We are working on a flex project
I need to display a number coming from a calculation in exponential format- essentially, show 80900 as 8.09* 10^4; 4.1e-4 as 4.1*10^-4 etc
Any simple way by which I can take the number and separate the exponent and the significand- without getting into string operations.
Try to look into Number.toExponential() method described here.
See if this helps:
"Let's create a custom formatter to
change a regular number format to an
exponential notation or a fixed-point
notation with two fractional digits"
Related
assuming the mathematical expression has less number of characters than the original number.
example-
20880467999847912034355032910578 can be expressed as (23^23 +10)
this looks like a good compression method. Will it work for compressing large files?
UPDATE- i didn't mean converting a file into a large binary number. lets say i have a text file and i replace all the characters in it with their ascii values. now i have a large number in the decimal number system. i can express it as a mathematical expression like in the example above.
The notion you're looking for is Kolmogorov complexity - it's a measure of how algorithmically incompressible a number is. See this wiki article for a rigorous definition and examples of such numbers.
If you take the contents of a file as a large binary number, and find an expression which evaluates to that number and can be stored more compactly than the number itself, then yes, you have compressed the file.
Unfortunately, for most files, you'll never find such an expression.
Simple logic (see the link posted by #OliCharlesworth) should convince you that it's impossible to find such an expression for all or even most files. Even for files which might have a suitable expression, finding it will be very, very difficult. If you want to convince yourself of this, try this challenge:
Take the following ASCII string:
"Holy Kolmogorov complexity, Batman! Compress this sucker down good and you'll get a pretty penny, my fine lad!"
Interpreted as a binary number, with the high-order digits coming first, that is: 2280899635869589768629811602006623364651019118009864206881173103187172975244099647369151382436996220022807793898568915685059542016541775658916080587423284053601554008368389985872997499032440860090224967472423163775276043175694884234152335588829534778866153948275745.
Try to find a polynomial which evaluates to that number. All the numbers used must be integral, and the total number of decimal digits appearing in the polynomial must be less than 80. If you succeed, I will send you a small cash prize by PayPal.
Yes, by definition. You have correctly defined compression as representing something larger with something smaller.
How do you propose to do this? How often will that work? There's the rub.
double r = 11.631;
double theta = 21.4;
In the debugger, these are shown as 11.631000000000000 and 21.399999618530273.
How can I avoid this?
These accuracy problems are due to the internal representation of floating point numbers and there's not much you can do to avoid it.
By the way, printing these values at run-time often still leads to the correct results, at least using modern C++ compilers. For most operations, this isn't much of an issue.
I liked Joel's explanation, which deals with a similar binary floating point precision issue in Excel 2007:
See how there's a lot of 0110 0110 0110 there at the end? That's because 0.1 has no exact representation in binary... it's a repeating binary number. It's sort of like how 1/3 has no representation in decimal. 1/3 is 0.33333333 and you have to keep writing 3's forever. If you lose patience, you get something inexact.
So you can imagine how, in decimal, if you tried to do 3*1/3, and you didn't have time to write 3's forever, the result you would get would be 0.99999999, not 1, and people would get angry with you for being wrong.
If you have a value like:
double theta = 21.4;
And you want to do:
if (theta == 21.4)
{
}
You have to be a bit clever, you will need to check if the value of theta is really close to 21.4, but not necessarily that value.
if (fabs(theta - 21.4) <= 1e-6)
{
}
This is partly platform-specific - and we don't know what platform you're using.
It's also partly a case of knowing what you actually want to see. The debugger is showing you - to some extent, anyway - the precise value stored in your variable. In my article on binary floating point numbers in .NET, there's a C# class which lets you see the absolutely exact number stored in a double. The online version isn't working at the moment - I'll try to put one up on another site.
Given that the debugger sees the "actual" value, it's got to make a judgement call about what to display - it could show you the value rounded to a few decimal places, or a more precise value. Some debuggers do a better job than others at reading developers' minds, but it's a fundamental problem with binary floating point numbers.
Use the fixed-point decimal type if you want stability at the limits of precision. There are overheads, and you must explicitly cast if you wish to convert to floating point. If you do convert to floating point you will reintroduce the instabilities that seem to bother you.
Alternately you can get over it and learn to work with the limited precision of floating point arithmetic. For example you can use rounding to make values converge, or you can use epsilon comparisons to describe a tolerance. "Epsilon" is a constant you set up that defines a tolerance. For example, you may choose to regard two values as being equal if they are within 0.0001 of each other.
It occurs to me that you could use operator overloading to make epsilon comparisons transparent. That would be very cool.
For mantissa-exponent representations EPSILON must be computed to remain within the representable precision. For a number N, Epsilon = N / 10E+14
System.Double.Epsilon is the smallest representable positive value for the Double type. It is too small for our purpose. Read Microsoft's advice on equality testing
I've come across this before (on my blog) - I think the surprise tends to be that the 'irrational' numbers are different.
By 'irrational' here I'm just referring to the fact that they can't be accurately represented in this format. Real irrational numbers (like π - pi) can't be accurately represented at all.
