Answer:
A). Using a flowchart, show the algorithm for the car collision avoidance system.
Explanation:
Answer:
- public class Main {
-
- public static void main (String [] args) {
- int[][] myArray = {{1,5,6}, {7, 9, 2}};
- fixArray(myArray, 1, 2, 12);
-
- System.out.println(myArray[1][2]);
- }
-
-
- private static void fixArray(int[][] array, int row, int col, int value){
- array[row][col] = value;
- }
- }
Explanation:
The solution code is written in Java.
Firstly, create the method fixArray with that takes four inputs, array, row, col and value (Line 11). Within the method body, use row and col as index to address a particular element from array and set the input value to it (Line 12).
Next, we test the method in the main program using a sample array (Line 4) and we try to change the row-1 and col-2 element from 2 to 12 (Line 5).
The print statement in Line 7 will display 12 in console.
Answer: Constructors can specify parameters but not return types.
Explanation:
public class Student {
int roll_no;
public Student(int a) {
roll_no = a;
}
public static void main(String[] args) {
Student abs = new Student(10);
System.out.println(abc.roll_no);
}
}
In the above code we have illustrated the working of constructors. We have a class with the name Student. then a constructor is created of the class called as the class constructor. In the main we create an object of the class and with this object we invoke the constructor and also pass a parameter. Here in the code we are passing the roll no of the student.
So we can say that constructor is called during the runtime when the object created invokes the constructor so a constructor can have many arguments but it does not have a return type.
Answer:
The answer is nearest-neighbor learning.
because nearest neighbor learning is classification algorithm.
It is used to identify the sample points that are separated into different classes and to predict that the new sample point belongs to which class.
it classify the new sample point based on the distance.
for example if there are two sample points say square and circle and we assume some center point initially for square and circle and all the other points are added to the either square or circle cluster based on the distance between sample point and center point.
while the goal of decision tree is to predict the value of the target variable by learning some rules that are inferred from the features.
In decision tree training data set is given and we need to predict output of the target variable.
Explanation:
