").Should().Be("0.1@+1");
new BigFloat("5").ToString("p2").Should().Be("05");
new BigFloat("-5.1").ToString("p2").Should().Be("-05");
new BigFloat("5").ToString("p2.2").Should().Be("05.00");
new BigFloat("5.67").ToString("p2.1").Should().Be("05.6");
new BigFloat("-5.67").ToString("p.1").Should().Be("-5.6");
new BigFloat("5.67").ToString("p.0").Should().Be("5");
new BigFloat("5.6789").ToString("p.").Should().Be("5.678");
new BigFloat("5.678").ToString("p.1#2").Should().Be("5.67");
new BigFloat("5.6").ToString("p.1#2").Should().Be("5.6");
new BigFloat("-5.6").ToString("p.#2").Should().Be("-5.600");
new BigFloat("5").ToString("p.#2").Should().Be("5.000");
new BigFloat("5.456789").ToString("p.#4").Should().Be("5.4567");
new BigFloat("5.456789").ToString("p#2").Should().Be("5.45");
new BigFloat("-5.456789").ToString("p#").Should().Be("-5.456789");
new BigFloat("500.4567").ToString("p4#2").Should().Be("0500.45");
new BigFloat("5.4567").ToString("p4#").Should().Be("0005.4567");
new BigFloat("512.4567").ToString("p4#0").Should().Be("0512");
new BigFloat("51234.4567").ToString("p4.0#0").Should().Be("51234");
new BigFloat("-5.4567").ToString("p4.#").Should().Be("-0005.4567");
new BigFloat("5.45").ToString("p4.#").Should().Be("0005.450");
new BigFloat("-1E-3").ToString("p").Should().Be("-0.001");
new BigFloat("5.67").ToString("pu").Should().Be("5.6699999999999999");
new BigFloat("5.67").ToString("pu#").Should().Be("5.6700000000000000");
new BigFloat("5.67").ToString("pu=").Should().Be("5.6699999999999999");
new BigFloat("5.67").ToString("eu").Should().Be("0.56699999999999999e+1");
new BigFloat("5.67").ToString("eu#").Should().Be("0.56700000000000000e+1");
new BigFloat("5.67").ToString("eu=").Should().Be("0.56699999999999999e+1");
new BigFloat("-1").ToString("E=0p=1").Should().Be("-1");
new BigFloat("-1").ToString("E=1p=1").Should().Be("-0.1E+1");
new BigFloat("-1").ToString("E=1p=0").Should().Be("-0.1E+1");
new BigFloat("100").ToString("E(-1,1)=3").Should().Be("0.1E+3");
new BigFloat("10").ToString("E(-1,1)=3").Should().Be("10");
new BigFloat("1").ToString("E(-1,1)=3").Should().Be("0.1E+1");
new BigFloat("0.1").ToString("E(-1,1)=3").Should().Be("0.1E+0");
new BigFloat("0.01").ToString("E(-1,1)=3").Should().Be("0.1E-1");
new BigFloat("0.001").ToString("E(-1,1)=3").Should().Be("0.001");
new BigFloat("100").ToString("p(-1,1)=3").Should().Be("100");
new BigFloat("10").ToString("p(-1,1)=3").Should().Be("0.1E+2");
new BigFloat("1").ToString("p=3(-1,1)").Should().Be("1");
new BigFloat("0.1").ToString("p(-1,1)=3").Should().Be("0.1");
new BigFloat("0.01").ToString("p(-1,1)=3").Should().Be("0.01");
new BigFloat("0.001").ToString("p=3(-1,1)=3").Should().Be("0.1E-2");
var flt = new BigFloat("10", precision: 100);
flt.Log();
flt.ToString("p").Should().Be("2.302585092994045684017991454683");
}
protected override BigFloat Multiply(BigFloat multiplicand, BigFloat multiplier)
{
return(multiplicand.Multiply(multiplier));
}
public override BigFloat Divide(
BigFloat dividend,
BigFloat divisor)
{
return(dividend.Divide(divisor));
}
/**
* Sets the coordinates of the location object.
*
* @param latitude
* the latitude, in degrees, of this location. North latitude is positive, south negative.
* @param longitude
* the longitude, in degrees, of this location. East longitude is positive, east negative.
*/
public void setLocation(String latitude, String longitude)
{
this.latitude = new BigFloat(latitude);
this.longitude = new BigFloat(longitude);
}
protected override BigFloat PreDecrement(ref BigFloat value)
{
value = BigFloat.Decrement(value);
return(value);
}
/// <summary>
/// GetSDTMultiplicityResult() calculates Singles, Doubles, and Triples rates plus related metadata.
