Logarithmic scale step - c

I'm building a keyboard light with AVR micro controller.
There are two buttons, BRIGHT and DIM, and a white LED.
The LED isn't really linear, so I need to use a logarithmic scale (increase brightness faster in higher values, and use tiny steps in lower).
To do that, I adjust the delay between 1 is added or subtracted to/from the PWM compare match control register.
while (1) {
if (btn_high() && OCR0A < 255) OCR0A += 1;
if (btn_low() && OCR0A > 0) OCR0A -= 1;
if (OCR0A < 25)
_delay_ms(30);
else if (OCR0A < 50)
_delay_ms(25);
else if (OCR0A < 128)
_delay_ms(17);
else
_delay_ms(5);
}
It works nice, but there's a visible step when it goes from one speed to another. It'd be much better if the delay adjusted smoothly.
Is there some simple formula I can use?
It must not contain division, modulo, sqrt, log or any other advanced math. I can use multiplication, add, sub, and bit operations. Also, I can't use float in it.
Or perhaps just some kind of lookup table? I'm not really happy with adding more branches to this if-else mess.

The posted transfer function is quite linear. Suggest a linear delay calculation.
delay = 32 - OCR0A/8;
After accept edit
Various look-up-tables lend themselves to a close fit simple equations (constructed to avoid intermediate values > 65535) such as
BRIGHTNESS_60 = (((index*index)>>2 + 128)*index)>>8;

The scaling isn't quite logarithmic so simply using log() isn't enough.
I have tackled this problem in the past by using a LUT with 18 entries and going an entire step at a time (i.e. the control variable varies from 0 to 17 and then is shoved through the LUT), but if finer control is required then having 52 or more is certainly doable. Make sure to put it in flash so that it doesn't consume any SRAM though.
Edit by MightyPork
Here's arrays I used in the end - obtained from the original array by linear interpolation.
Basic
#define BRIGHTNESS_LEN 60
const uint8_t BRIGHTNESS[] PROGMEM = {
0, 1, 1, 2, 2, 2, 3, 4, 4, 5, 6, 6, 7, 8, 9,
10, 11, 13, 14, 16, 18, 21, 24, 27, 30, 32,
35, 38, 40, 42, 45, 48, 50, 54, 58, 61, 65,
69, 72, 76, 80, 85, 90, 95, 100, 106, 112,
119, 125, 134, 142, 151, 160, 170, 180, 190,
200, 214, 228, 241, 255
};
Smoother
#define BRIGHTNESS_LEN 121
const uint8_t BRIGHTNESS[] PROGMEM = {
0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5,
6, 6, 6, 7, 7, 8, 8, 8, 9, 10, 10, 10, 11, 12, 13, 14, 14,
15, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 32, 34,
35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 48, 49, 50, 52, 54,
56, 58, 59, 61, 63, 65, 67, 69, 71, 72, 74, 76, 78, 80, 82,
85, 88, 90, 92, 95, 98, 100, 103, 106, 109, 112, 116, 119,
122, 125, 129, 134, 138, 142, 147, 151, 156, 160, 165, 170,
175, 180, 185, 190, 195, 200, 207, 214, 221, 228, 234, 241,
248, 255
};

It sounds like you really want to use some linear function of a logarithm, but without the overhead of the floating point math library. A crude fixed point logarithm can be coded as
uint_8 log2fix(uint_8 in)
{
if(in == 0)
return 0;
uint_8 out = 0;
while(in > 0)
{
in = in >> 1;
out++;
}
return out - 1;
}
This will give you a rough approximation. If you want more precision there is a fast fixed point algorithm that you should be able to modify for Q8.0 to Q3.5.

