Highest and lowest possible result of this javascript expression? - javascript

What is the highest number this javascript expression can evaluate to? What is the lowest number? Why?
+(''+Math.random()).substring(2)
Extra credit: How many different values can the expression evaluate to? Can it be every value from the minimum to the maximum, or are some intermediate values not obtainable due to rounding issues?
Response to Daniel's answer (deleted, was 10000000000000000 max, 0 min):
I was playing around in Chrome's console and got this:
Math.random();
>> 0.00012365682050585747
'12365682050585747'.length
>> 17
12365682050585747 > 10000000000000000
>> true
... so 10000000000000000 can't be the max!

It depends on how the random number is generated, and how the number will be converted to string. The ECMAScript spec doesn't specify both of these.
In practice, the number will have at most 17 significant figures, so the maximum should be at most 1017.
The spec does specify that a number will be displayed in decimal form (instead of scientific form) when the exponent is between -6 and 20 (10-6 ≤ x < 1021), so we just need to restrict our attention on numbers in [10-6, 1) when trying to seek the maximum exhaustively.
However, in this range a number must be representable as s × 2e, where 1 ≤ s ≤ 2 − 2-52 with a precision of Δs = 2-52 and -20 ≤ e ≤ -1. The spec recommends that ToNumber(ToString(x)) == x, so the number should be precise down to 2-52+e for a given e. Thus the "17-digit" number with (2 − n × 2-52) × 2e with the smallest n will be the biggest number representable with a given e, after chopping the initial 0..
v
(-20) 0.0000019073486328124998
(-19) 0.0000038146972656249996
(-18) 0.0000076293945312499975 (n=3)
(-17) 0.000015258789062499998
(-16) 0.000030517578124999997
(-15) 0.000061035156249999986 (n=2)
(-14) 0.00012207031249999999
(-13) 0.00024414062499999997
(-12) 0.00048828124999999995
(-11) 0.0009765624999999999 (always 16-digit?)
(-10) 0.0019531249999999998
(-9) 0.0039062499999999996
(-8) 0.0078124999999999965 (n=4)
(-7) 0.015624999999999998
(-6) 0.031249999999999997
(-5) 0.062499999999999986 (n=2)
(-4) 0.12499999999999999
(-3) 0.24999999999999997
(-2) 0.49999999999999994
(-1) 0.9999999999999999 (always 16-digit?)
From here we know that the absolute maximum is 78,124,999,999,999,965.
Math.random() can return any nonnegative numbers in the interval [0, 1), so the safe minimum is -324 from 5e-324 (the smallest subnormal number in double precision is 4.94 × 10-324).

For me the highest number is 1/0 (===Infinity) and the lowest obviously -1/0 (in Chromium browser).
Edit: You can also try parse a number from string to see which evaluates to Infinity.
var a = "1";
while(parseInt(a)!==Infinity) a=a+"0";
alert("Length of the highest number is: " + (a.length-1));
309 for me.

Related

Different hash results in Java and JavaScript [duplicate]

I'm in a computer systems course and have been struggling, in part, with two's complement. I want to understand it, but everything I've read hasn't brought the picture together for me. I've read the Wikipedia article and various other articles, including my text book.
What is two's complement, how can we use it and how can it affect numbers during operations like casts (from signed to unsigned and vice versa), bit-wise operations and bit-shift operations?
Two's complement is a clever way of storing integers so that common math problems are very simple to implement.
To understand, you have to think of the numbers in binary.
It basically says,
for zero, use all 0's.
for positive integers, start counting up, with a maximum of 2(number of bits - 1)-1.
for negative integers, do exactly the same thing, but switch the role of 0's and 1's and count down (so instead of starting with 0000, start with 1111 - that's the "complement" part).
Let's try it with a mini-byte of 4 bits (we'll call it a nibble - 1/2 a byte).
0000 - zero
0001 - one
0010 - two
0011 - three
0100 to 0111 - four to seven
That's as far as we can go in positives. 23-1 = 7.
For negatives:
1111 - negative one
1110 - negative two
1101 - negative three
1100 to 1000 - negative four to negative eight
Note that you get one extra value for negatives (1000 = -8) that you don't for positives. This is because 0000 is used for zero. This can be considered as Number Line of computers.
Distinguishing between positive and negative numbers
Doing this, the first bit gets the role of the "sign" bit, as it can be used to distinguish between nonnegative and negative decimal values. If the most significant bit is 1, then the binary can be said to be negative, where as if the most significant bit (the leftmost) is 0, you can say the decimal value is nonnegative.
"Sign-magnitude" negative numbers just have the sign bit flipped of their positive counterparts, but this approach has to deal with interpreting 1000 (one 1 followed by all 0s) as "negative zero" which is confusing.
"Ones' complement" negative numbers are just the bit-complement of their positive counterparts, which also leads to a confusing "negative zero" with 1111 (all ones).
You will likely not have to deal with Ones' Complement or Sign-Magnitude integer representations unless you are working very close to the hardware.
I wonder if it could be explained any better than the Wikipedia article.
The basic problem that you are trying to solve with two's complement representation is the problem of storing negative integers.
First, consider an unsigned integer stored in 4 bits. You can have the following
0000 = 0
0001 = 1
0010 = 2
...
1111 = 15
These are unsigned because there is no indication of whether they are negative or positive.
Sign Magnitude and Excess Notation
To store negative numbers you can try a number of things. First, you can use sign magnitude notation which assigns the first bit as a sign bit to represent +/- and the remaining bits to represent the magnitude. So using 4 bits again and assuming that 1 means - and 0 means + then you have
0000 = +0
0001 = +1
0010 = +2
...
1000 = -0
1001 = -1
1111 = -7
So, you see the problem there? We have positive and negative 0. The bigger problem is adding and subtracting binary numbers. The circuits to add and subtract using sign magnitude will be very complex.
What is
0010
1001 +
----
?
Another system is excess notation. You can store negative numbers, you get rid of the two zeros problem but addition and subtraction remains difficult.
So along comes two's complement. Now you can store positive and negative integers and perform arithmetic with relative ease. There are a number of methods to convert a number into two's complement. Here's one.
Convert Decimal to Two's Complement
Convert the number to binary (ignore the sign for now)
e.g. 5 is 0101 and -5 is 0101
If the number is a positive number then you are done.
e.g. 5 is 0101 in binary using two's complement notation.
If the number is negative then
3.1 find the complement (invert 0's and 1's)
e.g. -5 is 0101 so finding the complement is 1010
3.2 Add 1 to the complement 1010 + 1 = 1011.
Therefore, -5 in two's complement is 1011.
So, what if you wanted to do 2 + (-3) in binary? 2 + (-3) is -1.
What would you have to do if you were using sign magnitude to add these numbers? 0010 + 1101 = ?
Using two's complement consider how easy it would be.
2 = 0010
-3 = 1101 +
-------------
-1 = 1111
Converting Two's Complement to Decimal
Converting 1111 to decimal:
The number starts with 1, so it's negative, so we find the complement of 1111, which is 0000.
Add 1 to 0000, and we obtain 0001.
Convert 0001 to decimal, which is 1.
Apply the sign = -1.
Tada!
Like most explanations I've seen, the ones above are clear about how to work with 2's complement, but don't really explain what they are mathematically. I'll try to do that, for integers at least, and I'll cover some background that's probably familiar first.
Recall how it works for decimal: 2345 is a way of writing 2 × 103 + 3 × 102 + 4 × 101 + 5 × 100.
In the same way, binary is a way of writing numbers using just 0 and 1 following the same general idea, but replacing those 10s above with 2s. Then in binary, 1111is a way of writing 1 × 23 + 1 × 22 + 1 × 21 + 1 × 20and if you work it out, that turns out to equal 15 (base 10). That's because it is 8+4+2+1 = 15.
This is all well and good for positive numbers. It even works for negative numbers if you're willing to just stick a minus sign in front of them, as humans do with decimal numbers. That can even be done in computers, sort of, but I haven't seen such a computer since the early 1970's. I'll leave the reasons for a different discussion.
For computers it turns out to be more efficient to use a complement representation for negative numbers. And here's something that is often overlooked. Complement notations involve some kind of reversal of the digits of the number, even the implied zeroes that come before a normal positive number. That's awkward, because the question arises: all of them? That could be an infinite number of digits to be considered.
