s_data = numpy.array(random.sample(data_set.tolist(), 256)) s_data = minmax_scale(s_data) labels = s_data[:, -1] _, cols = s_data.shape s_index = random.sample(range(cols), 3) s_data = s_data[:, s_index] rows, cols = s_data.shape ae = AutoEncoder() ae.init_tf(fin=cols, fou=1, epochs=10) results = ae.get_results(s_data) split = random.uniform(min(results['inters']), max(results['inters'])) # print(results['inters'][numpy.where(results['inters'] > split)]) # print(results['weights']) # print(results['biases']) # print(results['inters']) exit() # split = (min(results['inters'])+max(results['inters'])) /2 x1, y1 = list(), list() x2, y2 = list(), list()
class DLForest:
def __init__(self):
self.sample_size = 0
self.height_limit = 0
self.leaf_size_limit = 10
self.attr_sample_size = 1
self.forest_size_limit = 10
self.forest = list()
self.ae = None
def setup(self,
sample_size=128,
height_limit=6,
leaf_size_limit=10,
attr_sample_size=2,
forest_size_limit=5):
self.sample_size = sample_size
self.height_limit = height_limit
self.leaf_size_limit = leaf_size_limit
self.attr_sample_size = attr_sample_size
self.forest_size_limit = forest_size_limit
def build(self, data):
i = 0
self.ae = AutoEncoder()
self.ae.init_tf(fin=self.attr_sample_size,
fou=1,
epochs=10,
l_rate=0.01)
while i < self.forest_size_limit:
sample_data = numpy.array(random.sample(data, self.sample_size))
tree = self.build_tree(sample_data, 0)
self.forest.append(tree)
i += 1
def build_tree(self, data, curH):
rows, cols = data.shape
if rows <= self.leaf_size_limit or curH >= self.height_limit:
node = Node()
node.size = rows
node.external = True
return node
else:
sample_index = random.sample(range(cols), self.attr_sample_size)
results = self.ae.get_results(data[:, sample_index])
node = Node()
node.index = sample_index
node.size = rows
node.weights = results['weights']
node.bias = results['biases']
node.split = random.uniform(min(results['inters']),
max(results['inters']))
left, right = self.split_data(results['inters'], node.split)
node.left = self.build_tree(data[left, :], curH + 1)
node.right = self.build_tree(data[right, :], curH + 1)
return node
def split_data(self, data, split_point):
left, right = list(), list()
for i in range(len(data)):
if data[i] <= split_point:
left.append(i)
else:
right.append(i)
return left, right
def evaluate(self, data, hlimit=6):
scores = list()
for item in data:
score = 0
for tree in self.forest:
score += self.path(item, tree, curH=0, hlimit=hlimit)
score /= self.forest_size_limit
scores.append(score)
return scores
def path(self, instance, tree, curH, hlimit):
if tree.external is True or curH >= hlimit:
return curH + self.cost(tree.size)
tag = sum(instance[tree.index] * tree.weights) + tree.bias
if tag <= tree.split:
return self.path(instance, tree.left, curH + 1, hlimit)
else:
return self.path(instance, tree.right, curH + 1, hlimit)
def cost(self, size):
if size == 2:
return 1
elif size < 2:
return 0
else:
return 2 * log(size - 1, 2) - float(2 * (size - 1)) / size
def show(self, tree):
if tree is None:
print('is None')
return None
print(tree.size)
self.show(tree.left)
self.show(tree.right)
