Classifying Moons Dataset
=========================

This tutorial demonstrates binary classification with ``VQCClassifier``.

Setup
------

.. code-block:: python

   import numpy as np
   from sklearn.datasets import make_moons
   from sklearn.model_selection import train_test_split
   from sklearn.metrics import accuracy_score
   from psipose.estimators import VQCClassifier

Generate Data
-------------

.. code-block:: python

   X, y = make_moons(n_samples=200, noise=0.2, random_state=42)
   X_train, X_test, y_train, y_test = train_test_split(
       X, y, test_size=0.3, random_state=42
   )

Train Classifier
----------------

.. code-block:: python

   clf = VQCClassifier(n_qubits=2, n_iter=100, random_state=42)
   clf.fit(X_train, y_train)

Evaluate
---------

.. code-block:: python

   y_pred = clf.predict(X_test)
   print(f"Accuracy: {accuracy_score(y_test, y_pred):.2%}")
   print(f"Loss history: {clf.loss_history_[:5]}...")