<span>They are made for vehicles that are slow moving or should travel at a low speed so as not to endanger the lives of other drivers on the road, they are signs that prevent bottlenecks with slow vehicles, which for one reason or another, they cannot travel at a higher speed than others cars in the road, this mostly is noticed in cargo trucks carrying heavy cargo during a long journey.</span>
<em>JSX - JavaScript Syntax Extension. JSX is a syntax extension to JavaScript. </em>
<em>Virtual DOM. React keeps a lightweight representation of the “real” DOM in the memory, and that is known as the “virtual” DOM (VDOM)</em>
<em>Performance. ...</em>
<em>Extensions. ...</em>
<em>One-way Data Binding. ...</em>
<em>Debugging. ..</em><em>.</em>
<em>Components. ...</em>
<em>State.</em>
Answer:
showing great care and perseverance.
Answer:
// C program to find sum and average of 10 elements.
#include <stdio.h>
// main function
int main(void) {
// array
double arr[10];
// variables
double average,sum=0;
int i;
// ask to enter 10 elements of the array
printf("Enter 10 elements of the array:");
for(i=0;i<10;i++)
{
// read elements of the array
scanf("%lf", &arr[i]);
// calculate the sum of elements
sum=sum+arr[i];
}
// fint the average
average=sum/10;
// print sum
printf("Sum of 10 elements of the array is:%f ",sum);
// print average
printf("\nAverage of 10 elements of the array is:%f ",average);
return 0;
}
Explanation:
Create an array of size 10.Then read 10 values from user and store them into array.Find the sum of 10 elements of the array and assign it to variable "sum". Find the average by dividing sum with 10.Print the sum and average.
Output:
Enter 10 elements of the array:3 2 4 5 1 4 8 9 12 10
Sum of 10 elements of the array is:58.000000
Average of 10 elements of the array is:5.800000
Answer:
b. the cluster centers for the current iteration are identical to the cluster centers for the previous iteration.
Explanation:
K-mean algorithm is one of the mot widely used algorithm for partitioning into groups of k clusters. This is done by partitioning observations into clusters which are similar to each other. When using k-mean algorithm, each of the different clusters are represented by their centroid and each point are placed only in clusters in which the point is close to cluster centroid.
The K-Means algorithm terminates when the cluster centers for the current iteration are identical to the cluster centers for the previous iteration.