You have over-complicated the issue. You have already turned the logarithmic problem into a linear one by defining a variable update rate rather than a variable PWM step - so you have essentially solved the problem, but not seen the simple arithmetic relationship.
If you take the OCR0A vs delay points you have selected (25,30), (50,25), (128,17), it can be seen that that is an approximately linear relationship described by (approximately) y = 0.125x + 32, which can be rearranged as y = 32 - x / 8
So what you need is:
while (1)
{
if (btn_high() && OCR0A < 255) OCR0A += 1;
if (btn_low() && OCR0A > 0) OCR0A -= 1;
_delay_ms( 32 - OCR0A / 8 ) ;
}

Related

multiplicative inverse with lookup table

I have an formula that I use multiple times in my subroutine, but my processor does not have division instruction(M0), so this is handled by the software library. To speed up this operation, I am considering using a lookup table to store the result of the inverse. However that would still take up 2kb in space (2 bytes per value). How can I optimize it further?
Formula is as follows, k is a constant known at compile time k = [10, 100]. x = [0, 1023]
(1000 * k) * ((1023/x) - 1)
EDITE: Clarification about precision. Since I have the "1000", I am considering using the result of the multiplication by 1000 to increase precision.
Assuming / is integer division
You don't need to store 1024 values, because many values of x result in the same value of 1023/x.
Specifically:
x: [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 42, 44, 46, 48, 51, 53, 56, 60, 63, 68, 73, 78, 85, 93, 102, 113, 127, 146, 170, 204, 255, 341, 511, 1023]
1023/x: [1023, 511, 341, 255, 204, 170, 146, 127, 113, 102, 93, 85, 78, 73, 68, 63, 60, 56, 53, 51, 48, 46, 44, 42, 40, 39, 37, 36, 35, 34, 33, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
You need only to store these 62 values of x and the 62 results of 1023/x.
As a bonus: if you look carefully, you'll notice those values are symmetric. The values for x are the exact mirror of the values for 1023/x. So you only need to store one of these two arrays.
You can easily shrink the lookup table to 256*2 bytes
static inline uint16_t get1023divxminus1(uint16_t x)
{
static const uint16_t table[256] = {0, 1022, 510, ....., 3};
if (x >= 512) return 0;
if (x >= 342) return 1;
if (x >= 256) return 2;
return table[x];
}
You could shrink the table even further, but I think it isn't worth the additional ifs.
You could compress the data in the table.
For example by storing full 2-byte values for every N-th value of x and store difference values for xs in between. The difference should fit in 1 byte in many cases.
If N would be 4, you'd store full values for x: 0, 4, 8, ... and difference values for x: 1, 2, 3, 5, 6, 7, 9, ...
To get the result for say x == 3, start with 2-byte value of 0 and add the 1-byte difference values of 1 and 2.
There will for sure be other 'tricks' to play if you'd have a close look at the data and think in the direction of data compression.
Accessing RAM is probably going to be slower than calculating long division, as long as your values fit within a register. In principle, calculating long division should be linear in the number of bits. Implement both and profile, but I am highly convinced that long division will be faster:
The algorithms is:
left shift the divisor until the MSD of the divisor equals the MSD of the dividend.
If the divisor is smaller than the dividend, write one, else write 0. Right shift the divisor by one. Repeat until the LSD of the divisor is also the LSD of the dividend.
Here is an explicit implementation:
https://codegolf.stackexchangechaschastitytity.com/questions/24541/divide-two-numbers-using-long-division

Viability of using the "tv_nsec" nanosecond component returned by "timespec_get()" on Linux as random number generator in C?