///
/// This utility method works for both Slow- and Fast-Background Multiplicity Analyzers.
///
/// Note that the input parameter "accidentalsHistogram" need not be normalized,
/// and that a normalized-accidentals distribution will be included in output and will be valid
/// for both slow and fast accidentals.
///
/// </summary>
/// <param name="realsPlusAccidentalsHistogram"> The histogram of gate populations. </param>
/// <param name="accidentalsHistogram"> The histogram of gate populations - NOT NORMALIZED. </param>
/// <param name="wasFastAccidentals"> true or false. Affects how PTsingles are calculated, etc. </param>
/// <param name="multiplicityGateWidth"> as a UInt64, in 100-nanosecond tics. </param>
/// <param name="multiplicityDeadDelay"> as a UInt64, in 100-nanosecond tics. </param>
/// <param name="accidentalsDelay"> as a UInt64, in 100-nanosecond tics. </param>
/// <param name="deadTimeCoeffTinNanoSecs"> as a double, in nanoseconds. </param>
/// <param name="deadTimeCoeffAinMicroSecs"> as a double, in microseconds. </param>
/// <param name="deadTimeCoeffBinPicoSecs"> as a double, in picoseconds. </param>
/// <param name="deadTimeCoeffCinNanoSecs"> as a double, in nanoseconds. </param>
/// <param name="totalMeasurementTime"> as a double, in seconds. </param>
/// <param name="normedAccidentalsHistogram"> UInt64[]. </param>
/// <returns></returns>
public MultiplicityResult GetSDTMultiplicityResult(UInt64[] realsPlusAccidentalsHistogram,
UInt64[] accidentalsHistogram,
Boolean wasFastAccidentals,
UInt64 multiplicityGateWidth,
UInt64 multiplicityDeadDelay,
UInt64 accidentalsDelay,
double deadTimeCoeffTinNanoSecs,
double deadTimeCoeffAinMicroSecs,
double deadTimeCoeffBinPicoSecs,
double deadTimeCoeffCinNanoSecs,
double totalMeasurementTime,
UInt64[] normedAccidentalsHistogram = null)
{
MultiplicityResult result;
double phi;
double gateInSeconds;
UInt32 biggestKey, biggestAKey;
int arrayLength;
double[] alpha;
double[] beta;
BigFloat[] α = new BigFloat[0], β = new BigFloat[0];
result = new MultiplicityResult();
if (wasFastAccidentals == true)
{
result.isSlowBackground = false;
}
else
{
result.isSlowBackground = true;
}
//store parameters
result.multiplicityGateWidth = multiplicityGateWidth;
result.multiplicityDeadDelay = multiplicityDeadDelay;
result.accidentalsDelay = accidentalsDelay;
result.deadTimeCoefficientTinNanoSecs = deadTimeCoeffTinNanoSecs;
result.deadTimeCoefficientAinMicroSecs = deadTimeCoeffAinMicroSecs;
result.deadTimeCoefficientBinPicoSecs = deadTimeCoeffBinPicoSecs;
result.deadTimeCoefficientCinNanoSecs = deadTimeCoeffCinNanoSecs;
//copy the real-plus-accidental multiplicity histogram
biggestKey = 0;
arrayLength = realsPlusAccidentalsHistogram.Length;
for (int i = 0; i < arrayLength; i++)
{
if (realsPlusAccidentalsHistogram[i] > 0)
{
result.realPlusAccidentalDistribution.Add((UInt64)i, realsPlusAccidentalsHistogram[i]);
biggestKey = (UInt32)i;
}
}
result.maxRABin = biggestKey;
//copy the accidental-only histogram
biggestAKey = 0;
arrayLength = accidentalsHistogram.Length;
for (int i = 0; i < arrayLength; i++)
{
if (accidentalsHistogram[i] > 0)
{
result.accidentalDistribution.Add((UInt32)i, accidentalsHistogram[i]);
biggestAKey = (UInt32)i;
}
}
result.maxABin = biggestAKey;
//************************************************************
//Normalize the AccidentalDistribution,
//scaling the FastBackgroundAnalysis result in proportion
//to the number of Real+Accidental gate
