def get_km(xs, n):
km = KMedoids(n_cluster=n, max_iter=1000, tol=1e-5)
km.fit(xs)
kmidx = list(km.medoids)
testidx = [i for i in range(len(xs)) if i not in kmidx]
np.random.shuffle(testidx)
return kmidx, testidx[:100]
def fit(self, X, y, **kwargs):
if self.verbose:
print("fitting", self)
clss = sorted(set(y))
meds = []
for c, k in enumerate(clss):
idxs_c = np.where(y == k)[0]
kmpp_idxs = kmeanspp(X[idxs_c], self.mk, seed=self.seed)
kmed = KMedoids(self.mk, init=kmpp_idxs)
kmed.fit(X[idxs_c], dist=False)
meds.append(idxs_c[kmed.medoids].tolist())
self.idxs = np.concatenate(meds, axis=0)
self.vecs = X[self.idxs]
cc = Counter(y[self.idxs])
assert set(cc.values()) == set({self.mk})
if self.verbose:
print("fitted KMedoids seed={},".format(self.seed), cc)
self.clf.fit(self.vecs, y[self.idxs])
return self
