I have implemented a quick sort algorithm in Dart which sorts a list of random integers in the range from 0 to 100. Most of the times, it throws a 'not in inclusive range' exception. Other times, it works. I don't understand what is wrong with my code or logic here.
import 'dart:math';
List<int> list = [];
void main() {
randomize();
quickSort(0, list.length - 1);
print(list);
}
void randomize() {
for (int i = 0; i < 100; ++i) {
list.add(Random().nextInt(100));
}
}
void quickSort(int l, int h) {
if (l < h) {
int mid = partition(l, h);
quickSort(l, mid - 1);
quickSort(mid + 1, h);
}
}
int partition(int l, int h) {
int pivot = l;
int i = l;
int j = h;
while (i < j) {
while (list[i] <= list[pivot]) {
++i;
}
while (list[j] > list[pivot]) {
--j;
}
if (i < j) {
int temp = list[i];
list[i] = list[j];
list[j] = temp;
}
}
int temp = list[pivot];
list[pivot] = list[j];
list[j] = temp;
return j;
}
I'm trying to write a code which print the sum of odd and even numbers from a group of inputs, but it doesn't work and I think it's a null safety issue.
This is the code
void main() {
List numbers = [1, 2, 3, 4];
List odd = [];
List even = [];
for (int i = 0; i < numbers.length; i++) {
var z = numbers[i] % 2;
if (z == 0) {
even.add(numbers[i]);
} //end if
else {
odd.add(numbers[i]);
} //end else
} //end for loop
int sumOdd = 0;
int sumEven = 0;
for (int i = 0; i < odd.length; i++) {
sumOdd = sumOdd + odd[i];
}//end for
for (int i = 0; i < even.length; i++) {
sumEven = sumEven + even[i];
}//end for
print("sum of odds numbers = $sumOdd");
print("sum of even numbers = $sumEven");
} //end main
i think its because you not specify the type of list.
by default it will define as a list of number.
but int sumOdd = 0; you define as integer.
thats why you got error when run here : sumOdd = sumOdd + odd[i];
sumOdd is integer
odd[i] is number .
solution:
List<int> numbers = [1, 2, 3, 4];
List <int> even =[];
List<int> odd =[];
second solution
you can parse odd[i] to integer
May I know how to store the N prime numbers, which I got from for loop in an array in dart?
import 'dart:io';
void main() {
// print('enter a start number');
// int a = int.parse(stdin.readLineSync()!);
print('enter a number');
int b = int.parse(stdin.readLineSync());
print('this are prime numbers');
primenum(b);
var z = '';
}
primenum(b) {
String string = "";
int a = 2;
outerLoop:
for (int i = a; i <= b; i++) {
for (int x = 2; x <= i / a; x++) {
if (i % x == 0) {
continue outerLoop;
}
}
var z = i.toString();
// print(z);
var h = z;
// String str = '';
string = string + h;
}
List d = string.split('');
print(d);
}
Using the above code, I am able to get those numbers in List. But the double-digit numbers are splitting.
May I know How to solve the above task? using dart.
The way you're doing string.split is splitting the string into a list of each individual character. Instead, you can add each prime number to a List directly without doing string manipulation.
primenum(b) {
List<String> d;
int a = 2;
outerLoop:
for (int i = a; i <= b; i++) {
for (int x = 2; x <= i / a; x++) {
if (i % x == 0) {
continue outerLoop;
}
}
d.add(i.toString());
}
print(d);
}
Below is a solution Network delay problem of leetcode. I have written a all test case success solution. But not able to analyse the time complexity. I believe its O(V^2 + E) where V is the number of nodes and E edges.
In this solution though I am adding all adjacents of each node every time, but not processing them further if there exists a min distance for that node already.
Leetcode question link https://leetcode.com/problems/network-delay-time
public int networkDelayTime(int[][] times, int n, int k) {
int[] distances = new int[n+1];
Arrays.fill(distances , -1);
if(n > 0){
List<List<int[]>> edges = new ArrayList<List<int[]>>();
for(int i = 0 ; i <= n ; i++){
edges.add(new ArrayList<int[]>());
}
for(int[] time : times){
edges.get(time[0]).add(new int[]{time[1] , time[2]});
}
Queue<Vertex> queue = new LinkedList<>();
queue.add(new Vertex(k , 0));
while(!queue.isEmpty()){
Vertex cx = queue.poll();
int index = cx.index;
int distance = cx.distance;
//process adjacents only if distance is updated
if(distances[index] == -1 || distances[index] > distance){
distances[index] = distance;
List<int[]> adjacents = edges.get(index);
for(int[] adjacent : adjacents){
queue.add(new Vertex(adjacent[0] , adjacent[1]+distance));
}
}
}
}
int sum = 0;
for(int i = 1 ; i <= n; i++){
int distance = distances[i];
if(distance == -1){
return -1;
}
sum = Math.max(sum , distance);
}
return sum;
}
public static class Vertex{
int index;
int distance;
public Vertex(int i , int d){
index = i;
distance = d;
}
}
You should use PriorityQueue instead of LinkedList
I am trying to understand the LASSO algorithm for linear regression. I have implemented the algorithm using naive coordinate descent method for optimization. However the coefficients that I obtained from my code, wasn't matching with those obtained from the 'glmnet'package for LASSO in R. I wanted to understand how I could make the algorithm more accurate, so that the coefficients match with those obtained from R. I think they use coordinate descent as well.
