I don't know why I am getting wrong bits when reading my byte. I have followed some indications found here, bit it is not working. I show you my code:
byte[] byte22And23 = _packet.ReadBytes(2);
DataFromGTAPackets.Packet8.ControlFlags.rightEngineSmoke = _packet.GetBit(byte22And23[0], 0);
DataFromGTAPackets.Packet8.ControlFlags.leftEngineSmoke = _packet.GetBit(byte22And23[0], 1);
DataFromGTAPackets.Packet8.ControlFlags.rightEngineFire = _packet.GetBit(byte22And23[0], 2);
DataFromGTAPackets.Packet8.ControlFlags.leftEngineFire = _packet.GetBit(byte22And23[0], 3);
DataFromGTAPackets.Packet8.ControlFlags.rightRotorFail = _packet.GetBit(byte22And23[0], 4);
DataFromGTAPackets.Packet8.ControlFlags.leftRotorFail = _packet.GetBit(byte22And23[0], 5);
etc...
public bool GetBit(byte b, int bitNumber)
{
bool bit = (b & (1 << bitNumber - 1)) != 0;
return bit;
}
My byte 22 has a value of 2, it is: 00000010. And my byte 23 has a value of 0. When I use this code to read byte 22, my 3rd variable (rightEngineFire) is getting "true" (1). It has no sense, it is wrong obviously. But I don't know what is wrong.
Thank you!
You GetBit method considers bit numbers to be 1...32, instead of being 0...31.
Simple test:
bool b1 = GetBit(1, 0); // false
bool b2 = GetBit(1, 1); // true
You should change the method to
public static bool GetBit(byte b, int bitNumber)
{
bool bit = (b & (1 << bitNumber)) != 0;
return bit;
}
Or you could write:
DataFromGTAPackets.Packet8.ControlFlags.rightEngineSmoke = _packet.GetBit(byte22And23[0], 1);
But in C-derived languages (and C# is a C-derived language) we count from 0, so the first solution is better (for me).
And as a sidenote:
1 << bitNumber - 1
is quite unreadable, because probably 9 out of 10 programmers don't know/don't remember that the expression means
1 << (bitNumber - 1)
because in the operator precedence table the - comes before the <<, so when you mix operators you should use (...) to make clear the priority.
The GetBit() implementation is wrong, you can check with the following test:
[TestMethod]
public void MyTestMethod()
{
var byte22And23 = new byte[] { 2, 0 };
var sb = new StringBuilder();
for (int i = 7; i >=0; i--)
{
var r = GetBit(byte22And23[0], i);
sb.Append((r) ? "1" : "0");
}
// result: 00000100
Assert.AreEqual("00000010", sb.ToString());
}
It looks like there is no need of -1 in GetBit() because you are indexing bits 0-based and not 1-based:
public bool GetBit(byte b, int bitNumber)
{
// no need of -1 here ------------ˇ
bool bit = (b & (1 << bitNumber - 1)) != 0;
return bit;
}
After the modification the tests runs green.
I have issue with the following method - I don't understand why it behaves the way it does
private static bool chksumCalc(ref byte[] receive_byte_array)
{
Console.WriteLine("receive_byte_array -> " + receive_byte_array.Length); //ok,151 bytes in my case
ArraySegment<byte> segment = new ArraySegment<byte>(receive_byte_array, 0, 149);
Console.WriteLine("segment # -> " + segment.Count); //ok,149 bytes
BitArray resultBits = new BitArray(8); //hold the result
Console.WriteLine("resultBits.Length -> " + resultBits.Length); //ok, 8bits
//now loop through the 149 bytes
for (int i = segment.Offset; i < (segment.Offset + segment.Count); ++i)
{
BitArray curBits = new BitArray(segment.Array[i]);
Console.WriteLine("curBits.Length -> " + curBits.Length); //gives me 229 not 8?
resultBits = resultBits.Xor(curBits);
}
//some more things to do ... return true...
