From bdde1f9183ea60eca6afeb05c10c9a9da60a0765 Mon Sep 17 00:00:00 2001
From: AnuragSingh0000 <143340413+AnuragSingh0000@users.noreply.github.com>
Date: Tue, 8 Oct 2024 02:00:51 +0530
Subject: [PATCH 1/9] Added gradient descent with momentum

---
 machine_learning/gradient_descent_momentum.py | 82 +++++++++++++++++++
 1 file changed, 82 insertions(+)
 create mode 100644 machine_learning/gradient_descent_momentum.py

diff --git a/machine_learning/gradient_descent_momentum.py b/machine_learning/gradient_descent_momentum.py
new file mode 100644
index 000000000000..399c7d4299ba
--- /dev/null
+++ b/machine_learning/gradient_descent_momentum.py
@@ -0,0 +1,82 @@
+import numpy as np
+
+# List of input, output pairs
+train_data = (
+    ((5, 2, 3), 15),
+    ((6, 5, 9), 25),
+    ((11, 12, 13), 41),
+    ((1, 1, 1), 8),
+    ((11, 12, 13), 41),
+)
+test_data = (((515, 22, 13), 555), ((61, 35, 49), 150))
+parameter_vector = [2, 4, 1, 5]
+m = len(train_data)
+LEARNING_RATE = 0.009
+MOMENTUM = 0.9
+
+# Initialize velocity (for momentum)
+velocity = [0] * len(parameter_vector)
+
+def _error(example_no, data_set="train"):
+    return calculate_hypothesis_value(example_no, data_set) - output(example_no, data_set)
+
+def _hypothesis_value(data_input_tuple):
+    hyp_val = 0
+    for i in range(len(parameter_vector) - 1):
+        hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
+    hyp_val += parameter_vector[0]
+    return hyp_val
+
+def output(example_no, data_set):
+    if data_set == "train":
+        return train_data[example_no][1]
+    elif data_set == "test":
+        return test_data[example_no][1]
+    return None
+
+def calculate_hypothesis_value(example_no, data_set):
+    if data_set == "train":
+        return _hypothesis_value(train_data[example_no][0])
+    elif data_set == "test":
+        return _hypothesis_value(test_data[example_no][0])
+    return None
+
+def summation_of_cost_derivative(index, end=m):
+    summation_value = 0
+    for i in range(end):
+        if index == -1:
+            summation_value += _error(i)
+        else:
+            summation_value += _error(i) * train_data[i][0][index]
+    return summation_value
+
+def get_cost_derivative(index):
+    return summation_of_cost_derivative(index, m) / m
+
+def run_gradient_descent_with_momentum():
+    global parameter_vector, velocity
+    absolute_error_limit = 0.000002
+    relative_error_limit = 0
+    j = 0
+    while True:
+        j += 1
+        temp_parameter_vector = [0] * len(parameter_vector)
+        for i in range(len(parameter_vector)):
+            cost_derivative = get_cost_derivative(i - 1)
+            velocity[i] = MOMENTUM * velocity[i] + cost_derivative
+            temp_parameter_vector[i] = parameter_vector[i] - LEARNING_RATE * velocity[i]
+        
+        if np.allclose(parameter_vector, temp_parameter_vector, atol=absolute_error_limit, rtol=relative_error_limit):
+            break
+        parameter_vector = temp_parameter_vector
+    print(("Number of iterations:", j))
+
+def test_gradient_descent():
+    for i in range(len(test_data)):
+        print(("Actual output value:", output(i, "test")))
+        print(("Hypothesis output:", calculate_hypothesis_value(i, "test")))
+
+if __name__ == "__main__":
+    run_gradient_descent_with_momentum()
+    print("\nTesting gradient descent with momentum for a linear hypothesis function.\n")
+    test_gradient_descent()

From 8a38a5fc274ea3bc713c4818e61aea164a4ac629 Mon Sep 17 00:00:00 2001
From: AnuragSingh0000 <143340413+AnuragSingh0000@users.noreply.github.com>
Date: Tue, 8 Oct 2024 11:49:33 +0530
Subject: [PATCH 2/9] Gradient Descent with Momentum completed