Note: I have generated some toy data with 11 observations, and 6
features(x,x^2 ,x^3,...,x^6). The last column contains the y values
generated from a dummy function (e^(-x^2)). I wanted to use LASSO to
estimate this function. Also, I have randomly picked the initial
weight vector, multiple times to crosscheck my results.
Here is my code:
#include<stdio.h>
#include<stdlib.h>
#include<string.h>
#include<math.h>
#include<time.h>
int num_dim = 6;
int num_obs = 11;
/*Computes the normalization factor*/
float norm_feature(int j,double arr[][7],int n){
float sum = 0.0;
int i;
for(i=0;i<n;i++){
sum = sum + pow(arr[i][j],2);
}
return sum;
}
/*Computes the partial sum*/
float approx(int dim,int d_ignore,float weights[],double arr[][7],int
i){
int flag = 1;
if(d_ignore == -1)
flag = 0;
int j;
float sum = 0.0;
for(j=0;j<dim;j++){
if(j != d_ignore)
sum = sum + weights[j]*arr[i][j];
else
continue;
}
return sum;
}
/* Computes rho-j */
float rho_j(double arr[][7],int n,int j,float weights[7]){
float sum = 0.0;
int i;
float partial_sum ;
for(i=0;i<n;i++){
partial_sum = approx(num_dim,j,weights,arr,i);
sum = sum + arr[i][j]*(arr[i][num_dim]-partial_sum);
}
return sum;
}
float intercept(float arr1[7],double arr[][7],int dim) {
int i;
float sum =0.0;
for (i = 0; i < num_obs; i++) {
sum = sum + pow((arr[i][num_dim]) - approx(num_dim, -1, arr1, arr,
i), 1);
}
return sum;
}
int main(){
double data[num_obs][7];
int i=0,j=0;
float a = 1.0;
float lambda = 0.1; //Setting lambda
float weights[7]; //weights[6] contains the intercept
srand((unsigned int) time(NULL));
/*Generating the data matrix */
for(i=0;i<11;i++)
data[i][0] = ((float)rand()/(float)(RAND_MAX)) * a;
for(i=0;i<11;i++)
for(j=1;j<6;j++)
data[i][j] = pow(data[i][0],j+1);
for(i=0;i<11;i++)
data[i][6] = exp(-pow(data[i][0],2)); // the last column in the
datamatrix contains the y values generated by the dummy function
/*Printing the data matrix */
printf("Data Matrix:\n");
for(i=0;i<11;i++){
for(j=0;j<7;j++){
printf("%lf ",data[i][j]);}
printf("\n");}
printf("\n");
int seed =0;
while(seed<20) {
//Initializing the weight vector
for (i = 0; i < 7; i++)
weights[i] = ((float) rand() / (float) (RAND_MAX)) * a;
int iter = 500;
int t = 0;
int r, l;
double rho[num_dim];
for (i = 0; i < 6; i++) {
rho[i] = rho_j(data, num_obs, r, weights);
}
// Intercept initialization
weights[num_dim] = intercept(weights,data,num_dim);
printf("Weights initialization: ");
for (i = 0; i < (num_dim+1); i++)
printf("%f ", weights[i]);
printf("\n");
while (t < iter) {
for (r = 0; r < num_dim; r++) {
rho[r] = rho_j(data, num_obs, r, weights);
//printf("rho %d:%f ",r,rho[r]);
if (rho[r] < -lambda / 2)
weights[r] = (rho[r] + lambda / 2) / norm_feature(r,
data, num_obs);
else if (rho[r] > lambda / 2)
weights[r] = (rho[r] - lambda / 2) / norm_feature(r,
data, num_obs);
else
weights[r] = 0;
weights[num_dim] = intercept(weights, data, num_dim);
}
/* printf("Iter(%d): ", t);
for (l = 0; l < 7; l++)
printf("%f ", weights[l]);
printf("\n");*/
t++;
}
//printf("\n");
printf("Final Weights: ");
for (i = 0; i < 7; i++)
printf("%f ", weights[i]);
printf("\n");
printf("\n");
seed++;
}
return 0;
}
PseudoCode: