Per DICOM specification, a UID is defined by: 9.1 UID Encoding Rules. In other words the following are valid DICOM UIDs:
"1.2.3.4.5"
"1.3.6.1.4.35045.103501438824148998807202626810206788999"
"1.2.826.0.1.3680043.2.1143.5028470438645158236649541857909059554"
while the following are illegal DICOM UIDs:
".1.2.3.4.5"
"1..2.3.4.5"
"1.2.3.4.5."
"1.2.3.4.05"
"12345"
"1.2.826.0.1.3680043.2.1143.50284704386451582366495418579090595540"
Therefore I know that the string is at most 64 bytes, and should match the following regex [0-9\.]+. However this regex is really a superset, since there are a lot less than (10+1)^64 (=4457915684525902395869512133369841539490161434991526715513934826241L) possibilities.
How would one computes precisely the number of possibilities to respect the DICOM UID rules ?
Reading the org root / suffix rule clearly indicates that I need at least one dot ('.'). In which case the combination is at least 3 bytes (char) in the form: [0-9].[0-9]. In which case there are 10x10=100 possibilities for UID of length 3.
Looking at the first answer, there seems to be something unclear about:
The first digit of each component shall not be zero unless the
component is a single digit.
What this means is that:
"0.0" is valid
"00.0" or "1.01" are not valid
Thus I would say a proper expression would be:
(([1-9][0-9]*)|0)(\.([1-9][0-9]*|0))+
Using a simple C code, I could find:
f(0) = 0
f(1) = 0
f(2) = 0
f(3) = 100
f(4) = 1800
f(5) = 27100
f(6) = 369000
f(7) = 4753000
f(8) = 59049000
The validation of the Root UID part is outside the scope of this question. A second validation step could take care of rejecting some OID that cannot possibly be registered (some people mention restriction on first and second arc for example). For simplicity we'll accept all possible (valid) Root UID.
While my other answer takes good care of this specific application, here is a more generic approach. It takes care of situations where you have a different regular expression describing the language in question. It also allows for considerably longer string lengths, since it only requires O(log n) arithmetic operations to compute the number of combinations for strings of length up to n. In this case the number of strings grows so quickly that the cost of these arithmetic operations will grow dramatically, but that may not be the case for other, otherwise similar situations.
Build a finite state automaton
Start with a regular expression description of your language in question. Translate that regular expression into a finite state automaton. In your case the regular expression can be given as
(([1-9][0-9]*)|0)(\.([1-9][0-9]*|0))+
The automaton could look like this:
Eliminate ε-transitions
This automaton usually contains ε-transitions (i.e. state transitions which do not correspond to any input character). Remove those, so that one transition corresponds to one character of input. Then add an ε-transition to the accepting state(s). If the accepting states have other outgoing transitions, don't add ε-loops to them, but instead add an ε-transition to an accepting state with no outgoing edges and then add the loop to that. This can be seen as padding the input with ε at its end, without allowing ε in the middle. Taken together, this transformation ensures that performing exactly n state transitions corresponds to processing an input of n characters or less. The modified automaton might look like this:
Note that both the construction of the first automaton from the regular expression and the elimination of ε-transitions can be performed automatically (and perhaps even in a single step. The resulting automata might be more complicated than what I constructed here manually, but the principle is the same.
Ensuring unique paths
You don't have to make the automaton deterministic in the sense that for every combination of source state and input character there is only one target state. That's not the case in my manually constructed one either. But you have to make sure that every complete input has only one possible path to the accepting state, since you'll essentially be counting paths. Making the automaton deterministic would ensure this weaker property, too, so go for that unless you can ensure unique paths without this. In my example the length of each component clearly dictates which path to use, so I didn't make it deterministic. But I've included an example with a deterministic approach at the end of this post.
Build transition matrix
Next, write down the transition matrix. Associate the rows and columns with your states (in order a, b, c, d, e, f in my example). For each arrow in your automaton, write the number of characters included in the label of that arrow in the column associated with the source state and the row associated with the target state of that arrow.