With the following simple code snippet:
struct timespec ts;
for (int i = 0; i < 100; i++) {
timespec_get(&ts, TIME_UTC);
printf("%ld, ", ts.tv_nsec % 100);
}
I get output like this:
58, 1, 74, 49, 5, 59, 89, 20, 52, 86, 17, 48, 79, 10, 41, 73, 3, 40, 72, 3, 36, 67, 98, 30, 61, 92, 24, 55, 86, 17, 49, 82, 14, 45, 76, 7, 40, 72, 3, 36, 71, 2, 35, 66, 97, 28, 66, 97, 28, 60, 90, 22, 52, 83, 15, 46, 77, 7, 41, 72, 3, 36, 67, 0, 44, 17, 82, 13, 45, 77, 8, 59, 90, 22, 54, 85, 17, 48, 80, 12, 43, 75, 6, 57, 89, 20, 52, 84, 15, 47, 79, 14, 50, 82, 16, 47, 79, 11, 43, 74,
I haven't studied the statistical distribution of the numbers and my searches have turned up blank, but the output does at first glance look similar to output of rand() or random(). Has anyone studied this or is able to express an opinion - could timespec_get() be used as random number generator? Would it be pseudo random or not? Why?
could timespec_get() be used as random number generator?
Of course. But that doesn't mean the output of such a RNG would have desirable or even acceptable statistical properties.
In particular, successive outputs are strongly correlated with each other. Your example hides that, somewhat, by discarding all the most-significant decimal digits. Additionally, the system clock is not required to have single-nanosecond resolution, though yours seems to have. In a system that didn`t have such resolution, the least-significant digits of all results would likely be correlated, and their distribution non-uniform.
Would it be pseudo random or not? Why?
No, actually. The output of a PRNG is deterministic with respect to the runtime state of the calling program at the time of the call. timespec_get(), on the other hand, depends on the program's execution context, not its own state.
The code you have provided is almost certainly guaranteed not to provide (pseudo-)random numbers!
Why?
Consider running this on an efficient CPU that can dedicate 100% of its time to your code (and with nothing else of 'significant impact') going on in the OS background: each run of the for loop executes an identical instruction sequence, so the intervals between successive calls to timespec_get will all be very similar - and a list of numbers with continuously similar intervals is certainly not random.
Even a fairly cursory glance through your generated number list shows that the only time a number is less than its precursor is when the value "rolls over" the 100 mark (this effect will be more noticeable if you increase your modulus from 100 to, say, 500 and run the test again).
could timespec_get() be used as random number generator?
I tried calling timespec_get(&ts, TIME_UTC); multiple times and received delta values of about 14 +/- 1 ns. To me this implies at best a non-predictable-ness (random-ness) of 1 bit per call (given the variability in the delta), not the 7 to 8 bits hoped for with timespec_get(&ts, TIME_UTC); ts.tv_nsec % 100. At worst, there is nearly zero bits of randomness.
.tv_nsec and .tv_sec could be used to initialize a random engine, but as as a source, it is very weak.
Would it be pseudo random or not? Why?
No. A PRNG is deterministic. Reading time is not deterministic enough.

How to Implement T-SQL CHECKSUM() in JavaScript for BigQuery?

