class KNNC:
"""docstring for KNNC"""
def __init__(self,X,K,labels=None,dist=euclidean):
'''
X is a N*M matrix where M is the case
labels is prepare for the predict.
dist is the similarity measurement way,
The distance function can be ‘braycurtis’, ‘canberra’,
‘chebyshev’, ‘cityblock’, ‘correlation’, ‘cosine’,
‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘kulsinski’,
‘mahalanobis’,
'''
self.X = np.array(X)
if labels==None:
self.labels=np.zeros((1,self.X.shape[1]))
else:
self.labels = np.array(labels).flatten(1)
assert self.X.shape[1]==self.labels.size
self.K = K
self.dist = dist
self.KDTrees=KDTree(X,labels,self.dist)
def predict(self,x,k):
ans=self.KDTrees.search_knn(self.KDTrees.P,x,k)
dc={}
maxx=0
y=0
for i in range(ans.counter+1):
if i==0:
continue
dc.setdefault(ans.heap[i].y,0)
dc[ans.heap[i].y]+=1
if dc[ans.heap[i].y]>maxx:
maxx=dc[ans.heap[i].y]
y=ans.heap[i].y
return y
def for_point(self,test_x,k=None):
if k==None:
k=self.K
ans=self.KDTrees.search_knn(self.KDTrees.P,np.array(test_x),k)
result=[]
for i in range(ans.counter+1):
if i==0:
continue
result.append(ans.heap[i].x)
return result
def pred(self,test_x,k=None):
'''
test_x is a N*TM matrix,and indicate TM test case
you can redecide the k
'''
if k==None:
k=self.K
test_case=np.array(test_x)
y=[]
for i in range(test_case.shape[1]):
y.append(self.predict(test_case[:,i].transpose(),k))
return y
class KNNC:
"""docstring for KNNC"""
def __init__(self,X,K,labels=None,dist=euclidean):
'''
X is a N*M matrix where M is the case
labels is prepare for the predict.
dist is the similarity measurement way,
The distance function can be ‘braycurtis’, ‘canberra’,
‘chebyshev’, ‘cityblock’, ‘correlation’, ‘cosine’,
‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘kulsinski’,
‘mahalanobis’,
'''
self.X = np.array(X)
if labels==None:
np.zeros((1,self.X.shape[1]))
self.labels = np.array(labels)
self.K = K
self.dist = dist
self.KDTrees=KDTree(X,labels,self.dist)
def predict(self,x,k):
ans=self.KDTrees.search_knn(self.KDTrees.P,x,k)
dc={}
maxx=0
y=0
for i in range(ans.counter+1):
if i==0:
continue
dc.setdefault(ans.heap[i].y,0)
dc[ans.heap[i].y]+=1
if dc[ans.heap[i].y]>maxx:
maxx=dc[ans.heap[i].y]
y=ans.heap[i].y
return y
def for_point(self,test_x,k=None):
if k==None:
k=self.K
ans=self.KDTrees.search_knn(self.KDTrees.P,np.array(test_x),k)
result=[]
for i in range(ans.counter+1):
if i==0:
continue
result.append(ans.heap[i].x)
return result
def pred(self,test_x,k=None):
'''
test_x is a N*TM matrix,and indicate TM test case
you can redecide the k
'''
if k==None:
k=self.K
test_case=np.array(test_x)
y=[]
for i in range(test_case.shape[1]):
y.append(self.predict(test_case[:,i].transpose(),k))
return y
def __init__(self,X,K,labels=None,dist=euclidean): ''' X is a N*M matrix where M is the case labels is prepare for the predict. dist is the similarity measurement way, The distance function can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, ‘correlation’, ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ''' self.X = np.array(X) if labels==None: np.zeros((1,self.X.shape[1])) self.labels = np.array(labels) self.K = K self.dist = dist self.KDTrees=KDTree(X,labels,self.dist)
