机器学习基石—作业1（15-20题PLA编程）

def mkDateSet(datPath):
dataSet = open(datPath, 'r').readlines()
m = len(dataSet)
X_train = np.zeros((m, 4))
Y_train = np.zeros((m, 1))

for i, item in enumerate(dataSet):
each = item.strip().split()
X_train[i] = [float(a) for a in each[:-1]]
Y_train[i] = int(each[-1])
X_train = np.hstack((np.ones((m, 1)), X_train))
return (X_train, Y_train)

def sign(w, x):
r = np.dot(w, x)
if np.dot(w, x)[0] > 0:
return 1
else:
return -1

def plaNaive(dataSet):
(X, Y) = dataSet
(m, n) = X.shape
w = np.zeros((n, 1))
errtimes = 0
iters = 0
while True:
iters += 1  # 所需遍历data的次数
hasError = False
for t in range(m):
if sign(X[t], w) != Y[t][0]:  # 发现错误点，进行一次梯度更新
w = w + Y[t][0] * np.transpose([X[t]])
# narray行向量x转置成列向量用np.transpose([x])。或者统一用matrix类，np.matrix(x).T
errtimes += 1
hasError = True
if hasError == False:  # 直到某个w在所有样本上都分类正确，才停止
break
return w, iters, errtimes

def plaRandom(dataSet, repeat, ita):
(X, Y) = dataSet
(m, n) = X.shape
w = np.zeros((n, 1))
errnum = 0
aveerrornum = 0
for i in range(repeat):
circle = random.sample(range(m), m)  # 每次试验选用相同的随机序列
while True:
hasError = False
for t in circle:
if sign(X[t], w) != Y[t][0]:
w = w + ita * Y[t][0] * np.transpose([X[t]])
errnum += 1
hasError = True
if hasError == False:  # 直到某个w在所有样本上都分类正确，才停止一次实验
break
aveerrornum += errnum
aveerrornum /= repeat
return w, aveerrornum

def plaPocket(dataSet, testSet, updates, ita):
(X, Y) = dataSet
(m, n) = X.shape
w = np.zeros((n, 1))
bestw = np.zeros(w.shape)
i = 0
minerror = 1  # 初始的最小错误率
while i < updates:
t = random.randint(0, m - 1)  # 每次随机找一个点
if sign(X[t], w) != Y[t][0]:
w = w + ita * Y[t][0] * np.transpose([X[t]])
errort = testW(w, dataSet)
if errort < minerror:
minerror = errort
bestw = w
i += 1  # 只查找50个错误点
return bestw, minerror

def testW(w, dataSet):
(X, Y) = dataSet
m = len(X)
errors = 0.0
for t in range(m):
if sign(X[t], w) == Y[t][0]:
continue
else:
errors += 1
errorrate = errors / m
return errorrate

if __name__ == '__main__':
dataSet1 = mkDateSet('hw1_15_train.dat')
dataSet2 = mkDateSet('hw1_18_train.dat')
testSet2 = mkDateSet('hw1_18_test.dat')
# errtimes = plaNaive(dataSet1)[2] #第15题
# errtimes = plaRandom(dataSet1,2000,0.5)[1] #第16/17题
##########第18题###########
errorrate = 0.0
for i in range(2000):
bestw = plaPocket(dataSet2, testSet2, 50, 0.5)[0]
errorrate += testW(bestw, testSet2)
print(i)
errorrate /= 2000
print(errorrate)  # 0.1328509999999997
#########第19题###########
# 将plaPocket返回的bestw 改为 w
# 0.352
#########第20题###########
# 将18题的updates=100
# errorrate = 0.11495600000000031