Develop Reference

Test method in Console c#

I want to test a binary search in console app. I've populalated my array. I have no errors. Code runs... I'm not sure what lines of code I need to test this.
Here is my code..
internal class Program
{
static void Main(string[] args)
{
//Initialise array
int[] arr = { 800, 11, 50, 771, 649, 770, 240, 9 };
int key = 0;
Program.BinarySearchDisplay(arr, key);
}
//Binary Search Method
public static object BinarySearchDisplay(int[] arr, int key)
{
int minNum = 0;
int maxNum = arr.Length - 1;
while (minNum <= maxNum)
{
int mid = (minNum + maxNum) / 2;
if (key == arr[mid])
{
return ++mid;
}
else if (key < arr[mid])
{
maxNum = mid - 1;
}
else
{
minNum = mid + 1;
}
}
return "None";
}
}
}

A couple of things of note here. Although technically correct, it is not a good idea to return values of different types from a function. C# being statically typed provides the opportunity to expect certain return types that are used to check the code doesn't contain errors at compile type.
A function needs to return a value, and so you need to assign this value to a variable. This is an example of how to do this for a function called Fun()
int x = Fun(a,b,c);
Here is one way to achieve what you are trying to do. Have a function that returns a value if successful or flag when it does not succeed. Return a bool type indicating success, and include an out parameter that gets assigned a value before the function exits.
Notice that you store the results of the function in a variable found of type bool and then act accordingly using an if(found) statement
class Program
{
static void Main(string[] args)
{
//Initialise array
int[] arr = { 800, 11, 50, 771, 649, 770, 240, 9 };
int key = 50;
Console.WriteLine($"Array: {string.Join(",", arr)}");
Console.WriteLine($"Key: {key}");
// call the function an store the results in a variable
bool found = BinarySearchDisplay(arr, key, out int index);
if (found)
{
Console.WriteLine($"Found arr[{index}] = {arr[index]}");
}
else
{
Console.WriteLine("Not Found.");
}
}
/// <summary>
/// Binary Search Method.
/// </summary>
/// <param name="arr">The array of values.</param>
/// <param name="key">The key to search for.</param>
/// <param name="index">The array index if key was found, -1 otherwise.</param>
/// <returns>true if key found, false otherwise.</returns>
public static bool BinarySearchDisplay(int[] arr, int key, out int index)
{
int minIndex = 0;
int maxIndex = arr.Length - 1;
index = -1;
while (minIndex <= maxIndex)
{
index = (minIndex + maxIndex) / 2;
if (key == arr[index])
{
return true;
}
else if (key < arr[index])
{
maxIndex = index - 1;
}
else
{
minIndex = index + 1;
}
}
return false;
}
}
Note that Program.BinarySearchDisplay() is replaced by just BinarySearchDisplay(). This is because the function is within the same class Program and there is no need to fully qualify its name.
The result of the above code is
Array: 800,11,50,771,649,770,240,9
Key: 50
Found arr[2] = 50
I think there was an error in the op code in return ++mid; as the next index value is returned. Maybe this was done because indexing is 0-based, and the op wanted a 1-based result. But mixing the two styles is dangerous and will lead to bugs.
This is a similar pattern to bool int.TryParse(string, out int) to convert a string to a number if possible, or return false otherwise.
if(int.TryParse("1150", out int x))
{
// x holds the value 1150
}

