public DiffFollow(DiffMethod method)
{
this.Follow = method;
this.Add = Flag.Check((int)method, (int)DiffMethod.Add);
this.Modify = Flag.Check((int)method, (int)DiffMethod.Modify);
this.Remove = Flag.Check((int)method, (int)DiffMethod.Remove);
}
public async Task DiffCreateAndApply(byte[] src, byte[] dest, DiffMethod method)
{
await using var ms = new MemoryStream();
switch (method)
{
case DiffMethod.Default:
await Utils.CreatePatch(src, dest, ms);
break;
case DiffMethod.BSDiff:
BSDiff.Create(src, dest, ms);
break;
case DiffMethod.OctoDiff:
OctoDiff.Create(src, dest, ms);
break;
default:
throw new ArgumentOutOfRangeException(nameof(method), method, null);
}
ms.Position = 0;
var patch = ms.ToArray();
await using var resultStream = new MemoryStream();
Utils.ApplyPatch(new MemoryStream(src), () => new MemoryStream(patch), resultStream);
Assert.Equal(dest, resultStream.ToArray());
}
public DiffFollow(DiffMethod method)
{
this.Follow = method;
this.Add = Flag.Check((int)method, (int)DiffMethod.Add);
this.Modify = Flag.Check((int)method, (int)DiffMethod.Modify);
this.Remove = Flag.Check((int)method, (int)DiffMethod.Remove);
}
public Column(string name, Type type, Getter getter, DiffMethod diffMethod, double colWidth)
{
this.name = name;
this.getter = getter;
this.colWidth = colWidth;
this.type = type;
this.diffMethod = diffMethod;
}
/// <summary>
/// Constructs a new Hybrid Monte Carlo sampler.
/// </summary>
/// <param name="x0">The initial sample.</param>
/// <param name="pdfLnP">The log density of the distribution we want to sample from.</param>
/// <param name="frogLeapSteps">Number frogleap simulation steps.</param>
/// <param name="stepSize">Size of the frogleap simulation steps.</param>
/// <param name="burnInterval">The number of iterations in between returning samples.</param>
/// <param name="diff">The method used for differentiation.</param>
/// <exception cref="ArgumentOutOfRangeException">When the number of burnInterval iteration is negative.</exception>
/// <exception cref="ArgumentNullException">When either x0, pdfLnP or diff is null.</exception>
public HybridMCGeneric(T x0, DensityLn <T> pdfLnP, int frogLeapSteps, double stepSize, int burnInterval, DiffMethod diff)
{
Energy = new DensityLn <T>(x => - pdfLnP(x));
FrogLeapSteps = frogLeapSteps;
StepSize = stepSize;
BurnInterval = burnInterval;
mCurrent = x0;
Diff = diff;
}
/// <summary>
/// Constructs a new Hybrid Monte Carlo sampler.
/// </summary>
/// <param name="x0">The initial sample.</param>
/// <param name="pdfLnP">The log density of the distribution we want to sample from.</param>
/// <param name="frogLeapSteps">Number frogleap simulation steps.</param>
/// <param name="stepSize">Size of the frogleap simulation steps.</param>
/// <param name="burnInterval">The number of iterations in between returning samples.</param>
/// <param name="randomSource">Random number generator used for sampling the momentum.</param>
/// <param name="diff">The method used for differentiation.</param>
/// <exception cref="ArgumentOutOfRangeException">When the number of burnInterval iteration is negative.</exception>
/// <exception cref="ArgumentNullException">When either x0, pdfLnP or diff is null.</exception>
protected HybridMCGeneric(T x0, DensityLn <T> pdfLnP, int frogLeapSteps, double stepSize, int burnInterval, Random randomSource, DiffMethod diff)
{
_energy = x => - pdfLnP(x);
FrogLeapSteps = frogLeapSteps;
StepSize = stepSize;
BurnInterval = burnInterval;
Current = x0;
_diff = diff;
RandomSource = randomSource;
}
public TaskSolution Solve(DiffMethod method, double a, double b, int n)
{
TablesManager manager = new TablesManager();
t0Table = manager.T0Table;
mTable = manager.MTable;
nTable = manager.NTable;
sigmaTable = manager.SigmaTable;
DiffEquationSys deSys = null;
switch (method)
{
case DiffMethod.RungeKutta:
deSys = new RungeKuttaDiffEquationSys(new DiffEquationSys.SeveralArgFun[]
{
(x, y) => ((y[1] - (Rk + Rp(y[0])) * y[0]) / Lk),
(x, y) => (-y[0] / Ck)
});
break;
case DiffMethod.ImplTrap:
deSys = new ImplTrapDiffEquationSys(new DiffEquationSys.SeveralArgFun[]
{
(x, y) => ((y[1] - (Rk + Rp(y[0])) * y[0]) / Lk),
(x, y) => (-y[0] / Ck)
});
break;
}
DiffEquationSolution[] sysSolution = deSys.FindSolution(a, b, new double[] { I0, Uc0 }, n);
DiffEquationSolution rpSolution = new DiffEquationSolution(a, b, n);
for (int i = 0; i <= n; i++)
{
rpSolution.Y[i] = Rp(sysSolution[0].Y[i]);
}
DiffEquationSolution ucpSolution = new DiffEquationSolution(a, b, n);
for (int i = 0; i <= n; i++)
{
ucpSolution.Y[i] = rpSolution.Y[i] * sysSolution[0].Y[i];
}
TaskSolution taskSolution;
taskSolution.I = sysSolution[0];
taskSolution.Uc = sysSolution[1];
taskSolution.Rp = rpSolution;
taskSolution.Ucp = ucpSolution;
return(taskSolution);
}
/// <summary>
/// Constructs a new Hybrid Monte Carlo sampler for a multivariate probability distribution.
