def testBuild(self):
kd = KDTree(dimensions = 3)
points = array([[5, 4, 3], [10, 2, 1], [4, 5, 6], [6, 1, 5], [1, 2, 3]])
labels = array([[1], [1], [1], [-1], [-1]])
kd.build(points, labels)
print(kd.searchKNN(([4, 4, 6]), 3)[0][0]._data)
pass
class KNN(object):
def __init__(self):
self._kdTree = None
def __init__(self, points, labels):
'''
suppose n is samples' amount, m is features' amount.
points: array, training data, n * m
labels: array, n * 1
'''
self.build(points, labels)
def build(self, points, labels):
'''
suppose n is samples' amount, m is features' amount.
points: array, training data, n * m
labels: array, n * 1
'''
dimensions = points.shape[1]
self._kdTree = KDTree(dimensions=dimensions)
self._kdTree.build(points, labels)
def add(self, x, y):
'''
add a point
x: single array
y: int
'''
pass
def add(self, x, y):
'''
suppose n is samples' amount, m is features' amount.
x: array, training data, n * m
y: array, n * 1
'''
pass
def getKNN(self, point, k=5):
'''
get nearest n points
'''
return self._kdTree.searchKNN(point, k)
def getLabel(self, point, k=5):
'''
get label of point
'''
nns = self._kdTree.searchKNN(point, k)
votes = {}
for item in nns:
label = item[0]._label[0]
if label not in votes:
votes[label] = 1
else:
votes[label] += 1
return sorted(votes.items(), key=lambda x: x[1], reverse=True)[0][0]