---
 machine_learning/gradient_descent_momentum.py | 74 +++++++++++++++++--
 1 file changed, 69 insertions(+), 5 deletions(-)

diff --git a/machine_learning/gradient_descent_momentum.py b/machine_learning/gradient_descent_momentum.py
index 399c7d4299ba..9f412a045218 100644
--- a/machine_learning/gradient_descent_momentum.py
+++ b/machine_learning/gradient_descent_momentum.py
@@ -1,3 +1,7 @@
+"""
+Implementation of gradient descent algorithm using momentum for minimizing cost of a linear hypothesis
+function.
+"""
 import numpy as np
 
 # List of input, output pairs
@@ -18,30 +22,73 @@
 velocity = [0] * len(parameter_vector)
 
 def _error(example_no, data_set="train"):
+    """
+    Calculate the error (difference between predicted and actual output) for a given example.
+    Args:
+        example_no (int): Index of the example in the dataset.
+        data_set (str): The dataset to use, either "train" or "test". 
+    Returns:
+        float: The difference between the predicted output and the actual output.
+    """
     return calculate_hypothesis_value(example_no, data_set) - output(example_no, data_set)
 
+
 def _hypothesis_value(data_input_tuple):
+    """
+    Compute the hypothesis value (predicted output) for a given input tuple.
+    Args:
+        data_input_tuple (tuple): The input tuple (features) for the example.
+    Returns:
+        float: The hypothesis value for the given input.
+    """
     hyp_val = 0
     for i in range(len(parameter_vector) - 1):
         hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
     hyp_val += parameter_vector[0]
     return hyp_val
 
+
 def output(example_no, data_set):
+    """
+    Retrieve the actual output (label) for a given example from the specified dataset.
+    Args:
+        example_no (int): Index of the example in the dataset.
+        data_set (str): The dataset to use, either "train" or "test".
+    Returns:
+        int: The actual output value for the specified example.
+    """
     if data_set == "train":
         return train_data[example_no][1]
     elif data_set == "test":
         return test_data[example_no][1]
     return None
 
+
 def calculate_hypothesis_value(example_no, data_set):
+    """
+    Calculate the hypothesis value (predicted output) for a given example.
+    Args:
+        example_no (int): Index of the example in the dataset.
+        data_set (str): The dataset to use, either "train" or "test".
+    Returns:
+        float: The hypothesis value for the specified example.
+    """
     if data_set == "train":
         return _hypothesis_value(train_data[example_no][0])
     elif data_set == "test":
         return _hypothesis_value(test_data[example_no][0])
     return None
 
+
 def summation_of_cost_derivative(index, end=m):
+    """
+    Calculate the summation of the cost derivative for a given index.
+    Args:
+        index (int): The index of the parameter for which the derivative is calculated.
+        end (int): The number of examples to consider (defaults to the size of the training set).
+    Returns:
+        float: The summation of the cost derivatives for the given parameter.
+    """
     summation_value = 0
     for i in range(end):
         if index == -1:
@@ -50,16 +97,29 @@ def summation_of_cost_derivative(index, end=m):
             summation_value += _error(i) * train_data[i][0][index]
     return summation_value
 
+
 def get_cost_derivative(index):
+    """
+    Compute the cost derivative with respect to a parameter.
+    Args:
+        index (int): The index of the parameter.
+    Returns:
+        float: The cost derivative for the specified parameter.
+    """
     return summation_of_cost_derivative(index, m) / m
 
+
 def run_gradient_descent_with_momentum():
+    """
+    Run gradient descent with momentum to minimize the cost function.
+    This function updates the parameter vector using velocity and the learning rate.
+    """
     global parameter_vector, velocity
     absolute_error_limit = 0.000002
     relative_error_limit = 0
-    j = 0
+    iteration = 0
     while True:
-        j += 1
+        iteration += 1
         temp_parameter_vector = [0] * len(parameter_vector)
         for i in range(len(parameter_vector)):
             cost_derivative = get_cost_derivative(i - 1)
@@ -69,12 +129,16 @@ def run_gradient_descent_with_momentum():
         if np.allclose(parameter_vector, temp_parameter_vector, atol=absolute_error_limit, rtol=relative_error_limit):
             break
         parameter_vector = temp_parameter_vector
-    print(("Number of iterations:", j))
+    print(f"Number of iterations: {iteration}")
+
 
 def test_gradient_descent():
+    """
+    Test the trained model on the test dataset and print actual vs predicted outputs.
+    """
     for i in range(len(test_data)):
-        print(("Actual output value:", output(i, "test")))
-        print(("Hypothesis output:", calculate_hypothesis_value(i, "test")))
+        print(f"Actual output value: {output(i, 'test')}")
+        print(f"Hypothesis output: {calculate_hypothesis_value(i, 'test')}")
 
 if __name__ == "__main__":
     run_gradient_descent_with_momentum()