The end result I'm looking for is to implement T-SQL CHECKSUM in BigQuery with a JavaScript UDF. I would settle for having the C/C++ source code to translate but if someone has already done this work then I'd love to use it.
Alternatively, if someone can think of a way to create an equivalent hash code between strings stored in Microsoft SQL Server compared to those in BigQuery then that would help me too.
UPDATE: I've found some source code through HABO's link in the comments which is written in T-SQL to perform the same CHECKSUM but I'm having difficulty converting it to JavaScript which inherently cannot handle 64bit integers. I'm playing with some small examples and have found that the algorithm works on the low nibble of each byte only.
UPDATE 2: I got really curious about replicating this algorithm and I can see some definite patterns but my brain isn't up to the task of distilling that into a reverse engineered solution. I did find that BINARY_CHECKSUM() and CHECKSUM() return different things so the work done on the former didn't help me with the latter.
I spent the day reverse engineering this by first dumping all results for single ASCII characters as well as pairs. This showed that each character has its own distinct "XOR code" and letters have the same one regardless of case. The algorithm was remarkably simple to figure out after that: rotate 4 bits left and xor by the code stored in a lookup table.
var xorcodes = [
0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28, 29, 30, 31,
0, 33, 34, 35, 36, 37, 38, 39, // !"#$%&'
40, 41, 42, 43, 44, 45, 46, 47, // ()*+,-./
132, 133, 134, 135, 136, 137, 138, 139, // 01234567
140, 141, 48, 49, 50, 51, 52, 53, 54, // 89:;<=>?#
142, 143, 144, 145, 146, 147, 148, 149, // ABCDEFGH
150, 151, 152, 153, 154, 155, 156, 157, // IJKLMNOP
158, 159, 160, 161, 162, 163, 164, 165, // QRSTUVWX
166, 167, 55, 56, 57, 58, 59, 60, // YZ[\]^_`
142, 143, 144, 145, 146, 147, 148, 149, // abcdefgh
150, 151, 152, 153, 154, 155, 156, 157, // ijklmnop
158, 159, 160, 161, 162, 163, 164, 165, // qrstuvwx
166, 167, 61, 62, 63, 64, 65, 66, // yz{|}~
];
function rol(x, n) {
// simulate a rotate shift left (>>> preserves the sign bit)
return (x<<n) | (x>>>(32-n));
}
function checksum(s) {
var checksum = 0;
for (var i = 0; i < s.length; i++) {
checksum = rol(checksum, 4);
var c = s.charCodeAt(i);
var xorcode = 0;
if (c < xorcodes.length) {
xorcode = xorcodes[c];
}
checksum ^= xorcode;
}
return checksum;
};
See https://github.com/neilodonuts/tsql-checksum-javascript for more info.
DISCLAIMER: I've only worked on compatibility with VARCHAR strings in SQL Server with collation set to SQL_Latin1_General_CP1_CI_AS. This won't work with multiple columns or integers but I'm sure the underlying algorithm uses the same codes so it wouldn't be hard to figure out. It also seems to differ from db<>fiddle possibly due to collation: https://github.com/neilodonuts/tsql-checksum-javascript/blob/master/data/dbfiddle-differences.png ... mileage may vary!
fyi, for those of you who are stuck in T-SQL legacy mode, here's a C# implementation that was tested and looks good for most strings/ints that I've been working with:
public static int[] xorcodes = {
0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28, 29, 30, 31,
0, 33, 34, 35, 36, 37, 38, 39, // !"#$%&'
40, 41, 42, 43, 44, 45, 46, 47, // ()*+,-./
132, 133, 134, 135, 136, 137, 138, 139, // 01234567
140, 141, 48, 49, 50, 51, 52, 53, 54, // 89:;<=>?#
142, 143, 144, 145, 146, 147, 148, 149, // ABCDEFGH
150, 151, 152, 153, 154, 155, 156, 157, // IJKLMNOP
158, 159, 160, 161, 162, 163, 164, 165, // QRSTUVWX
166, 167, 55, 56, 57, 58, 59, 60, // YZ[\]^_`
142, 143, 144, 145, 146, 147, 148, 149, // abcdefgh
150, 151, 152, 153, 154, 155, 156, 157, // ijklmnop
158, 159, 160, 161, 162, 163, 164, 165, // qrstuvwx
166, 167, 61, 62, 63, 64, 65, 66, // yz{|}~
};
public static int rol(int x, int n) {
// simulate a rotate shift left (>>> preserves the sign bit)
return ((int)x << n) | ((int)((uint)x >> (32 - n)));
}
public static int checksum(string s) {
int checksum = 0;
for (var i = 0; i < s.Length; i++) {
checksum = rol(checksum, 4);
var c = ((int)s[i]);
int xorcode = 0;
if (c < xorcodes.Length) {
xorcode = xorcodes[c];
}
checksum ^= xorcode;
}
return checksum;
}

