多标记KNN算法实现（Python3.6）
MLKNN算法
对于一个新实例，取其最近的k个实例，然后得到由k个实例组成的标签集合，最后通过先验概率与最大后验概率来确定新实例的标签集合。详细的内容参看周志华老师与张敏灵老师的《多标记学习》
算法实现
数据预处理
MLkNNDemo.py
#load file
data = sio.loadmat('scene.mat')
train_bags = data['train_bags']
test_bags = data['test_bags']
train_targets = data['train_targets']
test_targets = data['test_targets']
#train_bags:9*15 to 1*135
trainBagLine = len(train_bags)
train_data = []
for i in range(trainBagLine):
linshi = train_bags[i,0].flatten().tolist()
train_data.append(linshi)
train_data=np.array(train_data)
#test_bags:9*15 to 1*135
testBagLine = len(test_bags)
test_data = []
for i in range(testBagLine):
linshi = test_bags[i,0].flatten().tolist()
test_data.append(linshi)
test_data=np.array(test_data)
#const;Num is K's value
Num = 10
Smooth = 1
#training;
Prior,PriorN,Cond,CondN = MLkNNTrain.trainClass(train_data,train_targets,Num,Smooth)
#testing
outPuts,preLabels = MLkNNTest.testClass(train_data,train_targets,test_data,test_targets,Num,Prior,PriorN,Cond,CondN)训练
MLkNNTrain.py
#Train
def trainClass(train_data,train_targets,Num,Smooth):
#Get size of matrix
num_class,num_training = np.mat(train_targets).shape
dist_matrix = np.diagflat(ones((1,num_training))*sys.maxsize)
#Cumputing distance
for i in range((num_training-1)):
vector1 = train_data[i,:]
for j in range((i+1),(num_training)):
vector2 = train_data[j,:]
dist_matrix[i,j] = sum((vector1-vector2)**2)**0.5
dist_matrix[j,i] = dist_matrix[i,j]
#Prior and PriorN
Prior = zeros((num_class,1))
PriorN = zeros((num_class,1))
for i in range(num_class):
tempCi = sum((train_targets[i,:]==ones((1,num_training))))
Prior[i,0] = (tempCi+1)/(Smooth*2+num_training)
PriorN[i,0] = 1-Prior[i,0]
#Cond and CondN
#Sort by distance and get index
#find neighbors
disMatIndex = argsort(dist_matrix)
tempCi = zeros((num_class,Num+1))
tempNci = zeros((num_class,Num+1))
for i in range(num_training):
temp = zeros((1,num_class))
neighborLabels = []
for j in range(Num):
neighborLabels.append(train_targets[:,disMatIndex[i,j]])
neighborLabels = np.mat(neighborLabels)
neighborLabels = np.transpose(neighborLabels)
for j in range(num_class):
temp[0,j] = sum((neighborLabels[j,:] == ones((1,Num))))
for j in range(num_class):
t = int((temp[0,j]))
if(train_targets[j,i] == 1):
tempCi[j,t]=tempCi[j,t]+1
else:
tempNci[j,t] = tempNci[j,t]+1
#get 5*11 matrix
Cond = zeros((num_class,Num+1))
CondN = zeros((num_class,Num+1))
for i in range(num_class):
temp1 = sum((tempCi[i,:]))
temp2 = sum((tempNci[i,:]))
for j in range(Num+1):
Cond[i,j] = (Smooth+tempCi[i,j])/(Smooth*(Num+1)+temp1)
CondN[i,j] = (Smooth+tempNci[i,j])/(Smooth*(Num+1)+temp2)
return Prior,PriorN,Cond,CondN测试
MLkNNTest.py
def testClass(train_data,train_targets,test_data,test_targets,Num,Prior,PriorN,Cond,CondN):
num_class,num_training = np.mat(train_targets).shape
num_class,num_testing = np.mat(test_targets).shape
#init matrix about distance
distMatrix = zeros((num_testing,num_training))
for i in range(num_testing):
vector1 = test_data[i,:]
for j in range(num_training):
vector2 = train_data[j,:]
distMatrix[i,j] = sum((vector1-vector2)**2)**0.5
#Sort by distance and get index
#find neighbors
disMatIndex = argsort(distMatrix)
#computing outputs
outPuts = zeros((num_class,num_testing))
for i in range(num_testing):
temp = zeros((1,num_class))
neighborLabels = []
for j in range(Num):
neighborLabels.append(train_targets[:,disMatIndex[i,j]])
neighborLabels = np.mat(neighborLabels)
#transposition
neighborLabels = np.transpose(neighborLabels)
for j in range(num_class):
temp[0,j] = sum((neighborLabels[j,:] == ones((1,Num))))
for j in range(num_class):
t = int((temp[0,j]))
Prob_in=Prior[j]*Cond[j,t]
Prob_out=PriorN[j]*CondN[j,t]
if((Prob_in+Prob_out)==0):
outPuts[j,i]=Prior[j]
else:
outPuts[j,i]=Prob_in/[Prob_in+Prob_out]
#Evaluation
preLabels=zeros((num_class,num_testing))
for i in range(num_testing):
for j in range(num_class):
if(outPuts[j,i]>=0.5): #阈值为0.5
preLabels[j,i]=1
else:
preLabels[j,i]=-1
return outPuts,preLabels