From 7d710c812d65eb3985444576ad5f47ca283e9d4b Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 8 Oct 2024 06:29:23 +0000
Subject: [PATCH 3/9] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 machine_learning/gradient_descent_momentum.py | 22 ++++++++++++++-----
 1 file changed, 17 insertions(+), 5 deletions(-)

diff --git a/machine_learning/gradient_descent_momentum.py b/machine_learning/gradient_descent_momentum.py
index 9f412a045218..c8df37a36189 100644
--- a/machine_learning/gradient_descent_momentum.py
+++ b/machine_learning/gradient_descent_momentum.py
@@ -2,6 +2,7 @@
 Implementation of gradient descent algorithm using momentum for minimizing cost of a linear hypothesis
 function.
 """
+
 import numpy as np
 
 # List of input, output pairs
@@ -21,16 +22,19 @@
 # Initialize velocity (for momentum)
 velocity = [0] * len(parameter_vector)
 
+
 def _error(example_no, data_set="train"):
     """
     Calculate the error (difference between predicted and actual output) for a given example.
     Args:
         example_no (int): Index of the example in the dataset.
-        data_set (str): The dataset to use, either "train" or "test". 
+        data_set (str): The dataset to use, either "train" or "test".
     Returns:
         float: The difference between the predicted output and the actual output.
     """
-    return calculate_hypothesis_value(example_no, data_set) - output(example_no, data_set)
+    return calculate_hypothesis_value(example_no, data_set) - output(
+        example_no, data_set
+    )
 
 
 def _hypothesis_value(data_input_tuple):
@@ -125,8 +129,13 @@ def run_gradient_descent_with_momentum():
             cost_derivative = get_cost_derivative(i - 1)
             velocity[i] = MOMENTUM * velocity[i] + cost_derivative
             temp_parameter_vector[i] = parameter_vector[i] - LEARNING_RATE * velocity[i]
-        
-        if np.allclose(parameter_vector, temp_parameter_vector, atol=absolute_error_limit, rtol=relative_error_limit):
+
+        if np.allclose(
+            parameter_vector,
+            temp_parameter_vector,
+            atol=absolute_error_limit,
+            rtol=relative_error_limit,
+        ):
             break
         parameter_vector = temp_parameter_vector
     print(f"Number of iterations: {iteration}")
@@ -140,7 +149,10 @@ def test_gradient_descent():
         print(f"Actual output value: {output(i, 'test')}")
         print(f"Hypothesis output: {calculate_hypothesis_value(i, 'test')}")
 
+
 if __name__ == "__main__":
     run_gradient_descent_with_momentum()
-    print("\nTesting gradient descent with momentum for a linear hypothesis function.\n")
+    print(
+        "\nTesting gradient descent with momentum for a linear hypothesis function.\n"
+    )
     test_gradient_descent()

From 6cbd4ff0c3c5305833dcd02cd54ebd3ac5199f05 Mon Sep 17 00:00:00 2001
From: AnuragSingh0000 <143340413+AnuragSingh0000@users.noreply.github.com>
Date: Tue, 8 Oct 2024 12:26:54 +0530
Subject: [PATCH 4/9] Resolved Formatting issues

---
 machine_learning/frequent_pattern_growth.py   |  4 ++-
 machine_learning/gradient_descent_momentum.py | 26 +++++++++----------
 2 files changed, 16 insertions(+), 14 deletions(-)

diff --git a/machine_learning/frequent_pattern_growth.py b/machine_learning/frequent_pattern_growth.py
index 947f8692f298..08bf6c04deac 100644
--- a/machine_learning/frequent_pattern_growth.py
+++ b/machine_learning/frequent_pattern_growth.py
@@ -240,7 +240,9 @@ def ascend_tree(leaf_node: TreeNode, prefix_path: list[str]) -> None:
         ascend_tree(leaf_node.parent, prefix_path)
 
 
-def find_prefix_path(base_pat: frozenset, tree_node: TreeNode | None) -> dict:  # noqa: ARG001
+def find_prefix_path(
+    base_pat: frozenset, tree_node: TreeNode | None
+) -> dict:  # noqa: ARG001
     """
     Find the conditional pattern base for a given base pattern.
 