Create a list with millions of elements

I need to create and work with lists with 2**30 elements, but It's to slow. Is there any form to increase the speed?
My code:
sup = []
for i in range(2**30):
sup.append([i,pow(y,i,N)])
pow(y,i,n) == y**i*mod(N), modular exponentiation
I tried to use list comprehensions but isn't enough.
Different approach: why do you want to store those numbers in a list?
You have your formula right there; whenever some piece of code needs sup[i]; you just compute pow(y,i,N).
In other words: instead of storing values within a list; just compute them when you need them.
Edit: as it seems that you have good reasons to store that data in an array; I would then say: use the appropriate tool then.
Meaning: instead of doing computing intense things directly with python, you rather look into the numpy framework. That framework is designed for exactly such purposes. Beyond that, I would also look in the way you are storing/preparing your data. Example: you mention to later look for identical entries in that array. I am wondering if that would meant you should use a dictionary instead of a list; or did you really intend do check 2**30 entries each time you look for equal pow values?
Going by your comment and complementing the answer of GhostCat, go directly for the data you are looking for, for example like this
>>> from collections import defaultdict
>>> y = 2
>>> N = 10
>>> data = defaultdict(list)
>>> for i in range(100):
data[pow(y,i,N)].append(i)
>>> for x in data.items():
x
(8, [3, 7, 11, 15, 19, 23, 27, 31, 35, 39, 43, 47, 51, 55, 59, 63, 67, 71, 75, 79, 83, 87, 91, 95, 99])
(1, [0])
(2, [1, 5, 9, 13, 17, 21, 25, 29, 33, 37, 41, 45, 49, 53, 57, 61, 65, 69, 73, 77, 81, 85, 89, 93, 97])
(4, [2, 6, 10, 14, 18, 22, 26, 30, 34, 38, 42, 46, 50, 54, 58, 62, 66, 70, 74, 78, 82, 86, 90, 94, 98])
(6, [4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96])
>>>
or more specifically, as you need a random sample go for it from the start and don't waste time producing a gazillion stuff you would not need, for example
>>> import random
>>> random_data = defaultdict(list)
>>> for i in random.sample(range(2**30), 20):
random_data[pow(2,i,10)].append(i)
>>> for x in random_data.items():
x
(8, [633728687, 357300263, 208747091, 456291987, 1028949643, 23961003, 750842555])
(2, [602395153, 215460881, 144481457, 829193705])
(4, [752840814, 26689262])
(6, [423520476, 969809132, 326786996, 736424520, 929123176, 865279408, 338237708])
>>>
and depending of what you do with those i later on, you can instead try a more mathematical approach to uncover the underplaying patter that produce an i for which yi mod N is the same and that way you can produce as many i as you need for that particular modular class.
Which for this example is easy, it is
2i = 8 (mod 10) for all i=3 (mod 4) -> range(3,2**30,4)
2i = 2 (mod 10) for all i=1 (mod 4) -> range(1,2**30,4)
2i = 4 (mod 10) for all i=2 (mod 4) -> range(2,2**30,4)
2i = 6 (mod 10) for all i=0 (mod 4) -> range(4,2**30,4)
2i = 1 (mod 10) for i=0

Rand() seems to not work properly [duplicate]

