I have these 2 values:
decimal totalAmountDue = 1332.29m;
short installmentCount = 3;
I want to create 3 installments that have an even amount based on the totalAmountDue (extra pennies apply starting with the lowest installment number going to the highest installment number) using this class:
public class Installment
{
public Installment( short installmentNumber, decimal amount )
{
InstallmentNumber = installmentNumber;
Amount = amount;
}
public short InstallmentNumber { get; private set; }
public decimal Amount { get; private set; }
}
The installments should be as follows:
{ InstallmentNumber = 1, Amount = 444.10m }
{ InstallmentNumber = 2, Amount = 444.10m }
{ InstallmentNumber = 3, Amount = 444.09m }
I am looking for an interesting way to create my 3 installments. Using a simple LINQ to objects method would be nice. I have been trying to understand more about functional programming lately and this seems like it could be a fairly good exercise in recursion. The only decent way I can think of doing this is with a traditional while or for loop at the moment...
There's not a whole lot here that is "functional". I would approach the problem like this:
var pennies = (totalAmountDue * 100) % installmentCount;
var monthlyPayment = totalAmountDue / installmentCount;
var installments = from installment in Enumerable.Range(1, installmentCount)
let amount = monthlyPayment + (Math.Max(pennies--, 0m) / 100)
select new Installment(installment, amount);
You might be able to work something out where you constantly subtract the previous payment from the total amount and do the division rounding up to the nearest penny. In F# (C# is too wordy for this) it might be something like:
let calculatePayments totalAmountDue installmentCount =
let rec getPayments l (amountLeft:decimal) = function
| 0 -> l
| count -> let paymentAmount =
(truncate (amountLeft / (decimal)count * 100m)) / 100m
getPayments (new Installment(count, paymentAmount)::l)
(amountLeft - paymentAmount)
(count - 1)
getPayments [] totalAmountDue installmentCount
For those unfamiliar with F#, what that code is doing is setting up a recursive function (getPayments) and bootstrapping it with some initial values (empty list, starting values). Using match expressions it sets up a terminator (if installmentCount is 0) returning the list so far. Otherwise it calculates the payment amount and calls the recursive method adding the new installment to the front of the list, subtracting the payment amount from the amount left, and subtracting the count.
This is actually building the list in reverse (adding on to the front each time), so we throw away the extra pennies (the truncate) and eventually it catches up with us so the penny rounding works as expected. This is obviously more math intensive than the add/subtract code above since we divide and multiply in every iteration. But it is fully recursive and takes advantage of tail recursion so we'll never run out of stack.
The trouble with C# here is that you want a sequence of installments and recursion and there's no idiomatic built-in structure for doing that in C#. Here I'm using F#'s list which is immutable and O(1) operation to prepend.
You could possibly build something using the Scan() method in the Reactive Extensions to pass state from once instance to another.
Talljoe,
I think you are pushing me in the right direction. This code below seems to work. I had to switch out how the penny math was working but this looks pretty good (I think)
decimal totalAmountDue = 1332.29m;
short installmentCount = 8;
var pennies = (totalAmountDue * 100) % installmentCount;
var monthlyPayment = Math.Floor(totalAmountDue / installmentCount * 100);
var installments = from installmentNumber in Enumerable.Range(1, installmentCount)
let extraPenny = pennies-- > 0 ? 1 : 0
let amount = (monthlyPayment + extraPenny) / 100
select new Installment(installmentNumber, amount);
Related
Example 1:
``
Given S="300.01" and B-["300.00", "200.00*,*100.00"].
R[0]="150.00" (=300.01 300.00/600.00) R[1]="100.00" (=150.01* 200.00/300.00)
R[2]="50.01" (=50.01*100.00/100.00)
Example 2 (Pay careful attention to this one).
