def templateDistanceCalculation(self, cluster1, cluster2, type_measurement):
entry1 = cfentry(len(cluster1), linear_sum(cluster1), square_sum(cluster1));
entry2 = cfentry(len(cluster2), linear_sum(cluster2), square_sum(cluster2));
# check that the same distance from 1 to 2 and from 2 to 1.
distance12 = entry1.get_distance(entry2, type_measurement);
distance21 = entry2.get_distance(entry1, type_measurement);
assert distance12 == distance21;
# check with utils calculation
float_delta = 0.0000001;
if (type_measurement == measurement_type.CENTROID_EUCLIDIAN_DISTANCE):
assert distance12 == euclidean_distance_sqrt(entry1.get_centroid(), entry2.get_centroid());
elif (type_measurement == measurement_type.CENTROID_MANHATTAN_DISTANCE):
assert distance12 == manhattan_distance(entry1.get_centroid(), entry2.get_centroid());
elif (type_measurement == measurement_type.AVERAGE_INTER_CLUSTER_DISTANCE):
assert numpy.isclose(distance12, average_inter_cluster_distance(cluster1, cluster2)) == True;
elif (type_measurement == measurement_type.AVERAGE_INTRA_CLUSTER_DISTANCE):
assert numpy.isclose(distance12, average_intra_cluster_distance(cluster1, cluster2)) == True;
elif (type_measurement == measurement_type.VARIANCE_INCREASE_DISTANCE):
assert numpy.isclose(distance12, variance_increase_distance(cluster1, cluster2)) == True;
def templateDistanceCalculation(self, cluster1, cluster2, type_measurement):
entry1 = cfentry(len(cluster1), linear_sum(cluster1), square_sum(cluster1))
entry2 = cfentry(len(cluster2), linear_sum(cluster2), square_sum(cluster2))
# check that the same distance from 1 to 2 and from 2 to 1.
distance12 = entry1.get_distance(entry2, type_measurement)
distance21 = entry2.get_distance(entry1, type_measurement)
assert distance12 == distance21;
# check with utils calculation
float_delta = 0.0000001
if (type_measurement == measurement_type.CENTROID_EUCLIDEAN_DISTANCE):
assert distance12 == euclidean_distance_square(entry1.get_centroid(), entry2.get_centroid());
elif (type_measurement == measurement_type.CENTROID_MANHATTAN_DISTANCE):
assert distance12 == manhattan_distance(entry1.get_centroid(), entry2.get_centroid());
elif (type_measurement == measurement_type.AVERAGE_INTER_CLUSTER_DISTANCE):
assert numpy.isclose(distance12, average_inter_cluster_distance(cluster1, cluster2)) == True;
elif (type_measurement == measurement_type.AVERAGE_INTRA_CLUSTER_DISTANCE):
assert numpy.isclose(distance12, average_intra_cluster_distance(cluster1, cluster2)) == True;
elif (type_measurement == measurement_type.VARIANCE_INCREASE_DISTANCE):
assert numpy.isclose(distance12, variance_increase_distance(cluster1, cluster2)) == True;
def cluster_distances(path_sample, amount_clusters):
distances = [
'euclidian', 'manhattan', 'avr-inter', 'avr-intra', 'variance'
]
sample = utils.read_sample(path_sample)
agglomerative_instance = agglomerative(sample, amount_clusters)
agglomerative_instance.process()
obtained_clusters = agglomerative_instance.get_clusters()
print("Measurements for:", path_sample)
for index_cluster in range(len(obtained_clusters)):
for index_neighbor in range(index_cluster + 1, len(obtained_clusters),
1):
cluster1 = obtained_clusters[index_cluster]
cluster2 = obtained_clusters[index_neighbor]
center_cluster1 = utils.centroid(sample, cluster1)
center_cluster2 = utils.centroid(sample, cluster2)
for index_distance_type in range(len(distances)):
distance = None
distance_type = distances[index_distance_type]
if (distance_type == 'euclidian'):
distance = utils.euclidean_distance(
center_cluster1, center_cluster2)
elif (distance_type == 'manhattan'):
distance = utils.manhattan_distance(
center_cluster1, center_cluster2)
elif (distance_type == 'avr-inter'):
distance = utils.average_inter_cluster_distance(
cluster1, cluster2, sample)
elif (distance_type == 'avr-intra'):
distance = utils.average_intra_cluster_distance(
cluster1, cluster2, sample)
elif (distance_type == 'variance'):
distance = utils.variance_increase_distance(
