我有以下代码,使用Keras Scikit-Learn Wrapper:
from keras.models import Sequential
from sklearn import datasets
from keras.layers import Dense
from sklearn.model_selection import train_test_split
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
from sklearn import preprocessing
import pickle
import numpy as np
import json
def classifier(X, y):
"""
Description of classifier
"""
NOF_ROW, NOF_COL = X.shape
def create_model():
# create model
model = Sequential()
model.add(Dense(12, input_dim=NOF_COL, init='uniform', activation='relu'))
model.add(Dense(6, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', …例如,我有以下数据框.我想要做的是在该数据框中添加另一列(第7列).条件是if Sepal.Length >=5 assign "UP" else assign "DOWN".我们称之为"监管"栏目.
> iris
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5.0 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
...
在R中这样做的方法是什么？
我使用以下命令运行Java代码:
$ java -Xms4G -Xmx4G myjavacode
我的CPU的RAM容量是6GB.
但它总是无法执行给我这个错误消息:
Invalid initial heap size: -Xms5G
The specified size exceeds the maximum representable size.
Could not create the Java virtual machine
有没有办法设置Java选项,以便我们可以执行代码？
Update3: 如果你喜欢这个帖子,请不要赞成我,但请通过下面的DVK赞助天才答案.
我有以下子程序:
use warnings;
#Input
my @pairs = (
"fred bill",
"hello bye",
"hello fred",
"foo bar",
"fred foo");
#calling the subroutine
my @ccomp = connected_component(@pairs);
use Data::Dumper;
print Dumper \@ccomp;
sub connected_component {
my @arr = @_;
my %links;
foreach my $arrm ( @arr ) {
my ($x,$y) = split(/\s+/,$arrm);;
$links{$x}{$y} = $links{$y}{$x} = 1;
}
my %marked; # nodes we have already visited
my @stack;
my @all_ccomp;
for my $node (sort keys %links) {
next if exists …我有以下数据框只是单列.
import pandas as pd
tdf = pd.DataFrame({'s1' : [0,1,23.4,10,23]})
目前它具有以下形状.
In [54]: tdf.shape
Out[54]: (5, 1)
如何将其转换为系列或numpy向量,以便形状简单 (5,)
我尝试用Unix排序对这些数字进行排序,但它似乎不起作用:
2e-13
1e-91
2e-13
1e-104
3e-19
9e-99
这是我的命令:
sort -nr file.txt
什么是正确的方法呢？
我有包含单列和两列的数据行.我想要做的是提取只包含2列的行.
0333 foo
bar
23243 qux
只屈服:
0333 foo
23243 qux
请注意,它们是制表符分隔的,即使对于只有一列的行,您在开头有选项卡.
这样做的方法是什么？
我试过这个却失败了:
awk '$1!="";{print $1 "\t" $2}' myfile.txt
enter code here
我有一个数据,总是以下列格式(称为FASTQ)以四块为单位:
@SRR018006.2016 GA2:6:1:20:650 length=36
NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNGN
+SRR018006.2016 GA2:6:1:20:650 length=36
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!+!
@SRR018006.19405469 GA2:6:100:1793:611 length=36
ACCCGCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC
+SRR018006.19405469 GA2:6:100:1793:611 length=36
7);;).;);;/;*.2>/@@7;@77<..;)58)5/>/
是否有一种简单的sed/awk/bash方式将它们转换为这种格式(称为FASTA):
>SRR018006.2016 GA2:6:1:20:650 length=36
NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNGN
>SRR018006.19405469 GA2:6:100:1793:611 length=36
ACCCGCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC
原则上,我们想要在每个块中提取前两行并替换@为>.
我有以下代码:
import pandas as pd
import matplotlib
matplotlib.style.use('ggplot')
df = pd.DataFrame({ 'sample1':['foo','bar','bar','qux'], 'score':[5,9,1,7]})
sum_df = df.groupby("sample1").sum()
pie = sum_df.plot(kind="pie", figsize=(6,6), legend = False, use_index=False, subplots=True, colormap="Pastel1")
这就是饼图.我想要做的是将它保存到文件中.但为什么这会失败？
fig = pie.get_figure()
fig.savefig("~/Desktop/myplot.pdf")
我收到此错误:
'numpy.ndarray' object has no attribute 'get_figure'
我有以下代码,使用Keras Scikit-Learn Wrapper,它工作正常:
from keras.models import Sequential
from keras.layers import Dense
from sklearn import datasets
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
import numpy as np
def create_model():
# create model
model = Sequential()
model.add(Dense(12, input_dim=4, init='uniform', activation='relu'))
model.add(Dense(6, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
def main():
"""
Description of main
"""
iris = datasets.load_iris()
X, y = iris.data, iris.target
NOF_ROW, NOF_COL = X.shape
# evaluate …