From d47ad9c215d7ebe358e5ad47e3280c0b567dc02a Mon Sep 17 00:00:00 2001
From: Raisa Jose <168289547+raisaaajose@users.noreply.github.com>
Date: Thu, 31 Jul 2025 05:18:39 +0530
Subject: [PATCH 1/2] fix: added doctest for k-means-clustering

added two doctests and defined parameters
---
 .../k_means_clustering_tensorflow.py          | 33 ++++++++++++++++---
 1 file changed, 28 insertions(+), 5 deletions(-)

diff --git a/dynamic_programming/k_means_clustering_tensorflow.py b/dynamic_programming/k_means_clustering_tensorflow.py
index 8d3f6f0dfbcb..5e0f3f503136 100644
--- a/dynamic_programming/k_means_clustering_tensorflow.py
+++ b/dynamic_programming/k_means_clustering_tensorflow.py
@@ -6,15 +6,38 @@
 
 def tf_k_means_cluster(vectors, noofclusters):
     """
-    K-Means Clustering using TensorFlow.
-    'vectors' should be a n*k 2-D NumPy array, where n is the number
-    of vectors of dimensionality k.
-    'noofclusters' should be an integer.
+    K-Means Clustering using TensorFlow 1.x.
+
+    Parameters:
+    vectors (numpy.ndarray): A n*k 2-D NumPy array of dtype float32, 
+                            where n is the number of vectors and k is their dimensionality.
+    noofclusters (int): An integer representing the number of clusters (k).
+
+    (For reproducibility, set both Python's random seed and TensorFlow's random seed)
+    >>> import random
+    >>> random.seed(42)
+    >>> tf.random.set_seed(42)
+
+    Example 1
+    >>> data1 = numpy.array([[0.0, 0.0], [0.1, 0.1], [10.0, 10.0]], dtype=numpy.float32)
+    >>> centroids1, assignments1 = tf_k_means_cluster(data1, 2)
+    >>> print(centroids1)
+    [[ 0.05  0.05]
+     [10.   10.  ]]
+    >>> print(assignments1)
+    [0 0 1]
+
+    Example 2:
+    >>> data3 = numpy.array([[0.0, 0.0], [0.9, 0.9], [13.0, 15.0]], dtype=numpy.float32)
+    >>> tf_k_means_cluster(data3, 5)
+    Traceback (most recent call last):
+        ...
+    AssertionError
     """
 
     noofclusters = int(noofclusters)
     assert noofclusters < len(vectors)
-
+    
     # Find out the dimensionality
     dim = len(vectors[0])
 

From 9a3ae21387311b2b260885d162a884e74e63a7e4 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 30 Jul 2025 23:59:39 +0000
Subject: [PATCH 2/2] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 dynamic_programming/k_means_clustering_tensorflow.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/dynamic_programming/k_means_clustering_tensorflow.py b/dynamic_programming/k_means_clustering_tensorflow.py
index 5e0f3f503136..41e7244fbff9 100644
--- a/dynamic_programming/k_means_clustering_tensorflow.py
+++ b/dynamic_programming/k_means_clustering_tensorflow.py
@@ -9,7 +9,7 @@ def tf_k_means_cluster(vectors, noofclusters):
     K-Means Clustering using TensorFlow 1.x.
 
     Parameters:
-    vectors (numpy.ndarray): A n*k 2-D NumPy array of dtype float32, 
+    vectors (numpy.ndarray): A n*k 2-D NumPy array of dtype float32,
                             where n is the number of vectors and k is their dimensionality.
     noofclusters (int): An integer representing the number of clusters (k).
 
@@ -37,7 +37,7 @@ def tf_k_means_cluster(vectors, noofclusters):
 
     noofclusters = int(noofclusters)
     assert noofclusters < len(vectors)
-    
+
     # Find out the dimensionality
     dim = len(vectors[0])