⎛ 0 0 0 0 0 0⎞
⎜ 9 10 0 0 0 0⎟
⎜10 10 0 10 10 0⎟
⎜ 0 0 1 0 0 0⎟
⎜ 0 0 0 9 10 0⎟
⎝ 0 0 0 10 10 1⎠
Read result off that matrix
Now applying this matrix with a column vector once has the following meaning: if the number of possible ways to arrive in a given state is encoded in the input vector, the output vector gives you the number of ways one transition later. Take the 64th power of that matrix, concentrate on the first column (since ste start situation is encoded as (1,0,0,0,0,0), meaning only one way to end up in the start state) and sum up all the entries that correspond to accepting states (only the last one in this case). The bottom left element of the 64th power of this matrix is
1474472506836676237371358967075549167865631190000000000000000000000
which confirms my other answer.
Compute matrix powers efficiently
In order to actually compute the 64th power of that matrix, the easiest approach would be repeated squaring: after squaring the matrix 6 times you have an exponent of 26 = 64. If in some other scenario your exponent (i.e. maximal string length) is not a power of two, you can still perform exponentiation by squaring by multiplying the relevant squares according to the bit pattern of the exponent. This is what makes this approach take O(log n) arithmetic operations to compute the result for string length n, assuming a fixed number of states and therefore fixed cost for each matrix squaring.
Example with deterministic automaton
If you were to make my automaton deterministic using the usual powerset construction, you'd end up with
and sorting the states as a, bc, c, d, cf, cef, f one would get the transition matrix
⎛ 0 0 0 0 0 0 0⎞
⎜ 9 10 0 0 0 0 0⎟
⎜ 1 0 0 0 0 0 0⎟
⎜ 0 1 1 0 1 1 0⎟
⎜ 0 0 0 1 0 0 0⎟
⎜ 0 0 0 9 0 10 0⎟
⎝ 0 0 0 0 1 1 1⎠
and could sum the last three elements of the first column of its 64th power to obtain the same result as above.
Single component
Start by looking for ways to form a single component. The corresponding regular expression for a single component is
0|[1-9][0-9]*
so it is either zero or a non-zero digit followed by arbitrary many zero digits. (I had missed the possible sole zero case at first, but the comment by malat made me aware of this.) If the total length of such a component is to be n, and you write h(n) to denote the number of ways to form such a component of length exactly n, then you can compute that h(n) as
h(n) = if n = 1 then 10 else 9 * 10^(n - 1)
where the n = 1 case allows for all possible digits, and the other cases ensure a non-zero first digit.
One or more components
Subsection 9.1 only writes that a UID is a bunch of dot-separated number components, as outlined above. So in regular expressions that would be
(0|[1-9][0-9]*)(\.(0|[1-9][0-9]*))*
Suppose f(n) is the number of ways to write a UID of length n. Then you have
f(n) = h(n) + sum h(i) * f(n-i-1) for i from 1 to n-2
The first term describes the case of a single component, while the sum takes care of the case where it consists of more than one component. In that case you have a first component of length i, then a dot which accounts for the -1 in the formula, and then the remaining digits form one or more components which is expressed via the recursive use of f.
Two or more components
As the comment by cneller indicates, the part of section 9 before subsection 9.1 indicates that there has to be at least two components. So the proper regular expression would be more like
(0|[1-9][0-9]*)(\.(0|[1-9][0-9]*))+
with a + at the end indicating that we want at least one repetition of the parenthesized expression. Deriving an expression for this simply means leaving out the one-component-only case in the definition of f:
g(n) = sum h(i) * f(n-i-1) for i from 1 to n-2
If you sum all the g(n) for n from 3 (the minimal possible UID length) through 64 you get the number of possible UIDs as
1474472506836676237371358967075549167865631190000000000000000000000
or approximately 1.5e66. Which is considerably less than the 4.5e66 you get from your computation, in terms of absolute difference, although it's definitely on the same order of magnitude. By the way, your estimate doesn't explicitely mention UIDs shorter than 64, but you can always consider padding them with dots in your setup. I did the computation using a few lines of Python code:
f = [0]
g = [0]
h = [0, 10] + [9 * (10**(n-1)) for n in range(2, 65)]
s = 0
for n in range(1, 65):
x = 0
if n >= 3:
for i in range(1, n - 1):
x += h[i] * f[n-i-1]
g.append(x)
f.append(x + h[n])
s += x
print(h)
print(f)
print(g)
print(s)
Related
This problem gives you a positive integer number which is less than or equal to 100000 (10^5). You have to find out the following things for the number:
i. Is the number prime number? If it is a prime number, then print YES.
ii. If the number is not a prime number, then can we express the number as summation of unique prime numbers? If it is possible, then print YES. Here unique means, you can use any prime number only for one time.