//or else
return false;
}
I need to XOR 149 bytes and I don't understand why segment.Array[i] doesn't give me 1 byte. If I have array of 149 bytes if I use for example segment.Array[1] it has to yield the 2nd byte or am I that wrong? Where does the 229 come from? Can someone please clarify? Thank you.
This is the constructor you're calling: BitArray(int length)
Initializes a new instance of the BitArray class that can hold the specified number of bit values, which are initially set to false.
If you look, all of the constructors for BitArray read like that. I don't see why you need to use the BitArray class at all, though. Just use a byte to store your XOR result:
private static bool chksumCalc(ref byte[] receive_byte_array)
{
var segment = new ArraySegment<byte>(receive_byte_array, 0, 149);
byte resultBits = 0;
for (var i = segment.Offset; i < (segment.Offset + segment.Count); ++i)
{
var curBits = segment.Array[i];
resultBits = (byte)(resultBits ^ curBits);
}
//some more things to do ... return true...
//or else
return false;
}
I don't think you need the ArraySegment<T> either (not for the code presented), but I left it as is since it's beside the point of the question.
I am currently working in an environment where performance is critical and this is what I am doing :
var iso_8859_5 = System.Text.Encoding.GetEncoding("iso-8859-5");
var dataToSend = iso_8859_5.GetBytes(message);
The I need to group the bytes by 3 so I have a for loop that does this (i being the iterator of the loop):
byte[] dataByteArray = { dataToSend[i], dataToSend[i + 1], dataToSend[i + 2], 0 };
I then get an integer out of these 4 bytes
BitConverter.ToUInt32(dataByteArray, 0)
and finally the integer is converted to a hexadecimal string that I can place in a network packet.
The last two lines repeat about 150 times
I am currently hitting 50 milliseconds of execution times and ideally I would want to reach 0... Is there a faster way to do this that I am not aware of?
UPDATE
Just tried
string hex = BitConverter.ToString(dataByteArray);
hex.Replace("-", "")
to get the hex string directly but it is 3 times slower
Ricardo Silva's answer adapted
public byte[][] GetArrays(byte[] fullMessage, int size)
{
var returnArrays = new byte[(fullMessage.Length / size)+1][];
int i, j;
for (i = 0, j = 0; i < (fullMessage.Length - 2); i += size, j++)
{
returnArrays[j] = new byte[size + 1];
Buffer.BlockCopy(
src: fullMessage,
srcOffset: i,
dst: returnArrays[j],
dstOffset: 0,
count: size);
returnArrays[j][returnArrays[j].Length - 1] = 0x00;
}
switch ((fullMessage.Length % i))
{
case 0: {
returnArrays[j] = new byte[] { 0, 0, EOT, 0 };
} break;
case 1: {
returnArrays[j] = new byte[] { fullMessage[i], 0, EOT, 0 };
} break;
case 2: {
returnArrays[j] = new byte[] { fullMessage[i], fullMessage[i + 1], EOT, 0 };
} break;
}
return returnArrays;
}
After the line below you will get the total byte array.
var dataToSend = iso_8859_5.GetBytes(message);
My sugestion is work with Buffer.BlockCopy and test to see if this will be faster than your current method.
Try the code below and tell us if is faster than your current code:
public byte[][] GetArrays(byte[] fullMessage, int size)
{
var returnArrays = new byte[fullMessage.Length/size][];
for(int i = 0, j = 0; i < fullMessage.Length; i += size, j++)
{
returnArrays[j] = new byte[size + 1];
Buffer.BlockCopy(
src: fullMessage,
srcOffset: i,
dst: returnArrays[j],
dstOffset: 0,
count: size);
returnArrays[j][returnArrays[j].Length - 1] = 0x00;
}
return returnArrays;
}
EDIT1: I run the test below and the output was 245900ns (or 0,2459ms).
