Update multilayer perceptron classifier implementation

Refactored multilayer perceptron classifier with example usage.

Author: cclauss

machine_learning/multilayer_perceptron_classifier.py

@@ -1,99 +1,23 @@
-"""
-Multilayer Perceptron (MLP) Classifier
-
-A Multilayer Perceptron (MLP) is a type of feedforward artificial neural network
-that consists of at least three layers of nodes: an input layer, one or more hidden
-layers, and an output layer. Each node (except for the input nodes) is a neuron
-that uses a nonlinear activation function.
-
-Mathematical Concept:
----------------------
-MLPs learn a function f(·): R^m → R^o by training on a dataset, where m is the
-number of input features and o is the number of output classes. The network
-adjusts its weights using backpropagation to minimize the difference between
-predicted and actual outputs.
-
-Practical Use Cases:
---------------------
-- Handwritten digit recognition (e.g., MNIST dataset)
-- Binary and multiclass classification tasks
-- Predicting outcomes based on multiple features
-  (e.g., medical diagnosis, spam detection)
-
-Advantages:
------------
-- Can learn non-linear decision boundaries
-- Works well with complex pattern recognition
-- Flexible architecture for various problem types
-
-Limitations:
-------------
-- Requires careful tuning of hyperparameters
-- Sensitive to feature scaling
-- Can overfit on small datasets
-
-Time Complexity: O(n_samples * n_features * n_layers * n_epochs)
-Space Complexity: O(n_features * n_hidden_units + n_hidden_units * n_classes)
-
-References:
------------
-- https://en.wikipedia.org/wiki/Multilayer_perceptron
-- https://scikit-learn.org/stable/modules/neural_networks_supervised.html
-- https://medium.com/@aryanrusia8/multi-layer-perceptrons-explained-7cb9a6e318c3
-
-Example:
---------
->>> X = [[0, 0], [1, 1], [0, 1], [1, 0]]
->>> y = [0, 0, 1, 1]
->>> result = multilayer_perceptron_classifier(X, y, [[0, 0], [1, 1]])
->>> result in [[0, 0], [0, 1], [1, 0], [1, 1]]
-True
-"""
-
-from collections.abc import Sequence
-
 from sklearn.neural_network import MLPClassifier
 
+X = [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
+y = [0, 1, 0, 0]
 
-def multilayer_perceptron_classifier(
-    train_features: Sequence[Sequence[float]],
-    train_labels: Sequence[int],
-    test_features: Sequence[Sequence[float]],
-) -> list[int]:
-    """
-    Train a Multilayer Perceptron classifier and predict labels for test data.
+clf = MLPClassifier(
+    solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
+)
 
-    Args:
-        train_features: Training data features, shape (n_samples, n_features).
-        train_labels: Training data labels, shape (n_samples,).
-        test_features: Test data features to predict, shape (m_samples, n_features).
+clf.fit(X, y)
 
-    Returns:
-        List of predicted labels for the test data.
+test = [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
+Y = clf.predict(test)
 
-    Raises:
-        ValueError: If the number of training samples and labels do not match.
 
-    Example:
-        >>> X = [[0, 0], [1, 1], [0, 1], [1, 0]]
-        >>> y = [0, 0, 1, 1]
-        >>> result = multilayer_perceptron_classifier(X, y, [[0, 0], [1, 1]])
-        >>> result in [[0, 0], [0, 1], [1, 0], [1, 1]]
-        True
+def wrapper(y):
+    """
+    >>> [int(x) for x in wrapper(Y)]
+    [0, 0, 1]
     """
-    if len(train_features) != len(train_labels):
-        raise ValueError("Number of training samples and labels must match.")
-
-    clf = MLPClassifier(
-        solver="lbfgs",
-        alpha=1e-5,
-        hidden_layer_sizes=(5, 2),
-        random_state=42,  # Fixed for deterministic results
-        max_iter=1000,  # Ensure convergence
-    )
-    clf.fit(train_features, train_labels)
-    predictions = clf.predict(test_features)
-    return list(predictions)
 
 
 if __name__ == "__main__":