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example2.py
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example2.py
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import numpy as np
from qocsvm import QOCSVM
from scipy.stats import kstwobign
from scipy.stats import multivariate_normal as mvn
np.random.seed(666)
def multi_ks2samp(X, Y, alphas, gamma=None):
# two sample KS test in higher dimensions using QOCSVM sets
m, n = X.shape[0], Y.shape[0]
quants = QOCSVM(alphas, gamma=gamma)
quants.fit(X)
result1 = quants.transform(X)
result2 = quants.transform(Y)
F1 = result1.mean(axis=0)
F2 = result2.mean(axis=0)
max_delta = max(np.abs(F1-F2))
teststat = np.sqrt((n * m) / (n + m)) * max_delta
pval = kstwobign.sf(teststat)
return pval, teststat, max_delta
rv1 = mvn(mean=np.zeros(10)).rvs(100)
rv2 = mvn(mean=np.zeros(10), cov=.8).rvs(200)
alphas = np.arange(1,10)/10
result = multi_ks2samp(rv1, rv2, alphas)
print('p-value:\t{:.4f} \nstatistic:\t{:6.2f}'.format(result[0],result[1]))