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 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_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 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 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 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 __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 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) );