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 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 templateCfEntryValueDistance(self, cluster1, cluster2, value,
tolerance, type_measurment):
entry1 = cfentry(len(cluster1), linear_sum(cluster1),
square_sum(cluster1))
entry2 = cfentry(len(cluster2), linear_sum(cluster2),
square_sum(cluster2))
distance = entry1.get_distance(entry2, type_measurment)
assert ((value - tolerance < distance)
and (value + tolerance > distance))
def templateCfEntryDistance(self, type_measurement):
cluster1 = [[0.1, 0.1], [0.1, 0.2], [0.2, 0.1], [0.2, 0.2]];
cluster2 = [[0.4, 0.4], [0.4, 0.5], [0.5, 0.4], [0.5, 0.5]];
cluster3 = [[0.9, 0.9], [0.9, 1.0], [1.0, 0.9], [1.0, 1.0]];
entry1 = cfentry(len(cluster1), linear_sum(cluster1), square_sum(cluster1));
entry2 = cfentry(len(cluster2), linear_sum(cluster2), square_sum(cluster2));
entry3 = cfentry(len(cluster3), linear_sum(cluster3), square_sum(cluster3));
distance12 = entry1.get_distance(entry2, type_measurement);
distance23 = entry2.get_distance(entry3, type_measurement);
distance13 = entry1.get_distance(entry3, type_measurement);
assert distance12 < distance23;
assert distance23 < distance13;
def templateCfEntryDistance(self, type_measurement):
cluster1 = [[0.1, 0.1], [0.1, 0.2], [0.2, 0.1], [0.2, 0.2]]
cluster2 = [[0.4, 0.4], [0.4, 0.5], [0.5, 0.4], [0.5, 0.5]]
cluster3 = [[0.9, 0.9], [0.9, 1.0], [1.0, 0.9], [1.0, 1.0]]
entry1 = cfentry(len(cluster1), linear_sum(cluster1), square_sum(cluster1))
entry2 = cfentry(len(cluster2), linear_sum(cluster2), square_sum(cluster2))
entry3 = cfentry(len(cluster3), linear_sum(cluster3), square_sum(cluster3))
distance12 = entry1.get_distance(entry2, type_measurement)
distance23 = entry2.get_distance(entry3, type_measurement)
distance13 = entry1.get_distance(entry3, type_measurement)
assert distance12 < distance23;
assert distance23 < distance13;
def __get_nearest_feature(self, point, feature_collection):
"""!
@brief Find nearest entry for specified point.
@param[in] point (list): Pointer to point from input dataset.
@param[in] feature_collection (list): Feature collection that is used for
obtaining nearest feature for the specified point.
@return (double, uint) Tuple of distance to nearest entry to the specified point and index of that entry.
"""
minimum_distance = float("Inf")
index_nearest_feature = -1
for index_entry in range(0, len(feature_collection)):
point_entry = cfentry(1, linear_sum([point]), square_sum([point]))
distance = feature_collection[index_entry].get_distance(
point_entry, self.__measurement_type)
if distance < minimum_distance:
minimum_distance = distance
index_nearest_feature = index_entry
return minimum_distance, index_nearest_feature
def testCfTreeInserionOneLeafThreeEntries(self):
cluster1 = [[0.1, 0.1], [0.1, 0.2], [0.2, 0.1], [0.2, 0.2]];
cluster2 = [[0.4, 0.4], [0.4, 0.5], [0.5, 0.4], [0.5, 0.5]];
cluster3 = [[0.9, 0.9], [0.9, 1.0], [1.0, 0.9], [1.0, 1.0]];
tree = cftree(3, 4, 0.0);
tree.insert_cluster(cluster1);
tree.insert_cluster(cluster2);
tree.insert_cluster(cluster3);
entry1 = cfentry(len(cluster1), linear_sum(cluster1), square_sum(cluster1));
