quantum-distance-based-classifier 0.0.4

The Quantum Distance-based classifier is a technique inspired by the classical k-Nearest Neighbors that leverage quantum properties to perform prediction. The package has been implemented in Qiskit

The Quantum Distance-based classifier is a technique inspired by the classical k-Nearest Neighbors that leverage quantum properties to perform prediction. The package has been implemented in Qiskit.

    ```
    from quantum_distance_based_classifier.quantum_distance_based_classifier import QuantumDistaceBasedClassifier
    from sklearn import preprocessing
    from sklearn.datasets import load_iris
    from sklearn.preprocessing import StandardScaler
    import numpy as np


    X, y = load_iris(return_X_y=True)

    n_features = 2
    X = X[:, :n_features] # Keep only n_features

    # Standardize and normalize the features
    X = StandardScaler().fit_transform(X)
    X = preprocessing.normalize(X, axis=1)

    # Initialize variables to store sampled instances
    sampled_X = []
    sampled_y = []

    # Loop through each class to sample instances
    for class_label in np.unique(y):
        class_indices = np.where(y == class_label)[0]
        sampled_indices = np.random.choice(class_indices, size=instances_per_class, replace=False)
        sampled_X.extend(X[sampled_indices])
        sampled_y.extend(y[sampled_indices])

    # Convert lists to numpy arrays
    sampled_X = np.array(sampled_X)
    sampled_y = np.array(sampled_y)

    qdbc = QuantumDistaceBasedClassifier()
    qdbc.fit(sampled_X, sampled_y)
    result = qdbc.predict(sampled_X[0])
    print(f"Classification result: {result}")
    ```