def main(cluster_num):
print "Basic global clustering"
print "Iterations: " + str(iterations)
print "Clusters: " + str(cluster_num)
start = time.time()
# Performing Global Histogram-Based Color Image Clustering
globalHistogramKMeans = GlobalHistogramKMeans(cluster_num, 'k-means++', 10, 300, 1e-4, 0)
predicted_labels = globalHistogramKMeans.fit_predict(histograms)
end = time.time()
print "Time: " + str(end - start)
# Adequate to sklearn's inertia_
print "Sum distances: %s" % globalHistogramKMeans.sum_distances_
true_labels = dataset.get_true_labels()
# Evaluation
purity = Evaluator.calculate_purity(true_labels, predicted_labels)
precision, recall = Evaluator.calculate_precision_recall(true_labels, predicted_labels)
ri = Evaluator.calculate_ri(true_labels, predicted_labels)
# Show results
print "Purity: %s" % purity
print "Precision: %s" % precision
print "Recall: %s" % recall
print "Random index value: %s" % ri
dataset.write_results(os.path.join("results_global/", str(cluster_num), "no-pca"), predicted_labels)
print "PCA global clustering"
print "Dimensions left " + str(dimensions_left)
print "Iterations: " + str(iterations)
print "Clusters: " + str(cluster_num)
start = time.time()
# Changing dimensions from 768 to selected value.
pca = Pca(dimensions_left)
reduced_histograms = pca.fit_transform(histograms)
# Clustering and predicting
predicted_labels = globalHistogramKMeans.fit_predict(reduced_histograms)
end = time.time()
print "Time: " + str(end - start)
# Adequate to sklearn's inertia_
print "Sum distances: %s" % globalHistogramKMeans.sum_distances_
# Evaluation
purity = Evaluator.calculate_purity(true_labels, predicted_labels)
precision, recall = Evaluator.calculate_precision_recall(true_labels, predicted_labels)
ri = Evaluator.calculate_ri(true_labels, predicted_labels)
# Show results
print "Purity: %s" % purity
print "Precision: %s" % precision
print "Recall: %s" % recall
print "Random index value: %s" % ri
dataset.write_results(os.path.join("results_global/", str(cluster_num), "pca"), predicted_labels)
def main(cluster_num):
print "Local clustering"
print "Iterations: " + str(iterations)
print "Clusters: " + str(cluster_num)
start = time.time()
# Performing Local Histogram-Based Color Image Clustering
localHistogramKMeans = LocalHistogramKMeans(cluster_num, 'k-means++', 10, 300, 1e-4, 0)
predicted_labels = localHistogramKMeans.fit_predict(part_images)
end = time.time()
print "Time: " + str(end - start)
# Adequate to sklearn's inertia_
print "Sum similarities: %s" % localHistogramKMeans.sum_similarities_
true_labels = dataset.get_true_labels()
# Evaluation
purity = Evaluator.calculate_purity(true_labels, predicted_labels)
precision, recall = Evaluator.calculate_precision_recall(true_labels, predicted_labels)
ri = Evaluator.calculate_ri(true_labels, predicted_labels)
# Show results
print "Purity: %s" % purity
print "Precision: %s" % precision
print "Recall: %s" % recall
print "Random index value: %s" % ri
dataset.write_results(os.path.join("results_local/", str(cluster_num)), predicted_labels)