entry2 = cfentry(len(cluster2), linear_sum(cluster2), square_sum(cluster2));
entry3 = cfentry(len(cluster3), linear_sum(cluster3), square_sum(cluster3));
assert tree.find_nearest_leaf(entry1) == tree.find_nearest_leaf(entry2);
assert tree.find_nearest_leaf(entry2) == tree.find_nearest_leaf(entry3);
def testCfTreeInserionOneLeafThreeEntries(self):
cluster1 = [[0.1, 0.1], [0.1, 0.2], [0.2, 0.1], [0.2, 0.2]];
cluster2 = [[0.4, 0.4], [0.4, 0.5], [0.5, 0.4], [0.5, 0.5]];
cluster3 = [[0.9, 0.9], [0.9, 1.0], [1.0, 0.9], [1.0, 1.0]];
tree = cftree(3, 4, 0.0);
tree.insert_cluster(cluster1);
tree.insert_cluster(cluster2);
tree.insert_cluster(cluster3);
entry1 = cfentry(len(cluster1), linear_sum(cluster1), square_sum(cluster1));
entry2 = cfentry(len(cluster2), linear_sum(cluster2), square_sum(cluster2));
entry3 = cfentry(len(cluster3), linear_sum(cluster3), square_sum(cluster3));
assert tree.find_nearest_leaf(entry1) == tree.find_nearest_leaf(entry2);
assert tree.find_nearest_leaf(entry2) == tree.find_nearest_leaf(entry3);
def insert_point(self, point):
"""!
@brief Insert point that is represented by list of coordinates.
@param[in] point (list): Point represented by list of coordinates that should be inserted to CF tree.
"""
entry = cfentry(len([point]), linear_sum([point]), square_sum([point]))
self.insert(entry)
def testCfEntryIncrease(self):
cluster = [[0.1, 0.1], [0.2, 0.2], [0.5, 0.5], [0.4, 0.4], [0.6, 0.6]]
entry1 = cfentry(len(cluster), linear_sum(cluster), square_sum(cluster))
entry2 = entry1 + entry1
assert cfentry(10, [3.6, 3.6], 3.28) == entry2
entry2 = entry2 + entry2
assert cfentry(20, [7.2, 7.2], 6.56) == entry2
def testCfEntryIncrease(self):
tolerance = 0.00001;
cluster = [ [0.1, 0.1], [0.2, 0.2], [0.5, 0.5], [0.4, 0.4], [0.6, 0.6] ];
entry1 = cfentry(len(cluster), linear_sum(cluster), square_sum(cluster));
entry2 = entry1 + entry1;
assert cfentry(10, [3.6, 3.6], 3.28) == entry2;
entry2 = entry2 + entry2;
assert cfentry(20, [7.2, 7.2], 6.56) == entry2;
def insert_cluster(self, cluster):
"""!
@brief Insert cluster that is represented as list of points where each point is represented by list of coordinates.
@details Clustering feature is created for that cluster and inserted to the tree.
@param[in] cluster (list): Cluster that is represented by list of points that should be inserted to the tree.
"""
entry = cfentry(len(cluster), linear_sum(cluster), square_sum(cluster));
self.insert(entry);
def testCfEntryIncrease(self):
tolerance = 0.00001;
cluster = [ [0.1, 0.1], [0.2, 0.2], [0.5, 0.5], [0.4, 0.4], [0.6, 0.6] ];
entry1 = cfentry(len(cluster), linear_sum(cluster), square_sum(cluster));
entry2 = entry1 + entry1;
assert cfentry(10, [3.6, 3.6], 3.28) == entry2;
entry2 = entry2 + entry2;
assert cfentry(20, [7.2, 7.2], 6.56) == entry2;
def insert_cluster(self, cluster,hadoop_address_2):
"""!
@brief Insert cluster that is represented as list of points where each point is represented by list of coordinates.
@details Clustering feature is created for that cluster and inserted to the tree.
@param[in] cluster (list): Cluster that is represented by list of points that should be inserted to the tree.
