[ENH] concat operation on distributions

.all-contributorsrc

@@ -140,6 +140,15 @@
         "code"
       ]
     },
+    {
+      "login": "sairevanth25",
+      "name": "Sai Revanth Gowravajhala",
+      "avatar_url": "https://avatars.githubusercontent.com/SaiRevanth25",
+      "profile": "https://github.com/SaiRevanth25",
+      "contributions": [
+        "code"
+      ]
+    },
     {
       "login": "malikrafsan",
       "name": "Malik Akbar Hashemi Rafsanjani",

skpro/distributions/concat.py

@@ -0,0 +1,96 @@
+# copyright: skpro developers, BSD-3-Clause License (see LICENSE file)
+"""Concat operation."""
+
+__author__ = ["SaiRevanth25"]
+
+import pandas as pd
+
+
+class concat:
+    """Concatenate the given distributions along specified axis.
+
+    Parameters
+    ----------
+    distributions : list
+        list of distributions
+    axis : {0/'index', 1/'columns'}, default 0
+        The axis to concatenate along
+
+    Examples
+    --------
+    >>> import skpro.distributions.concat as skpro
+    >>> d1 = Normal(mu=[[1, 2], [3, 4]], sigma=1)
+    >>> d2 = Normal(mu=0, sigma = [[2, 42]])
+    >>> skpro.concat([d1,d2]).mean()
+
+            0	1
+        0	1	2
+        1	3	4
+        2	0	0
+
+    >>> skpro.concat([d1,d2]).var()
+
+            0	1
+        0	1	1
+        1	1	1
+        2	4	1764
+
+
+    >>> d3 = Gamma(alpha=[[5, 2]], beta=4)
+    >>> d4 = Laplace(mu= [5,7], scale=[2,8])
+
+    >>> skpro.concat([d2,d3,d4]).pdf(x=1)
+
+                    0	 1
+        Normal	4.0000	1764.000
+        Gamma	0.3125	0.125
+        Laplace	8.0000	128.000
+
+    """
+
+    def __init__(self, distributions, axis=0):
+        """Initialize concat with list of distributions and axis for concatenation."""
+        self.distributions = distributions
+        self.axis = axis
+        self.distribution_names = [dist.name for dist in distributions]
+
+    def mean(self):
+        """Calculate and concatenate means for each distribution."""
+        means = [dist.mean() for dist in self.distributions]
+        concatenated = pd.concat(means, axis=self.axis, ignore_index=True)
+        concatenated.index = self._generate_index(len(concatenated))
+        return concatenated
+
+    def var(self):
+        """Calculate and concatenate variances for each distribution."""
+        variances = [dist.var() for dist in self.distributions]
+        concatenated = pd.concat(variances, axis=self.axis, ignore_index=True)
+        concatenated.index = self._generate_index(len(concatenated))
+        return concatenated
+
+    def pdf(self, x):
+        """Concatenate PDFs of the distributions for a given value of `x`."""
+        pdfs = []
+        for dist in self.distributions:
+            try:
+                pdf_values = dist.pdf(x)
+                pdfs.append(pdf_values)
+            except ValueError as e:
+                raise ValueError(
+                    f"Error in pdf computation for distribution {dist.name}: {str(e)}"
+                )
+
+        concatenated = pd.concat(pdfs, axis=self.axis, ignore_index=True)
+        concatenated.index = self._generate_index(len(concatenated))
+        return concatenated
+
+    def _generate_index(self, length):
+        """Generate index for concatenated result."""
+        if length != len(self.distribution_names):
+            return pd.RangeIndex(start=0, stop=length)
+        return self.distribution_names
+
+    # todo: Constructing a new distribution when the two distributions are same.
+    def _concat_distr(self):
+        """Construct a new distrbution when the distributions are same."""
+        pass