Fortunately, computers don't represent infinities. Numbers are constrained to a particular length (or width, if you prefer). So let's return to positive binary numbers, but with a particular size. I'll use 8 digits ("bits") for these examples. So our binary number would really be 00001111or 0 × 27 + 0 × 26 + 0 × 25 + 0 × 24 + 1 × 23 + 1 × 22 + 1 × 21 + 1 × 20
To form the 2's complement negative, we first complement all the (binary) digits to form 11110000and add 1 to form 11110001but how are we to understand that to mean -15?
The answer is that we change the meaning of the high-order bit (the leftmost one). This bit will be a 1 for all negative numbers. The change will be to change the sign of its contribution to the value of the number it appears in. So now our 11110001 is understood to represent -1 × 27 + 1 × 26 + 1 × 25 + 1 × 24 + 0 × 23 + 0 × 22 + 0 × 21 + 1 × 20Notice that "-" in front of that expression? It means that the sign bit carries the weight -27, that is -128 (base 10). All the other positions retain the same weight they had in unsigned binary numbers.
Working out our -15, it is -128 + 64 + 32 + 16 + 1 Try it on your calculator. it's -15.
Of the three main ways that I've seen negative numbers represented in computers, 2's complement wins hands down for convenience in general use. It has an oddity, though. Since it's binary, there have to be an even number of possible bit combinations. Each positive number can be paired with its negative, but there's only one zero. Negating a zero gets you zero. So there's one more combination, the number with 1 in the sign bit and 0 everywhere else. The corresponding positive number would not fit in the number of bits being used.
What's even more odd about this number is that if you try to form its positive by complementing and adding one, you get the same negative number back. It seems natural that zero would do this, but this is unexpected and not at all the behavior we're used to because computers aside, we generally think of an unlimited supply of digits, not this fixed-length arithmetic.
This is like the tip of an iceberg of oddities. There's more lying in wait below the surface, but that's enough for this discussion. You could probably find more if you research "overflow" for fixed-point arithmetic. If you really want to get into it, you might also research "modular arithmetic".
2's complement is very useful for finding the value of a binary, however I thought of a much more concise way of solving such a problem(never seen anyone else publish it):
take a binary, for example: 1101 which is [assuming that space "1" is the sign] equal to -3.
using 2's complement we would do this...flip 1101 to 0010...add 0001 + 0010 ===> gives us 0011. 0011 in positive binary = 3. therefore 1101 = -3!
What I realized:
instead of all the flipping and adding, you can just do the basic method for solving for a positive binary(lets say 0101) is (23 * 0) + (22 * 1) + (21 * 0) + (20 * 1) = 5.
Do exactly the same concept with a negative!(with a small twist)
take 1101, for example:
for the first number instead of 23 * 1 = 8 , do -(23 * 1) = -8.
then continue as usual, doing -8 + (22 * 1) + (21 * 0) + (20 * 1) = -3
Imagine that you have a finite number of bits/trits/digits/whatever. You define 0 as all digits being 0, and count upwards naturally:
00
01
02
..
Eventually you will overflow.
98
99
00
We have two digits and can represent all numbers from 0 to 100. All those numbers are positive! Suppose we want to represent negative numbers too?
What we really have is a cycle. The number before 2 is 1. The number before 1 is 0. The number before 0 is... 99.
So, for simplicity, let's say that any number over 50 is negative. "0" through "49" represent 0 through 49. "99" is -1, "98" is -2, ... "50" is -50.
This representation is ten's complement. Computers typically use two's complement, which is the same except using bits instead of digits.
The nice thing about ten's complement is that addition just works. You do not need to do anything special to add positive and negative numbers!
I read a fantastic explanation on Reddit by jng, using the odometer as an analogy.
It is a useful convention. The same circuits and logic operations that
add / subtract positive numbers in binary still work on both positive
and negative numbers if using the convention, that's why it's so
useful and omnipresent.
Imagine the odometer of a car, it rolls around at (say) 99999. If you
increment 00000 you get 00001. If you decrement 00000, you get 99999
(due to the roll-around). If you add one back to 99999 it goes back to
00000. So it's useful to decide that 99999 represents -1. Likewise, it is very useful to decide that 99998 represents -2, and so on. You have
to stop somewhere, and also by convention, the top half of the numbers
are deemed to be negative (50000-99999), and the bottom half positive
just stand for themselves (00000-49999). As a result, the top digit
being 5-9 means the represented number is negative, and it being 0-4
means the represented is positive - exactly the same as the top bit
representing sign in a two's complement binary number.
Understanding this was hard for me too. Once I got it and went back to
re-read the books articles and explanations (there was no internet
back then), it turned out a lot of those describing it didn't really
understand it. I did write a book teaching assembly language after
that (which did sell quite well for 10 years).
Two complement is found out by adding one to 1'st complement of the given number.
Lets say we have to find out twos complement of 10101 then find its ones complement, that is, 01010 add 1 to this result, that is, 01010+1=01011, which is the final answer.
Lets get the answer 10 – 12 in binary form using 8 bits:
What we will really do is 10 + (-12)
We need to get the compliment part of 12 to subtract it from 10.
12 in binary is 00001100.
10 in binary is 00001010.
To get the compliment part of 12 we just reverse all the bits then add 1.
12 in binary reversed is 11110011. This is also the Inverse code (one's complement).
Now we need to add one, which is now 11110100.
So 11110100 is the compliment of 12! Easy when you think of it this way.
Now you can solve the above question of 10 - 12 in binary form.
00001010
11110100
-----------------
11111110
Looking at the two's complement system from a math point of view it really makes sense. In ten's complement, the idea is to essentially 'isolate' the difference.