Given S="1.00" and B=["0.05","1.00"]. 1. First we consider 1.00 because it is the largest,
a. 1.00*1.00/1.05~0.95238...
b. Round 0.95238... to "0.95". Rounding down to carry pennies to smaller departments. c. Set R[1]=0.95. Notice, this is in the same place as 1.00. It is the 2nd value in the result! 2. Now we have 0.05 left
Next we look at the smaller B[0]=0.05 department
a. 0.05 0.05/0.05 = 0.05 b. No rounding required
c. Set R[0]=0.05. R=["0.05", "0.95"]
`
Write a function:
class Solution { public String[] solution(String 5, String[] B); }
that, given a string S representing the total excess billables and an array B consisting of K strings representing the undiscounted bills for each customer. The return value should be an array of strings R (length M) in the same order as B representing the amount of the discount to each customer.
Notes:
The total S should be completely refunded. Neither more nor less than S should be
returned. Don't lose or gain a penny!
Be careful with the types you choose to represent currencies. Floating points numbers are notoriously error prone for precise calculations with currencies.
Test Output
Amounts should be rounded down to the nearest $0.01. By design, fractional pennies are pushed to groups with smaller unadjusted invoices.
Results should be represented with 2 decimal places of precision as strings, even if these are both zeroes. ex. "100.00" 5. You may assume sensible inputs. The total to be discounted will never exceed the total of the
unadjusted invoices.
Please do pay attention to returning the discounts in the same order as the incoming invoices.
Answer:::
def solution(A):
answer = 0
current_sum = 0
#Currently there is one empty subarray with sum 0
prefixSumCount = {0:1}
for list_element in A:
current_sum = current_sum + list_element
if current_sum in prefixSumCount:
answer = answer + prefixSumCount[current_sum]
if current_sum not in prefixSumCount:
prefixSumCount[current_sum] = 1
else:
prefixSumCount[current_sum] = prefixSumCount[current_sum] + 1
if answer > 1000000000:
return -1
else:
return answer
#Sample run
A = [2,-2,3,0,4,-7]
print(solution(A))
strong text
Find my solution for JavaScript
You can avoid function sumArray and use the sum funciton with reducer within solution function.
enter code here
function solution(S, B) {
// write your code in JavaScript (Node.js 8.9.4)
let copyArray=[...B];
let solutionObj={};
//ordered array to consider last first
copyArray.sort();
//calculating sum of values within array
let sumArray=B.reduce((acc,value)=> acc+Number(value),0);
//calculating values of array
//loop for ading on to solvin array
let initial=S;
for(i=copyArray.length-1;i>=0;i--){
//obtaining index of value addded to solution array
let index=B.indexOf(copyArray[i]);
let value=initial*copyArray[i]/sumArray;
value=i==0?Math.ceil(value*100)/100:Math.floor(value*100)/100;
solutionObj[index]=value.toFixed(2);
}
return Object.values(solutionObj) ;
}
console.log(solution(300.01,["300.00","200.00","100.00"]))
console.log(solution(1.00,["0.05","1.00"]))
These are the resulting entries
Solution in java for the same coding exercise
public String[] solution(String S, String[] B) {
List<Double> list = Arrays.stream(B).sorted(Comparator.comparingDouble(v->Double.valueOf((String) v)).reversed()).map(Double::parseDouble).collect(Collectors.toList());
Double S1 = Double.valueOf(S);
String R[] = new String[B.length];
Double total = 0.00;
for (int i = 0; i< list.size(); i++){
Double individualValue = list.get(i);
Double sumTotal = 0.0;
for(int j = i+1;j < list.size(); j++){
sumTotal+=list.get(j);
}
BigDecimal data = new BigDecimal(S1 * (individualValue / (individualValue + sumTotal)));
data = data.setScale(2, RoundingMode.FLOOR);
total+=data.doubleValue();
R[i] = String.valueOf(data);
S1 = S1 - Double.valueOf(R[i]);
}
Double diff = new BigDecimal(Double.valueOf(S) - total).setScale(2, RoundingMode.HALF_DOWN).doubleValue();
if (diff > 0) {
R[B.length - 1] = String.valueOf(Double.valueOf(R[B.length - 1]) + diff);
}
return R;
}
In my game,if I touch a particular object,coin objects will come out of them at random speeds and occupy random positions.
public void update(delta){
if(isTouched()&& getY()<Constants.WORLD_HEIGHT/2){
setY(getY()+(randomSpeed * delta));
setX(getX()-(randomSpeed/4 * delta));
}
}
Now I want to make this coins occupy positions in some patterns.Like if 3 coins come out,a triangle pattern or if 4 coins, rectangular pattern like that.
