def __init__(self, k, datas):
self.datas = [Vec(data[:-1]) for data in datas]
self.lab = {self.datas[i]: datas[i][-1] for i in range(len(datas))}
Vec.sta(self.datas)
# ini_centers = [self.datas[random.randint(0,len(self.datas)-1)] for i in range(k)]
ini_centers = random.sample(self.datas, k)
while 1:
ini_cls = {cent: [] for cent in ini_centers}
for data in self.datas:
sho_dis, cls = float("inf"), None
for cent in ini_centers:
dis = data.distance(cent)
if dis < sho_dis:
sho_dis, cls = dis, cent
ini_cls[cls].append(data)
new_centers = [
Vec.get_clustercenter(ini_cls[key]) for key in ini_cls
]
if Vec.is_converg(ini_centers, new_centers, 1e-5):
self.cls = ini_cls
break
else:
ini_centers = new_centers
def predict(self, sam: list):
sam = Vec(sam)
dis_dict = {}
for data in self.datas:
dis = sam.distance(data)
if dis not in dis_dict:
dis_dict[dis] = [data]
else:
dis_dict[dis].append(data)
largest_dis = heapq.nlargest(self.k, dis_dict.keys())
kn_datas = []
for dis in largest_dis:
kn_datas.extend(dis_dict[dis])
if len(kn_datas) >= self.k:
break
fre = {}
for data in kn_datas:
fre[self.lab[data]] = fre.get(self.lab[data], 0) + 1
lab, max_fre = None, 0
for k in fre:
if fre[k] > max_fre:
lab, max_fre = k, fre[k]
return lab
def __init__(self, datas: list, k: int):
self.datas = [Vec(data[:-1]) for data in datas]
Vec.sta(self.datas)
self.lab = {self.datas[i]: datas[i][-1] for i in range(len(datas))}
self.k = int(sqrt(len(datas))) if k > sqrt(len(datas)) else k
def rows(self):
l = []
for row in self.mat:
l.append(Vec(row))
return l
def egi_vecs(self):
url = "http://www.yunsuanzi.com/cgi-bin/eigen_decomp.py"
text = egi.send_to_ysz(self.mat, url)
return [Vec(vec) for vec in egi.parse_egi_result(text)[1]]
def orth_by_row(self):
row_vecs = self.rows()
return Mat.construct_by_rows(Vec.orthogonal(row_vecs))
def orth_by_col(self):
col_vecs = self.cols()
return Mat.conconstruct_by_cols(Vec.orthogonal(col_vecs))
def getCol(self, n):
return Vec([self.mat[row][n] for row in range(len(self.mat))])
def getRow(self, n):
return Vec(self.mat[n][:])