Example: 63 - 24 = x
We add the complement of 24 which is really just (100 - 24). So really, all we are doing is adding 100 on both sides of the equation.
Now the equation is: 100 + 63 - 24 = x + 100, that is why we remove the 100 (or 10 or 1000 or whatever).
Due to the inconvenient situation of having to subtract one number from a long chain of zeroes, we use a 'diminished radix complement' system, in the decimal system, nine's complement.
When we are presented with a number subtracted from a big chain of nines, we just need to reverse the numbers.
Example: 99999 - 03275 = 96724
That is the reason, after nine's complement, we add 1. As you probably know from childhood math, 9 becomes 10 by 'stealing' 1. So basically it's just ten's complement that takes 1 from the difference.
In Binary, two's complement is equatable to ten's complement, while one's complement to nine's complement. The primary difference is that instead of trying to isolate the difference with powers of ten (adding 10, 100, etc. into the equation) we are trying to isolate the difference with powers of two.
It is for this reason that we invert the bits. Just like how our minuend is a chain of nines in decimal, our minuend is a chain of ones in binary.
Example: 111111 - 101001 = 010110
Because chains of ones are 1 below a nice power of two, they 'steal' 1 from the difference like nine's do in decimal.
When we are using negative binary number's, we are really just saying:
0000 - 0101 = x
1111 - 0101 = 1010
1111 + 0000 - 0101 = x + 1111
In order to 'isolate' x, we need to add 1 because 1111 is one away from 10000 and we remove the leading 1 because we just added it to the original difference.
1111 + 1 + 0000 - 0101 = x + 1111 + 1
10000 + 0000 - 0101 = x + 10000
Just remove 10000 from both sides to get x, it's basic algebra.
The word complement derives from completeness. In the decimal world the numerals 0 through 9 provide a complement (complete set) of numerals or numeric symbols to express all decimal numbers. In the binary world the numerals 0 and 1 provide a complement of numerals to express all binary numbers. In fact The symbols 0 and 1 must be used to represent everything (text, images, etc) as well as positive (0) and negative (1).
In our world the blank space to the left of number is considered as zero:
35=035=000000035.
In a computer storage location there is no blank space. All bits (binary digits) must be either 0 or 1. To efficiently use memory numbers may be stored as 8 bit, 16 bit, 32 bit, 64 bit, 128 bit representations. When a number that is stored as an 8 bit number is transferred to a 16 bit location the sign and magnitude (absolute value) must remain the same. Both 1's complement and 2's complement representations facilitate this.
As a noun:
Both 1's complement and 2's complement are binary representations of signed quantities where the most significant bit (the one on the left) is the sign bit. 0 is for positive and 1 is for negative.
2s complement does not mean negative. It means a signed quantity. As in decimal the magnitude is represented as the positive quantity. The structure uses sign extension to preserve the quantity when promoting to a register [] with more bits:
[0101]=[00101]=[00000000000101]=5 (base 10)
[1011]=[11011]=[11111111111011]=-5(base 10)
As a verb:
2's complement means to negate. It does not mean make negative. It means if negative make positive; if positive make negative. The magnitude is the absolute value:
if a >= 0 then |a| = a
if a < 0 then |a| = -a = 2scomplement of a
This ability allows efficient binary subtraction using negate then add.
a - b = a + (-b)
The official way to take the 1's complement is for each digit subtract its value from 1.
1'scomp(0101) = 1010.
This is the same as flipping or inverting each bit individually. This results in a negative zero which is not well loved so adding one to te 1's complement gets rid of the problem.
To negate or take the 2s complement first take the 1s complement then add 1.
Example 1 Example 2
0101 --original number 1101
1's comp 1010 0010
add 1 0001 0001
2's comp 1011 --negated number 0011
In the examples the negation works as well with sign extended numbers.
Adding:
1110 Carry 111110 Carry
0110 is the same as 000110
1111 111111
sum 0101 sum 000101
SUbtracting:
1110 Carry 00000 Carry
0110 is the same as 00110
-0111 +11001
---------- ----------
sum 0101 sum 11111
Notice that when working with 2's complement, blank space to the left of the number is filled with zeros for positive numbers butis filled with ones for negative numbers. The carry is always added and must be either a 1 or 0.
Cheers
2's complement is essentially a way of coming up with the additive inverse of a binary number. Ask yourself this: Given a number in binary form (present at a fixed length memory location), what bit pattern, when added to the original number (at the fixed length memory location), would make the result all zeros ? (at the same fixed length memory location). If we could come up with this bit pattern then that bit pattern would be the -ve representation (additive inverse) of the original number; as by definition adding a number to its additive inverse always results in zero. Example: take 5 which is 101 present inside a single 8 bit byte. Now the task is to come up with a bit pattern which when added to the given bit pattern (00000101) would result in all zeros at the memory location which is used to hold this 5 i.e. all 8 bits of the byte should be zero. To do that, start from the right most bit of 101 and for each individual bit, again ask the same question: What bit should I add to the current bit to make the result zero ? continue doing that taking in account the usual carry over. After we are done with the 3 right most places (the digits that define the original number without regard to the leading zeros) the last carry goes in the bit pattern of the additive inverse. Furthermore, since we are holding in the original number in a single 8 bit byte, all other leading bits in the additive inverse should also be 1's so that (and this is important) when the computer adds "the number" (represented using the 8 bit pattern) and its additive inverse using "that" storage type (a byte) the result in that byte would be all zeros.