UInt64 numAccidentalGates = 0;
UInt64 numRealPlusAccidentalGates = 0;
foreach (KeyValuePair <UInt64, UInt64> pair in result.realPlusAccidentalDistribution)
{
numRealPlusAccidentalGates += pair.Value;
}
// compute the normalization param and recompute the unnormalizaed array from this one (code does not seem to work JFL)
if (normedAccidentalsHistogram != null)
{
for (int i = 0; i < normedAccidentalsHistogram.Length; i++)
{
if (normedAccidentalsHistogram[i] > 0)
{
result.normalizedAccidentalDistribution.Add((UInt32)i, normedAccidentalsHistogram[i]);
}
}
UInt64 numNAccidentalGates = 0;
foreach (ulong no in normedAccidentalsHistogram)
{
numNAccidentalGates += no;
}
double denormalizingRatio;
UInt64 denormalizedRate;
denormalizingRatio = ((double)numNAccidentalGates) / ((double)numRealPlusAccidentalGates);
result.accidentalDistribution.Clear();
foreach (KeyValuePair <UInt64, UInt64> pair in result.normalizedAccidentalDistribution)
{
denormalizedRate = (UInt64)(pair.Value * denormalizingRatio);
result.accidentalDistribution.Add(pair.Key, denormalizedRate);
biggestAKey = (UInt32)pair.Key;
}
result.maxABin = biggestAKey;
foreach (KeyValuePair <UInt64, UInt64> pair in result.accidentalDistribution)
{
numAccidentalGates += pair.Value;
}
}
else
{
foreach (KeyValuePair <UInt64, UInt64> pair in result.accidentalDistribution)
{
numAccidentalGates += pair.Value;
}
double normalizingRatio;
UInt64 normalizedRate;
normalizingRatio = ((double)numRealPlusAccidentalGates) / ((double)numAccidentalGates);
result.normalizedAccidentalDistribution.Clear();
foreach (KeyValuePair <UInt64, UInt64> pair in result.accidentalDistribution)
{
normalizedRate = (UInt64)(pair.Value * normalizingRatio);
result.normalizedAccidentalDistribution.Add(pair.Key, normalizedRate);
}
}
//*** END Normalizing the AccidentalDistribution *************
//store the bigger key...
if (biggestAKey > biggestKey)
{
biggestKey = biggestAKey;
}
if (biggestKey < 2)
{
biggestKey = 2; //...minimum size for data-output arrays...
}
alpha = new double[biggestKey + 1];
beta = new double[biggestKey + 1];
gateInSeconds = ((double)multiplicityGateWidth) * this.ticSizeInSeconds;
phi = (deadTimeCoeffTinNanoSecs / 1E9) / gateInSeconds;
bool standard = true; // dev note: this toggle signals use of BigNum when FP overflow is detected
int axover = 0, bxover = 0;
//calculate the alphas
alpha[0] = 0.0;
alpha[1] = 1.0;
for (int n = 2; n <= biggestKey; n++)
{
if (phi > 1e-20)
{
alpha[n] = 1.0;
if (standard)
{
for (int k = 0; k <= (n - 2); k++)
{
double alphaCoeff;
alphaCoeff = this.binomialCoefficient((n - 1), (k + 1))
* Math.Pow((double)(k + 1), (double)k)
* Math.Pow(phi, (double)k)
/ (Math.Pow((1.0 - ((k + 1) * phi)), (double)(k + 2)));
alpha[n] += alphaCoeff;
if (Double.IsInfinity(alpha[n]) || Double.IsNaN(alpha[n]))
{
result.warnings.Add("Overflow alpha at n = " + n + ", k = " + k);
alpha[n] = 0;
axover = n;
standard = false; k = n; n = n - 1; // redo the loop
α = new BigFloat[biggestKey + 1];
}
}
}
else
{
// INCCCycleConditioning.calc_alpha_beta
PrecisionSpec ps128 = new PrecisionSpec(128, PrecisionSpec.BaseType.BIN);
BigFloat one = new BigFloat(1, ps128);
BigFloat combination;
BigFloat sum;
double raise1, power1, power2;
double log1, log2, log3;
BigFloat exp1, exp2, exp3;
/* calculate alpha array */
sum = new BigFloat(0, ps128);
for (int k = 0; k <= (n - 2); k++)
{
combination = new BigFloat(binomialCoefficient((n - 1), (k + 1)), ps128);