I tried to make it work,but coins are coming out and moved,but overlapping each other.Not able to create any patterns.
patterns like:
This is what I tried
int a = Math.abs(rndNo.nextInt() % 3)+1;//no of coins
int no =0;
float coinxPos = player.getX()-coins[0].getWidth()/2;
float coinyPos = player.getY();
int minCoinGap=20;
switch (a) {
case 1:
for (int i = 0; i < coins.length; i++) {
if (!coins[i].isCoinVisible() && no < a) {
coins[i].setCoinVisible(true);
coinxPos = coinxPos+rndNo.nextInt()%70;
coinyPos = coinyPos+rndNo.nextInt()%70;
coins[i].setPosition(coinxPos, coinyPos);
no++;
}
}
break;
case 2:
for (int i = 0; i < coins.length; i++) {
if (!coins[i].isCoinVisible() && no < a) {
coins[i].setCoinVisible(true);
coinxPos = coinxPos+minCoinGap+rndNo.nextInt()%70;
coinyPos = coinyPos+rndNo.nextInt()%150;
coins[i].setPosition(coinxPos, coinyPos);
no++;
}
}
break:
......
......
default:
break;
may be this is a simple logic to implement,but I wasted a lot of time on it and got confused of how to make it work.
Any help would be appreciated.
In my game, when I want some object at X,Y to reach some specific coordinates Xe,Ye at every frame I'm adding to it's coordinates difference between current and wanted position, divided by constant and multiplied by time passed from last frame. That way it starts moving quickly and goes slowly and slowly as it's closer, looks kinda cool.
X += ((Xe - X)* dt)/ CONST;
Y += ((Ye - Y)* dt)/ CONST;
You'll experimentally get that CONST value, bigger value means slower movement. If you want it to look even cooler you can add velocity variable and instead of changing directly coordinates depending on distance from end position you can adjust that velocity. That way even if object at some point reaches the end position it will still have some velocity and it will keep moving - it will have inertia. A bit more complex to achieve, but movement would be even wilder.
And if you want that Xe,Ye be some specific position (not random), then just set those constant values. No need to make it more complicated then that. Set like another constat OFFSET:
static final int OFFSET = 100;
Xe1 = X - OFFSET; // for first coin
Ye1 = Y - OFFSET;
Xe2 = X + OFFSET; // for second coin
Ye2 = Y - OFFSET;
...
I'm trying to get a mobile robot to map an arena based on what it can see from a camera. I've created a map, and managed to get the robot to identify items placed in the arena and give an estimated location, however, as I'm only using an RGB camera the resulting numbers can vary slightly ever frame due to noise, or change in lighting, etc. What am now trying to do is create a probability map using Bayes' formula to give a better map of the arena.
Bayes' Formula
P(i | x) = (p(i)p(x|i))/(sum(p(j)(p(x|j))
This is what I've got so far. All points on the map are initialised to 0.5.
// Gets the Likely hood of the event being correct
// Para 1 = Is the object likely to be at that location
// Para 2 = is the sensor saying it's at that location
private double getProbabilityNum(bool world, bool sensor)
{
if (world && sensor)
{
// number to test the function works
return 0.6;
}
else if (world && !sensor)
{
// number to test the function works
return 0.4;
}
else if (!world && sensor)
{
// number to test the function works
return 0.2;
}
else //if (!world && !sensor)
{
// number to test the function works
return 0.8;
}
}
// A function to update the map's probability of an object being at location (x,y)
// Para 3 = does the sensor pick up the an object at (x,y)
public double probabilisticMap(int x,int y,bool sensor)
{
// gets the current likelihood from the map (prior Probability)
double mapProb = get(x,y);
//decide if object is at location (x,y)
bool world = (mapProb < threshold);
//Bayes' formula to update the probability
double newProb =
(getProbabilityNum(world, sensor) * mapProb) / ((getProbabilityNum(world, sensor) * mapProb) + (getProbabilityNum(!world, sensor) * (1 - mapProb)));
// update the location on the map
set(x,y,newProb);
// return the probability as well
return newProb;
}
It does work, but the numbers seem to jump rapidly, and then flicker when they are at the top, it also errors if the numbers drop too near to zero. Anyone have any idea why this might be happening? I think it's something to do with the way the equations is coded, but I'm not too sure. (I found this, but I don't quite understand it, so I'm not sure of it's relevents, but it seems to be talking about the same thing
Thanks in Advance.
