Python nDimAlg Examples

Example #1

        for k in range(len(X)):
            y[k] = y[k] - 1

    for j in range(0, 4):
        if j == 0:
            kmeans = KMeans(n_clusters=kmeansValues[i]).fit(X)
            labels = kmeans.labels_
        elif j == 1:
            if i == 1:
                min_samples = np.log(len(X)) * 10
            else:
                min_samples = np.log(len(X))
            db = DBSCAN(eps=epsValues[i], min_samples=min_samples).fit(X)
            labels = db.labels_
        elif j == 2:
            labels = alg.nDimAlg(X, n, 1)
        elif j == 3:
            labels = alg.nDimAlg(X, n, 2)

        label_color = [f.LABEL_COLOR_MAP[l] for l in labels]
        fig = plt.figure()
        plt.scatter(X[:, 0],
                    X[:, 1],
                    marker='o',
                    c=label_color,
                    s=25,
                    edgecolor='k')
        plt.show()

        # f.benchmark(labels, y)
        print(dataName[i] + " - " + algName[j] + " - " + "ARI:" +

Example #2

File: benchmark_unbalance.py Project: keg-tucn/DensityBasedClustering_SpikeSorting

plt.show()

print('### KMEANS CORRECTNESS')
#f.benchmark(labels, y)
print("ARI:" + str(metrics.adjusted_rand_score(labels, y)))
print("AMI:" + str(metrics.adjusted_mutual_info_score(labels, y)))

###### UNBALANCE DBSCAN ######
#eps = 5000
#min_samples=np.log(len(X))*10
eps = 18000
min_samples = np.log(len(X))
db = DBSCAN(eps=eps, min_samples=min_samples).fit(X)
labels = db.labels_
label_color = [f.LABEL_COLOR_MAP[l] for l in labels]
unique, counts = np.unique(labels, return_counts=True)
#print('#FINAL:'+ str(dict(zip(unique, counts))))
fig = plt.figure()
plt.scatter(X[:, 0], X[:, 1], marker='o', c=label_color, s=25, edgecolor='k')
plt.show()

print('### DBSCAN CORRECTNESS')
#f.benchmark(labels, y)
print("ARI:" + str(metrics.adjusted_rand_score(labels, y)))
print("AMI:" + str(metrics.adjusted_mutual_info_score(labels, y)))

###### UNBALANCE MYALG #####
labels = alg.nDimAlg(X, n)
#f.benchmark(labels, y)
print("ARI:" + str(metrics.adjusted_rand_score(labels, y)))
print("AMI:" + str(metrics.adjusted_mutual_info_score(labels, y)))

Example #3

plt.show()

print("ARI:" + str(metrics.adjusted_rand_score(labels, y)))
print("AMI:" + str(metrics.adjusted_mutual_info_score(labels, y)))

NN = NearestNeighbors(n_neighbors=int(np.log(len(X)))).fit(X)
distances, indices = NN.kneighbors(X)

fig = plt.figure()
plt.plot(np.sort(distances[:, distances.shape[1] - 1]), color='red')

###### S1 DBSCAN ######
eps = 0.25
min_samples = np.log(len(X))
db = DBSCAN(eps=eps, min_samples=min_samples).fit(X)
labels = db.labels_
label_color = [f.LABEL_COLOR_MAP[l] for l in labels]
unique, counts = np.unique(labels, return_counts=True)
#print('#FINAL:'+ str(dict(zip(unique, counts))))
fig = plt.figure()
plt.scatter(X[:, 0], X[:, 1], marker='o', c=label_color, s=25, edgecolor='k')
plt.show()

print("ARI:" + str(metrics.adjusted_rand_score(labels, y)))
print("AMI:" + str(metrics.adjusted_mutual_info_score(labels, y)))

###### S1 MYALG #####
labels = alg.nDimAlg(X, n, True)
plt.show()
print("ARI:" + str(metrics.adjusted_rand_score(labels, y)))
print("AMI:" + str(metrics.adjusted_mutual_info_score(labels, y)))