raise1 = (double)(k + 1);
power1 = (double)k;
power2 = (double)(k + 2);
log1 = Math.Log(raise1);
log2 = Math.Log(phi);
log3 = Math.Log(1.0 - raise1 * phi);
exp1 = BigFloat.Exp(new BigFloat(log1 * power1, ps128));
exp2 = BigFloat.Exp(new BigFloat(log2 * power1, ps128));
exp3 = BigFloat.Exp(new BigFloat(log3 * power2, ps128));
sum += combination * exp1 * exp2 / exp3;
}
α[n] = new BigFloat(one + sum, ps128);
}
}
else
{
alpha[n] = 1.0;
}
}
//calculate the betas
standard = true;
beta[0] = 0.0;
beta[1] = 0.0;
beta[2] = alpha[2] - 1.0;
for (int n = 3; n <= biggestKey; n++)
{
if (phi > 1e-20)
{
beta[n] = alpha[n] - 1.0;
if (standard)
{
for (int k = 0; k <= (n - 3); k++)
{
double betaCoeff;
betaCoeff = this.binomialCoefficient((n - 1), (k + 2))
* (k + 1)
* Math.Pow((double)(k + 2), (double)k)
* Math.Pow(phi, (double)k)
/ (Math.Pow((1.0 - ((k + 2) * phi)), (double)(k + 3)));
beta[n] += betaCoeff;
if (Double.IsInfinity(beta[n]) || Double.IsNaN(beta[n]))
{
result.warnings.Add("Overflow beta at n = " + n + ", k = " + k);
beta[n] = 0;
bxover = n;
standard = false; k = n; n = n - 1; // redo the loop
β = new BigFloat[biggestKey + 1];
}
}
}
else
{
PrecisionSpec ps128 = new PrecisionSpec(128, PrecisionSpec.BaseType.BIN);
BigFloat one = new BigFloat(1, ps128);
BigFloat combination;
BigFloat sum;
double raise1, power1, power2;
double log1, log2, log3;
BigFloat exp1, exp2, exp3;
sum = new BigFloat(0, ps128);
for (int k = 0; k <= n - 3; k++)
{
combination = new BigFloat(binomialCoefficient((n - 1), (k + 2)), ps128);
raise1 = (double)(k + 2);
power1 = (double)k;
power2 = (double)(k + 3);
log1 = Math.Log(raise1);
log2 = Math.Log(phi);
log3 = Math.Log(1.0 - raise1 * phi);
exp1 = BigFloat.Exp(new BigFloat(log1 * power1, ps128));
exp2 = BigFloat.Exp(new BigFloat(log2 * power1, ps128));
exp3 = BigFloat.Exp(new BigFloat(log3 * power2, ps128));
sum += combination * (new BigFloat(k + 1, ps128)) * exp1 * exp2 / exp3;
}
β[n] = α[n] - one + sum;
}
}
else
{
beta[n] = 0.0;
}
}
//store the alpha and beta coefficients
result.alpha = new double[biggestKey + 1];
result.beta = new double[biggestKey + 1];
for (int i = 0; i <= biggestKey; i++)
{
result.alpha[i] = alpha[i];
result.beta[i] = beta[i];
}
double lastGoodD = 0;
for (int i = axover; axover > 0 && i <= biggestKey; i++)
{
double d;
bool good = Double.TryParse(α[i].ToString(), System.Globalization.NumberStyles.Any, System.Globalization.CultureInfo.InvariantCulture.NumberFormat, out d); // what to do when it is really larger than a double?
if (!good)
{
result.alpha[i] = lastGoodD;
result.warnings.Add(String.Format("α[{0}] conversion failed on {1}", i, α[i].ToString()));
}
else
{
lastGoodD = d;
result.alpha[i] = d;
}
}
for (int i = bxover; bxover > 0 && i <= biggestKey; i++)
{
double d;
bool good = Double.TryParse(β[i].ToString(), System.Globalization.NumberStyles.Any, System.Globalization.CultureInfo.InvariantCulture.NumberFormat, out d);
if (!good)
{
result.beta[i] = lastGoodD;
result.warnings.Add(String.Format("β[{0}] conversion failed on {1}", i, β[i].ToString())); // URGENT: alpha/beta arrays are to be transformed from doubles to BigFloat types when this conversion happens to overflow
}
else
{
lastGoodD = d;
result.beta[i] = d;
}
}
//NOTE: in the following calculations,
//variables named RAxxx refer to "Reals Plus Accidentals" phenomena, and
//variables named Axxx refer to "Accidentals" phenomena (such as, fast-background counting)
//calculate the factorial moments
