fix(random): correct size parameter delegation in np.random.* functions

Fixed circular delegation and incorrect type conversion in 18 RandomSampling files.
All overloads now properly delegate to Shape constructor:

- int[] → new Shape(size)
- params long[] → new Shape(size)
- int → new int[] { size } to int[] overload

Shape constructor accepts both int[] and long[], eliminating need for
Array.ConvertAll or Shape.ToIntArray in delegation chain.

Files fixed: dirichlet, gumbel, hypergeometric, laplace, logistic, logseries,
multivariate_normal, negative_binomial, noncentral_chisquare, pareto, power,
rayleigh, standard_cauchy, standard_exponential, standard_gamma, standard_t,
vonmises, zipf

Author: Nucs

src/NumSharp.Core/RandomSampling/np.random.dirichlet.cs

@@ -154,7 +154,7 @@ public NDArray dirichlet(double[] alpha, params long[] size)
         /// <param name="size">Number of samples to draw.</param>
         /// <returns>Drawn samples from the Dirichlet distribution.</returns>
         public NDArray dirichlet(double[] alpha, int size)
-            => dirichlet(alpha, new Shape(size));
+            => dirichlet(alpha, new int[] { size });
 
         /// <summary>
         ///     Sample a single Dirichlet vector and store at the given offset.

src/NumSharp.Core/RandomSampling/np.random.gumbel.cs

@@ -76,7 +76,7 @@ public NDArray gumbel(double loc, double scale, params long[] size)
         /// <param name="size">Output shape as single int.</param>
         /// <returns>Drawn samples from the parameterized Gumbel distribution.</returns>
         public NDArray gumbel(double loc, double scale, int size)
-            => gumbel(loc, scale, new Shape(size));
+            => gumbel(loc, scale, new int[] { size });
 
         /// <summary>
         ///     Sample from the Gumbel distribution using the same algorithm as NumPy.

src/NumSharp.Core/RandomSampling/np.random.hypergeometric.cs

@@ -69,7 +69,7 @@ public NDArray hypergeometric(long ngood, long nbad, long nsample, params long[]
         /// <param name="size">Output shape as single int.</param>
         /// <returns>Drawn samples from the hypergeometric distribution.</returns>
         public NDArray hypergeometric(long ngood, long nbad, long nsample, int size)
-            => hypergeometric(ngood, nbad, nsample, new Shape(size));
+            => hypergeometric(ngood, nbad, nsample, new int[] { size });
 
         /// <summary>
         ///     Draw a single sample from a Hypergeometric distribution.

src/NumSharp.Core/RandomSampling/np.random.laplace.cs

@@ -76,7 +76,7 @@ public NDArray laplace(double loc, double scale, params long[] size)
         /// <param name="size">Output shape as single int.</param>
         /// <returns>Drawn samples from the parameterized Laplace distribution.</returns>
         public NDArray laplace(double loc, double scale, int size)
-            => laplace(loc, scale, new Shape(size));
+            => laplace(loc, scale, new int[] { size });
 
         /// <summary>
         ///     Sample from the Laplace distribution using the same algorithm as NumPy.

src/NumSharp.Core/RandomSampling/np.random.logistic.cs

@@ -74,7 +74,7 @@ public NDArray logistic(double loc, double scale, params long[] size)
         /// <param name="size">Output shape as single int.</param>
         /// <returns>Drawn samples from the parameterized logistic distribution.</returns>
         public NDArray logistic(double loc, double scale, int size)
-            => logistic(loc, scale, new Shape(size));
+            => logistic(loc, scale, new int[] { size });
 
         /// <summary>
         ///     Sample from the logistic distribution using inverse transform method.

src/NumSharp.Core/RandomSampling/np.random.logseries.cs

@@ -91,7 +91,7 @@ public NDArray logseries(double p, Shape size)
         /// <param name="size">Output shape as single int.</param>
         /// <returns>Drawn samples from the parameterized logarithmic series distribution.</returns>
         public NDArray logseries(double p, int size)
-            => logseries(p, new Shape(size));
+            => logseries(p, new int[] { size });
 
         private static void ValidateLogseriesP(double p)
         {

src/NumSharp.Core/RandomSampling/np.random.negative_binomial.cs

@@ -77,7 +77,7 @@ public NDArray negative_binomial(double n, double p, params long[] size)
         /// <param name="size">Output shape as single int.</param>
         /// <returns>Drawn samples from the parameterized negative binomial distribution.</returns>
         public NDArray negative_binomial(double n, double p, int size)
-            => negative_binomial(n, p, new Shape(size));
+            => negative_binomial(n, p, new int[] { size });
 
         /// <summary>
         ///     Sample from the negative binomial distribution using gamma-Poisson mixture.

src/NumSharp.Core/RandomSampling/np.random.noncentral_chisquare.cs

@@ -66,7 +66,7 @@ public NDArray noncentral_chisquare(double df, double nonc, params long[] size)
         /// <param name="size">Output shape as single int.</param>
         /// <returns>Drawn samples from the parameterized noncentral chi-square distribution.</returns>
         public NDArray noncentral_chisquare(double df, double nonc, int size)
-            => noncentral_chisquare(df, nonc, new Shape(size));
+            => noncentral_chisquare(df, nonc, new int[] { size });
 
         /// <summary>
         ///     Draw a single sample from a noncentral chi-square distribution.

src/NumSharp.Core/RandomSampling/np.random.pareto.cs

@@ -65,7 +65,7 @@ public NDArray pareto(double a, params long[] size)
         /// <param name="size">Output shape as single int.</param>
         /// <returns>Drawn samples from the parameterized Pareto distribution.</returns>
         public NDArray pareto(double a, int size)
-            => pareto(a, new Shape(size));
+            => pareto(a, new int[] { size });
 
         /// <summary>
         ///     Draw a single sample from a Pareto II or Lomax distribution.

src/NumSharp.Core/RandomSampling/np.random.power.cs

@@ -62,7 +62,7 @@ public NDArray power(double a, params long[] size)
         /// <param name="size">Output shape as single int.</param>
         /// <returns>Drawn samples from the parameterized power distribution, in range [0, 1].</returns>
         public NDArray power(double a, int size)
-            => power(a, new Shape(size));
+            => power(a, new int[] { size });
 
         /// <summary>
         ///     Draw a single sample from a power distribution.