盘荣博
JCHPJP 1 year ago
parent 321434f837
commit 2b2079026a

@ -1,2 +0,0 @@
a=[0]*int(2e4)
print(len(a))

@ -1,54 +0,0 @@
from functools import cmp_to_key
c=[[],[]]
string=[[],[]]
for i in range(2):
a = eval(input())
while a>0:
s=input().split()
s[1],s[2]=eval(s[1]),eval(s[2])
c[i].append(s)
string[i].append(s[0])
a-=1
def cmp(a,b):
if a[1]>b[1]:
return -1
elif a[1]==b[1]:
if a[2]<b[2] :
return -1
else:
return 1
else :
return 1
c[0]=sorted(c[0],key=cmp_to_key(cmp))
# print(c[0])
c[1]=sorted(c[1],key=cmp_to_key(cmp))
# print(c[1])
# m='123'
# n='123'
# print(m==n)
st="lzr010506"
s=set(string[0])&set(string[1])
# for i in c[0]:
# for j in c[1]:
# if i[0] == j[0] :
# s.append(j[0])
# print(s)
ans1=0
ans2=0
i=0
while c[0][i][0] != st:
# print(c[0][i][0])
if c[0][i][0] in s:
ans1+=1
i+=1
ans1= i+1-ans1
i=0
while c[1][i][0]!=st:
# print(c[1][i][0])
if c[1][i][0] in s:
ans2+=1
i+=1
ans2=i+1-ans2
# print(ans1,ans2)
print(min(ans1,ans2))
# print(c[0],'\n',c[1])

@ -1,18 +0,0 @@
import heapq
import ctypes
a=int(input())
b=input().split()
for i in range(len(b)):
b[i]=int(b[i])
heapq.heapify(b)
# print(b)
sum=0
while len(b) != 1:
c,d=heapq.nsmallest(2,b)
# heapq.heappop(b)
# heapq.heappop(b)
b.remove(c)
b.remove(d)
sum+=c+d
heapq.heappush(b,c+d)
print(sum)

@ -1,28 +0,0 @@
import functools
a,b =map(int, input().split(' '))
nums=[]
for i in range(b):
m,n=map(int,input().split(' '))
if m>n:continue;
nums.append([m,n])
def cmp(a,b):#升序
if(a[0]>b[0]):
return 1
else :
return -1#不变
nums=sorted(nums,key=functools.cmp_to_key(cmp))
# for i in nums:
# print(i)
sum = 0
start,end=nums[0][0],nums[0][1]
count=1
while count<len(nums):
if nums[count][0]>=start and nums[count][0]<=end:
if end<=nums[count][1]:
end=nums[count][1]
else :
sum+=end-start+1
start=nums[count][0];end=nums[count][1]
count+=1
sum+=end-start+1
print(a-sum+1)

@ -1,7 +0,0 @@
a,b =map(int, input().split(' '))
nums=[]
for i in range(b):
m,n=map(int,input().split(' '))
nums.extend(range(m,n+1))
nums=set(nums)
print(a+1-len(nums))

@ -0,0 +1,68 @@
import pandas as pd
<<<<<<< HEAD
import numpy as np
from scipy.stats import zscore
from sklearn.decomposition import PCA
from scipy.stats import zscore
import matplotlib.pyplot as plt
from matplotlib.pyplot import ylabel
df = pd.read_excel("棉花产量论文作业的数据.xlsx")
# plt.plot(df["年份"],df["单产"])
plt.rcParams['font.sans-serif']="SimHei"
# plt.rcParams['size'] =10
# plt.ylabel('单产')
# plt.xlabel('年份')
# print(df)
d = df.to_numpy()[:,1:]
print(d)
plt.subplot(4,1,1)
plt.scatter(d[:,:1],d[:,1:2],c='r')
ylabel('原始数据'),plt.title("单产和种子费用的关系")
#公式调用标准化,遵守标准正态分布
data = zscore(d)
print(data)
plt.subplot(4,1,2)
plt.scatter(data[:,:1],data[:,1:2],c='b',)
ylabel('zscore')
print(d.max(axis=0))
print(d.std(axis=0))
print(d.mean(axis=0))
#手写标准正态分布
data1=(d-d.mean(axis=0))/d.std(axis=0)
print(data1)
plt.subplot(4,1,3)
plt.scatter(data1[:,:1],data1[:,1:2],c='y')
ylabel('手写标准正态分布')
data2=(d-d.min(axis=0))/(d.max(axis=0)-d.min(axis=0))
plt.subplot(4,1,4)
plt.scatter(data2[:,:1],data2[:,1:2],c='g')
plt.xlabel('压缩到0~1')
print(data==data1)
# plt.savefig("shuju.jpg",dpi=2000)
# plt.show()
md= PCA().fit(data)
cf = np.cov(data.T)#求协方差矩阵
print(cf)
c, d= np.linalg.eig(cf)
print("特征值:\n",c)
print(md.explained_variance_)
e=c/c.sum()
# for _ in range(len(e)):
# if(_!=0):
# e[_]+=e[_-1]
print('贡献率:')
print(e)
print(md.explained_variance_ratio_)
print('特征向量:')
print(d.T)
print(md.components_)
print(md.components_-d.T<=0.1)
plt.savefig("shuju.jpg",dpi=2000)
plt.show()
Loading…
Cancel
Save