Use log-likelihoods when doing numerical computations involving probabilities.
Consider
P(i | x) = (p(i)p(x|i))/(sum(p(j)(p(x|j)).
Because x is fixed, the denominator, p(x), is a constant. Thus
P(i | x) ~ p(i)p(x|i)
where ~ denotes "is proportional to."
The log-likelihood function is just the log of this. That is,
L(i | x) = log(p(i)) + log(p(x|i)).
Ok, so I have a histogram (represented by an array of ints), and I'm looking for the best way to find local maxima and minima. Each histogram should have 3 peaks, one of them (the first one) probably much higher than the others.
I want to do several things:
Find the first "valley" following the first peak (in order to get rid of the first peak altogether in the picture)
Find the optimum "valley" value in between the remaining two peaks to separate the picture
I already know how to do step 2 by implementing a variant of Otsu.
But I'm struggling with step 1
In case the valley in between the two remaining peaks is not low enough, I'd like to give a warning.
Also, the image is quite clean with little noise to account for
What would be the brute-force algorithms to do steps 1 and 3? I could find a way to implement Otsu, but the brute-force is escaping me, math-wise. As it turns out, there is more documentation on doing methods like otsu, and less on simply finding peaks and valleys. I am not looking for anything more than whatever gets the job done (i.e. it's a temporary solution, just has to be implementable in a reasonable timeframe, until I can spend more time on it)
I am doing all this in c#
Any help on which steps to take would be appreciated!
Thank you so much!
EDIT: some more data:
most histogram are likely to be like the first one, with the first peak representing background.
Use peakiness-test. It's a method to find all the possible peak between two local minima, and measure the peakiness based on a formula. If the peakiness higher than a threshold, the peak is accepted.
Source: UCF CV CAP5415 lecture 9 slides
Below is my code:
public static List<int> PeakinessTest(int[] histogram, double peakinessThres)
{
int j=0;
List<int> valleys = new List<int> ();
//The start of the valley
int vA = histogram[j];
int P = vA;
//The end of the valley
int vB = 0;
//The width of the valley, default width is 1
int W = 1;
//The sum of the pixels between vA and vB
int N = 0;
//The measure of the peaks peakiness
double peakiness=0.0;
int peak=0;
bool l = false;
try
{
while (j < 254)
{
l = false;
vA = histogram[j];
P = vA;
W = 1;
N = vA;
int i = j + 1;
//To find the peak
while (P < histogram[i])
{
P = histogram[i];
W++;
N += histogram[i];
i++;
}
//To find the border of the valley other side
peak = i - 1;
vB = histogram[i];
N += histogram[i];
i++;
W++;
l = true;
while (vB >= histogram[i])
{
vB = histogram[i];
W++;
N += histogram[i];
i++;
}
//Calculate peakiness
peakiness = (1 - (double)((vA + vB) / (2.0 * P))) * (1 - ((double)N / (double)(W * P)));
if (peakiness > peakinessThres & !valleys.Contains(j))
{
//peaks.Add(peak);
valleys.Add(j);
valleys.Add(i - 1);
}
j = i - 1;
}
}
catch (Exception)
{
if (l)
{
vB = histogram[255];
peakiness = (1 - (double)((vA + vB) / (2.0 * P))) * (1 - ((double)N / (double)(W * P)));
if (peakiness > peakinessThres)
valleys.Add(255);
//peaks.Add(255);
return valleys;
}
}
//if(!valleys.Contains(255))
// valleys.Add(255);
return valleys;
}
While coding Euler problems, I ran across what I think is bizarre:
The method toString.map is slower than toString.toArray.map.