If above two conditions fail for any integer number, then print NO. For more clarification please see the input, output section and their explanations.
Input
At first you are given an integer T (T<=100), which is the number of test cases. For each case you will be given a positive integer X which is less than or equal 100000.
Output
For every test case, print only YES or NO.
Sample
Input Output
3
7
6
10 YES
NO
YES
Case – 1 Explanation: 7 is a prime number.
Case – 2 Explanation: 6 is not a prime number. 6 can be expressed as 6 = 3 + 3 or 6 = 2 + 2 + 2. But you can’t use any prime number more than 1 time. Also there is no way to express 6 as two or three unique prime numbers summation.
Case – 3 Explanation: 10 is not prime number but 10 can be expressed as 10 = 3 + 7 or 10 = 2 + 3 + 5. In this two expressions, every prime number is used only for one time.
Without employing any mathematical tricks (not sure if any exist...you'd think as a mathematician I'd have more insight here), you will have to iterate over every possible summation. Hence, you'll definitely need to iterate over every possible prime, so I'd recommend the first step being to find all the primes at most 10^5. A basic (Sieve of Eratosthenes)[https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes] will probably be good enough, though faster sieves exist nowadays. I know your question is language agnostic, but you could consider the following as vectorized pseudocode for such a sieve.
import numpy as np
def sieve(n):
index = np.ones(n+1, dtype=bool)
index[:2] = False
for i in range(2, int(np.sqrt(n))):
if index[i]:
index[i**2::i] = False
return np.where(index)[0]
There are some other easy optimizations, but for simplicity this assumes that we have an array index where the indices correspond exactly to whether the number is prime or not. We start with every number being prime, mark 0 and 1 as not prime, and then for every prime we find we mark every multiple of it as not prime. The np.where() at the end just returns the indices where our index corresponds to True.
From there, we can consider a recursive algorithm for actually solving your problem. Note that you might feasibly have a huge number of distinct primes necessary. The number 26 is the sum of 4 distinct primes. It is also the sum of 3 and 23. Since the checks are more expensive for 4 primes than for 2, I think it's reasonable to start by checking the smallest number possible.
In this case, the way we're going to do that is to define an auxiliary function to find whether a number is the sum of precisely k primes and then sequentially test that auxiliary function for k from 1 to whatever the maximum possible number of addends is.
primes = sieve(10**5)
def sum_of_k_primes(x, k, excludes=()):
if k == 1:
if x not in excludes and x in primes:
return (x,)+excludes
else:
return ()
for p in (p for p in primes if p not in excludes):
if x-p < 2:
break
temp = sum_of_k_primes(x-p, k-1, (p,)+excludes)
if temp:
return temp
return ()
Running through this, first we check the case where k is 1 (this being the base case for our recursion). That's the same as asking if x is prime and isn't in one of the primes we've already found (the tuple excludes, since you need uniqueness). If k is at least 2, the rest of the code executes instead. We check all the primes we might care about, stopping early if we'd get an impossible result (no primes in our list are less than 2). We recursively call the same function for smaller k, and if we succeed we propagate that result up the call stack.
Note that we're actually returning the smallest possible tuple of unique prime addends. This is empty if you want your answer to be "NO" as specified, but otherwise it allows you to easily come up with an explanation for why you answered "YES".
partial = np.cumsum(primes)
def max_primes(x):
return np.argmax(partial > x)
def sum_of_primes(x):
for k in range(1, max_primes(x)+1):
temp = sum_of_k_primes(x, k)
if temp:
return temp
return ()
For the rest of the code, we store the partial sums of all the primes up to a given point (e.g. with primes 2, 3, 5 the partial sums would be 2, 5, 10). This gives us an easy way to check what the maximum possible number of addends is. The function just sequentially checks if x is prime, if it is a sum of 2 primes, 3 primes, etc....