double RAfactorialMoment0, RAfactorialMoment1, RAfactorialMoment2, RAfactorialMoment3;
double AfactorialMoment0, AfactorialMoment1, AfactorialMoment2, AfactorialMoment3;
double RAfactorialMomentAlpha1, AfactorialMomentAlpha1;
double RAfactorialMomentBeta2, AfactorialMomentBeta2;
RAfactorialMoment0 = 0.0;
RAfactorialMoment1 = 0.0;
RAfactorialMoment2 = 0.0;
RAfactorialMoment3 = 0.0;
AfactorialMoment0 = 0.0;
AfactorialMoment1 = 0.0;
AfactorialMoment2 = 0.0;
AfactorialMoment3 = 0.0;
RAfactorialMomentAlpha1 = 0.0;
AfactorialMomentAlpha1 = 0.0;
RAfactorialMomentBeta2 = 0.0;
AfactorialMomentBeta2 = 0.0;
for (int i = 0; i <= biggestKey; i++)
{
UInt64 gateCount;
int j = i + 1;
int k = i + 2;
int L = i + 3;
if (result.realPlusAccidentalDistribution.TryGetValue((UInt64)i, out gateCount))
{
RAfactorialMoment0 += (double)gateCount;
}
if (result.accidentalDistribution.TryGetValue((UInt64)i, out gateCount))
{
AfactorialMoment0 += gateCount;
}
if (j <= biggestKey)
{
if (result.realPlusAccidentalDistribution.TryGetValue((UInt64)j, out gateCount))
{
RAfactorialMoment1 += (double)(((UInt64)j) * gateCount);
RAfactorialMomentAlpha1 += alpha[j] * ((double)gateCount);
}
if (result.accidentalDistribution.TryGetValue((UInt64)j, out gateCount))
{
AfactorialMoment1 += (double)(((UInt64)j) * gateCount);
AfactorialMomentAlpha1 += alpha[j] * ((double)gateCount);
}
}
if (k <= biggestKey)
{
if (result.realPlusAccidentalDistribution.TryGetValue((UInt32)k, out gateCount))
{
RAfactorialMoment2 += (double)(((UInt64)(k * (k - 1) / 2)) * gateCount);
RAfactorialMomentBeta2 += beta[k] * ((double)gateCount);
}
if (result.accidentalDistribution.TryGetValue((UInt32)k, out gateCount))
{
AfactorialMoment2 += (double)(((UInt64)(k * (k - 1) / 2)) * gateCount);
AfactorialMomentBeta2 += beta[k] * ((double)gateCount);
}
}
if (L <= biggestKey)
{
if (result.realPlusAccidentalDistribution.TryGetValue((UInt32)L, out gateCount))
{
RAfactorialMoment3 += ((double)(((UInt64)(L * (L - 1) * (L - 2))) * gateCount)) / 6.0;
}
if (result.accidentalDistribution.TryGetValue((UInt32)L, out gateCount))
{
AfactorialMoment3 += ((double)(((UInt64)(L * (L - 1) * (L - 2))) * gateCount)) / 6.0;
}
}
}
//store the factorial moments
result.RAfactorialMoment0 = RAfactorialMoment0;
result.RAfactorialMoment1 = RAfactorialMoment1;
result.RAfactorialMoment2 = RAfactorialMoment2;
result.RAfactorialMoment3 = RAfactorialMoment3;
result.AfactorialMoment0 = AfactorialMoment0;
result.AfactorialMoment1 = AfactorialMoment1;
result.AfactorialMoment2 = AfactorialMoment2;
result.AfactorialMoment3 = AfactorialMoment3;
//NOTE: in the following calculations,
//variables named PTxxx refer to "Pulse Triggered" phenomena, and
//variables named RTxxx refer to "Regularly Triggered" phenomena (such as, fast-background counting)
//penultimately, use all this to calculate the SDT rates
double PTsingles, RTsingles, normRAfactorialMoment1, normRAfactorialMoment2;
double normRAfactorialMomentAlpha1, normRAfactorialMomentBeta2;
double normAfactorialMoment0, normAfactorialMoment1, normAfactorialMoment2, normAfactorialMoment3;
double normAfactorialMomentAlpha1, normAfactorialMomentBeta2;
if (wasFastAccidentals)
{
PTsingles = RAfactorialMoment0;
double gateFactor = numAccidentalGates / Math.Floor(totalMeasurementTime / (multiplicityGateWidth * this.ticSizeInSeconds));
RTsingles = AfactorialMoment1 / gateFactor;
normRAfactorialMoment1 = RAfactorialMoment1 / PTsingles;
normRAfactorialMoment2 = RAfactorialMoment2 / PTsingles;