Find Minimum and Maximum values within a multi dimensional struct array

For a school project in learning c# I am making a data collection console application which saves floating point entries for 4 different locations along with the time/date, and user who recorded the entry. I am required to use a multi dimensional struct array.
I need to have a view that displays the average of the values, along with the minimum and maximum values and the first and last date. I've got the average figured out by counting through the values and adding cumulatively but I can't figure out how to find the minimum and maximum values.
I tried searching and came across this page: http://www.dotnetperls.com/max
I tried to implement this syntax into my code to no avail due to it being a much more complex array.
It worked in my test with integers:
class Program
{
static int[][] intarray = new int[4][] { new int[10], new int[10], new int[10], new int[10] };
static void Main(string[] args)
{
intarray[0][0] = 5;
intarray[0][1] = 3;
intarray[0][2] = 10;
intarray[0][3] = 4;
intarray[0][4] = 2;
Console.WriteLine(intarray[0].Max());
Console.ReadLine();
}
}
The above code perfectly displays the number 10! :)
But when I tried to implement this in to my program with a struct array, it doesn't work:
class Program
{
static byte location = 0;
static float entriesmin;
static float entriesmax;
static Entry[][] entriesarray = new Entry[4][] { new Entry[10], new Entry[10], new Entry[10], new Entry[10] };
struct Entry
{
public float value;
public string user;
public DateTime datetime;
}
static byte LoopEntries(bool display)
{
float runningtotal = 0;
byte entrycount = 0;
foreach (Entry entry in entriesarray[0])
{
if (entry.user != null)
{
if (display)
{
string ten;
if (entrycount == 9)
{
ten = " #";
}
else
{
ten = " #";
}
Console.WriteLine(ten + (entrycount + 1) + " " + entry.datetime + " " + entry.user + new string(' ', 16 - entry.user.Length) + entry.value);
}
runningtotal += entry.value;
entrycount += 1;
}
}
entriesmin = (entriesarray[location]).value.Min();
entriesmax = (entriesarray[location]).value.Max();
if (entrycount == 0 && display)
{
Console.WriteLine("No entries to show!");
}
return entrycount;
}
I need to hand this project in on Monday! I really hope someone can help me! ;)

What you have is not a multidimensional array, it's an array of array (a.k.a. a jagged array), but you work with them in a similar way.
To loop through the array of arrays you need a loop in a loop:
foreach (Entry[] arr in entriesarray) {
foreach (Entry entry in arr) {
// ...
}
}
You could use the Min and Max extension methods to get the minimum and maximum value of each inner array, but you still would have to find the minimum and maximum between those values. As you are already looping through all the entries anyway to get the average you can just keep a record of the smallest and largest values found:
float runningtotal = 0;
int entrycount = 0;
float min = float.MaxValue;
float max = float.MinValue;
foreach (Entry[] arr in entriesarray) {
foreach (Entry entry in arr) {
runningtotal += entry.value;
entrycount++;
if (entry.value < min) {
min = entry.value;
}
if (entry.value > max) {
max = entry.value;
}
}
}

How to take X amount of questions, that sum up Y amount of difficulty

I have a table
Questions -> Question(string), Difficulty (int, 1-10)
I need to create a method, that as the title mentions, takes X amount of questions, whose difficulty should sum up Y.
For example:
getQuestions(2,10) -> Question1 (diff: 4), Question2 (diff: 6)
getQuestions(3,15) -> Question3 (diff: 5), Question4 (diff: 5), Question5 (diff: 5)
How can I achieve something like this with LINQ?