This question already has answers here:
Closed 10 years ago.
Possible Duplicate:
Why do I always get the same sequence of random numbers with rand()?
I've been experimenting with generating random numbers in C, and I've come across something weird. I don't know if it's only on my compiler but whenever I try to generate a pseudo-random number with the rand() function, it returns a very predictable number — the number generated with the parameter before plus 3.125 to be exact. It's hard to explain but here's an example.
srand(71);
int number = rand();
printf("%d", number);
This returns 270.
srand(72);
int number = rand();
printf("%d", number);
This returns 273.
srand(73);
int number = rand();
printf("%d", number);
This returns 277.
srand(74);
int number = rand();
printf("%d", number);
This returns 280.
Every eighth number is 4 higher. Otherwise it's 3.
This can't possibly be right. Is there something wrong with my compiler?
Edit: I figured it out — I created a function where I seed only once, then I loop the rand() and it generates random numbers. Thank you all!
The confusion here is about how pseudorandom number generators work.
Pseudorandom number generators like C's rand work by having a number representing the current 'state'. Every time the rand function is called, some deterministic computations are done on the 'state' number to produce the next 'state' number. Thus, if the generator is given the same input (the same 'state'), it will produce the same output.
So, when you seed the generator with srand(74), it will always generate the same string of numbers, every time. When you seed the generator with srand(75), it will generate a different string of numbers, etc.
The common way to ensure different output each time is to always provide a different seed, usually done by seeding the generator with the current time in seconds/milliseconds, e.g. srand(time(NULL)).
EDIT: Here is a Python session demonstrating this behavior. It is entirely expected.
>>> import random
If we seed the generator with the same number, it will always output the same sequence:
>>> random.seed(500)
>>> [random.randint(0, 100) for _ in xrange(20)]
[80, 95, 58, 25, 76, 37, 80, 34, 57, 79, 1, 33, 40, 29, 92, 6, 45, 31, 13, 11]
>>> random.seed(500)
>>> [random.randint(0, 100) for _ in xrange(20)]
[80, 95, 58, 25, 76, 37, 80, 34, 57, 79, 1, 33, 40, 29, 92, 6, 45, 31, 13, 11]
>>> random.seed(500)
>>> [random.randint(0, 100) for _ in xrange(20)]
[80, 95, 58, 25, 76, 37, 80, 34, 57, 79, 1, 33, 40, 29, 92, 6, 45, 31, 13, 11]
If we give it a different seed, even a slightly different one, the numbers will be totally different from the old seed, yet still the same if the same (new) seed is used:
>>> random.seed(501)
>>> [random.randint(0, 100) for _ in xrange(20)]
[64, 63, 24, 81, 33, 36, 72, 35, 95, 46, 37, 2, 76, 21, 46, 68, 47, 96, 39, 36]
>>> random.seed(501)
>>> [random.randint(0, 100) for _ in xrange(20)]
[64, 63, 24, 81, 33, 36, 72, 35, 95, 46, 37, 2, 76, 21, 46, 68, 47, 96, 39, 36]
>>> random.seed(501)
>>> [random.randint(0, 100) for _ in xrange(20)]
[64, 63, 24, 81, 33, 36, 72, 35, 95, 46, 37, 2, 76, 21, 46, 68, 47, 96, 39, 36]
How do we make our program have different behavior each time? If we supply the same seed, it will always behave the same. We can use the time.time() function, which will yield a different number each time we call it:
>>> import time
>>> time.time()
1347917648.783
>>> time.time()
1347917649.734
>>> time.time()
1347917650.835
So if we keep re-seeding it with a call to time.time(), we will get a different sequence of numbers each time, because the seed is different each time:
>>> random.seed(time.time())
>>> [random.randint(0, 100) for _ in xrange(20)]
[60, 75, 60, 26, 19, 70, 12, 87, 58, 2, 79, 74, 1, 79, 4, 39, 62, 20, 28, 19]
>>> random.seed(time.time())
>>> [random.randint(0, 100) for _ in xrange(20)]
[98, 45, 85, 1, 67, 25, 30, 88, 17, 93, 44, 17, 94, 23, 98, 32, 35, 90, 56, 35]
>>> random.seed(time.time())
>>> [random.randint(0, 100) for _ in xrange(20)]
[44, 17, 10, 98, 18, 6, 17, 15, 60, 83, 73, 67, 18, 2, 40, 76, 71, 63, 92, 5]
Of course, even better than constantly re-seeding it is to seed it once and keep going from there:
>>> random.seed(time.time())
>>> [random.randint(0, 100) for _ in xrange(20)]
[94, 80, 63, 66, 31, 94, 74, 15, 20, 29, 76, 90, 50, 84, 43, 79, 50, 18, 58, 15]
>>> [random.randint(0, 100) for _ in xrange(20)]
[30, 53, 75, 19, 35, 11, 73, 88, 3, 67, 55, 43, 37, 91, 66, 0, 9, 4, 41, 49]
>>> [random.randint(0, 100) for _ in xrange(20)]
[69, 7, 25, 68, 39, 57, 72, 51, 33, 93, 81, 89, 44, 61, 78, 77, 43, 10, 33, 8]
Every invocation of rand() returns the next number in a predefined sequence where the starting number is the seed supplied to srand(). That' why it's called a pseudo-random number generator, and not a random number generator.
rand() is implemented by a pseudo random number generator.
The distribution of numbers generated by consecutive calls to rand() have the properties of being random numbers, but the order is pre-determined.
The 'start' number is determined by the seed that you provide.
You should give a PRNG a single seed only. Providing it with multiple seeds can radically alter the randomness of the generator. In addition, providing it the same seed over and over removes all randomness.
Generating a "random" number regardless of the implementation is dependent on a divergent infinite sequence. The infinite sequence is generated using the seed of the random function and it is actually pseudo random because of its nature. This would explain to you why your number is actually very dependent on the seed that you give the function.
In some implementations the sequence is only one and the seed is the starting member of the sequence. In others there are difference sequences depending on the seed. If a seed is not provided then the seed is determined by the internal "clock".
The number is truncated when using an upper and lower bounds for your random number by respectively doing randValue % upperBound and randValue + lowerBound. Random implementation is very similar to Hash Functions. Depending on architecture the upper bound of the random value is set depending on what it the largest integer/double that it can carry out if not set lower by the user.

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