1 1 1
----------
1 0 1
1 0 1 1 ---> additive inverse
---------
0 0 0
Many of the answers so far nicely explain why two's complement is used to represent negative numbers, but do not tell us what two's complement number is, particularly not why a '1' is added, and in fact often added in a wrong way.
The confusion comes from a poor understanding of the definition of a complement number. A complement is the missing part that would make something complete.
The radix complement of an n digit number x in radix b is, by definition, b^n-x.
In binary 4 is represented by 100, which has 3 digits (n=3) and a radix of 2 (b=2). So its radix complement is b^n-x = 2^3-4=8-4=4 (or 100 in binary).
However, in binary obtaining a radix's complement is not as easy as getting its diminished radix complement, which is defined as (b^n-1)-y, just 1 less than that of radix complement. To get a diminished radix complement, you simply flip all the digits.
100 -> 011 (diminished (one's) radix complement)
to obtain the radix (two's) complement, we simply add 1, as the definition defined.
011 +1 ->100 (two's complement).
Now with this new understanding, let's take a look of the example given by Vincent Ramdhanie (see above second response):
Converting 1111 to decimal:
The number starts with 1, so it's negative, so we find the complement of 1111, which is 0000.
Add 1 to 0000, and we obtain 0001.
Convert 0001 to decimal, which is 1.
Apply the sign = -1.
Tada!
Should be understood as:
The number starts with 1, so it's negative. So we know it is a two's complement of some value x. To find the x represented by its two's complement, we first need find its 1's complement.
two's complement of x: 1111
one's complement of x: 1111-1 ->1110;
x = 0001, (flip all digits)
Apply the sign -, and the answer =-x =-1.
I liked lavinio's answer, but shifting bits adds some complexity. Often there's a choice of moving bits while respecting the sign bit or while not respecting the sign bit. This is the choice between treating the numbers as signed (-8 to 7 for a nibble, -128 to 127 for bytes) or full-range unsigned numbers (0 to 15 for nibbles, 0 to 255 for bytes).
It is a clever means of encoding negative integers in such a way that approximately half of the combination of bits of a data type are reserved for negative integers, and the addition of most of the negative integers with their corresponding positive integers results in a carry overflow that leaves the result to be binary zero.
So, in 2's complement if one is 0x0001 then -1 is 0x1111, because that will result in a combined sum of 0x0000 (with an overflow of 1).
2’s Complements: When we add an extra one with the 1’s complements of a number we will get the 2’s complements. For example: 100101 it’s 1’s complement is 011010 and 2’s complement is 011010+1 = 011011 (By adding one with 1's complement) For more information
this article explain it graphically.
Two's complement is mainly used for the following reasons:
To avoid multiple representations of 0
To avoid keeping track of carry bit (as in one's complement) in case of an overflow.
Carrying out simple operations like addition and subtraction becomes easy.
Two's complement is one of the ways of expressing a negative number and most of the controllers and processors store a negative number in two's complement form.
In simple terms, two's complement is a way to store negative numbers in computer memory. Whereas positive numbers are stored as a normal binary number.
Let's consider this example,
The computer uses the binary number system to represent any number.
x = 5;
This is represented as 0101.
x = -5;
When the computer encounters the - sign, it computes its two's complement and stores it.
That is, 5 = 0101 and its two's complement is 1011.
The important rules the computer uses to process numbers are,
If the first bit is 1 then it must be a negative number.
If all the bits except first bit are 0 then it is a positive number, because there is no -0 in number system (1000 is not -0 instead it is positive 8).
If all the bits are 0 then it is 0.
Else it is a positive number.
To bitwise complement a number is to flip all the bits in it. To two’s complement it, we flip all the bits and add one.
Using 2’s complement representation for signed integers, we apply the 2’s complement operation to convert a positive number to its negative equivalent and vice versa. So using nibbles for an example, 0001 (1) becomes 1111 (-1) and applying the op again, returns to 0001.
The behaviour of the operation at zero is advantageous in giving a single representation for zero without special handling of positive and negative zeroes. 0000 complements to 1111, which when 1 is added. overflows to 0000, giving us one zero, rather than a positive and a negative one.
A key advantage of this representation is that the standard addition circuits for unsigned integers produce correct results when applied to them. For example adding 1 and -1 in nibbles: 0001 + 1111, the bits overflow out of the register, leaving behind 0000.
For a gentle introduction, the wonderful Computerphile have produced a video on the subject.
The question is 'What is “two's complement”?'
The simple answer for those wanting to understand it theoretically (and me seeking to complement the other more practical answers): 2's complement is the representation for negative integers in the dual system that does not require additional characters, such as + and -.
Two's complement of a given number is the number got by adding 1 with the ones' complement of the number.
Suppose, we have a binary number: 10111001101
Its 1's complement is: 01000110010
And its two's complement will be: 01000110011
Reference: Two's Complement (Thomas Finley)
I invert all the bits and add 1. Programmatically:
// In C++11
int _powers[] = {
1,
2,
4,
8,
16,
32,
64,
128
};
int value = 3;
int n_bits = 4;
int twos_complement = (value ^ ( _powers[n_bits]-1)) + 1;
You can also use an online calculator to calculate the two's complement binary representation of a decimal number: http://www.convertforfree.com/twos-complement-calculator/
The simplest answer:
1111 + 1 = (1)0000. So 1111 must be -1. Then -1 + 1 = 0.
It's perfect to understand these all for me.