[TestClass()]
public class Form1Tests
{
[TestMethod()]
public void GetArraysTest()
{
var expected = new byte[] { 0x30, 0x31, 0x32, 0x00 };
var size = 3;
var stopWatch = new Stopwatch();
stopWatch.Start();
var iso_8859_5 = System.Text.Encoding.GetEncoding("iso-8859-5");
var target = iso_8859_5.GetBytes("012");
var arrays = Form1.GetArrays(target, size);
BitConverter.ToUInt32(arrays[0], 0);
stopWatch.Stop();
foreach(var array in arrays)
{
for(int i = 0; i < expected.Count(); i++)
{
Assert.AreEqual(expected[i], array[i]);
}
}
Console.WriteLine(string.Format("{0}ns", stopWatch.Elapsed.TotalMilliseconds * 1000000));
}
}
EDIT 2
I looked to your code and I have only one suggestion. I understood that you need to add EOF message and the length of input array will not be Always multiple of size that you want to break.
BUT, now the code below has TWO responsabilities, that break the S of SOLID concept.
The S talk about Single Responsability - Each method has ONE, and only ONE responsability.
The code you posted has TWO responsabilities (break input array into N smaller arrays and add EOF). Try think a way to create two totally independente methods (one to break an array into N other arrays, and other to put EOF in any array that you pass). This will allow you to create unit tests for each method (and guarantee that they Works and will never be breaked for any changed), and call the two methods from your class that make the system integration.
In this answer, the below code was posted for creating unique random alphanumeric strings. Could someone clarify for me how exactly they are ensured to be unique in this code and to what extent these are unique? If I rerun this method on different occasions would I still get unique strings?
Or did I just misunderstand the reply and these are not generating unique keys at all, only random?
I already asked this in a comment to that answer but the user seems to be inactive.
public static string GetUniqueKey()
{
int maxSize = 8;
char[] chars = new char[62];
string a;
a = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890";
chars = a.ToCharArray();
int size = maxSize;
byte[] data = new byte[1];
RNGCryptoServiceProvider crypto = new RNGCryptoServiceProvider();
crypto.GetNonZeroBytes(data);
size = maxSize;
data = new byte[size];
crypto.GetNonZeroBytes(data);
StringBuilder result = new StringBuilder(size);
foreach (byte b in data)
{ result.Append(chars[b % (chars.Length - 1)]); }
return result.ToString();
}
There is nothing in the code that guarantees that the result is unique. To get a unique value you either have to keep all previous values so that you can check for duplicates, or use a lot longer codes so that duplicates are practically impossible (e.g. a GUID). The code contains less than 48 bits of information, which is a lot less than the 128 bits of a GUID.
The string is just random, and although a crypto strength random generator is used, that is ruined by how the code is generated from the random data. There are some issues in the code:
A char array is created, that is just thrown away and replaced with another.
A one byte array of random data is created for no apparent reason at all, as it's not used for anything.
The GetNonZeroBytes method is used instead of the GetBytes method, which adds a skew to the distribution of characters as the code does nothing to handle the lack of zero values.
The modulo (%) operator is used to reduce the random number down to the number of characters used, but the random number can't be evenly divided into the number of characters, which also adds a skew to the distribution of characters.
chars.Length - 1 is used instead of chars.Length when the number is reduced, which means that only 61 of the predefined 62 characters can occur in the string.
Although those issues are minor, they are important when you are dealing with crypo strength randomness.
A version of the code that would produce a string without those issues, and give a code with enough information to be considered practically unique:
public static string GetUniqueKey() {
int size = 16;
byte[] data = new byte[size];
RNGCryptoServiceProvider crypto = new RNGCryptoServiceProvider();
crypto.GetBytes(data);
return BitConverter.ToString(data).Replace("-", String.Empty);
}
Uniqueness and randomness are mutually exclusive concepts. If a random number generator is truly random, then it can return the same value. If values are truly unique, although they may not be deterministic, they certainly aren't truly random, because every value generated removes a value from the pool of allowed values. This means that every run affects the outcome of subsequent runs, and at a certain point the pool is exhausted (barring of course the possibility of an infinitely-sized pool of allowed values, but the only way to avoid collisions in such a pool would be the use of a deterministic method of choosing values).
The code you're showing generates values that are very random, but not 100% guaranteed to be unique. After enough runs, there will be a collision.