As some example output, we have
>>> sum_of_primes(1001)
(991, 7, 3)
>>> sum_of_primes(26)
(23, 3)
>>> sum_of_primes(27)
(19, 5, 3)
>>> sum_of_primes(6)
()
At a first glance, I thought caching some intermediate values might help, but I'm not convinced that the auxiliary function would ever be called with the same arguments twice. There might be a way to use dynamic programming to do roughly the same thing but in a table with a minimum number of computations to prevent any duplicated efforts with the recursion. I'd have to think more about it.
As far as the exact output your teacher is expecting and the language this needs to be coded in, that'll be up to you. Hopefully this helps on the algorithmic side of things a little.
So in my text book there is this example of a recursive function using f#
let rec gcd = function
| (0,n) -> n
| (m,n) -> gcd(n % m,m);;
with this function my text book gives the example by executing:
gcd(36,116);;
and since the m = 36 and not 0 then it ofcourse goes for the second clause like this:
gcd(116 % 36,36)
gcd(8,36)
gcd(36 % 8,8)
gcd(4,8)
gcd(8 % 4,4)
gcd(0,4)
and now hits the first clause stating this entire thing is = 4.
What i don't get is this (%)percentage sign/operator or whatever it is called in this connection. for an instance i don't get how
116 % 36 = 8
I have turned this so many times in my head now and I can't figure how this can turn into 8?
I know this is probably a silly question for those of you who knows this but I would very much appreciate your help the same.
% is a questionable version of modulo, which is the remainder of an integer division.
In the positive, you can think of % as the remainder of the division. See for example Wikipedia on Euclidean Divison. Consider 9 % 4: 4 fits into 9 twice. But two times four is only eight. Thus, there is a remainder of one.
If there are negative operands, % effectively ignores the signs to calculate the remainder and then uses the sign of the dividend as the sign of the result. This corresponds to the remainder of an integer division that rounds to zero, i.e. -2 / 3 = 0.
This is a mathematically unusual definition of division and remainder that has some bad properties. Normally, when calculating modulo n, adding or subtracting n on the input has no effect. Not so for this operator: 2 % 3 is not equal to (2 - 3) % 3.
I usually have the following defined to get useful remainders when there are negative operands:
/// Euclidean remainder, the proper modulo operation
let inline (%!) a b = (a % b + b) % b
So far, this operator was valid for all cases I have encountered where a modulo was needed, while the raw % repeatedly wasn't. For example:
When filling rows and columns from a single index, you could calculate rowNumber = index / nCols and colNumber = index % nCols. But if index and colNumber can be negative, this mapping becomes invalid, while Euclidean division and remainder remain valid.
If you want to normalize an angle to (0, 2pi), angle %! (2. * System.Math.PI) does the job, while the "normal" % might give you a headache.
Because
116 / 36 = 3
116 - (3*36) = 8
Basically, the % operator, known as the modulo operator will divide a number by other and give the rest if it can't divide any longer. Usually, the first time you would use it to understand it would be if you want to see if a number is even or odd by doing something like this in f#
let firstUsageModulo = 55 %2 =0 // false because leaves 1 not 0
When it leaves 8 the first time means that it divided you 116 with 36 and the closest integer was 8 to give.
Just to help you in future with similar problems: in IDEs such as Xamarin Studio and Visual Studio, if you hover the mouse cursor over an operator such as % you should get a tooltip, thus:
Module operator tool tip
Even if you don't understand the tool tip directly, it'll give you something to google.
i want to know is it possible to validate that deviding two number has remaining zero in result or not?
for example dividing number 4 on number two has zero in remaining.
4/2=0 (this is true)
but 4/3=1 (this is not true)
is there any expression for validation such case?
Better Question :
Is There any validation expression to validate this sentence ?
Remainder is zero
thank you
You can use a Modulo operator. The modulo operation finds the remainder of division of one number by another
y mod x
5 mod 2 =1 (2x2=4, 5-4=1)
9 mod 3 = 0 (3*3=9)
You can think of it, how many times does x fit in y and then take the remainder.
In computing the modulo operator is integrated in most programming languages, along with division, substraction etc. Check modulo and then your language on google (probably its mod).