Here's one way to do it, using a modified version of the recursive solution found here: Finding all possible combinations of numbers to reach a given sum
First, a public method that will do some quick validation and then call a recursive method to get the results:
/// <summary>
/// Gets lists of numQuestions length of all combinations
/// of questions whose difficulties add up to sumDifficulty
/// </summary>
/// <param name="questions">The list of questions to search</param>
/// <param name="numQuestions">The number of questions required</param>
/// <param name="sumDifficulty">The amount that the difficulties should sum to</param>
/// <returns></returns>
public static List<List<Question>> GetQuestions(List<Question> questions,
int numQuestions, int sumDifficulty)
{
if (questions == null) throw new ArgumentNullException("questions");
var results = new List<List<Question>>();
// Fail fast argument validation
if (numQuestions < 1 ||
numQuestions > questions.Count ||
sumDifficulty < numQuestions * Question.MinDifficulty ||
sumDifficulty > numQuestions * Question.MaxDifficulty)
{
return results;
}
// If we only need single questions, no need to do any recursion
if (numQuestions == 1)
{
results.AddRange(questions.Where(q => q.Difficulty == sumDifficulty)
.Select(q => new List<Question> {q}));
return results;
}
// We can remove any questions who have a difficulty that's higher
// than the sumDifficulty minus the number of questions plus one
var candidateQuestions =
questions.Where(q => q.Difficulty <= sumDifficulty - numQuestions + 1)
.ToList();
if (!candidateQuestions.Any())
{
return results;
}
GetSumsRecursively(candidateQuestions, sumDifficulty, new List<Question>(),
numQuestions, results);
return results;
}
And then the recursive method that does the heavy lifting:
private static void GetSumsRecursively(IReadOnlyList<Question> questions,
int sumDifficulty, List<Question> candidates, int numQuestions,
ICollection<List<Question>> results)
{
int candidateSum = candidates.Sum(x => x.Difficulty);
if (candidateSum == sumDifficulty && candidates.Count == numQuestions)
{
results.Add(candidates);
}
if (candidateSum >= sumDifficulty)
return;
for (int i = 0; i < questions.Count; i++)
{
var remaining = new List<Question>();
for (int j = i + 1; j < questions.Count; j++)
{
remaining.Add(questions[j]);
}
var filteredCandidates = new List<Question>(candidates) {questions[i]};
GetSumsRecursively(remaining, sumDifficulty, filteredCandidates,
numQuestions, results);
}
}
Here's an example usage:
public static void Main()
{
const int numberOfQuestions = 3;
const int sumOfDifficulty = 15;
// Since I don't have your table, I'm using a list of objects to fake it
var questions = new List<Question>();
for (int i = 1; i < 11; i++)
{
questions.Add(new Question {Difficulty = i % 10 + 1,
QuestionString = "Question #" + i});
}
var results = GetQuestions(questions, numberOfQuestions, sumOfDifficulty);
// Write output to console to verify results
foreach (var result in results)
{
Console.WriteLine("{0} = {1}", string.Join(" + ",
result.Select(r => r.Difficulty)), sumOfDifficulty);
}
}
And just so you have everything to make this work, here's my Question class used to fake your table:
internal class Question
{
public const int MinDifficulty = 1;
public const int MaxDifficulty = 10;
private int _difficulty;
public int Difficulty
{
get { return _difficulty; }
set
{
if (value < MinDifficulty) _difficulty = MinDifficulty;
else if (value > MaxDifficulty) _difficulty = MaxDifficulty;
else _difficulty = value;
}
}
public string QuestionString { get; set; }
}

Combination Algorithm

Length = input Long(can be 2550, 2880, 2568, etc)
List<long> = {618, 350, 308, 300, 250, 232, 200, 128}
The program takes a long value, for that particular long value we have to find the possible combination from the above list which when added give me a input result(same value can be used twice). There can be a difference of +/- 30.
Largest numbers have to be used most.
Ex:Length = 868
For this combinations can be
Combination 1 = 618 + 250
Combination 2 = 308 + 232 + 200 +128
Correct Combination would be Combination 1
But there should also be different combinations.
public static void Main(string[] args)
{
//subtotal list
List<int> totals = new List<int>(new int[] { 618, 350, 308, 300, 250, 232, 200, 128 });
// get matches
List<int[]> results = KnapSack.MatchTotal(2682, totals);
// print results
foreach (var result in results)
{
Console.WriteLine(string.Join(",", result));
}
Console.WriteLine("Done.");
}
internal static List<int[]> MatchTotal(int theTotal, List<int> subTotals)
{
List<int[]> results = new List<int[]>();
while (subTotals.Contains(theTotal))
{
results.Add(new int[1] { theTotal });
subTotals.Remove(theTotal);
}
if (subTotals.Count == 0)
return results;
subTotals.Sort();
double mostNegativeNumber = subTotals[0];
if (mostNegativeNumber > 0)
mostNegativeNumber = 0;
if (mostNegativeNumber == 0)
subTotals.RemoveAll(d => d > theTotal);
for (int choose = 0; choose <= subTotals.Count; choose++)
{
IEnumerable<IEnumerable<int>> combos = Combination.Combinations(subTotals.AsEnumerable(), choose);
results.AddRange(from combo in combos where combo.Sum() == theTotal select combo.ToArray());
}
return results;
}
public static class Combination
{
public static IEnumerable<IEnumerable<T>> Combinations<T>(this IEnumerable<T> elements, int choose)
{
return choose == 0 ?
new[] { new T[0] } :
elements.SelectMany((element, i) =>
elements.Skip(i + 1).Combinations(choose - 1).Select(combo => (new[] { element }).Concat(combo)));
}
}
I Have used the above code, can it be more simplified, Again here also i get unique values. A value can be used any number of times. But the largest number has to be given the most priority.
I have a validation to check whether the total of the sum is greater than the input value. The logic fails even there..

