我有一个形状为 public static void readRecord(String searchTerm,String filepath){
boolean found = false;
boolean toUpdate = false;
String techID="";
String service="";
String firstName="";
String lastName="";
String salary="";
String position="";
String password="";
try
{
Scanner x = new Scanner(new File(filepath));
x.useDelimiter("[\\|]");
while(x.hasNext()&& !found)
{
techID = x.next();
service=x.next();
firstName=x.next();
lastName=x.next();
salary=x.next();
position=x.next();
password=x.next();
if(techID.contains(searchTerm)){
found = true;
if(!techID.equals(searchTerm)) {
toUpdate = true;
}
}
}
if(found)
{
if(toUpdate) {
techID = techID.substring(4, techID.length());
}
System.out.print("ID: "+techID+"\n"+"Service : "+service+"\n"+"First Name: "+firstName+"\n"+"Last Name : "+lastName+"\n" + "Salary : "+salary
+"\n" + "Position : "+position);
}
else
{
System.out.print("ID not found");
}
x.close();
}
catch(Exception e)
{
e.printStackTrace();
}
finally {
}
}
}
的1d numpy ndarray。
Calendar.getInstance().get(Calendar.YEAR) - employmentYear >= n
我想将其转换为形状为(1,2)的ndarray，使其看起来像这样；
nd = array[1,0]
(n,2)中有nd_new = [
[1,0]
[1,0]
[1,0]
...
...
[1,0]
]
行。
答案 0 :(得分：1)
您可以按以下方式使用np.tile：
>>> np.tile(nd, (n, 1))
答案 1 :(得分：0)
我会回答我自己的问题。使用来自keras的一种热门编码工具。
from keras.utils import to_categorical
y_nd_ones = [1] * n
y_nd = to_categorical(y_nd_ones)