This is called the modulo function. It essentially gives the remainder of a division of two integer number. So you can test for the modulo funtion returning zero. For example, in Python you would write
if a % b == 0:
# a can be divided by b with zero remainder
When I compute the difference between the largest and the smallest number in an empty vector(v←⍳0) using ⌈⌿(⌈/c)- ⌊⌿(⌊/c) , it gives me a domain error. This statement works fine with normal vectors and matrices.
How do I handle the exception such that it does not give me an error when the vector is empty? It should not return anything or just return a zero.
A guard is the best way to do this:
{0=⍴⍵:0 ⋄ (⌈/⍵)-⌊/⍵}
Note that the use of two reductions, one with axis specfication, is not really needed or correct actually. That is, if you want it to work on all of the elements of a simple array of any dimension, simply ravel the argument first:
{0=⍴⍵:0 ⋄ (⌈/⍵)-⌊/⍵},10 10 ⍴⍳100
99
Or for an array of any structure or depth, you can use "super ravel":
{0=⍴⍵:0 ⋄ (⌈/⍵)-⌊/⍵}∊(1 2 3)(7 8 9 10)
9
Note that quadML (Migration Level) must be set to 3 to ensure that epsilon is "super ravel."
Note also the equivalence of the following when operating on a matrix:
⌈⌿⌈/10 10 ⍴⍳100
99
⌈/⌈/10 10 ⍴⍳100
99
⌈/⌈⌿10 10 ⍴⍳100
99
⌈⌿⌈⌿10 10 ⍴⍳100
99
Using reduction with axis is not needed in this case, and obscures the intent and is also potentially more expensive. Better to just ravel the whole thing.
As I mentioned in the comments, Dyalog APL has guards, which can be used for conditional execution, and thus you can simply check for the empty vector and give a different answer.
This can be implemented in a more traditional/pure APL method however.
This version only works in 1-dimension
In the APL font:
Z←DIFFERENCE V
⍝ Calculate difference between vectors, with empty set protection
⍝ Difference is calculated by a reduced ceiling subtracted from the reduced floor
⍝ eg. (⌈⌿(⌈V)) - (⌊⌿(⌊V))
⍝ Protection is implemented by comparison against the empty set ⍬≡V
⍝ Which yields 0 or 1, and using that result to select an answer from a tuple
⍝ If empty, then it drops the first element, yielding just a zero, otherwise both are retained
⍝ eg. <condition>↓(a b) => 0 = (a b), 1 = (b)
⍝ The final operation is first ↑, to remove the first element from the tuple.
Z←↑(⍬≡V)↓(((⌈⌿(⌈V)) - (⌊⌿(⌊V))) 0)
Or in brace notation, for people without the font.
Z{leftarrow}DIFFERENCE V
{lamp} Calculate difference between vectors, with empty set protection
{lamp} Difference is calculated by a reduced ceiling subtracted from the reduced floor
{lamp} eg. ({upstile}{slashbar}({upstile}V)) - ({downstile}{slashbar}({downstile}V))
{lamp} Protection is implemented by comparison against the empty set {zilde}{equalunderbar}V
{lamp} Which yields 0 or 1, and using that result to select an answer from a tuple
{lamp} If empty, then it drops the first element, yielding just a zero, otherwise both are retained
{lamp} eg. <condition>{downarrow}(a b) => 0 = (a b), 1 = (b)
{lamp} The final operation is first {uparrow}, to remove the first element from the tuple.
Z{leftarrow}{uparrow}({zilde}{equalunderbar}V){downarrow}((({upstile}{slashbar}({upstile}V)) - ({downstile}{slashbar}({downstile}V))) 0)
and an image for the sake of preservation...
Updated. multi-dimensional
Z←DIFFERENCE V
⍝ Calculate difference between vectors, with empty set protection
⍝ Initially enlist the vector to get reduce to single dimension
⍝ eg. ∊V
⍝ Difference is calculated by a reduced ceiling subtracted from the reduced floor
⍝ eg. (⌈/V) - (⌊/V)
⍝ Protection is implemented by comparison against the empty set ⍬≡V
⍝ Which yields 0 or 1, and using that result to select an answer from a tuple
⍝ If empty, then it drops the first element, yielding just a zero, otherwise both are retained
⍝ eg. <condition>↓(a b) => 0 = (a b), 1 = (b)
⍝ The final operation is first ↑, to remove the first element from the tuple.