The algorithm you have shown assumes that the list is sorted in ascending order. If not, then you shall first have to sort the list in O(nlogn) time and then execute the algorithm.
Also, it assumes that you are only considering combinations of pairs and you exit on the first match.
If you want to find all combinations, then instead of "break", just output the combination and increment startIndex or decrement endIndex.
Moreover, you should check for ranges (targetSum - 30 to targetSum + 30) rather than just the exact value because the problem says that a margin of error is allowed.
This is the best solution according to me because its complexity is O(nlogn + n) including the sorting.

V4 - Recursive Method, using Stack structure instead of stack frames on thread
It works (tested in VS), but there could be some bugs remaining.
static int Threshold = 30;
private static Stack<long> RecursiveMethod(long target)
{
Stack<long> Combination = new Stack<long>(establishedValues.Count); //Can grow bigger, as big as (target / min(establishedValues)) values
Stack<int> Index = new Stack<int>(establishedValues.Count); //Can grow bigger
int lowerBound = 0;
int dimensionIndex = lowerBound;
long fail = -1 * Threshold;
while (true)
{
long thisVal = establishedValues[dimensionIndex];
dimensionIndex++;
long afterApplied = target - thisVal;
if (afterApplied < fail)
lowerBound = dimensionIndex;
else
{
target = afterApplied;
Combination.Push(thisVal);
if (target <= Threshold)
return Combination;
Index.Push(dimensionIndex);
dimensionIndex = lowerBound;
}
if (dimensionIndex >= establishedValues.Count)
{
if (Index.Count == 0)
return null; //No possible combinations
dimensionIndex = Index.Pop();
lowerBound = dimensionIndex;
target += Combination.Pop();
}
}
}
Maybe V3 - Suggestion for Ordered solution trying every combination
Although this isn't chosen as the answer for the related question, I believe this is a good approach - https://stackoverflow.com/a/17258033/887092(, otherwise you could try the chosen answer (although the output for that is only 2 items in set being summed, rather than up to n items)) - it will enumerate every option including multiples of the same value. V2 works but would be slightly less efficient than an ordered solution, as the same failing-attempt will likely be attempted multiple times.
V2 - Random Selection - Will be able to reuse the same number twice
I'm a fan of using random for "intelligence", allowing the computer to brute force the solution. It's also easy to distribute - as there is no state dependence between two threads trying at the same time for example.
static int Threshold = 30;
public static List<long> RandomMethod(long Target)
{
List<long> Combinations = new List<long>();
Random rnd = new Random();
//Assuming establishedValues is sorted
int LowerBound = 0;
long runningSum = Target;
while (true)
{
int newLowerBound = FindLowerBound(LowerBound, runningSum);
if (newLowerBound == -1)
{
//No more beneficial values to work with, reset
runningSum = Target;
Combinations.Clear();
LowerBound = 0;
continue;
}
LowerBound = newLowerBound;
int rIndex = rnd.Next(LowerBound, establishedValues.Count);
long val = establishedValues[rIndex];
runningSum -= val;
Combinations.Add(val);
if (Math.Abs(runningSum) <= 30)
return Combinations;
}
}
static int FindLowerBound(int currentLowerBound, long runningSum)
{
//Adjust lower bound, so we're not randomly trying a number that's too high
for (int i = currentLowerBound; i < establishedValues.Count; i++)
{
//Factor in the threshold, because an end aggregate which exceeds by 20 is better than underperforming by 21.
if ((establishedValues[i] - Threshold) < runningSum)
{
return i;
}
}
return -1;
}
V1 - Ordered selection - Will not be able to reuse the same number twice
Add this very handy extension function (uses a binary algorithm to find all combinations):
//Make sure you put this in a static class inside System namespace
public static IEnumerable<List<T>> EachCombination<T>(this List<T> allValues)
{
var collection = new List<List<T>>();
for (int counter = 0; counter < (1 << allValues.Count); ++counter)
{
List<T> combination = new List<T>();
for (int i = 0; i < allValues.Count; ++i)
{
if ((counter & (1 << i)) == 0)
combination.Add(allValues[i]);
}
if (combination.Count == 0)
continue;
yield return combination;
}
}
Use the function
static List<long> establishedValues = new List<long>() {618, 350, 308, 300, 250, 232, 200, 128, 180, 118, 155};
//Return is a list of the values which sum to equal the target. Null if not found.
List<long> FindFirstCombination(long target)
{
foreach (var combination in establishedValues.EachCombination())
{
//if (combination.Sum() == target)
if (Math.Abs(combination.Sum() - target) <= 30) //Plus or minus tolerance for difference
return combination;
}
return null; //Or you could throw an exception
}
Test the solution
var target = 858;
var result = FindFirstCombination(target);
bool success = (result != null && result.Sum() == target);
//TODO: for loop with random selection of numbers from the establishedValues, Sum and test through FindFirstCombination