//NOT USED: double normRAfactorialMoment3 = RAfactorialMoment3 / PTsingles;
normRAfactorialMomentAlpha1 = RAfactorialMomentAlpha1 / PTsingles;
normRAfactorialMomentBeta2 = RAfactorialMomentBeta2 / PTsingles;
normAfactorialMoment0 = AfactorialMoment0 / numAccidentalGates;
normAfactorialMoment1 = AfactorialMoment1 / numAccidentalGates;
normAfactorialMoment2 = AfactorialMoment2 / numAccidentalGates;
normAfactorialMoment3 = AfactorialMoment3 / numAccidentalGates;
normAfactorialMomentAlpha1 = AfactorialMomentAlpha1 / numAccidentalGates;
normAfactorialMomentBeta2 = AfactorialMomentBeta2 / numAccidentalGates;
}
else
{
PTsingles = AfactorialMoment0; //XXX SHOULDN'T THIS BE RAfactorialMoment0 not AfactorialMoment0???, answer, no, the two values should be the same, RA and A of 0 are identical for "slow"
RTsingles = AfactorialMoment0;
normRAfactorialMoment1 = RAfactorialMoment1 / PTsingles;
normRAfactorialMoment2 = RAfactorialMoment2 / PTsingles;
//NOT USED: double normRAfactorialMoment3 = RAfactorialMoment3 / PTsingles;
normRAfactorialMomentAlpha1 = RAfactorialMomentAlpha1 / PTsingles;
normRAfactorialMomentBeta2 = RAfactorialMomentBeta2 / PTsingles;
normAfactorialMoment0 = AfactorialMoment0 / RTsingles;
normAfactorialMoment1 = AfactorialMoment1 / RTsingles;
normAfactorialMoment2 = AfactorialMoment2 / RTsingles;
normAfactorialMoment3 = AfactorialMoment3 / RTsingles;
normAfactorialMomentAlpha1 = AfactorialMomentAlpha1 / RTsingles;
normAfactorialMomentBeta2 = AfactorialMomentBeta2 / RTsingles;
}
double RTdoubles = (0.5 / (multiplicityGateWidth * this.ticSizeInSeconds)) * ((2.0 * normAfactorialMoment2) - Math.Pow(normAfactorialMoment1, 2.0));
double RTtriples = (0.16667 / (multiplicityGateWidth * this.ticSizeInSeconds))
* ((6.0 * normAfactorialMoment3) - (6.0 * normAfactorialMoment1 * normAfactorialMoment2) + (2.0 * Math.Pow(normAfactorialMoment1, 3)));
double PTdoubles = PTsingles * (normRAfactorialMoment1 - normAfactorialMoment1);
double PTtriples;
double PTtriplesDTcoef;
if (AfactorialMoment0 != 0.0)
{
PTtriples = PTsingles * ((normRAfactorialMoment2 - normAfactorialMoment2)
- ((normAfactorialMoment1 / normAfactorialMoment0)
* (normRAfactorialMoment1 - normAfactorialMoment1)));
PTtriplesDTcoef = PTsingles * ((normRAfactorialMomentBeta2 - normAfactorialMomentBeta2)
- ((normAfactorialMomentAlpha1 / normAfactorialMoment0)
* (normRAfactorialMomentAlpha1 - normAfactorialMomentAlpha1)));
}
else
{
PTtriples = 0.0;
PTtriplesDTcoef = 0.0;
}
if (totalMeasurementTime > 1E-12)
{
PTsingles /= totalMeasurementTime;
PTdoubles /= totalMeasurementTime;
PTtriples /= totalMeasurementTime;
PTtriplesDTcoef /= totalMeasurementTime;
}
else
{
PTsingles = 0.0;
PTdoubles = 0.0;
PTtriples = 0.0;
PTtriplesDTcoef = 0.0;
}
//store the SDT rates
result.singlesRatePerSecond = PTsingles;
result.doublesRatePerSecond = PTdoubles;
result.triplesRatePerSecond = PTtriples;
//now that rates are calculated, calculate the dead-time corrections
// dead time correction coefficients for RT rates (INCC as well as H&C)
// determined experimentally using D/T ratio - see analysisComparison/triplesDeadTime.xls
// the best fit (poly3) used to reproduce the trend
// note: valid only for sources in the range of ~100n/s - ~500,000 n/s
/** NOT USED ***
* double DTcoeffT0_RT = 3.42854465;
* double DTcoeffT1_RT = 3.35351651E-6;
* double DTcoeffT2_RT = -5.83706327E-12;
* double DTcoeffT3_RT = 2.03604973E-17;
