def test_coreset():
kmeans = KMeans(n_clusters=15, random_state=0).fit(X=coreset,
sample_weight=weights)
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1])
cost = utils.cost_function(data, kmeans.predict(X=data),
kmeans.cluster_centers_)
return cost
def test_no_coreset():
kmeans = KMeans(n_clusters=15, random_state=0).fit(X=data)
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1])
cost = utils.cost_function(data, kmeans.labels_, kmeans.cluster_centers_)
return cost
#Computing geometric decomposition coreset
geo = agc.GeometricDecomposition(data, 5000, 15, 0.5)
coreset = geo.compute()
@utils.timeit
def test_no_coreset():
kmeans = KMeans(n_clusters=15, random_state=0).fit(X=data)
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1])
cost = utils.cost_function(data, kmeans.labels_, kmeans.cluster_centers_)
return cost
@utils.timeit
def test_coreset():
kmeans = KMeans(n_clusters=15, random_state=0).fit(X=coreset)
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1])
cost = utils.cost_function(data, kmeans.predict(X=data),
kmeans.cluster_centers_)
return cost
cost = test_no_coreset()
cost_cs = test_coreset()
cost_opt = utils.cost_function(data, opt, centers)
print("cost no coreset ", cost)
print("cost coreset ", cost_cs)
print("coreset improvment: {:.1%} ".format(np.abs(cost - cost_cs) / cost))
#plt.show()