last_tree = self.trees[i-1]
residual = last_tree.next_residual()
print >>sys.stderr, 'training tree #%d' % (i)
tree.train(residual)
self.trees.append(tree)
def predict(self, x):
y = self.F0
for tree in self.trees:
y += tree.predict(x)
return y
if __name__ == '__main__':
param = DTreeParameter()
param.max_level = 4
param.split_threshold = 0.8
param.max_attr_try_time = 1000
param.tree_number = 20
param.learning_rate = 0.5
sample = DTreeSample()
sample.load_liblinear('heart_scale.txt')
gbdt = GBDT(sample)
gbdt.train(param)
print gbdt.predict([0.708333,1,1,-0.320755,-0.105023,-1,1,-0.419847,-1,-0.225806,0,1,-1])
print gbdt.predict([0.583333,-1,0.333333,-0.603774,1,-1,1,0.358779,-1,-0.483871,0,-1,1])
tree.train(residual)
self.trees.append(tree)
def predict(self, x):
y = self.F0
for tree in self.trees:
y += tree.predict(x)
return y
if __name__ == '__main__':
param = DTreeParameter()
param.max_level = 4
param.split_threshold = 0.8
param.max_attr_try_time = 1000
param.tree_number = 20
param.learning_rate = 0.5
sample = DTreeSample()
sample.load_liblinear('heart_scale.txt')
gbdt = GBDT(sample)
gbdt.train(param)
print gbdt.predict([
0.708333, 1, 1, -0.320755, -0.105023, -1, 1, -0.419847, -1, -0.225806,
0, 1, -1
])
print gbdt.predict([
0.583333, -1, 0.333333, -0.603774, 1, -1, 1, 0.358779, -1, -0.483871,
0, -1, 1
])