V←∊V
Z←↑(⍬≡V)↓(((⌈/V) - (⌊/V)) 0)
I have come across this problem in a calculation I do in my code, where the divisor is 0 if the divident is 0 too. In my code I return 0 for that case. I am wondering, while division by zero is generally undefined, why not make an exception for this case? My understanding why division by zero is undefined is basically that it cannot be reversed. However, I do not see this problem in the case 0/0.
EDIT OK, so this question spawned a lot of discussion. I made the mistake of over-eagerly accepting an answer based on the fact that it received a lot of votes. I now accepted AakashM's answer, because it provides an idea on how to analyze the problem.
Let's say:
0/0 = x
Now, rearranging the equation (multiplying both sides by 0) gives:
x * 0 = 0
Now do you see the problem? There are an infinite number of values for x as anything multiplied by 0 is 0.
This is maths rather than programming, but briefly:
It's in some sense justifiable to assign a 'value' of positive-infinity to some-strictly-positive-quantity / 0, because the limit is well-defined
However, the limit of x / y as x and y both tend to zero depends on the path they take. For example, lim (x -> 0) 2x / x is clearly 2, whereas lim (x -> 0) x / 5x is clearly 1/5. The mathematical definition of a limit requires that it is the same whatever path is followed to the limit.
(Was inspired by Tony Breyal's rather good answer to post one of my own)
Zero is a tricky and subtle beast - it does not conform to the usual laws of algebra as we know them.
Zero divided by any number (except zero itself) is zero. Put more mathematically:
0/n = 0 for all non-zero numbers n.
You get into the tricky realms when you try to divide by zero itself. It's not true that a number divided by 0 is always undefined. It depends on the problem. I'm going to give you an example from calculus where the number 0/0 is defined.
Say we have two functions, f(x) and g(x). If you take their quotient, f(x)/g(x), you get another function. Let's call this h(x).
You can also take limits of functions. For example, the limit of a function f(x) as x goes to 2 is the value that the function gets closest to as it takes on x's that approach 2. We would write this limit as:
lim{x->2} f(x)
This is a pretty intuitive notion. Just draw a graph of your function, and move your pencil along it. As the x values approach 2, see where the function goes.
Now for our example. Let:
f(x) = 2x - 2
g(x) = x - 1
and consider their quotient:
h(x) = f(x)/g(x)
What if we want the lim{x->1} h(x)? There are theorems that say that
lim{x->1} h(x) = lim{x->1} f(x) / g(x)
= (lim{x->1} f(x)) / (lim{x->1} g(x))
= (lim{x->1} 2x-2) / (lim{x->1} x-1)
=~ [2*(1) - 2] / [(1) - 1] # informally speaking...
= 0 / 0
(!!!)
So we now have:
lim{x->1} h(x) = 0/0
But I can employ another theorem, called l'Hopital's rule, that tells me that this limit is also equal to 2. So in this case, 0/0 = 2 (didn't I tell you it was a strange beast?)
Here's another bit of weirdness with 0. Let's say that 0/0 followed that old algebraic rule that anything divided by itself is 1. Then you can do the following proof:
We're given that:
0/0 = 1
Now multiply both sides by any number n.
n * (0/0) = n * 1
Simplify both sides:
(n*0)/0 = n
(0/0) = n
Again, use the assumption that 0/0 = 1:
1 = n
So we just proved that all other numbers n are equal to 1! So 0/0 can't be equal to 1.
walks on back to her home over at mathoverflow.com
Here's a full explanation:
http://en.wikipedia.org/wiki/Division_by_zero
( Including the proof that 1 = 2 :-) )
You normally deal with this in programming by using an if statement to get the desired behaviour for your application.
The problem is with the denominator. The numerator is effectively irrelevant.