***************/
// dead time correction coefficients for PT rates with background calculated using H&C consecutive gates
// determined experimentally using D/T ratio - see analysisComparison/triplesDeadTime.xls
// the best fit (poly3) used to reproduce the trend
// note: valid only for sources in the range of ~100n/s - ~500,000 n/s
/** NOT USED ***
* double DTcoeffT0_PT = 2.78760077;
* double DTcoeffT1_PT = 2.86078894E-6;
* double DTcoeffT2_PT = -8.21994836E-12;
* double DTcoeffT3_PT = 9.45195862E-17;
***************/
/** NOT USED ***
* double DTcoeffA_RT = 0.2063; // these values were determined using two source method
* double DTcoeffB_RT = 0.04256;
***************/
double DTcoeffA = deadTimeCoeffAinMicroSecs;
double DTcoeffB = deadTimeCoeffBinPicoSecs;
double DTcoeffC = deadTimeCoeffCinNanoSecs;
double DTcoeffT = deadTimeCoeffTinNanoSecs;
double exponent = ((DTcoeffA / 1E6) + ((DTcoeffB / 1E12) * PTsingles)) * PTsingles;
double PTsinglesDTcorr = PTsingles * Math.Exp(exponent / 4.0);
double PTdoublesDTcorr = PTdoubles * Math.Exp(exponent);
double PTtriplesDTcorr = PTtriplesDTcoef * Math.Exp((DTcoeffT / 1E9) * PTsingles);
/** NOT USED ***
* double RTsinglesDTcorr = RTsingles * Math.Exp(( ((DTcoeffA/1E6) + ((DTcoeffB/1E12)*RTsingles)) *RTsingles)/4.0);
* double RTdoublesDTcorr = RTdoubles * Math.Exp( ((DTcoeffA_RT/1E6) + ((DTcoeffB_RT/1E12)*RTsingles)) *RTsingles);
* double RTtriplesDTcorr = RTtriples* (( (DTcoeffT3_RT*Math.Pow(RTsingles,3))
+ (DTcoeffT2_RT*Math.Pow(RTsingles,2))
+ (DTcoeffT1_RT*RTsingles)
+ DTcoeffT0_RT)
+ /DTcoeffT0_RT);
***************/
//store the dead-time corrected values
result.deadTimeCorrectedSinglesRate = PTsinglesDTcorr;
result.deadTimeCorrectedDoublesRate = PTdoublesDTcorr;
result.deadTimeCorrectedTriplesRate = PTtriplesDTcorr;
//Calculate the Dytlewski Dead-Time-Corrected Rates, based upon
//N. Dytlewski, Dead-time corrections for multiplicity counters,
//Nuclear Instruments and Methods in Physics Research A305(1991)492-494
double P03, P02, P13, P12, P23;
P03 = Math.Pow((1.0 - (2.0 * phi)), 3.0);
P02 = Math.Pow((1.0 - phi), 2.0);
P13 = (2.0 * Math.Pow((1.0 - phi), 3.0)) - (2.0 * P03);
P12 = 1.0 - P02;
P23 = 1.0 + P03 - (2.0 * Math.Pow((1.0 - phi), 3.0));
result.dytlewskiDeadTimeCorrectedTriplesRate = PTtriples / P03;
//Martyn made me do it. hn 2.6.2015
result.deadTimeCorrectedTriplesRate = result.dytlewskiDeadTimeCorrectedTriplesRate;
result.dytlewskiDeadTimeCorrectedDoublesRate = (PTdoubles / P02) + ((PTtriples * P13) / (P02 * P03));
result.dytlewskiDeadTimeCorrectedSinglesRate = PTsingles + ((P12 * PTdoubles) / P02) + (PTtriples * (((P12 * P13) / (P02 * P03)) - (P23 / P03)));
return(result);
}
protected override BigFloat Negate(BigFloat value)
{
return(BigFloat.Negate(value));
}
public async Task GetBalance()
{
BigFloat balance = await rpc.GetBalance("tz1hmK2ru6ism15MxXbnhKWWKGJ6hqWssMc5");
}
public void TestExtremeMathPi(int precision)
{
var pi = BigFloat.GetPi(AccuracyGoal.Absolute(precision));
}
private void DoComputeRows(BigFloat startX, BigFloat startY, BigFloat dx, BigFloat dy, int startRow, int numRows, int width, int maxDwell)
{
BigInfo info;
info.startX = startX;
info.startY = startY;
info.dx = dx;
info.dy = dy;
info.bailout = BigFloat.Bailout;
info.maxDwell = maxDwell;
for (int v = startRow; v < startRow + numRows && !m_aborted; ++v)
{
info.v = v;
for (int h = 0; h < width && !m_aborted; ++h)
{
info.h = h;
double mag;
ushort dwell;
BigCompute(info, out mag, out dwell);
