Question

我试图通过制作一条线（感知器）f并使一侧的点+1和-1在另一侧制作数据点的训练集。然后通过w = w + y（t）x（t）更新w = w + y（t）x（t）来创建新的g并尽可能接近f，其中w是权重，y（t）是+ 1，-1和x（t））是未分类点的坐标。实施这个后，我从g到f都没有得到很好的契合。这是我的代码和一些示例输出。

import random

random.seed()

points = [ [1, random.randint(-25, 25), random.randint(-25,25), 0] for k in range(1000)]

weights = [.1,.1,.1]

misclassified = []

############################################################# Function f

interceptf = (0,random.randint(-5,5))

slopef = (random.randint(-10, 10),random.randint(-10,10))

point1f = ((interceptf[0] + slopef[0]),(interceptf[1] + slopef[1]))
point2f = ((interceptf[0] - slopef[0]),(interceptf[1] - slopef[1]))

############################################################# Function G starting
interceptg = (-weights[0],weights[2])

slopeg = (-weights[1],weights[2])

point1g = ((interceptg[0] + slopeg[0]),(interceptg[1] + slopeg[1]))
point2g = ((interceptg[0] - slopeg[0]),(interceptg[1] - slopeg[1]))
#############################################################

def isLeft(a, b, c):
      return ((b[0] - a[0])*(c[1] - a[1]) - (b[1] - a[1])*(c[0] - a[0])) > 0


for i in points:
    if isLeft(point1f,point2f,i):
        i[3]=1
    else:
        i[3]=-1

for i in points:
    if (isLeft(point1g,point2g,i)) and (i[3] == -1):
        misclassified.append(i)

    if (not isLeft(point1g,point2g,i)) and (i[3] == 1):
        misclassified.append(i)

print len(misclassified)


while misclassified:
    first = misclassified[0]
    misclassified.pop(0)

    a = [first[0],first[1],first[2]]
    b = first[3]

    a[:] = [x*b for x in a]

    weights = [(x + y) for x, y in zip(weights,a)]

interceptg = (-weights[0],weights[2])

slopeg = (-weights[1],weights[2])

point1g = ((interceptg[0] + slopeg[0]),(interceptg[1] + slopeg[1]))
point2g = ((interceptg[0] - slopeg[0]),(interceptg[1] - slopeg[1]))

check = 0

for i in points:
    if (isLeft(point1g,point2g,i)) and (i[3] == -1):
        check += 1

    if (not isLeft(point1g,point2g,i)) and (i[3] == 1):
        check += 1

print weights
print check

117＆lt; ---原始未分类数量与g

[ - 116.9，-300.9,190.1]＆lt; ---最终权重

617＆lt; ---算法之后带有g的原始未分类数

956＆lt; ---原始未分类数与g

[ - 33.9，-12769.9，-572.9]＆lt; ---最终重量

461＆lt; ---算法后带有g的原始未分类数

Answer 1

您的算法至少存在一些问题：

你的“同时”条件是错误的 - 感知器学习不是像现在一样通过所有错误分类的点重复一次。只要其中任何一个被错误分类，算法就应该遍历所有点。特别是 - 每次更新都可以将一些正确分类的点作为错误的点，因此您必须总是遍历所有这些并检查一切是否正常。

我非常确定您真正想要的是(y(i)-p(i))x(i)形式的更新规则，其中p(i)是预测标签而y(i)是真正的标签（但这显然是退化的）如果您只更新错误分类，请使用您的方法

感知器问题

1 个答案: