def logistic_l1():
traindir ='./data/training'
testdir = './data/test'
tokens = list(nb.read('tokens'))
train_x,train_y,category = vec.func2(traindir,tokens)
train_x = np.array(train_x)
train_y = np.array(train_y)
print train_x.shape
clf = LogisticRegression(penalty='l2')
clf.fit(train_x,train_y)
category = nb.read('category')
result,test_x,test_file= vec.func3(testdir,tokens,category)
test_x = np.array(test_x)
print test_x.shape
predict = np.array(clf.predict(test_x))
test_file = np.array(test_file)
predict = np.column_stack((test_file,predict))
category = nb.read('category_nb_eventmodel')
category_convert = nb.convert(category)
result = nb.read('result')
path = './data/logistic_l1.csv'
evaluate = nb.sta_result(predict,category_convert,result,path)
def logistic_l1():
traindir = './data/training'
testdir = './data/test'
tokens = list(nb.read('tokens'))
train_x, train_y, category = vec.func2(traindir, tokens)
train_x = np.array(train_x)
train_y = np.array(train_y)
print train_x.shape
clf = LogisticRegression(penalty='l2')
clf.fit(train_x, train_y)
category = nb.read('category')
result, test_x, test_file = vec.func3(testdir, tokens, category)
test_x = np.array(test_x)
print test_x.shape
predict = np.array(clf.predict(test_x))
test_file = np.array(test_file)
predict = np.column_stack((test_file, predict))
category = nb.read('category_nb_eventmodel')
category_convert = nb.convert(category)
result = nb.read('result')
path = './data/logistic_l1.csv'
evaluate = nb.sta_result(predict, category_convert, result, path)
def logistic_own():
train_x = nb.read('train_x')
train_y = nb.read('train_y')
category = nb.read('category')
result =nb.read('result')
test_x = nb.read('test_x')
test_file = nb.read('test_file')
m,n=train_x.shape
temp = np.ones((m,1))
train_x = np.column_stack((temp,train_x))
temp = np.ones((len(test_x),1))
test_x = np.column_stack((temp,test_x))
predict = np.zeros((len(test_x),1))
train_x = np.mat(train_x)
train_y = np.mat(train_y).transpose()
test_x = np.mat(test_x)
#由于要实现多分类，我们可以通过多个二分类来实现预测
for i in range(10):
binary_y = np.mat(np.zeros((m,1)).astype(int))
for index in range(len(train_y)):
if train_y[index]==i:
binary_y[index]=1
else:
binary_y[index]=0
weight = np.mat(np.ones((n+1,1)))
alpha = 0.0001
maxitem = 100
for k in range(maxitem):
h = sigmoid(train_x*weight)
#我们在计算代价函数的时候,不能简单的用公式实现,应当进行判断
J = calj(binary_y,h,m)
#J = 1.0/m*(-binary_y.transpose()*np.log2(h)-(1-binary_y.transpose())*np.log2(1-h))
error = h-binary_y
weight -= alpha*(train_x.transpose()*error)
binary_predict = test_x*weight
for index in range(len(binary_predict)):
if binary_predict[index]>0:
predict[index]=i
predict = np.array(predict).astype(int)
test_file = np.array(test_file)
predict = np.column_stack((test_file,predict))
category = nb.read('category_nb_eventmodel')
category_convert = nb.convert(category)
result = nb.read('result')
path = './data/logistic_l1.csv'
evaluate = nb.sta_result(predict,category_convert,result,path)
def logistic_own():
train_x = nb.read('train_x')
train_y = nb.read('train_y')
category = nb.read('category')
result = nb.read('result')
test_x = nb.read('test_x')
test_file = nb.read('test_file')
m, n = train_x.shape
temp = np.ones((m, 1))
train_x = np.column_stack((temp, train_x))
temp = np.ones((len(test_x), 1))
test_x = np.column_stack((temp, test_x))
predict = np.zeros((len(test_x), 1))
train_x = np.mat(train_x)
train_y = np.mat(train_y).transpose()
test_x = np.mat(test_x)
#由于要实现多分类，我们可以通过多个二分类来实现预测
for i in range(10):
binary_y = np.mat(np.zeros((m, 1)).astype(int))
for index in range(len(train_y)):
if train_y[index] == i:
binary_y[index] = 1
else:
binary_y[index] = 0
weight = np.mat(np.ones((n + 1, 1)))
alpha = 0.0001
maxitem = 100
for k in range(maxitem):
h = sigmoid(train_x * weight)
#我们在计算代价函数的时候,不能简单的用公式实现,应当进行判断
J = calj(binary_y, h, m)
#J = 1.0/m*(-binary_y.transpose()*np.log2(h)-(1-binary_y.transpose())*np.log2(1-h))
error = h - binary_y
weight -= alpha * (train_x.transpose() * error)
binary_predict = test_x * weight
for index in range(len(binary_predict)):