selection based on percentage weighting

I have a set of values, and an associated percentage for each:
a: 70% chance
b: 20% chance
c: 10% chance
I want to select a value (a, b, c) based on the percentage chance given.
how do I approach this?
my attempt so far looks like this:
r = random.random()
if r <= .7:
return a
elif r <= .9:
return b
else:
return c
I'm stuck coming up with an algorithm to handle this. How should I approach this so it can handle larger sets of values without just chaining together if-else flows.
(any explanation or answers in pseudo-code are fine. a python or C# implementation would be especially helpful)

Here is a complete solution in C#:
public class ProportionValue<T>
{
public double Proportion { get; set; }
public T Value { get; set; }
}
public static class ProportionValue
{
public static ProportionValue<T> Create<T>(double proportion, T value)
{
return new ProportionValue<T> { Proportion = proportion, Value = value };
}
static Random random = new Random();
public static T ChooseByRandom<T>(
this IEnumerable<ProportionValue<T>> collection)
{
var rnd = random.NextDouble();
foreach (var item in collection)
{
if (rnd < item.Proportion)
return item.Value;
rnd -= item.Proportion;
}
throw new InvalidOperationException(
"The proportions in the collection do not add up to 1.");
}
}
Usage:
var list = new[] {
ProportionValue.Create(0.7, "a"),
ProportionValue.Create(0.2, "b"),
ProportionValue.Create(0.1, "c")
};
// Outputs "a" with probability 0.7, etc.
Console.WriteLine(list.ChooseByRandom());