cluster1, cluster2, sample)
print("\tDistance", distance_type, "from", index_cluster, "to",
index_neighbor, "is:", distance)
def cluster_distances(path_sample, amount_clusters):
distances = ['euclidian', 'manhattan', 'avr-inter', 'avr-intra', 'variance'];
sample = utils.read_sample(path_sample);
agglomerative_instance = agglomerative(sample, amount_clusters);
agglomerative_instance.process();
obtained_clusters = agglomerative_instance.get_clusters();
print("Measurements for:", path_sample);
for index_cluster in range(len(obtained_clusters)):
for index_neighbor in range(index_cluster + 1, len(obtained_clusters), 1):
cluster1 = obtained_clusters[index_cluster];
cluster2 = obtained_clusters[index_neighbor];
center_cluster1 = utils.centroid(sample, cluster1);
center_cluster2 = utils.centroid(sample, cluster2);
for index_distance_type in range(len(distances)):
distance = None;
distance_type = distances[index_distance_type];
if (distance_type == 'euclidian'):
distance = utils.euclidean_distance(center_cluster1, center_cluster2);
elif (distance_type == 'manhattan'):
distance = utils.manhattan_distance(center_cluster1, center_cluster2);
elif (distance_type == 'avr-inter'):
distance = utils.average_inter_cluster_distance(cluster1, cluster2, sample);
elif (distance_type == 'avr-intra'):
distance = utils.average_intra_cluster_distance(cluster1, cluster2, sample);
elif (distance_type == 'variance'):
distance = utils.variance_increase_distance(cluster1, cluster2, sample);
print("\tDistance", distance_type, "from", index_cluster, "to", index_neighbor, "is:", distance);
def display_two_dimensional_cluster_distances(path_sample, amount_clusters):
distances = [
'euclidian', 'manhattan', 'avr-inter', 'avr-intra', 'variance'
]
ajacency = [[0] * amount_clusters for i in range(amount_clusters)]
sample = utils.read_sample(path_sample)
agglomerative_instance = agglomerative(sample, amount_clusters)
agglomerative_instance.process()
obtained_clusters = agglomerative_instance.get_clusters()
stage = utils.draw_clusters(sample,
obtained_clusters,
display_result=False)
for index_cluster in range(len(ajacency)):
for index_neighbor_cluster in range(index_cluster + 1, len(ajacency)):
if ((index_cluster == index_neighbor_cluster) or
(ajacency[index_cluster][index_neighbor_cluster] is True)):
continue
ajacency[index_cluster][index_neighbor_cluster] = True
ajacency[index_neighbor_cluster][index_cluster] = True
cluster1 = obtained_clusters[index_cluster]
cluster2 = obtained_clusters[index_neighbor_cluster]
center_cluster1 = utils.centroid(sample, cluster1)
center_cluster2 = utils.centroid(sample, cluster2)
x_maximum, x_minimum, y_maximum, y_minimum = None, None, None, None
x_index_maximum, y_index_maximum = 1, 1
if (center_cluster2[0] > center_cluster1[0]):
x_maximum = center_cluster2[0]
x_minimum = center_cluster1[0]
x_index_maximum = 1
else:
x_maximum = center_cluster1[0]
x_minimum = center_cluster2[0]
x_index_maximum = -1
if (center_cluster2[1] > center_cluster1[1]):
y_maximum = center_cluster2[1]
y_minimum = center_cluster1[1]
y_index_maximum = 1
else:
y_maximum = center_cluster1[1]
y_minimum = center_cluster2[1]
y_index_maximum = -1
print("Cluster 1:", cluster1, ", center:", center_cluster1)
print("Cluster 2:", cluster2, ", center:", center_cluster2)
stage.annotate(s='',
xy=(center_cluster1[0], center_cluster1[1]),
xytext=(center_cluster2[0], center_cluster2[1]),
arrowprops=dict(arrowstyle='<->'))
for index_distance_type in range(len(distances)):
distance = None
distance_type = distances[index_distance_type]
if (distance_type == 'euclidian'):
distance = utils.euclidean_distance(
center_cluster1, center_cluster2)
elif (distance_type == 'manhattan'):
distance = utils.manhattan_distance(
center_cluster1, center_cluster2)
elif (distance_type == 'avr-inter'):
distance = utils.average_inter_cluster_distance(
cluster1, cluster2, sample)
elif (distance_type == 'avr-intra'):