I need to generate 7 characters of an alphanumeric string. With a small search, I wrote the below code. Performance results are uploaded above
I have used hashtable Class to guarantee uniqueness and also used RNGCryptoServiceProvider Class to get high-quality random chars
results of generating 100.000 - 1.000.000 - 10.000.000 sample
Generating unique strings;
thanks to nipul parikh
public static Tuple<List<string>, List<string>> GenerateUniqueList(int count)
{
uniqueHashTable = new Hashtable();
nonUniqueList = new List<string>();
uniqueList = new List<string>();
for (int i = 0; i < count; i++)
{
isUniqueGenerated = false;
while (!isUniqueGenerated)
{
uniqueStr = GetUniqueKey();
try
{
uniqueHashTable.Add(uniqueStr, "");
isUniqueGenerated = true;
}
catch (Exception ex)
{
nonUniqueList.Add(uniqueStr);
// Non-unique generated
}
}
}
uniqueList = uniqueHashTable.Keys.Cast<string>().ToList();
return new Tuple<List<string>, List<string>>(uniqueList, nonUniqueList);
}
public static string GetUniqueKey()
{
int size = 7;
char[] chars = new char[62];
string a = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890";
chars = a.ToCharArray();
RNGCryptoServiceProvider crypto = new RNGCryptoServiceProvider();
byte[] data = new byte[size];
crypto.GetNonZeroBytes(data);
StringBuilder result = new StringBuilder(size);
foreach (byte b in data)
result.Append(chars[b % (chars.Length - 1)]);
return Convert.ToString(result);
}
Whole Console Application Code below;
class Program
{
static string uniqueStr;
static Stopwatch stopwatch;
static bool isUniqueGenerated;
static Hashtable uniqueHashTable;
static List<string> uniqueList;
static List<string> nonUniqueList;
static Tuple<List<string>, List<string>> generatedTuple;
static void Main(string[] args)
{
int i = 0, y = 0, count = 100000;
while (i < 10 && y < 4)
{
stopwatch = new Stopwatch();
stopwatch.Start();
generatedTuple = GenerateUniqueList(count);
stopwatch.Stop();
Console.WriteLine("Time elapsed: {0} --- {1} Unique --- {2} nonUnique",
stopwatch.Elapsed,
generatedTuple.Item1.Count().ToFormattedInt(),
generatedTuple.Item2.Count().ToFormattedInt());
i++;
if (i == 9)
{
Console.WriteLine(string.Empty);
y++;
count *= 10;
i = 0;
}
}
Console.ReadLine();
}
public static Tuple<List<string>, List<string>> GenerateUniqueList(int count)
{
uniqueHashTable = new Hashtable();
nonUniqueList = new List<string>();
uniqueList = new List<string>();
for (int i = 0; i < count; i++)
{
isUniqueGenerated = false;
while (!isUniqueGenerated)
{
uniqueStr = GetUniqueKey();
try
{
uniqueHashTable.Add(uniqueStr, "");
isUniqueGenerated = true;
}
catch (Exception ex)
{
nonUniqueList.Add(uniqueStr);
// Non-unique generated
}
}
}
uniqueList = uniqueHashTable.Keys.Cast<string>().ToList();
return new Tuple<List<string>, List<string>>(uniqueList, nonUniqueList);
}
public static string GetUniqueKey()
{
int size = 7;
char[] chars = new char[62];
string a = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890";
chars = a.ToCharArray();
RNGCryptoServiceProvider crypto = new RNGCryptoServiceProvider();
byte[] data = new byte[size];
crypto.GetNonZeroBytes(data);
StringBuilder result = new StringBuilder(size);
foreach (byte b in data)
result.Append(chars[b % (chars.Length - 1)]);
return Convert.ToString(result);
}
}
public static class IntExtensions
{
public static string ToFormattedInt(this int value)
{
return string.Format(CultureInfo.InvariantCulture, "{0:0,0}", value);
}
}
Using strictly alphanumeric characters restricts the pool you draw from to 62. Using the complete printable character set(ASCII 32-126) increases your pool to 94, decreasing the likelihood of collision and eliminating creating the pool separately.
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.