@len([cluster[0][:len(cluster[0])-1]] is the actual point after taking of the hadoop address from the data point
@[cluster[0][len(cluster[0])-1]] is the hadoop address of a point
"""
entry = cfentry(len(cluster), linear_sum(cluster), square_sum(cluster),hadoop_address_2);
self.insert(entry);
def insert_cluster(self, cluster):
"""!
@brief Insert cluster that is represented as list of points where each point is represented by list of coordinates.
@details Clustering feature is created for that cluster and inserted to the tree.
@param[in] cluster (list): Cluster that is represented by list of points that should be inserted to the tree.
"""
entry = cfentry(len(cluster), linear_sum(cluster), square_sum(cluster));
self.insert(entry);
def templateCfClusterRepresentation(self, cluster, centroid, radius, diameter, tolerance):
entry = cfentry(len(cluster), linear_sum(cluster), square_sum(cluster));
assertion_centroid = centroid;
if (type(centroid) != list):
assertion_centroid = [ centroid ];
if (type(centroid) == list):
for dimension in range(0, len(assertion_centroid)):
assert (assertion_centroid[dimension] - tolerance < ( entry.get_centroid() )[dimension]) and (( entry.get_centroid() )[dimension] < assertion_centroid[dimension] + tolerance);
assert (radius - tolerance < entry.get_radius()) and (entry.get_radius() < radius + tolerance);
assert (diameter - tolerance < entry.get_diameter()) and (entry.get_diameter() < diameter + tolerance);
def templateCfClusterRepresentation(self, cluster, centroid, radius, diameter, tolerance):
entry = cfentry(len(cluster), linear_sum(cluster), square_sum(cluster));
assertion_centroid = centroid;
if (type(centroid) != list):
assertion_centroid = [ centroid ];
if (type(centroid) == list):
for dimension in range(0, len(assertion_centroid)):
assert (assertion_centroid[dimension] - tolerance < ( entry.get_centroid() )[dimension]) and (( entry.get_centroid() )[dimension] < assertion_centroid[dimension] + tolerance);
assert (radius - tolerance < entry.get_radius()) and (entry.get_radius() < radius + tolerance);
assert (diameter - tolerance < entry.get_diameter()) and (entry.get_diameter() < diameter + tolerance);
def templateCfClusterRepresentation(self, cluster, centroid, radius, diameter, tolerance):
entry = cfentry(len(cluster), linear_sum(cluster), square_sum(cluster))
assertion_centroid = centroid
if type(centroid) != list:
assertion_centroid = [centroid]
if type(centroid) == list:
for dimension in range(0, len(assertion_centroid)):
self.assertAlmostEqual(assertion_centroid[dimension], (entry.get_centroid())[dimension], tolerance)
self.assertAlmostEqual(radius, entry.get_radius(), tolerance)
self.assertAlmostEqual(diameter, entry.get_diameter(), tolerance)
def __get_nearest_feature(self, point, feature_collection):
minimum_distance = float("Inf")
index_nearest_feature = -1
for index_entry in range(0, len(feature_collection)):
point_entry = cfentry(1, linear_sum([point]), square_sum([point]))
distance = feature_collection[index_entry].get_distance(
point_entry, self.__measurement_type)
if distance < minimum_distance:
minimum_distance = distance
index_nearest_feature = index_entry
return minimum_distance, index_nearest_feature
def testCfTreeEntryAbsorbing(self):
tree = cftree(2, 1, 10000.0);
absorbing_entry = cfentry(0, [0.0, 0.0], 0.0);
for offset in range(0, 10):
cluster = [ [random() + offset, random() + offset] for i in range(10)];
entry = cfentry(len(cluster), linear_sum(cluster), square_sum(cluster));
absorbing_entry += entry;
tree.insert(entry);
assert 1 == tree.amount_entries;
assert 1 == tree.amount_nodes;
assert 1 == tree.height;
assert None == tree.root.parent;
assert absorbing_entry == tree.root.feature;
def testCfTreeEntryAbsorbing(self):
tree = cftree(2, 1, 10000.0)