if binary_predict[index] > 0:
predict[index] = i
predict = np.array(predict).astype(int)
test_file = np.array(test_file)
predict = np.column_stack((test_file, predict))
category = nb.read('category_nb_eventmodel')
category_convert = nb.convert(category)
result = nb.read('result')
path = './data/logistic_l1.csv'
evaluate = nb.sta_result(predict, category_convert, result, path)
def logistic_x():
train_x,train_y,category,result,test_x,test_file = preprocess()
clf = LogisticRegression(penalty='l1')
clf.fit(train_x,train_y)
predict = clf.predict(test_x)
predict = np.array(predict)
predict = np.column_stack((test_file,predict))
category = nb.read('category_nb_eventmodel')
category_convert = nb.convert(category)
result = nb.read('result')
path = './data/logistic_l1.csv'
evaluate = nb.sta_result(predict,category_convert,result,path)
def logistic_x():
train_x, train_y, category, result, test_x, test_file = preprocess()
clf = LogisticRegression(penalty='l1')
clf.fit(train_x, train_y)
predict = clf.predict(test_x)
predict = np.array(predict)
predict = np.column_stack((test_file, predict))
category = nb.read('category_nb_eventmodel')
category_convert = nb.convert(category)
result = nb.read('result')
path = './data/logistic_l1.csv'
evaluate = nb.sta_result(predict, category_convert, result, path)
def sto_logistic():
train_x = nb.read('train_x')
train_y = nb.read('train_y')
category = nb.read('category')
result =nb.read('result')
test_x = nb.read('test_x')
test_file = nb.read('test_file')
m,n=train_x.shape
temp = np.ones((m,1))
train_x = np.column_stack((temp,train_x))
temp = np.ones((len(test_x),1))
test_x = np.column_stack((temp,test_x))
predict = np.zeros((len(test_x),1))
train_x = np.mat(train_x)
train_y = np.mat(train_y).transpose()
test_x = np.mat(test_x)
#由于要实现多分类，我们可以通过多个二分类来实现预测
for i in range(10):
binary_y = np.mat(np.zeros((m,1)).astype(int))
for index in range(len(train_y)):
if train_y[index]==i:
binary_y[index]=1
else:
binary_y[index]=0
weight = np.mat(np.ones((n+1,1)))
alpha = 0.001
maxitem =5000
for k in range(maxitem):
index = random.randrange(m)
h = sigmoid(train_x[index]*weight)
error = h - binary_y[index]
weight -= alpha*(train_x[index].transpose()*error)
binary_predict = test_x*weight
for index in range(len(binary_predict)):
if binary_predict[index]>0:
predict[index]=i
predict = np.array(predict).astype(int)
test_file = np.array(test_file)
predict = np.column_stack((test_file,predict))
category = nb.read('category_nb_eventmodel')
category_convert = nb.convert(category)
result = nb.read('result')
path = './data/logistic_l1.csv'
evaluate = nb.sta_result(predict,category_convert,result,path)
def sto_logistic():
train_x = nb.read('train_x')
train_y = nb.read('train_y')
category = nb.read('category')
result = nb.read('result')
test_x = nb.read('test_x')
test_file = nb.read('test_file')
m, n = train_x.shape
temp = np.ones((m, 1))
train_x = np.column_stack((temp, train_x))
temp = np.ones((len(test_x), 1))
test_x = np.column_stack((temp, test_x))
predict = np.zeros((len(test_x), 1))
train_x = np.mat(train_x)
train_y = np.mat(train_y).transpose()
test_x = np.mat(test_x)
#由于要实现多分类，我们可以通过多个二分类来实现预测
for i in range(10):
binary_y = np.mat(np.zeros((m, 1)).astype(int))
for index in range(len(train_y)):
if train_y[index] == i:
binary_y[index] = 1
else:
binary_y[index] = 0
weight = np.mat(np.ones((n + 1, 1)))
alpha = 0.001
maxitem = 5000
for k in range(maxitem):
index = random.randrange(m)
h = sigmoid(train_x[index] * weight)
error = h - binary_y[index]
weight -= alpha * (train_x[index].transpose() * error)
binary_predict = test_x * weight
for index in range(len(binary_predict)):
if binary_predict[index] > 0:
predict[index] = i
predict = np.array(predict).astype(int)
test_file = np.array(test_file)
predict = np.column_stack((test_file, predict))
category = nb.read('category_nb_eventmodel')
category_convert = nb.convert(category)
result = nb.read('result')
path = './data/logistic_l1.csv'
evaluate = nb.sta_result(predict, category_convert, result, path)