/// The components of the momentum will be sampled from a normal distribution with standard deviations
/// given by pSdv. This constructor will set the burn interval, the method used for
/// numerical differentiation and the random number generator.
/// </summary>
/// <param name="x0">The initial sample.</param>
/// <param name="pdfLnP">The log density of the distribution we want to sample from.</param>
/// <param name="frogLeapSteps">Number frogleap simulation steps.</param>
/// <param name="stepSize">Size of the frogleap simulation steps.</param>
/// <param name="burnInterval">The number of iterations in between returning samples.</param>
/// <param name="pSdv">The standard deviations of the normal distributions that are used to sample
/// the components of the momentum.</param>
/// <param name="randomSource">Random number generator used for sampling the momentum.</param>
/// <param name="diff">The method used for numerical differentiation.</param>
/// <exception cref="ArgumentOutOfRangeException">When the number of burnInterval iteration is negative.</exception>
/// <exception cref="ArgumentOutOfRangeException">When the length of pSdv is not the same as x0.</exception>
public HybridMC(double[] x0, DensityLn <double[]> pdfLnP, int frogLeapSteps, double stepSize, int burnInterval, double[] pSdv, System.Random randomSource, DiffMethod diff)
: base(x0, pdfLnP, frogLeapSteps, stepSize, burnInterval, randomSource, diff)
{
_length = x0.Length;
MomentumStdDev = pSdv;
Initialize(x0);
Burn(BurnInterval);
}
/// <summary>
/// Constructs a new Hybrid Monte Carlo sampler for a multivariate probability distribution.
/// The components of the momentum will be sampled from a normal distribution with standard deviations
/// given by pSdv using the default <see cref="System.Random"/> random
/// number generator. This constructor will set both the burn interval and the method used for
/// numerical differentiation.
/// </summary>
/// <param name="x0">The initial sample.</param>
/// <param name="pdfLnP">The log density of the distribution we want to sample from.</param>
/// <param name="frogLeapSteps">Number frogleap simulation steps.</param>
/// <param name="stepSize">Size of the frogleap simulation steps.</param>
/// <param name="burnInterval">The number of iterations in between returning samples.</param>
/// <param name="pSdv">The standard deviations of the normal distributions that are used to sample
/// the components of the momentum.</param>
/// <param name="diff">The method used for numerical differentiation.</param>
/// <exception cref="ArgumentOutOfRangeException">When the number of burnInterval iteration is negative.</exception>
/// <exception cref="ArgumentOutOfRangeException">When the length of pSdv is not the same as x0.</exception>
public HybridMC(double[] x0, DensityLn <double[]> pdfLnP, int frogLeapSteps, double stepSize, int burnInterval, double[] pSdv, DiffMethod diff)
: base(x0, pdfLnP, frogLeapSteps, stepSize, burnInterval, diff)
{
Length = x0.Count();
MomentumStdDev = pSdv;
Initialize(x0);
Burn(BurnInterval);
}
/// <summary>
/// Constructs a new Hybrid Monte Carlo sampler for a multivariate probability distribution.
/// The momentum will be sampled from a normal distribution with standard deviation
/// given by pSdv using a random
/// number generator provided by the user. This constructor will set both the burn interval and the method used for
/// numerical differentiation.