distance = utils.average_intra_cluster_distance(
cluster1, cluster2, sample)
elif (distance_type == 'variance'):
distance = utils.variance_increase_distance(
cluster1, cluster2, sample)
print("\tCluster distance -", distance_type, ":", distance)
x_multiplier = index_distance_type + 3
if (x_index_maximum < 0):
x_multiplier = len(distances) - index_distance_type + 3
y_multiplier = index_distance_type + 3
if (y_index_maximum < 0):
y_multiplier = len(distances) - index_distance_type + 3
x_text = x_multiplier * (x_maximum - x_minimum) / (
len(distances) + 6) + x_minimum
y_text = y_multiplier * (y_maximum - y_minimum) / (
len(distances) + 6) + y_minimum
#print(x_text, y_text, "\n");
stage.text(x_text,
y_text,
distance_type + " {:.3f}".format(distance),
fontsize=9,
color='blue')
plt.show()
def display_two_dimensional_cluster_distances(path_sample, amount_clusters):
distances = ['euclidian', 'manhattan', 'avr-inter', 'avr-intra', 'variance'];
ajacency = [ [0] * amount_clusters for i in range(amount_clusters) ];
sample = utils.read_sample(path_sample);
agglomerative_instance = agglomerative(sample, amount_clusters);
agglomerative_instance.process();
obtained_clusters = agglomerative_instance.get_clusters();
stage = utils.draw_clusters(sample, obtained_clusters, display_result = False);
for index_cluster in range(len(ajacency)):
for index_neighbor_cluster in range(index_cluster + 1, len(ajacency)):
if ( (index_cluster == index_neighbor_cluster) or (ajacency[index_cluster][index_neighbor_cluster] is True) ):
continue;
ajacency[index_cluster][index_neighbor_cluster] = True;
ajacency[index_neighbor_cluster][index_cluster] = True;
cluster1 = obtained_clusters[index_cluster];
cluster2 = obtained_clusters[index_neighbor_cluster];
center_cluster1 = utils.centroid(sample, cluster1);
center_cluster2 = utils.centroid(sample, cluster2);
x_maximum, x_minimum, y_maximum, y_minimum = None, None, None, None;
x_index_maximum, y_index_maximum = 1, 1;
if (center_cluster2[0] > center_cluster1[0]):
x_maximum = center_cluster2[0];
x_minimum = center_cluster1[0];
x_index_maximum = 1;
else:
x_maximum = center_cluster1[0];
x_minimum = center_cluster2[0];
x_index_maximum = -1;
if (center_cluster2[1] > center_cluster1[1]):
y_maximum = center_cluster2[1];
y_minimum = center_cluster1[1];
y_index_maximum = 1;
else:
y_maximum = center_cluster1[1];
y_minimum = center_cluster2[1];
y_index_maximum = -1;
print("Cluster 1:", cluster1, ", center:", center_cluster1);
print("Cluster 2:", cluster2, ", center:", center_cluster2);
stage.annotate(s = '', xy = (center_cluster1[0], center_cluster1[1]), xytext = (center_cluster2[0], center_cluster2[1]), arrowprops = dict(arrowstyle = '<->'));
for index_distance_type in range(len(distances)):
distance = None;
distance_type = distances[index_distance_type];
if (distance_type == 'euclidian'):
distance = utils.euclidean_distance(center_cluster1, center_cluster2);
elif (distance_type == 'manhattan'):
distance = utils.manhattan_distance(center_cluster1, center_cluster2);
elif (distance_type == 'avr-inter'):
distance = utils.average_inter_cluster_distance(cluster1, cluster2, sample);
elif (distance_type == 'avr-intra'):
distance = utils.average_intra_cluster_distance(cluster1, cluster2, sample);
elif (distance_type == 'variance'):
distance = utils.variance_increase_distance(cluster1, cluster2, sample);
print("\tCluster distance -", distance_type, ":", distance);
x_multiplier = index_distance_type + 3;
if (x_index_maximum < 0):
x_multiplier = len(distances) - index_distance_type + 3;
y_multiplier = index_distance_type + 3;
if (y_index_maximum < 0):
y_multiplier = len(distances) - index_distance_type + 3;
x_text = x_multiplier * (x_maximum - x_minimum) / (len(distances) + 6) + x_minimum;
y_text = y_multiplier * (y_maximum - y_minimum) / (len(distances) + 6) + y_minimum;
#print(x_text, y_text, "\n");
stage.text(x_text, y_text, distance_type + " {:.3f}".format(distance), fontsize = 9, color='blue');
plt.show();
