只是想知道但是你会如何使用k表示对这个数据集进行聚类？ 我被限制使用任何包或模块。 https://raw.githubusercontent.com/gsprint23/cpts215/master/progassignments/files/simple.csv
这个数据集是对这个数据集的培训
https://raw.githubusercontent.com/gsprint23/cpts215/master/progassignments/files/cancer.csv
一直试图解决这个问题，尝试了几件事，但似乎没有一件事能奏效。不需要代码，但如果有人能给我一个通用的思考过程来解决这个问题，我将非常感激。
这是我目前的思维方式。我试图将他的数据放入热图中 我目前的思考过程是首先随机选择中心。 然后为每个中心的距离创建一个列表列表。 找到每个中心每个点的最小距离索引。 创建与数据集大小相同的数据框，并使用该点最接近的中心索引填充每个元素的每个索引。 通过采用具有相同中心索引的点的平均值来重新计算中心 多次重复此过程，直到索引数据框不更改为止。 创建一个新数据框，并在框架中添加具有相同中心点的点。 然后创建热图。
这似乎不起作用。 只是想知道，我是在正确的轨道还是我完全关闭，如果我在正确的轨道上，我需要更改哪些部分才能解决问题。如果没有，请指出我在正确的轨道上。
以下是要查看的代码
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import math
import random
#%matplotlib inline
def truncate(f, n):
return math.floor(f * 10 ** n) / 10 ** n
def chooseCenter(data, centers):
length = data.shape
cent = []
while len(cent) < centers :
x = random.randrange(0,length[0])
y = random.randrange(0,length[1])
if data.iloc[x][y] not in cent:
d = truncate(data.iloc[x][y],2)
cent.append(d)
return cent
def distance(val, center):
return math.sqrt((val- center)**2)
def getDistances(centers, data):
length = data.shape
dist = []
for i in range(length[0]):
for j in range(length[1]):
y = []
for k in range(len(centers)):
val = distance(data.iloc[i][j], centers[k])
y.append(truncate(val,3))
dist.append(y)
return dist
def findClosest(data, dist):
close = data.copy()
length = close.shape
indexes = []
for i in range(len(dist)):
pt = min(dist[i])
idx = dist[i].index(pt)
indexes.append(idx)
#print(indexes)
length = data.shape
n = np.array(indexes)
n = pd.DataFrame(np.reshape(n, (length[0],length[1])))
#reshape this data frame into the same shape as the data
#keep running the find closest until there is no change
#try heatmap on this?
#this should cluster it, but to make sure test it
#might need to do some tweaking to this
return n
# for i in range(length[0]):
# for j in range(length[1]):
# print('dist[i]', dist[j])
# pt = min(dist[j])
# print(pt)
# idx = dist[j].index(pt)
# close.iloc[i][j] = int(idx)
#return close
def computeNewCenter(data, close):
d = dict()
for i in range(len(close)):
for j in range(len(close[0])):
d[close.iloc[i][j]] = []
for i in range(len(data)):
for j in range(len(data[0])):
if close.iloc[i][j] in d:
d[close.iloc[i][j]].append(data.iloc[i][j])
newCenters = []
for key, value in d.items():
m = np.mean(value)
newCenters.append(truncate(m, 3))
return newCenters
# lst = [[] * numcenters]
# for i in range(len(close)):
# for j in range(len(close[0])):
# if close.iloc[i][j]
def main():
data = np.array(pd.read_csv('https://raw.githubusercontent.com/gsprint23/cpts215/master/progassignments/files/simple.csv', header=None))
data = data.T
#print(data)
df = pd.DataFrame(data[1:], columns=data[0], dtype=float).T
df = df.iloc[::-1]
# print(df)
# print(df.iloc[1][9])
# print(df)
# print(df.iloc[0][1])
# heatmap = plt.pcolor(df, cmap=plt.cm.bwr)
# plt.colorbar(heatmap)
c = chooseCenter(df, 3)
print(c)
#print(len(c))
dist = getDistances(c, df)
#print(dist)
y = findClosest(df, dist)
# q = []
# for i in range(len(c)):
# q.append([])
# #print(q)
j = computeNewCenter(df, y)
#print(j)
length = df.shape
oldFrame = pd.DataFrame(np.ndarray((length[0],length[1])))
oldFrame = oldFrame.fillna(0)
ct=0
while y.equals(oldFrame) == False:
ct+=1
oldFrame = y.copy()
c = computeNewCenter(df, oldFrame)
#print(c)
dist = getDistances(c, df)
#print(dist)
y = findClosest(df, dist)
#print(y)
#plt.pcolor(df, cmap=plt.cm.bwr)
l = []
for i in range(len(y)):
for j in range(len(y[0])):
if y.iloc[i][j] == 1:
l.append(df.iloc[i][j])
for i in range(len(y)):
for j in range(len(y[0])):
if y.iloc[i][j] == 2:
l.append(df.iloc[i][j])
for i in range(len(y)):
for j in range(len(y[0])):
if y.iloc[i][j] == 0:
l.append(df.iloc[i][j])
l = np.ndarray((length[0],length[1]))
l = pd.DataFrame(l)
print(l)
hm = plt.pcolor(l, cmap=plt.cm.bwr)
plt.colorbar(hm)
# print(y)
# print(c)
# print(ct)
#plt.pcolor(y, cmap=plt.cm.bwr)
if __name__ == '__main__':
main()
感谢您阅读