diff --git a/machine_learning/gradient_descent_momentum.py b/machine_learning/gradient_descent_momentum.py
index c8df37a36189..7f28ab9b4fd9 100644
--- a/machine_learning/gradient_descent_momentum.py
+++ b/machine_learning/gradient_descent_momentum.py
@@ -1,6 +1,6 @@
 """
-Implementation of gradient descent algorithm using momentum for minimizing cost of a linear hypothesis
-function.
+Implementation of gradient descent algorithm using momentum
+for minimizing cost of a linear hypothesis function.
 """
 
 import numpy as np
@@ -25,23 +25,23 @@
 
 def _error(example_no, data_set="train"):
     """
-    Calculate the error (difference between predicted and actual output) for a given example.
+    Calculate the error for a given example.
     Args:
         example_no (int): Index of the example in the dataset.
         data_set (str): The dataset to use, either "train" or "test".
     Returns:
-        float: The difference between the predicted output and the actual output.
+        float: The difference between predicted output and actual output.
     """
-    return calculate_hypothesis_value(example_no, data_set) - output(
-        example_no, data_set
-    )
+    hypo_value = calculate_hypothesis_value(example_no, data_set)
+    output_value = output(example_no, data_set)
+    return hypo_value - output_value
 
 
 def _hypothesis_value(data_input_tuple):
     """
     Compute the hypothesis value (predicted output) for a given input tuple.
     Args:
-        data_input_tuple (tuple): The input tuple (features) for the example.
+        data_input_tuple: The input tuple (features) for the example.
     Returns:
         float: The hypothesis value for the given input.
     """
@@ -54,7 +54,8 @@ def _hypothesis_value(data_input_tuple):
 
 def output(example_no, data_set):
     """
-    Retrieve the actual output (label) for a given example from the specified dataset.
+    Retrieve the actual output (label) for a given example
+    from the specified dataset.
     Args:
         example_no (int): Index of the example in the dataset.
         data_set (str): The dataset to use, either "train" or "test".
@@ -89,7 +90,8 @@ def summation_of_cost_derivative(index, end=m):
     Calculate the summation of the cost derivative for a given index.
     Args:
         index (int): The index of the parameter for which the derivative is calculated.
-        end (int): The number of examples to consider (defaults to the size of the training set).
+        end (int): The number of examples to consider
+                    (defaults to the size of the training set).
     Returns:
         float: The summation of the cost derivatives for the given parameter.
     """
@@ -152,7 +154,5 @@ def test_gradient_descent():
 
 if __name__ == "__main__":
     run_gradient_descent_with_momentum()
-    print(
-        "\nTesting gradient descent with momentum for a linear hypothesis function.\n"
-    )
+    print("\nTesting gradient descent momentum for a linear hypothesis function.\n")
     test_gradient_descent()

From a095eb0fd9b178619faf4b7c4b6ea066679d1710 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 8 Oct 2024 06:57:47 +0000
Subject: [PATCH 5/9] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 machine_learning/frequent_pattern_growth.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/machine_learning/frequent_pattern_growth.py b/machine_learning/frequent_pattern_growth.py
index 08bf6c04deac..947f8692f298 100644
--- a/machine_learning/frequent_pattern_growth.py
+++ b/machine_learning/frequent_pattern_growth.py
@@ -240,9 +240,7 @@ def ascend_tree(leaf_node: TreeNode, prefix_path: list[str]) -> None:
         ascend_tree(leaf_node.parent, prefix_path)
 
 
-def find_prefix_path(
-    base_pat: frozenset, tree_node: TreeNode | None
-) -> dict:  # noqa: ARG001
+def find_prefix_path(base_pat: frozenset, tree_node: TreeNode | None) -> dict:  # noqa: ARG001
     """
     Find the conditional pattern base for a given base pattern.
 

