pyqpanda_alg.QKmeans.QuantumKmeans

Module Contents

Classes

QuantumKmeans

QKMeans (Quantum K-Means) is a quantum algorithm that is used for clustering data into k clusters.

class pyqpanda_alg.QKmeans.QuantumKmeans.QuantumKmeans(k=2, tol=0.01)

QKMeans (Quantum K-Means) is a quantum algorithm that is used for clustering data into k clusters.

Parameters:

k: int

k is a user-specified parameter.

fit(data)

Classify the input data.

Parameters:

data: ndarray

Input array, can be complex.

Returns:

centers_new: int

Cluster center of input data.

clusters: int

Category number of the entered data.

Examples

import os
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
from pyqpanda_alg.QKmeans.QuantumKmeans import QuantumKmeans
from pyqpanda_alg import QKmeans


def ReadCSV(path):
    import csv
    csvFile = open(path, "r")
    reader = csv.reader(csvFile)
    data = []
    label = []
    for item in reader:
        data0 = []
        if reader.line_num == 1:
            continue
        for i in range(1, 5):
            data0.append(np.float64(item[i]))
        data.append(data0)
        label.append(item[5])
    csvFile.close()
    return np.array(data), label


if __name__ == '__main__':
    data_path = QKmeans.__path__[0]
    data_file = os.path.join(data_path, 'dataset/Iris.csv')
    data, label = ReadCSV(data_file)
    data = data[:, 2:]
    label = [i for i in label]
    for _ in range(len(label)):
        if label[_] == 'Iris-setosa':
            label[_] = 'r'
        elif label[_] == 'Iris-versicolor':
            label[_] = 'b'
        else:
            label[_] = 'g'
    rows, col = data.shape[0], data.shape[1]

    scaler = MinMaxScaler(feature_range=(-1, 1))
    data = scaler.fit_transform(data)

    n_clusters = 3
    Colors = ['black', 'blue', 'green', 'yellow', 'red', 'purple', 'orange', 'brown', 'pink']

    print('Classical K_means start...')
    Ctest = KMeans(n_clusters)
    Ctest.fit(data)
    Ccenter = Ctest.cluster_centers_
    print('Final Classical Result:', Ccenter)

    print('Quantum K_means start...')
    Qtest = QuantumKmeans(n_clusters)
    Qcenters, clusters = Qtest.fit(data)
    print('Final Quantum Result:', Qcenters)

    x0 = data[clusters == 0]
    x1 = data[clusters == 1]
    x2 = data[clusters == 2]
    plt.scatter(x0[:, 0], x0[:, 1], c="red", marker='o', label='label0')
    plt.scatter(x1[:, 0], x1[:, 1], c="green", marker='*', label='label1')
    plt.scatter(x2[:, 0], x2[:, 1], c="blue", marker='+', label='label2')

    plot_title = 'Iris Quantum K_means Result'
    plt.title(plot_title)
    plt.show()