Need for a JavaScript/ecma262 ToInt32 algorithm explanation

I'm trying to understand how JS engines convert a JS Number (Float64) to a 32-bit signed integer. I read that one can quickly convert a 64 bit float to a 32 bit signed integer with the bitwise OR like:
-8589934590 | 0 // which gives 2
I can't understand where does the 2 come from. According to the spec, the ToInt32 algorithm does this (the bold text is mine, not the spec's):
Let number be ? ToNumber(argument): -8589934590 is already a Number
If number is NaN, +0, -0, +∞, or -∞, return +0.: No
Let int be the Number value that is the same sign as number and whose magnitude is floor(abs(number)): -8589934590 is already an integer
Let int32bit be int modulo 2³² Since 2³² is positive the result should also be positive. In JS the remainder operator uses the sign of the left operand, so to get a modulo in this case (where -8589934590 is negative), we negate it: let int32bit = 8589934590 % 2**32 // 4294967294 which has 32 bit length 0b11111111111111111111111111111110
If int32bit ≥ 2³¹, return int32bit - 2³²; otherwise return int32bit. int32bit is smaller 2³¹ (since it's negative), so I use int32bit which equals -2 (Even if we consider 0b11111111111111111111111111111110 an unsigned integer, then it's greater 2³¹ and int32bit - 2³² still equals -2
Could someone, please, explain, do I correctly understand the ToInt32 algorithm and the bitwise OR operator?
Your step 4 is wrong. Modulo is defined by the spec as:
The notation “x modulo y” (y must be finite and nonzero) computes a value k of the same sign as y (or zero) such that abs(k) < abs(y) and x-k = q × y for some integer q.
So -8589934590 is our x, and 2**32 is our y, from that we also know that k must be positive. If we choose q = -1 we can solve the equation to k = -4294967294. That is however not a valid solution, as k (negative) does not have the same sign as y (positive). If we choose q = -2 instead, we get k = 2.
So for negative numbers x and positive numbers y, q * y will always have to result in a smaller number than x for k to be positive. Thus if we are transforming that to positive numbers (like you did), we are looking for the larger multiple of the number not the smaller one. E.g. if we take 2 % 3, that'll return 2 (2 - 2 = 3 * 0), whereas -2 modulo 3 will return 1 (-2 -1 = 3 * -1).

Javascript's Shift right with zero-fill operator (>>>) yielding unexpected result

