Plotting HQC

This section shows a scatterplot of the Helstrom Quantum Centroid (HQC) classifier and the Python code to generate the scatterplot.

Out:

/home/docs/checkouts/readthedocs.org/user_builds/helstrom-quantum-centroid-classifier/envs/latest/lib/python3.7/site-packages/numpy/core/fromnumeric.py:87: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)

# Example
import hqc
import numpy as np
from matplotlib import pyplot as plt

X = [[0, 0], [1, 1]]
y = [0, 1]
clf = hqc.HQC()   # Using default hyperparameter values
clf.fit(X, y)

rng = np.random.RandomState(13)
X_test = rng.rand(500, 2)
y_pred = clf.predict(X_test)

X_0 = X_test[y_pred == 0]
X_1 = X_test[y_pred == 1]


p0 = plt.scatter(0, 0, c='red', s=100)
p1 = plt.scatter(1, 1, c='blue', s=100)

ax0 = plt.scatter(X_0[:, 0], X_0[:, 1], c='crimson', s=50)
ax1 = plt.scatter(X_1[:, 0], X_1[:, 1], c='deepskyblue', s=50)

leg = plt.legend([p0, p1, ax0, ax1],
                 ['Point 0', 'Point 1', 'Class 0', 'Class 1'],
                 loc='upper left', fancybox=True, scatterpoints=1)
leg.get_frame().set_alpha(0.5)

plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.xlim([-.5, 1.5])

plt.show()