10 / n
10 / 1 = 10
10 / 0.1 = 100
10 / 0.001 = 1,000
10 / 0.0001 = 10,000
Therefore: 10 / 0 = infinity (in the limit as n reaches 0)
The Pattern is that as n gets smaller, the results gets bigger. At n = 0, the result is infinity, which is a unstable or non-fixed point. You can't write infinity down as a number, because it isn't, it's a concept of an ever increasing number.
Otherwise, you could think of it mathematically using the laws on logarithms and thus take division out of the equation altogther:
log(0/0) = log(0) - log(0)
BUT
log(0) = -infinity
Again, the problem is the the result is undefined because it's a concept and not a numerical number you can input.
Having said all this, if you're interested in how to turn an indeterminate form into a determinate form, look up l'Hopital's rule, which effectively says:
f(x) / g(x) = f'(x) / g'(x)
assuming the limit exists, and therefore you can get a result which is a fixed point instead of a unstable point.
Hope that helps a little,
Tony Breyal
P.S. using the rules of logs is often a good computational way to get around the problems of performing operations which result in numbers which are so infinitesimal small that given the precision of a machine’s floating point values, is indistinguishable from zero. Practical programming example is 'maximum likelihood' which generally has to make use of logs in order to keep solutions stable
Look at division in reverse: if a/b = c then c*b = a. Now, if you substitute a=b=0, you end up with c*0 = 0. But ANYTHING multiplied by zero equals zero, so the result can be anything at all. You would like 0/0 to be 0, someone else might like it to be 1 (for example, the limiting value of sin(x)/x is 1 when x approaches 0). So the best solution is to leave it undefined and report an error.
You may want to look at Dr. James Anderson's work on Transarithmetic. It isn't widely accepted.
Transarithmetic introduces the term/number 'Nullity' to take the value of 0/0, which James likens to the introduction 'i' and 'j'.
The structure of modern math is set by mathematicians who think in terms of axioms.
Having additional axioms that aren't productive and don't really allow one to do more stuff is against the ideal of having clear math.
How many times does 0 go into 0? 5. Yes - 5 * 0 = 0, 11. Yes - 11 * 0 = 0, 43. Yes - 43 * 0 = 0. Perhaps you can see why it's undefined now? :)
Since x/y=z should be equivalent to x=yz, and any z would satisfy 0=0z, how useful would such an 'exception' be?
Another explanation of why 0/0 is undefined is that you could write:
0/0 = (4 - 4)/0 = 4/0 - 4/0
And 4/0 is undefined.
If a/b = c, then a = b * c.
In the case of a=0 and b=0, c can be anything because 0 * c = 0 will be true for all possible values of c. Therefore, 0/0 is undefined.
This is only a Logical answer not a mathamatical one,
imagine Zero as empty how can you Divide an empty by an empty this is the case in division by zero also how can you divide by something which is empty.
0 means nothing, so if you have nothing, it does not imply towards anything to distribute to anything. Some Transit Facilities when they list out the number of trips of a particular line, trip number 0 is usually the special route that is routed in a different way. Typically, a good example would be in the Torrance Transit Systems where Line 2 has a trip before the first trip known as trip number 0 that operates on weekdays only, that trip in particular is trip number 0 because it is a specialized route that is routed differently from all the other routes.
See the following web pages for details:
http://transit.torrnet.com/PDF/Line-2_MAP.pdf
http://transit.torrnet.com/PDF/Line-2_Time_PDF.pdf
On the map, trip number 0 is the trip that is mapped in dotted line, the solid line maps the regular routing.
Sometimes 0 can be used on numbering the trips a route takes where it is considered the "Express Service" route.
why not make an exception for this
case?
Because:
as others said, it's not that easy;)
there's no application for defining 0/0 - adding exception would complicate mathematics for no gains.
This is what I'd do:
function div(a, b) {
if(b === 0 && a !== 0) {
return undefined;
}
if(b === 0 && a === 0) {
return Math.random;
}
return a/b;
}
When you type in zero divided by zero, there's an error because whatever you multiply zero from will be zero so it could be any number.
As Andrzej Doyle said:
Anything dived by zero is infinity. 0/0 is also infinity. You can't get 0/0 = 1. That's the basic principle of maths. That's how the whole world goes round. But you can sure edit a program to say "0/0 is not possible" or "Cannot divide by zero" as they say in cell phones.