Here's an example:
def main(args: Array[String])
{
def toDigit(num : Int) = num.toString.map(_ - 48) //2137 ms
def toDigitFast(num : Int) = num.toString.toArray.map(_ - 48) //592 ms
val startTime = System.currentTimeMillis;
(1 to 1200000).map(toDigit)
println(System.currentTimeMillis - startTime)
}
Shouldn't the method map on String fallback to a map over the array? Why is there such a noticeable difference? (Note that increasing the number even causes an stack overflow on the non-array case).
Original
Could be because toString.map uses the WrappedString implicit, while toString.toArray.map uses the WrappedArray implicit to resolve map.
Let's see map, as defined in TraversableLike:
def map[B, That](f: A => B)(implicit bf: CanBuildFrom[Repr, B, That]): That = {
val b = bf(repr)
b.sizeHint(this)
for (x <- this) b += f(x)
b.result
}
WrappedString uses a StringBuilder as builder:
def +=(x: Char): this.type = { append(x); this }
def append(x: Any): StringBuilder = {
underlying append String.valueOf(x)
this
}
The String.valueOf call for Any uses Java Object.toString on the Char instances, possibly getting boxed first. These extra ops might be the cause of speed difference, versus the supposedly shorter code paths of the Array builder.
This is a guess though, would have to measure.
Edit
After revising, the general point still stands, but the I referred the wrong implicits, since the toDigit methods return an Int sequence (or like), not a translated string as I misread.
toDigit uses LowPriorityImplicits.fallbackStringCanBuildFrom[T]: CanBuildFrom[String, T, immutable.IndexedSeq[T]], with T = Int, which just defers to a general IndexedSeq builder.
toDigitFast uses a direct Array implicit of type CanBuildFrom[Array[_], T, Array[T]], which is unarguably faster.
Passing the following CBF for toDigit explicitly makes the two methods on par:
object FastStringToArrayBuild {
def canBuildFrom[T : ClassManifest] = new CanBuildFrom[String, T, Array[T]] {
private def newBuilder = scala.collection.mutable.ArrayBuilder.make()
def apply(from: String) = newBuilder
def apply() = newBuilder
}
}
You're being fooled by running out of memory. The toDigit version does create more intermediate objects, but if you have plenty of memory then the GC won't be heavily impacted (and it'll all run faster). For example, if instead of creating 1.2 million numbers, I create 12k 100x in a row, I get approximately equal times for the two methods. If I create 1.2k 5-digit numbers 1000x in a row, I find that toDigit is about 5% faster.
Given that the toDigit method produces an immutable collection, which is better when all else is equal since it is easier to reason about, and given that all else is equal for all but highly demanding tasks, I think the library is as it should be.
When trying to improve performance, of course one needs to keep all sorts of tricks in mind; one of these is that arrays have better memory characteristics for collections of known length than do the fancy collections in the Scala library. Also, one needs to know that map isn't the fastest way to get things done; if you really wanted this to be fast you should
final def toDigitReallyFast(num: Int, accum: Long = 0L, iter: Int = 0): Array[Byte] = {
if (num==0) {
val ans = new Array[Byte](math.max(1,iter))
var i = 0
var ac = accum
while (i < ans.length) {
ans(ans.length-i-1) = (ac & 0xF).toByte
ac >>= 4
i += 1
}
ans
}
else {
val next = num/10
toDigitReallyFast(next, (accum << 4) | (num-10*next), iter+1)
}
}
which on my machine is at 4x faster than either of the others. And you can get almost 3x faster yet again if you leave everything in a Long and pack the results in an array instead of using 1 to N:
final def toDigitExtremelyFast(num: Int, accum: Long = 0L, iter: Int = 0): Long = {
if (num==0) accum | (iter.toLong << 48)
else {
val next = num/10
toDigitExtremelyFast(next, accum | ((num-10*next).toLong<<(4*iter)), iter+1)
}
}
// loop, instead of 1 to N map, for the 1.2k number case
{
var i = 10000
val a = new Array[Long](1201)
while (i<=11200) {
a(i-10000) = toDigitReallyReallyFast(i)
i += 1
}
a
}
As with many things, performance tuning is highly dependent on exactly what you want to do. In contrast, library design has to balance many different concerns. I do think it's worth noticing where the library is sub-optimal with respect to performance, but this isn't really one of those cases IMO; the flexibility is worth it for the common use cases.