For Python:
>>> import random
>>> dst = 70, 20, 10
>>> vls = 'a', 'b', 'c'
>>> picks = [v for v, d in zip(vls, dst) for _ in range(d)]
>>> for _ in range(12): print random.choice(picks),
...
a c c b a a a a a a a a
>>> for _ in range(12): print random.choice(picks),
...
a c a c a b b b a a a a
>>> for _ in range(12): print random.choice(picks),
...
a a a a c c a c a a c a
>>>
General idea: make a list where each item is repeated a number of times proportional to the probability it should have; use random.choice to pick one at random (uniformly), this will match your required probability distribution. Can be a bit wasteful of memory if your probabilities are expressed in peculiar ways (e.g., 70, 20, 10 makes a 100-items list where 7, 2, 1 would make a list of just 10 items with exactly the same behavior), but you could divide all the counts in the probabilities list by their greatest common factor if you think that's likely to be a big deal in your specific application scenario.
Apart from memory consumption issues, this should be the fastest solution -- just one random number generation per required output result, and the fastest possible lookup from that random number, no comparisons &c. If your likely probabilities are very weird (e.g., floating point numbers that need to be matched to many, many significant digits), other approaches may be preferable;-).

Knuth references Walker's method of aliases. Searching on this, I find http://code.activestate.com/recipes/576564-walkers-alias-method-for-random-objects-with-diffe/ and http://prxq.wordpress.com/2006/04/17/the-alias-method/. This gives the exact probabilities required in constant time per number generated with linear time for setup (curiously, n log n time for setup if you use exactly the method Knuth describes, which does a preparatory sort you can avoid).

Take the list of and find the cumulative total of the weights: 70, 70+20, 70+20+10. Pick a random number greater than or equal to zero and less than the total. Iterate over the items and return the first value for which the cumulative sum of the weights is greater than this random number:
def select( values ):
variate = random.random() * sum( values.values() )
cumulative = 0.0
for item, weight in values.items():
cumulative += weight
if variate < cumulative:
return item
return item # Shouldn't get here, but just in case of rounding...
print select( { "a": 70, "b": 20, "c": 10 } )
This solution, as implemented, should also be able to handle fractional weights and weights that add up to any number so long as they're all non-negative.

Let T = the sum of all item weights
Let R = a random number between 0 and T
Iterate the item list subtracting each item weight from R and return the item that causes the result to become <= 0.

def weighted_choice(probabilities):
random_position = random.random() * sum(probabilities)
current_position = 0.0
for i, p in enumerate(probabilities):
current_position += p
if random_position < current_position:
return i
return None
Because random.random will always return < 1.0, the final return should never be reached.

import random
def selector(weights):
i=random.random()*sum(x for x,y in weights)
for w,v in weights:
if w>=i:
break
i-=w
return v
weights = ((70,'a'),(20,'b'),(10,'c'))
print [selector(weights) for x in range(10)]
it works equally well for fractional weights
weights = ((0.7,'a'),(0.2,'b'),(0.1,'c'))
print [selector(weights) for x in range(10)]
If you have a lot of weights, you can use bisect to reduce the number of iterations required
import random
import bisect
def make_acc_weights(weights):
acc=0
acc_weights = []
for w,v in weights:
acc+=w
acc_weights.append((acc,v))
return acc_weights
def selector(acc_weights):
i=random.random()*sum(x for x,y in weights)
return weights[bisect.bisect(acc_weights, (i,))][1]
weights = ((70,'a'),(20,'b'),(10,'c'))
acc_weights = make_acc_weights(weights)
print [selector(acc_weights) for x in range(100)]
Also works fine for fractional weights
weights = ((0.7,'a'),(0.2,'b'),(0.1,'c'))
acc_weights = make_acc_weights(weights)
print [selector(acc_weights) for x in range(100)]

today, the update of python document give an example to make a random.choice() with weighted probabilities:
If the weights are small integer ratios, a simple technique is to build a sample population with repeats:
>>> weighted_choices = [('Red', 3), ('Blue', 2), ('Yellow', 1), ('Green', 4)]
>>> population = [val for val, cnt in weighted_choices for i in range(cnt)]
>>> random.choice(population)
'Green'
A more general approach is to arrange the weights in a cumulative distribution with itertools.accumulate(), and then locate the random value with bisect.bisect():
>>> choices, weights = zip(*weighted_choices)
>>> cumdist = list(itertools.accumulate(weights))
>>> x = random.random() * cumdist[-1]
>>> choices[bisect.bisect(cumdist, x)]
'Blue'
one note: itertools.accumulate() needs python 3.2 or define it with the Equivalent.