First, (-1 >>> 0) === (2**32 - 1) which I expect is due to adding a new zero to the left, thus converting the number into 33-bit number?
But, Why is (-1 >>> 32) === (2**32 - 1) as well, while I expect it (after shifting the 32-bit number 32 times and replacing the Most Significant Bits with zeros) to be 0.
Shouldn't it be equal ((-1 >>> 31) >>> 1) === 0? or Am I missing something?
When you execute (-1 >>> 0) you are executing an unsigned right shift. The unsigned here is key. Per the spec, the result of >>> is always unsigned. -1 is represented as the two's compliment of 1. This in binary is all 1s (In an 8 bit system it'd be 11111111).
So now you are making it unsigned by executing >>> 0. You are saying, "shift the binary representation of -1, which is all 1s, by zero bits (make no changes), but make it return an unsigned number.” So, you get the value of all 1s. Go to any javascript console in a browser and type:
console.log(2**32 - 1) //4294967295
// 0b means binary representation, and it can have a negative sign
console.log(0b11111111111111111111111111111111) //4294967295
console.log(-0b1 >>> 0) //4294967295
Remember 2 ** any number minus 1 is always all ones in binary. It's the same number of ones as the power you raised two to. So 2**32 - 1 is 32 1s. For example, two to the 3rd power (eight) minus one (seven) is 111 in binary.
So for the next one (-1 >>> 32) === (2**32 - 1).... let's look at a few things. We know the binary representation of -1 is all 1s. Then shift it right one digit and you get the same value as having all 1s but precede it with a zero (and return an unsigned number).
console.log(-1 >>> 1) //2147483647
console.log(0b01111111111111111111111111111111) //2147483647
And keep shifting until you have 31 zeros and a single 1 at the end.
console.log(-1 >>> 31) //1
This makes sense to me, we have 31 0s and a single 1 now for our 32 bits.
So then you hit the weird case, shifting one more time should make zero right?
Per the spec:
6.1.6.1.11 Number::unsignedRightShift ( x, y )
Let lnum be ! ToInt32(x).
Let rnum be ! ToUint32(y).
Let shiftCount be the result of masking out all but the least significant 5 bits of rnum, that is, compute rnum & 0x1F.
Return the result of performing a zero-filling right shift of lnum by shiftCount bits. Vacated bits are filled with zero. The result is an unsigned 32-bit integer.
So we know we already have -1, which is all 1s in twos compliment. And we are going to shift it per the last step of the docs by shiftCount bits (which we think is 32). And shiftCount is:
Let shiftCount be the result of masking out all but the least significant 5 bits of rnum, that is, compute rnum & 0x1F.
So what is rnum & 0x1F? Well & means a bitwise AND operation. lnum is the number left of the >>> and rnum is the number right of it. So we are saying 32 AND 0x1F. Remember 32 is 100000. 0x is hexadecimal where each character can be represented by 4 bits. 1 is 0001 and F is 1111. So 0x1F is 00011111 or 11111 (31 in base 10, 2**5 - 1 also).
console.log(0x1F) //31 (which is 11111)
32: 100000 &
0x1F: 011111
---------
000000
The number of bits to shift if zero. This is because the leading 1 in 32 is not part of the 5 most significant bits! 32 is six bits. So we take 32 1s and shift it zero bits! That's why. The answer is still 32 1s.
On the example -1 >>> 31 this made sense because 31 is <= 5 bits. So we did
31: 11111 &
0x1F: 11111
-------
11111
And shifted it 31 bits.... as expected.
Let's test this further.... let's do
console.log(-1 >>> 33) //2147483647
console.log(-1 >>> 1) //2147483647
That makes sense, just shift it one bit.
33: 100001 &
0x1F: 011111
---------
00001
So, go over 5 bits with a bitwise operator and get confused. Want to play stump the dummy with a person who hasn't researched the ECMAScript to answer a stackoverflow post? Just ask why are these the same.
console.log(-1 >>> 24033) //2147483647
console.log(-1 >>> 1) //2147483647
Well of course it's because
console.log(0b101110111100001) // 24033
console.log(0b000000000000001) // 1
// ^^^^^ I only care about these bits!!!
When you do (-1 >>> 0), you are turning the sign bit into zero while keeping the rest of the number the same, therefore ending up as 2**32 - 1.
The next behaviour is documented in the ECMAScript specification. The actual number of shifts is going to be "the result of masking out all but the least significant 5 bits of rnum, that is, compute rnum & 0x1F".
Since 32 & 0x1F === 0, both of your results will be identical.

Javascript numbers representing limits of inequality comparisons

I am working with a set of values such that 0 < value < 1. I want to validate some data such that a value outside this range is modified to the closest valid value. After some experimentation, I discovered Number.MIN_VALUE as the best way of representing the lower limit. However, the upper limit, 1 - Number.MIN_VALUE, has unexpected behaviour:
Whereas:
Number.MIN_VALUE; //5e-324 and Number.MIN_VALUE > 0; //true
1 - Number.MIN_VALUE; //1 and 1 - Number.MIN_VALUE < 1; //false
So, what is the best way of generating the smallest and largest numbers in Javascript that satisfy an inequality of the form a < value < b?
The mantissa of a JavaScript float has 52 bits. With denormalization, the minimum float can be extremely small, 5e-324 -- this is represented as something like 51 zero bits, 1 one bit, and the most negative exponent.
But you can't get that close to 1, because the representation of this will be all one bits, and you can't increase the range using exponents. It's limited by the floating point precision.
So the value will be as given below:
console.log(1 - .5**53);

Firefox/Chrome JSON parsing numeric value in JSON as numeric value -1 for 1 specific value [duplicate]