absorbing_entry = cfentry(0, [0.0, 0.0], 0.0)
for offset in range(0, 10):
cluster = [[random() + offset, random() + offset] for i in range(10)]
entry = cfentry(len(cluster), linear_sum(cluster), square_sum(cluster))
absorbing_entry += entry
tree.insert(entry)
assert 1 == tree.amount_entries
assert 1 == tree.amount_nodes
assert 1 == tree.height
assert None == tree.root.parent
assert absorbing_entry == tree.root.feature
def testGetNearestEntry(self):
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1)
tree = cftree(10, 100, 0.2, measurement_type.CENTROID_EUCLIDEAN_DISTANCE)
self.assertEqual(10, tree.branch_factor)
self.assertEqual(100, tree.max_entries)
self.assertEqual(0.2, tree.threshold)
self.assertEqual(measurement_type.CENTROID_EUCLIDEAN_DISTANCE, tree.type_measurement)
for index_point in range(len(sample)):
tree.insert_point(sample[index_point])
cluster = [[0.1, 0.1], [0.2, 0.2]]
entry = cfentry(len(cluster), linear_sum(cluster), square_sum(cluster))
leaf = tree.find_nearest_leaf(entry)
found_entry = leaf.get_nearest_entry(entry, measurement_type.CENTROID_EUCLIDEAN_DISTANCE)
found_index_entry = leaf.get_nearest_index_entry(entry, measurement_type.CENTROID_EUCLIDEAN_DISTANCE)
self.assertEqual(leaf.entries[found_index_entry], found_entry)
def __get_nearest_feature(self, point):
"""!
@brief Find nearest entry for specified point.
@param[in] point (list): Pointer to point from input dataset.
@return (uint) Index of nearest entry to the specified point.
"""
minimum_distance = float("Inf");
index_nearest_feature = -1;
for index_entry in range(0, len(self.__features)):
point_entry = cfentry(1, linear_sum([ point ]), square_sum([ point ]),self.__hadoop_address);
distance = self.__features[index_entry].get_distance(point_entry, self.__measurement_type);
if (distance < minimum_distance):
minimum_distance = distance;
index_nearest_feature = index_entry;
return index_nearest_feature;
def __get_nearest_feature(self, point):
"""!
@brief Find nearest entry for specified point.
@param[in] point (list): Pointer to point from input dataset.
@return (uint) Index of nearest entry to the specified point.
"""
minimum_distance = float("Inf")
index_nearest_feature = -1
for index_entry in range(0, len(self.__features)):
point_entry = cfentry(1, linear_sum([point]), square_sum([point]))
distance = self.__features[index_entry].get_distance(
point_entry, self.__measurement_type)
if (distance < minimum_distance):
minimum_distance = distance
index_nearest_feature = index_entry
return index_nearest_feature
def __get_nearest_feature(self, point, feature_collection):
"""!
@brief Find nearest entry for specified point.
@param[in] point (list): Pointer to point from input dataset.
@param[in] feature_collection (list): Feature collection that is used for obtaining nearest feature for the specified point.
@return (double, uint) Tuple of distance to nearest entry to the specified point and index of that entry.
"""
minimum_distance = float("Inf");
index_nearest_feature = -1;
for index_entry in range(0, len(feature_collection)):
point_entry = cfentry(1, linear_sum([ point ]), square_sum([ point ]));
distance = feature_collection[index_entry].get_distance(point_entry, self.__measurement_type);
if (distance < minimum_distance):
minimum_distance = distance;
index_nearest_feature = index_entry;
return (minimum_distance, index_nearest_feature);
def templateCfEntryValueDistance(self, cluster1, cluster2, value, tolerance, type_measurment):
entry1 = cfentry(len(cluster1), linear_sum(cluster1), square_sum(cluster1));
entry2 = cfentry(len(cluster2), linear_sum(cluster2), square_sum(cluster2));
distance = entry1.get_distance(entry2, type_measurment);
assert ( (value - tolerance < distance) and (value + tolerance > distance) );