/// </summary>
/// <param name="x0">The initial sample.</param>
/// <param name="pdfLnP">The log density of the distribution we want to sample from.</param>
/// <param name="frogLeapSteps">Number frogleap simulation steps.</param>
/// <param name="stepSize">Size of the frogleap simulation steps.</param>
/// <param name="burnInterval">The number of iterations in between returning samples.</param>
/// <param name="pSdv">The standard deviation of the normal distribution that is used to sample
/// the momentum.</param>
/// <param name="diff">The method used for numerical differentiation.</param>
/// <param name="randomSource">Random number generator used for sampling the momentum.</param>
/// <exception cref="ArgumentOutOfRangeException">When the number of burnInterval iteration is negative.</exception>
public UnivariateHybridMC(double x0, DensityLn <double> pdfLnP, int frogLeapSteps, double stepSize, int burnInterval, double pSdv, System.Random randomSource, DiffMethod diff)
: base(x0, pdfLnP, frogLeapSteps, stepSize, burnInterval, randomSource, diff)
{
MomentumStdDev = pSdv;
_distribution = new Normal(0.0, MomentumStdDev, RandomSource);
Burn(BurnInterval);
}
/// <summary>
/// Parses the command-line options
/// </summary>
/// <param name="options">
/// The program options array
/// </param>
/// <returns>
/// True if the arguments are valid
/// False otherwise
/// </returns>
static Boolean ParseOptions(String[] options)
{
// initialize options
password = "";
diffMethod = DiffMethod.Timestamp;
sourcePaths = new List<IO.Path>();
includeFilter = new List<Regex>();
excludeFilter = new List<Regex>();
deleteArchive = false;
maxRetries = 5;
maxFailures = 5;
checkpointLength = 1024;
rateLimit = Int32.MaxValue / 1024;
compress = false;
// parse options
try
{
new Options.OptionSet()
{
{ "c|connect=", v => connectionString = v },
{ "a|archive=", v => archiveName = v },
{ "p|password="******"r|max-retry=", (Int32 v) => maxRetries = v },
{ "f|max-fail=", (Int32 v) => maxFailures = v },
{ "s|source=", v => sourcePaths.Add((IO.Path)v) },
{ "n|include=", v => includeFilter.Add(new Regex(v, RegexOptions.IgnoreCase)) },
{ "x|exclude=", v => excludeFilter.Add(new Regex(v, RegexOptions.IgnoreCase)) },
{ "k|delete", v => deleteArchive = (v != null) },
{ "d|diff=", (DiffMethod v) => diffMethod = v },
{ "t|checkpoint=", (Int32 v) => checkpointLength = v },
{ "l|rate=", (Int32 v) => rateLimit = v },
{ "z|compress", v => compress = (v != null) },
}.Parse(options);
}
catch { return false; }
// validate options
if (String.IsNullOrWhiteSpace(connectionString))
return false;
if (String.IsNullOrWhiteSpace(archiveName))
return false;
if (maxRetries < 0)
return false;
if (maxFailures < 0)
return false;
if (!sourcePaths.Any())
sourcePaths.Add(IO.Path.Current);
if (sourcePaths.Any(p => String.IsNullOrWhiteSpace(p)))
return false;
if (diffMethod == 0)
return false;
if (checkpointLength <= 0)
return false;
if (rateLimit <= 0)
return false;
return true;
}
/// <summary>
/// Diff the given columns of two given files using a given tolerance and method reporting any information back to the StringBuilder passed by ref
/// </summary>
/// <param name="origfile">the original file</param>
/// <param name="newfile">the new file to diff against the original file</param>
/// <param name="colnames">the colnames to focus on</param>
/// <param name="tolerance">the toleracne to use (absolute value/dp/%age depedning on 'method')</param>
/// <param name="method">the method to use when diffing (absolute value/dp/%age)</param>
/// <param name="output">a StringBuilder for reporting</param>
/// <returns>true if files are approximately the same (within the given tolerance)</returns>
internal static bool Diff(InputFile origfile, InputFile newfile, List <string> colnames, decimal tolerance, DiffMethod method, ref StringBuilder output)
{
Dictionary <string, int>
orig_lookup = new Dictionary <string, int>(),
new_lookup = new Dictionary <string, int>();
List <string>
orig_cols = new List <string>(origfile.ColNames),
new_cols = new List <string>(newfile.ColNames);
//basic little factory for setting up our comparer object
CompareMethod comparer =
method == DiffMethod.pct ? (CompareMethod) new PctCompare(tolerance) :
method == DiffMethod.abs ? (CompareMethod) new AbsCompare(tolerance) :
(CompareMethod) new DPCompare(tolerance);
//set up a lookup dictionary to translate between column name and index
foreach (string col in colnames)
{
orig_lookup.Add(col, orig_cols.IndexOf(col));
new_lookup.Add(col, new_cols.IndexOf(col));
}
//grab the raw data to diff
object[][]
origdata = origfile.GetData(true),
newdata = newfile.GetData(true);
string result = "";
if (origdata.Length != newdata.Length)
{
result += "The 2 files had different lengths, diff will end at bottom of shortest file" + Environment.NewLine;
}
object diff;
int
badhits = 0,
ibadline = -1,
numlines = (int)Math.Min(origdata.Length, newdata.Length);
//for each line in our files
for (int i = 0; i < numlines; i++)
{
//for each column of interest
foreach (string col in colnames)
{
//if we don't have a 'match' (comparer.match takes care of the tolerances for us)
if (!comparer.match(origdata[i][orig_lookup[col]], newdata[i][new_lookup[col]], out diff))
{
badhits++;
//if we haven't picked up any fails on this line yet
if (ibadline != i)
{
//if this isn't the first line of our report then make sure we add a newline
if (ibadline >= 0)
{
output.AppendLine();
}
//if it is the first line then make sure we make it clear the file failed
else
{
output.AppendLine("FAIL");
}
//print the line number (remember this is the first fail for this line)
output.AppendFormat("\t{0,-4}:", i);
ibadline = i;
}
//print out the column name of the value that failed, along with the difference
output.AppendFormat("\t{0}:{1}", col, diff);
}
}
}
if (badhits == 0)
{
output.AppendLine("PASS");
}
return(badhits == 0);
}
