def __init__(self, width, height, dataset, iteration_num=1000):
self.radius = max(width, height) / 2.0
self.time_constant = iteration_num / math.log10(self.radius)
self.learning_rate = 0.09 #TODO to determine the init learning rate
self.current_learning_rate = 0.0
self.iteration_num = iteration_num
self.current_iteration = 1
self.neighbour_radius = 0.0
self.width = width
self.height = height
self.nodes = []
self.bmu = None
self.mh = mahalanobis_helper(
dataset) # using given samples to calculate covariance matrix
self.init_map_node(dataset)
'''for x in range(self.width):
for y in range(self.height):
wv = [] # init weights vector for map node
#wv.append(random.randint(*self.app_index_range))
wv.append(random.randint(*self.duration_range))
wv.append(random.randint(*self.num_events_range))
wv.append(random.randint(*self.num_sys_objs_range))
wv.append(random.randint(*self.token_index_range))
wv.append(random.randint(*self.num_db_range))
wv.append(random.randint(*self.num_cp_range))
wv.append(random.randint(*self.num_network_range))
# init new node and append to map
self.nodes.append(Node(self, x, y, wv))'''
# init the first round parameters
self.update_neighbour_radius()
self.update_learning_rate()
def __init__(self, width, height, dataset, iteration_num = 50):
self.radius = max(width, height) / 2.0
self.time_constant = iteration_num / math.log10(self.radius)
self.learning_rate = 2.0 #TODO to determine the init learning rate
self.current_learning_rate = 0.0
self.iteration_num = iteration_num
self.current_iteration = 1
self.neighbour_radius = 0.0
self.width = width
self.height = height
self.nodes = []
self.bmu = None
self.mh = mahalanobis_helper(dataset) # using given samples to calculate covariance matrix
self.init_map_node(dataset)
for x in range(self.width):
for y in range(self.height):
wv = [] # init weights vector for map node
wv.append(random.randint(*self.obj_range))
wv.append(random.randint(*self.pid_range))
wv.append(random.randint(*self.date_range))
wv.append(random.randint(*self.obj_occur_range))
wv.append(random.randint(*self.pid_occur_range))
self.nodes.append(Node(self, x, y, wv))
# init the first round parameters
self.update_neighbour_radius()
self.update_learning_rate()
def __init__(self, width, height, dataset, iteration_num = 1000):
self.radius = max(width, height) / 2.0
self.time_constant = iteration_num / math.log10(self.radius)
self.learning_rate = 0.09 #TODO to determine the init learning rate
self.current_learning_rate = 0.0
self.iteration_num = iteration_num
self.current_iteration = 1
self.neighbour_radius = 0.0
self.width = width
self.height = height
self.nodes = []
self.bmu = None
self.mh = mahalanobis_helper(dataset) # using given samples to calculate covariance matrix
self.init_map_node(dataset)
'''for x in range(self.width):
for y in range(self.height):
wv = [] # init weights vector for map node
#wv.append(random.randint(*self.app_index_range))
wv.append(random.randint(*self.duration_range))
wv.append(random.randint(*self.num_events_range))
wv.append(random.randint(*self.num_sys_objs_range))
wv.append(random.randint(*self.token_index_range))
wv.append(random.randint(*self.num_db_range))
wv.append(random.randint(*self.num_cp_range))
wv.append(random.randint(*self.num_network_range))
# init new node and append to map
self.nodes.append(Node(self, x, y, wv))'''
# init the first round parameters
self.update_neighbour_radius()
self.update_learning_rate()
