【发布时间】：2013-02-19 03:22:34
【问题描述】：
我正在尝试在 python 中实现梯度下降；当我尝试使用 training_set1 时，该实现有效，但当我尝试使用 training_set 时，它返回的不是数字（nan）。知道为什么我的代码被破坏了吗？
from collections import namedtuple
TrainingInstance = namedtuple("TrainingInstance", ['X', 'Y'])
training_set1 = [TrainingInstance(0, 4), TrainingInstance(1, 7),
TrainingInstance(2, 7), TrainingInstance(3, 8),
TrainingInstance(8, 12)]
training_set = [TrainingInstance(60, 3.1), TrainingInstance(61, 3.6),
TrainingInstance(62, 3.8), TrainingInstance(63, 4),
TrainingInstance(65, 4.1)]
def grad_desc(x, x1):
# minimize a cost function of two variables using gradient descent
training_rate = 0.1
iterations = 5000
#while sqrd_error(x, x1) > 0.0000001:
while iterations > 0:
#print sqrd_error(x, x1)
x, x1 = x - (training_rate * deriv(x, x1)), x1 - (training_rate * deriv1(x, x1))
iterations -= 1
return x, x1
def sqrd_error(x, x1):
sum = 0.0
for inst in training_set:
sum += ((x + x1 * inst.X) - inst.Y)**2
return sum / (2.0 * len(training_set))
def deriv(x, x1):
sum = 0.0
for inst in training_set:
sum += ((x + x1 * inst.X) - inst.Y)
return sum / len(training_set)
def deriv1(x, x1):
sum = 0.0
for inst in training_set:
sum += ((x + x1 * inst.X) - inst.Y) * inst.X
return sum / len(training_set)
if __name__ == "__main__":
print grad_desc(2, 2)
【问题讨论】：
标签： machine-learning linear-regression statistics