def stocGradAscent0(dataMatrix, classLabels):
m, n = shape(dataMatrix)
alpha = 0.5
weights = ones(n) # initialize to all ones
weightsHistory = zeros((500 * m, n))
for j in range(500):
for i in range(m):
h = logRegres.sigmoid(sum(dataMatrix[i] * weights))
error = classLabels[i] - h
weights = weights + alpha * error * dataMatrix[i]
weightsHistory[j * m + i, :] = weights
return weightsHistory
def stocGradAscent0(dataMatrix, classLabels):
m,n = shape(dataMatrix)
alpha = 0.5
weights = ones(n) #initialize to all ones
weightsHistory=zeros((500*m,n))
for j in range(500):
for i in range(m):
h = logRegres.sigmoid(sum(dataMatrix[i]*weights))
error = classLabels[i] - h
weights = weights + alpha * error * dataMatrix[i]
weightsHistory[j*m + i,:] = weights
return weightsHistory
def stocGradAscent1(dataMatrix, classLabels):
m, n = shape(dataMatrix)
alpha = 0.4
weights = ones(n) # initialize to all ones
weightsHistory = zeros((40 * m, n))
for j in range(40):
dataIndex = range(m)
for i in range(m):
alpha = 4 / (1.0 + j + i) + 0.01
randIndex = int(random.uniform(0, len(dataIndex)))
h = logRegres.sigmoid(sum(dataMatrix[randIndex] * weights))
error = classLabels[randIndex] - h
# print error
weights = weights + alpha * error * dataMatrix[randIndex]
weightsHistory[j * m + i, :] = weights
del (dataIndex[randIndex])
print weights
return weightsHistory
def stocGradAscent1(dataMatrix, classLabels):
m,n = shape(dataMatrix)
alpha = 0.4
weights = ones(n) #initialize to all ones
weightsHistory=zeros((40*m,n))
for j in range(40):
dataIndex = range(m)
for i in range(m):
alpha = 4/(1.0+j+i)+0.01
randIndex = int(random.uniform(0,len(dataIndex)))
h = logRegres.sigmoid(sum(dataMatrix[randIndex]*weights))
error = classLabels[randIndex] - h
#print error
weights = weights + alpha * error * dataMatrix[randIndex]
weightsHistory[j*m + i,:] = weights
del(dataIndex[randIndex])
print weights
return weightsHistory
def stocGradAscent1(dataMatrix, classLabels):
# 获取矩阵的维数 m × n,m 等于100
m, n = shape(dataMatrix)
alpha = 0.4
# 生成 n × 1 矩阵
weights = ones(n) # initialize to all ones
weightsHistory = zeros((40 * m, n))
for j in range(40):
dataIndex = range(m)
for i in range(m):
alpha = 4 / (1.0 + j + i) + 0.01
# 随机选取一条数据记录
randIndex = int(random.uniform(0, len(dataIndex)))
h = logRegres.sigmoid(sum(dataMatrix[randIndex] * weights))
error = classLabels[randIndex] - h
# print error
weights = weights + alpha * error * dataMatrix[randIndex]
# 记录weights，注意这个地方的 j * m + i ，j为迭代的次数，m为数据总量100
weightsHistory[j * m + i, :] = weights
del (dataIndex[randIndex])
print weights
return weightsHistory