Consider this code (node v5.0.0)
const a = Math.pow(2, 53)
const b = Math.pow(2, 53) + 1
const c = Math.pow(2, 53) + 2
console.log(a === b) // true
console.log(a === c) // false
Why a === b is true?
What is the maximum integer value javascript can handle?
I'm implementing random integer generator up to 2^64. Is there any pitfall I should be aware of?
How javascript treat large integers?
JS does not have integers. JS numbers are 64 bit floats. They are stored as a mantissa and an exponent.
The precision is given by the mantissa, the magnitude by the exponent.
If your number needs more precision than what can be stored in the mantissa, the least significant bits will be truncated.
9007199254740992; // 9007199254740992
(9007199254740992).toString(2);
// "100000000000000000000000000000000000000000000000000000"
// \ \ ... /\
// 1 10 53 54
// The 54-th is not stored, but is not a problem because it's 0
9007199254740993; // 9007199254740992
(9007199254740993).toString(2);
// "100000000000000000000000000000000000000000000000000000"
// \ \ ... /\
// 1 10 53 54
// The 54-th bit should be 1, but the mantissa only has 53 bits!
9007199254740994; // 9007199254740994
(9007199254740994).toString(2);
// "100000000000000000000000000000000000000000000000000010"
// \ \ ... /\
// 1 10 53 54
// The 54-th is not stored, but is not a problem because it's 0
Then, you can store all these integers:
-9007199254740992, -9007199254740991, ..., 9007199254740991, 9007199254740992
The second one is called the minimum safe integer:
The value of Number.MIN_SAFE_INTEGER is the smallest integer n such
that n and n − 1 are both exactly representable as a Number value.
The value of Number.MIN_SAFE_INTEGER is −9007199254740991
(−(253−1)).
The second last one is called the maximum safe integer:
The value of Number.MAX_SAFE_INTEGER is the largest integer n such
that n and n + 1 are both exactly representable as a Number value.
The value of Number.MAX_SAFE_INTEGER is 9007199254740991
(253−1).
Answering your second question, here is your maximum safe integer in JavaScript:
console.log( Number.MAX_SAFE_INTEGER );
All the rest is written in MDN:
The MAX_SAFE_INTEGER constant has a value of 9007199254740991. The
reasoning behind that number is that JavaScript uses double-precision
floating-point format numbers as specified in IEEE 754 and can only
safely represent numbers between -(2 ** 53 - 1) and 2 ** 53 - 1.
Safe in this context refers to the ability to represent integers
exactly and to correctly compare them. For example,
Number.MAX_SAFE_INTEGER + 1 === Number.MAX_SAFE_INTEGER + 2 will
evaluate to true, which is mathematically incorrect. See
Number.isSafeInteger() for more information.
.:: JavaScript only supports 53 bit integers ::.
All numbers in JavaScript are floating point which means that integers are always represented as
sign × mantissa × 2exponent
The mantissa has 53 bits. You can use the exponent to get higher integers, but then they won’t be contiguous, any more. For example, you generally need to multiply the mantissa by two (exponent 1) in order to reach the 54th bit.
However, if you multiply by two, you will only be able to represent every second integer:
Math.pow(2, 53) // 54 bits 9007199254740992
Math.pow(2, 53) + 1 // 9007199254740992
Math.pow(2, 53) + 2 //9007199254740994
Math.pow(2, 53) + 3 //9007199254740996
Math.pow(2, 53) + 4 //9007199254740996
Rounding effects during the addition make things unpredictable for odd increments (+1 versus +3). The actual representation is a bit more complicated but this explanation should help you understand the basic problem.
You can safely use strint library to encode large integers in strings and perform arithmetic operations on them too.
Here is the full article.
Number.MAX_VALUE will tell you the largest floating-point value representable in your JS implementation. The answer will likely be: 1.7976931348623157e+308. But that doesn't mean that every integer up to 10^308 can be represented exactly. As your example code shows, beyond 2^53 only even numbers can be represented, and as you go farther out on the number line the gaps get much wider.
If you need exact integers larger than 2^53, you probably want to work with a bignum package, which allows for arbitrarily large integers (within the bounds of available memory). Two packages that I happen to know are:
BigInt by Leemon
and
Crunch
To supplement to other answers here, it's worth mentioning that BigInt exists. This allows JavaScript to handle arbitrarily large integers.
Use the n suffix on your numbers and use regular operators like 2n ** 53n + 2n. Important to point out that a BigInt is not a Number, but you can do range-limited interoperation with Number via explicit conversions.
Some examples at the Node.js REPL:
> 999999999999999999999999999999n + 1n
1000000000000000000000000000000n
> 2n ** 53n
9007199254740992n
> 2n ** 53n + 1n
9007199254740993n
> 2n ** 53n == 2n ** 53n + 1n
false
> typeof 1n
'bigint'
> 3 * 4n
TypeError: Cannot mix BigInt and other types, use explicit conversions
> BigInt(3) * 4n
12n
> 3 * Number(4n)
12
> Number(2n ** 53n) == Number(2n ** 53n + 1n)
true

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