I think you can have an array of small objects (I implemented in Java although I know a little bit C# but I am afraid can write wrong code), so you may need to port it yourself. The code in C# will be much smaller with struct, var but I hope you get the idea
class PercentString {
double percent;
String value;
// Constructor for 2 values
}
ArrayList<PercentString> list = new ArrayList<PercentString();
list.add(new PercentString(70, "a");
list.add(new PercentString(20, "b");
list.add(new PercentString(10, "c");
double percent = 0;
for (int i = 0; i < list.size(); i++) {
PercentString p = list.get(i);
percent += p.percent;
if (random < percent) {
return p.value;
}
}

If you are really up to speed and want to generate the random values quickly, the Walker's algorithm mcdowella mentioned in https://stackoverflow.com/a/3655773/1212517 is pretty much the best way to go (O(1) time for random(), and O(N) time for preprocess()).
For anyone who is interested, here is my own PHP implementation of the algorithm:
/**
* Pre-process the samples (Walker's alias method).
* #param array key represents the sample, value is the weight
*/
protected function preprocess($weights){
$N = count($weights);
$sum = array_sum($weights);
$avg = $sum / (double)$N;
//divide the array of weights to values smaller and geq than sum/N
$smaller = array_filter($weights, function($itm) use ($avg){ return $avg > $itm;}); $sN = count($smaller);
$greater_eq = array_filter($weights, function($itm) use ($avg){ return $avg <= $itm;}); $gN = count($greater_eq);
$bin = array(); //bins
//we want to fill N bins
for($i = 0;$i<$N;$i++){
//At first, decide for a first value in this bin
//if there are small intervals left, we choose one
if($sN > 0){
$choice1 = each($smaller);
unset($smaller[$choice1['key']]);
$sN--;
} else{ //otherwise, we split a large interval
$choice1 = each($greater_eq);
unset($greater_eq[$choice1['key']]);
}
//splitting happens here - the unused part of interval is thrown back to the array
if($choice1['value'] >= $avg){
if($choice1['value'] - $avg >= $avg){
$greater_eq[$choice1['key']] = $choice1['value'] - $avg;
}else if($choice1['value'] - $avg > 0){
$smaller[$choice1['key']] = $choice1['value'] - $avg;
$sN++;
}
//this bin comprises of only one value
$bin[] = array(1=>$choice1['key'], 2=>null, 'p1'=>1, 'p2'=>0);
}else{
//make the second choice for the current bin
$choice2 = each($greater_eq);
unset($greater_eq[$choice2['key']]);
//splitting on the second interval
if($choice2['value'] - $avg + $choice1['value'] >= $avg){
$greater_eq[$choice2['key']] = $choice2['value'] - $avg + $choice1['value'];
}else{
$smaller[$choice2['key']] = $choice2['value'] - $avg + $choice1['value'];
$sN++;
}
//this bin comprises of two values
$choice2['value'] = $avg - $choice1['value'];
$bin[] = array(1=>$choice1['key'], 2=>$choice2['key'],
'p1'=>$choice1['value'] / $avg,
'p2'=>$choice2['value'] / $avg);
}
}
$this->bins = $bin;
}
/**
* Choose a random sample according to the weights.
*/
public function random(){
$bin = $this->bins[array_rand($this->bins)];
$randValue = (lcg_value() < $bin['p1'])?$bin[1]:$bin[2];
}

