class KNearest:
def __init__(self, k_neighbours=5, dense=False, balanced=False):
self.k_neighbours = k_neighbours
self.dense = dense
self.balanced = balanced
self.data = None
def fit(self, X, y):
self.label_set = set(y)
data = list()
for con, lab in it.izip(X, y):
if not self.dense:
con = con.toarray()
con = con[0]
data.append((con, lab))
# Create a KDTree using the data given and store it
self.data = KDTree(data, self.k_neighbours, balanced=self.balanced)
def predict(self, X_test):
predictions = list()
if not self.dense:
X_test = toArray(X_test)
for u in X_test:
dists = list()
# neighbours = bucket of vectors to compare u with
neighbours = self.data.search(u)
# Make a list of distances between u and each neighbour
for n in neighbours:
dists.append((self.__distance(u, n[0]), n[1]))
# Sort the list so we can get the k closest neighbours
dists = sorted(dists, key=lambda dist: dist[0])
nearest = dists[:self.k_neighbours]
# Find Majority
predictions.append(self.__findMajority(nearest))
return predictions
def __findMajority(self, dists):
labels = dict()
# Make a dict of occurences of each label in dists
for l in self.label_set:
labels[l] = 0
for __dist, lab in dists:
labels[lab] += 1
# Find max label
maxval = 0
maxkey = -1
for key, value in labels.iteritems():
if value > maxval:
maxval = value
maxkey = key
return maxkey
# Euclidean distance of 2 vectors
def __distance(self, U, V):
s = 0
for xu, xv in it.izip(U, V):
s += (xu - xv)**2
dist = sqrt(s)
return dist
