Added Gradient Descent with Momentum in Machine Learning Algorithms

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)

machine_learning/gradient_descent_momentum.py

@@ -0,0 +1,157 @@
+"""
+Implementation of gradient descent algorithm using momentum
+for minimizing cost of a linear hypothesis function.
+"""
+
+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 = [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
+
+
+def _error(example_no, data_set="train") -> float:
+    """
+    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 predicted output and actual output.
+    """
+    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) -> float:
+    """
+    Compute the hypothesis value (predicted output) for a given input tuple.
+    Args:
+        data_input_tuple: The input tuple (features) for the example.
+    Returns:
+        float: The hypothesis value for the given input.
+    """
+    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]
+    return hyp_val
+
+
+def output(example_no, data_set) -> int:
+    """
+    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 -1
+
+
+def calculate_hypothesis_value(example_no, data_set) -> float:
+    """
+    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 -1
+
+
+def summation_of_cost_derivative(index, end=m) -> float:
+    """
+    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 = 0.0
+    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) -> float:
+    """
+    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() -> None:
+    """
+    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
+    iteration = 0
+
+    while True:
+        iteration += 1
+        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
+            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(f"Number of iterations: {iteration}")
+
+
+def test_gradient_descent() -> None:
+    """
+    Test the trained model on the test dataset and print actual vs predicted outputs.
+    """
+    for i in range(len(test_data)):
+        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 momentum for a linear hypothesis function.\n")
+    test_gradient_descent()