From 785622c0a5de15d7ee599a13364e5fa06ecefcb9 Mon Sep 17 00:00:00 2001
From: AnuragSingh0000 <143340413+AnuragSingh0000@users.noreply.github.com>
Date: Tue, 8 Oct 2024 19:07:01 +0530
Subject: [PATCH 6/9] Added return type

---
 machine_learning/gradient_descent_momentum.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/machine_learning/gradient_descent_momentum.py b/machine_learning/gradient_descent_momentum.py
index 7f28ab9b4fd9..2b01ec93d86d 100644
--- a/machine_learning/gradient_descent_momentum.py
+++ b/machine_learning/gradient_descent_momentum.py
@@ -23,7 +23,7 @@
 velocity = [0] * len(parameter_vector)
 
 
-def _error(example_no, data_set="train"):
+def _error(example_no, data_set="train") -> float:
     """
     Calculate the error for a given example.
     Args:
@@ -37,7 +37,7 @@ def _error(example_no, data_set="train"):
     return hypo_value - output_value
 
 
-def _hypothesis_value(data_input_tuple):
+def _hypothesis_value(data_input_tuple) -> float:
     """
     Compute the hypothesis value (predicted output) for a given input tuple.
     Args:
@@ -52,7 +52,7 @@ def _hypothesis_value(data_input_tuple):
     return hyp_val
 
 
-def output(example_no, data_set):
+def output(example_no, data_set) -> int:
     """
     Retrieve the actual output (label) for a given example
     from the specified dataset.
@@ -69,7 +69,7 @@ def output(example_no, data_set):
     return None
 
 
-def calculate_hypothesis_value(example_no, data_set):
+def calculate_hypothesis_value(example_no, data_set) -> float:
     """
     Calculate the hypothesis value (predicted output) for a given example.
     Args:
@@ -85,7 +85,7 @@ def calculate_hypothesis_value(example_no, data_set):
     return None
 
 
-def summation_of_cost_derivative(index, end=m):
+def summation_of_cost_derivative(index, end=m) -> float:
     """
     Calculate the summation of the cost derivative for a given index.
     Args:
@@ -104,7 +104,7 @@ def summation_of_cost_derivative(index, end=m):
     return summation_value
 
 
-def get_cost_derivative(index):
+def get_cost_derivative(index) -> float:
     """
     Compute the cost derivative with respect to a parameter.
     Args:
@@ -115,7 +115,7 @@ def get_cost_derivative(index):
     return summation_of_cost_derivative(index, m) / m
 
 
-def run_gradient_descent_with_momentum():
+def run_gradient_descent_with_momentum() -> None:
     """
     Run gradient descent with momentum to minimize the cost function.
     This function updates the parameter vector using velocity and the learning rate.
@@ -143,7 +143,7 @@ def run_gradient_descent_with_momentum():
     print(f"Number of iterations: {iteration}")
 
 
-def test_gradient_descent():
+def test_gradient_descent() -> None:
     """
     Test the trained model on the test dataset and print actual vs predicted outputs.
     """

From dc755229733aa18bf7bd0093838f8da8187151a3 Mon Sep 17 00:00:00 2001
From: AnuragSingh0000 <143340413+AnuragSingh0000@users.noreply.github.com>
Date: Tue, 8 Oct 2024 19:28:36 +0530
Subject: [PATCH 7/9] Fixed more return type issues

---
 machine_learning/frequent_pattern_growth.py   |  4 +++-
 machine_learning/gradient_descent_momentum.py | 17 ++++++++---------
 2 files changed, 11 insertions(+), 10 deletions(-)

diff --git a/machine_learning/frequent_pattern_growth.py b/machine_learning/frequent_pattern_growth.py
index 947f8692f298..08bf6c04deac 100644
--- a/machine_learning/frequent_pattern_growth.py
+++ b/machine_learning/frequent_pattern_growth.py
@@ -240,7 +240,9 @@ def ascend_tree(leaf_node: TreeNode, prefix_path: list[str]) -> None:
         ascend_tree(leaf_node.parent, prefix_path)
 
 
-def find_prefix_path(base_pat: frozenset, tree_node: TreeNode | None) -> dict:  # noqa: ARG001
+def find_prefix_path(
+    base_pat: frozenset, tree_node: TreeNode | None
+) -> dict:  # noqa: ARG001
     """
     Find the conditional pattern base for a given base pattern.
 
diff --git a/machine_learning/gradient_descent_momentum.py b/machine_learning/gradient_descent_momentum.py
index 2b01ec93d86d..883c5da0841a 100644
--- a/machine_learning/gradient_descent_momentum.py
+++ b/machine_learning/gradient_descent_momentum.py
@@ -14,14 +14,12 @@
     ((11, 12, 13), 41),
 )
 test_data = (((515, 22, 13), 555), ((61, 35, 49), 150))
-parameter_vector = [2, 4, 1, 5]
+parameter_vector = [0.0, 0.0, 0.0, 0.0]
+velocity = [0.0] * len(parameter_vector)
 m = len(train_data)
 LEARNING_RATE = 0.009
 MOMENTUM = 0.9
 
