def score(ar):
m, n = shape(ar)
scoretable = np.zeros((m, 3))
groupids = set(ar[:, -1])
#groupids = np.unique(ar[:, -1])
A, B, S = range(3)
for i in range(m):
item = ar[i, :]
# get a(i)
subgroup = ar[np.where(ar[:, -1]==item[-1])]
scoretable[i, A] = np.mean(distance(subgroup[:, :-1], item[:-1]))
# get b(i)
scoretable[i, B] = np.min([np.min(distance(ar[np.where(ar[:, -1]==gid)][:, :-1], item[:-1])) for gid in groupids - {item[-1]}])
# get s(i)
scoretable[i, S] = (scoretable[i, B] - scoretable[i, A]) /\
max(scoretable[i, B], scoretable[i, A])
#print(scoretable[i, :])
return scoretable[:, S].mean()
def assigngroup(cls, data, k=0, centroid=None):
m, n = shape(data)
category = np.zeros((m, 1))
scoretable = np.zeros((m, k))
if centroid:
cls.centroid = centroid
if k:
cls.k = k
cdata = np.hstack((data, category))
for c in range(cls.k):
scoretable[:, c] = distance(cdata[:, :-1], cls.centroid[c, :])
cdata[:, -1] = scoretable.argmin(axis=1)
return cdata
def train(cls, data, k=3, itercount=60):
m, n = shape(data)
category = np.zeros((m, 1))
cls.centroid = np.array([[5, 3, 5, 1], [4, 3, 1, 1], [5, 3, 1, 0]])
#cls.centroid = get_randomseed(data, (k, n))
cls.k = k
'''
u = m / k
d = [(u*i, u*(i+1), i) for i in range(k)]
for s, e, i in d:
category[s:e] = i
'''
cdata = np.hstack((data, category))
scoretable = np.zeros((m, k))
old_centroid = None
count = 0
while not np.array_equal(old_centroid, cls.centroid) and \
count < itercount:
old_centroid = cls.centroid.copy()
count += 1
for c in range(cls.k):
scoretable[:, c] = distance(data, cls.centroid[c, :])
#check the number of group
group = scoretable.argmin(axis=1)
if len(np.unique(group)) != cls.k:
cls.centroid = get_randomseed(data, (k, n))
continue
cdata[:, -1] = group
for c in range(k):
cls.centroid[c, :] = \
cdata[np.where(cdata[:, -1]==c)].mean(axis=0)[:-1]
cls.assigneddata = cdata
def regionquery(self, item):
indexes = np.where(distance(self.data, item)<self.eps)
return indexes[0]