float sample;
if (dwell < maxDwell)
sample = (float) (dwell + (A - Math.Log(0.5*Math.Log(mag)))/B); // Continuous Smoothing (see http://en.wikipedia.org/wiki/Mandelbrot_set#Continuous_.28smooth.29_coloring)
else
sample = float.PositiveInfinity;
m_samples[h, v] = sample;
m_minDwell = Math.Min(m_minDwell, dwell);
m_maxDwell = Math.Max(m_maxDwell, dwell);
if (!float.IsInfinity(sample))
{
m_minSample = Math.Min(m_minSample, sample);
m_maxSample = Math.Max(m_maxSample, sample);
}
}
}
}
protected override BigFloat PreDecrement(ref BigFloat value)
{
return(--value);
}
public static Tuple<List<BigFloat>, List<BigFloat>> Solve(int n, int n_iters, BigFloat tolerance, List<BigFloat> initialGuess, List<BigFloat> zi)
{
int m = initialGuess.Count, i, j, k;
BigFloat d = 0, pa, pb, a, b, qa, qb, k1, k2, k3, na, nb, s1, s2;
for (i = 0; i < n_iters; ++i)
{
d = 0.0;
for (j = 0; j < m; ++j)
{
//Read in zj
pa = initialGuess[j];
pb = zi[j];
//Compute denominator
//
// (zj - z0) * (zj - z1) * ... * (zj - z_{n-1})
//
a = 1.0;
b = 0.0;
for (k = 0; k < m; ++k)
{
if (k == j)
{
continue;
}
qa = pa - initialGuess[k];
qb = pb - zi[k];
if (qa * qa + qb * qb < tolerance)
{
continue;
}
k1 = qa * (a + b);
k2 = a * (qb - qa);
k3 = b * (qa + qb);
a = k1 - k3;
b = k1 + k2;
}
//Compute numerator
na = RealNumbers[n - 1];
nb = ImaginaryNumbers[n - 1];
s1 = pb - pa;
s2 = pa + pb;
for (k = n - 2; k >= 0; --k)
{
k1 = pa * (na + nb);
k2 = na * s1;
k3 = nb * s2;
na = k1 - k3 + RealNumbers[k];
nb = k1 + k2 + ImaginaryNumbers[k];
}
//Compute reciprocal
k1 = a * a + b * b;
if (BigFloat.Abs(k1) > Epsilon)
{
a /= k1;
b /= -k1;
}
else
{
a = 1.0;
b = 0.0;
}
//Multiply and accumulate
k1 = na * (a + b);
k2 = a * (nb - na);
k3 = b * (na + nb);
qa = k1 - k3;
qb = k1 + k2;
initialGuess[j] = pa - qa;
zi[j] = pb - qb;
d = BigFloat.Max(d, BigFloat.Max(BigFloat.Abs(qa), BigFloat.Abs(qb)));
}
//If converged, exit early
if (d < tolerance)
{
break;
}
}
// Post process: Combine any repeated roots
int count;
for (i = 0; i < m; ++i)
{
count = 1;
a = initialGuess[i];
b = zi[i];
for (j = 0; j < m; ++j)
{
if (i == j)
{
continue;
}
if (Near(initialGuess[i], zi[i], initialGuess[j], zi[j], tolerance))
{
++count;
a += initialGuess[j];
b += zi[j];
}
}
if (count > 1)
{
a /= count;
b /= count;
for (j = 0; j < m; ++j)
{
if (i == j)
{
continue;
}
if (Near(initialGuess[i], zi[i], initialGuess[j], zi[j], tolerance))
{
initialGuess[j] = a;
zi[j] = b;
}
}
initialGuess[i] = a;
zi[i] = b;
}
}
for(i=0;i<initialGuess.Count;i++)
{
if(BigFloat.Abs(zi[i]) < Epsilon)
{
zi[i] = 0;
}
if (BigFloat.Abs(initialGuess[i]) < Epsilon)
{
initialGuess[i] = 0;
}
}
// Order by size
List<Tuple<BigFloat, BigFloat>> Elements = initialGuess.Zip(zi, (x, y) => Tuple.Create(x, y)).OrderByDescending(x => x.Item2.IsZero()).ToList();
Elements = Elements.OrderBy(x => x.Item1).ToList();
// Clear
zi = new List<BigFloat>();
initialGuess = new List<BigFloat>();
for (i = 0; i < Elements.Count; i++)
{
bool found = false;
for (j = 0; j < zi.Count; j++)
{
if (zi[j] == Elements[i].Item2 && initialGuess[j] == Elements[i].Item1)
{
found = true;
}
}
if (!found)
{
zi.Add(Elements[i].Item2);
initialGuess.Add(Elements[i].Item1);
}
}
// Round the result
foreach(BigFloat s in initialGuess)
{
BigFloat c = new BigFloat(s);
c.FPart();
if (BigFloat.Abs(c) < tolerance)
{
s.Floor();
}
}
return new Tuple<List<BigFloat>, List<BigFloat>>(initialGuess, zi);
}
protected override BigFloat Negate(BigFloat value)
{
return(-value);
}