我有以下矩阵：
mat <- matrix(1:16, 4, 4)
> mat
[,1] [,2] [,3] [,4]
[1,] 1 5 9 13
[2,] 2 6 10 14
[3,] 3 7 11 15
[4,] 4 8 12 16
我想按照行的顺序将上三角形（没有对角线）转换为向量：如果我是这样的：
> mat1<-as.vector(mat[upper.tri(mat)])
> mat1
[1] 5 9 10 13 14 15
我想按行顺序获取向量（mat1），如下所示： 5,9,13,10,14,15
我使用以下代码。对于相同的随机种子，我希望获得相同的结果。我使用相同的随机种子（在这种情况下为1）并获得不同的结果。这是代码：
import pandas as pd
import numpy as np
from random import seed
# Load scikit's random forest classifier library
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
seed(1) ### <-----
file_path = 'https://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/sonar/sonar.all-data'
dataset2 = pd.read_csv(file_path, header=None, sep=',')
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
#Encoding
y = le.fit_transform(dataset2[60])
dataset2[60] = y
train, test = train_test_split(dataset2, test_size=0.1)
y = train[60]
y_test = test[60]
clf = RandomForestClassifier(n_jobs=100, random_state=0)
features = train.columns[0:59]
clf.fit(train[features], y)
# Apply the Classifier we trained to …我有以下数据框（data6）：
数据6
n S_ID EID VO
1: 1 41883100 1 A1
2: 2 41883100 2 B22
3: 3 41883100 3 C13
4: 4 41883100 4 D18
5: 5 41883100 5 T5-7
6: 6 41883098 1 HJ89
7: 7 41883098 2 I982
8: 8 41884555 1 ZX567
9: 9 41997896 1 TYU12
我想在data6中将其最大EID列值大于每个S_ID的所有行保留在data6中（删除每个S_ID的EID值是1或2）。因此结果如下：
数据6
n S_ID EID VO
1: 1 41883100 1 A1
2: 2 41883100 2 B22
3: 3 41883100 3 C13
4: 4 41883100 4 D18
5: 5 41883100 …我有以下树:
library (data.tree)
data (acme)
t1<-acme
> acme
levelName
1 Acme Inc.
2 ¦--Accounting
3 ¦ ¦--New Software
4 ¦ °--New Accounting Standards
5 ¦--Research
6 ¦ ¦--New Product Line
7 ¦ °--New Labs
8 °--IT
9 ¦--Outsource
10 ¦--Go agile
11 °--Switch to R
我想通过在每个节点名称中添加行数来枚举树节点名称,如下所示:
> t1
levelName
1 Acme Inc._1
2 ¦--Accounting_2
3 ¦ ¦--New Software_3
4 ¦ °--New Accounting Standards_4
5 ¦--Research_5
6 ¦ ¦--New Product Line_6
7 ¦ °--New Labs_7
8 °--IT_8
9 ¦--Outsource_9
10 ¦--Go …我想做以下配对t检验:
str1<-' ENSEMBLE 0.934 0.934 0.934 0.934 '
str2<-' J48 0.934 0.934 0.934 0.934 '
df1 <- read.table(text=scan(text=str1, what='', quiet=TRUE), header=TRUE)
df2 <- read.table(text=scan(text=str2, what='', quiet=TRUE), header=TRUE)
t.test ( df1$ENSEMBLE, df2$J48, mu=0 , alt="two.sided", paired = T, conf.level = 0.95)
我得到以下结果:
Paired t-test
data: df1$ENSEMBLE and df2$J48
t = NaN, df = 3, p-value = NA
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
NaN NaN
sample estimates:
mean of the differences
0 …