-# Initialize velocity (for momentum)
-velocity = [0] * len(parameter_vector)
-
 
 def _error(example_no, data_set="train") -> float:
     """
@@ -45,7 +43,7 @@ def _hypothesis_value(data_input_tuple) -> float:
     Returns:
         float: The hypothesis value for the given input.
     """
-    hyp_val = 0
+    hyp_val = 0.0
     for i in range(len(parameter_vector) - 1):
         hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
     hyp_val += parameter_vector[0]
@@ -66,7 +64,7 @@ def output(example_no, data_set) -> int:
         return train_data[example_no][1]
     elif data_set == "test":
         return test_data[example_no][1]
-    return None
+    return -1
 
 
 def calculate_hypothesis_value(example_no, data_set) -> float:
@@ -82,7 +80,7 @@ def calculate_hypothesis_value(example_no, data_set) -> float:
         return _hypothesis_value(train_data[example_no][0])
     elif data_set == "test":
         return _hypothesis_value(test_data[example_no][0])
-    return None
+    return -1
 
 
 def summation_of_cost_derivative(index, end=m) -> float:
@@ -95,7 +93,7 @@ def summation_of_cost_derivative(index, end=m) -> float:
     Returns:
         float: The summation of the cost derivatives for the given parameter.
     """
-    summation_value = 0
+    summation_value = 0.0
     for i in range(end):
         if index == -1:
             summation_value += _error(i)
@@ -124,9 +122,10 @@ def run_gradient_descent_with_momentum() -> None:
     absolute_error_limit = 0.000002
     relative_error_limit = 0
     iteration = 0
+
     while True:
         iteration += 1
-        temp_parameter_vector = [0] * len(parameter_vector)
+        temp_parameter_vector = [0.0] * len(parameter_vector)
         for i in range(len(parameter_vector)):
             cost_derivative = get_cost_derivative(i - 1)
             velocity[i] = MOMENTUM * velocity[i] + cost_derivative

From e64627d1829d333734b18aa77e0e196b219b54db Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 8 Oct 2024 13:59:08 +0000
Subject: [PATCH 8/9] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 machine_learning/frequent_pattern_growth.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/machine_learning/frequent_pattern_growth.py b/machine_learning/frequent_pattern_growth.py
index 08bf6c04deac..947f8692f298 100644
--- a/machine_learning/frequent_pattern_growth.py
+++ b/machine_learning/frequent_pattern_growth.py
@@ -240,9 +240,7 @@ def ascend_tree(leaf_node: TreeNode, prefix_path: list[str]) -> None:
         ascend_tree(leaf_node.parent, prefix_path)
 
 
-def find_prefix_path(
-    base_pat: frozenset, tree_node: TreeNode | None
-) -> dict:  # noqa: ARG001
+def find_prefix_path(base_pat: frozenset, tree_node: TreeNode | None) -> dict:  # noqa: ARG001
     """
     Find the conditional pattern base for a given base pattern.
 

From 60e7323618dcab9d767e9d5d54219141f4d014f2 Mon Sep 17 00:00:00 2001
From: AnuragSingh0000 <AnuragSingh0000@users.noreply.github.com>
Date: Wed, 9 Oct 2024 21:47:33 +0000
Subject: [PATCH 9/9] updating DIRECTORY.md

---
 DIRECTORY.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/DIRECTORY.md b/DIRECTORY.md
index f0a34a553946..d6598578e9a5 100644
--- a/DIRECTORY.md
+++ b/DIRECTORY.md
@@ -589,6 +589,7 @@
   * [Frequent Pattern Growth](machine_learning/frequent_pattern_growth.py)
   * [Gradient Boosting Classifier](machine_learning/gradient_boosting_classifier.py)
   * [Gradient Descent](machine_learning/gradient_descent.py)
+  * [Gradient Descent Momentum](machine_learning/gradient_descent_momentum.py)
   * [K Means Clust](machine_learning/k_means_clust.py)
   * [K Nearest Neighbours](machine_learning/k_nearest_neighbours.py)
   * [Linear Discriminant Analysis](machine_learning/linear_discriminant_analysis.py)