Here is my version that can apply to any IList and normalize the weight. It is based on Timwi's solution : selection based on percentage weighting
/// <summary>
/// return a random element of the list or default if list is empty
/// </summary>
/// <param name="e"></param>
/// <param name="weightSelector">
/// return chances to be picked for the element. A weigh of 0 or less means 0 chance to be picked.
/// If all elements have weight of 0 or less they all have equal chances to be picked.
/// </param>
/// <returns></returns>
public static T AnyOrDefault<T>(this IList<T> e, Func<T, double> weightSelector)
{
if (e.Count < 1)
return default(T);
if (e.Count == 1)
return e[0];
var weights = e.Select(o => Math.Max(weightSelector(o), 0)).ToArray();
var sum = weights.Sum(d => d);
var rnd = new Random().NextDouble();
for (int i = 0; i < weights.Length; i++)
{
//Normalize weight
var w = sum == 0
? 1 / (double)e.Count
: weights[i] / sum;
if (rnd < w)
return e[i];
rnd -= w;
}
throw new Exception("Should not happen");
}

I've my own solution for this:
public class Randomizator3000
{
public class Item<T>
{
public T value;
public float weight;
public static float GetTotalWeight<T>(Item<T>[] p_itens)
{
float __toReturn = 0;
foreach(var item in p_itens)
{
__toReturn += item.weight;
}
return __toReturn;
}
}
private static System.Random _randHolder;
private static System.Random _random
{
get
{
if(_randHolder == null)
_randHolder = new System.Random();
return _randHolder;
}
}
public static T PickOne<T>(Item<T>[] p_itens)
{
if(p_itens == null || p_itens.Length == 0)
{
return default(T);
}
float __randomizedValue = (float)_random.NextDouble() * (Item<T>.GetTotalWeight(p_itens));
float __adding = 0;
for(int i = 0; i < p_itens.Length; i ++)
{
float __cacheValue = p_itens[i].weight + __adding;
if(__randomizedValue <= __cacheValue)
{
return p_itens[i].value;
}
__adding = __cacheValue;
}
return p_itens[p_itens.Length - 1].value;
}
}
And using it should be something like that (thats in Unity3d)
using UnityEngine;
using System.Collections;
public class teste : MonoBehaviour
{
Randomizator3000.Item<string>[] lista;
void Start()
{
lista = new Randomizator3000.Item<string>[10];
lista[0] = new Randomizator3000.Item<string>();
lista[0].weight = 10;
lista[0].value = "a";
lista[1] = new Randomizator3000.Item<string>();
lista[1].weight = 10;
lista[1].value = "b";
lista[2] = new Randomizator3000.Item<string>();
lista[2].weight = 10;
lista[2].value = "c";
lista[3] = new Randomizator3000.Item<string>();
lista[3].weight = 10;
lista[3].value = "d";
lista[4] = new Randomizator3000.Item<string>();
lista[4].weight = 10;
lista[4].value = "e";
lista[5] = new Randomizator3000.Item<string>();
lista[5].weight = 10;
lista[5].value = "f";
lista[6] = new Randomizator3000.Item<string>();
lista[6].weight = 10;
lista[6].value = "g";
lista[7] = new Randomizator3000.Item<string>();
lista[7].weight = 10;
lista[7].value = "h";
lista[8] = new Randomizator3000.Item<string>();
lista[8].weight = 10;
lista[8].value = "i";
lista[9] = new Randomizator3000.Item<string>();
lista[9].weight = 10;
lista[9].value = "j";
}
void Update ()
{
Debug.Log(Randomizator3000.PickOne<string>(lista));
}
}
In this example each value has a 10% chance do be displayed as a debug =3

Based loosely on python's numpy.random.choice(a=items, p=probs), which takes an array and a probability array of the same size.
public T RandomChoice<T>(IEnumerable<T> a, IEnumerable<double> p)
{
IEnumerator<T> ae = a.GetEnumerator();
Random random = new Random();
double target = random.NextDouble();
double accumulator = 0;
foreach (var prob in p)
{
ae.MoveNext();
accumulator += prob;
if (accumulator > target)
{
break;
}
}
return ae.Current;
}
The probability array p must sum to (approx.) 1. This is to keep it consistent with the numpy interface (and mathematics), but you could easily change that if you wanted.