Most people are familiar with 1/3 not working in decimal: 0.3333333333333...
The odd thing is that 1.1 doesn't work in floats. People expect decimal values to work in floating point numbers because of how they think of them:
1.1 is 11 x 10^-1
When actually they're in base-2
1.1 is 154811237190861 x 2^-47
You can't avoid it, you just have to get used to the fact that some floats are 'irrational', in the same way that 1/3 is.
One way you can avoid this is to use a library that uses an alternative method of representing decimal numbers, such as BCD
If you are using Java and you need accuracy, use the BigDecimal class for floating point calculations. It is slower but safer.
Seems to me that 21.399999618530273 is the single precision (float) representation of 21.4. Looks like the debugger is casting down from double to float somewhere.
You cant avoid this as you're using floating point numbers with fixed quantity of bytes. There's simply no isomorphism possible between real numbers and its limited notation.
But most of the time you can simply ignore it. 21.4==21.4 would still be true because it is still the same numbers with the same error. But 21.4f==21.4 may not be true because the error for float and double are different.
If you need fixed precision, perhaps you should try fixed point numbers. Or even integers. I for example often use int(1000*x) for passing to debug pager.
Dangers of computer arithmetic
If it bothers you, you can customize the way some values are displayed during debug. Use it with care :-)
Enhancing Debugging with the Debugger Display Attributes
Refer to General Decimal Arithmetic
Also take note when comparing floats, see this answer for more information.
According to the javadoc
"If at least one of the operands to a numerical operator is of type double, then the
operation is carried out using 64-bit floating-point arithmetic, and the result of the
numerical operator is a value of type double. If the other operand is not a double, it is
first widened (§5.1.5) to type double by numeric promotion (§5.6)."
Here is the Source
I am looking a general algorithm that would convert from one (arbitrary) numerical base to another (also arbitrary) without storing the result in a large integer and performing arithmetic operations on it in between.
The algorithm I am looking for takes an array of numerical values in a given base (that would mostly be a string of characters) and returns the result alike.
Thank you for help.
I would say it is not possible. For certain bases it would be possible to convert from one string to another, by just streaming the chars through (e.g. if one base is a multiple of the other, like octal->hex), but for arbitrary bases it is not possible without arithmetic operations.
If you would do it with strings/chars in between it would be still big integer arithmetic, but your integers were just in a (unnecessary big) unusual format.
So you have just the choice between: Either reprogram arithmetic operations with char encoded numbers, or do the step and use a big integer library and walk the convert(char(base1->bigInt), convert(bigInt->base2) path.
It's computable, but it's not pretty.
Seriously, it'd probably be easier and faster to include one of the many bignum libraries or write your own.
In Mma, for example, I want to calculate
1.0492843824838929890231*0.2323432432432432^3
But it does not show the full precision. I tried N or various other functions but none seemed to work. How to achieve this? Many thanks.
When you specify numbers using decimal point, it takes them to have MachinePrecision, roughly 16 digits, hence the results typically have less than 16 meaningful digits. You can do infinite precision by using rational/algebraic numbers. If you want finite precision that's better than default, specify your numbers like this
123.23`100
This makes Mathematica interpret the number as having 100 digits of precision. So you can do
ans=1.0492843824838929890231`100*0.2323432432432432`100^3
Check precision of the final answer using Precision
Precision[ans]
Check tutorial/ArbitraryPrecisionNumbers for more details
You may do:
r[x_]:=Rationalize[x,0];
n = r#1.0492843824838929890231 (r#0.2323432432432432)^3
Out:
228598965838025665886943284771018147212124/17369643723462006556253010609136949809542531
And now, for example
N[n,100]
0.01316083216659453615093767083090600540780118249299143245357391544869\
928014026433963352910151464006549
Sometimes you just want to see more of the machine precision result. These are a few methods.
(1) Put the cursor at the end of the output line, and press Enter (not on the numeric keypad) to copy the output to a new input line, showing all digits.
(2) Use InputForm as in InputForm[1.0/7]
(3) Change the setting of PrintPrecision using the Options Inspector.
Why this code 7.30 - 7.20 in ruby returns 0.0999999999999996, not 0.10?
But if i'll write 7.30 - 7.16, for example, everything will be ok, i'll get 0.14.
What the problem, and how can i solve it?
What Every Computer Scientist Should Know About Floating-Point Arithmetic
The problem is that some numbers we can easily write in decimal don't have an exact representation in the particular floating point format implemented by current hardware. A casual way of stating this is that all the integers do, but not all of the fractions, because we normally store the fraction with a 2**e exponent. So, you have 3 choices:
Round off appropriately. The unrounded result is always really really close, so a rounded result is invariably "perfect". This is what Javascript does and lots of people don't even realize that JS does everything in floating point.
Use fixed point arithmetic. Ruby actually makes this really easy; it's one of the only languages that seamlessly shifts to Class Bignum from Fixnum as numbers get bigger.
Use a class that is designed to solve this problem, like BigDecimal
To look at the problem in more detail, we can try to represent your "7.3" in binary. The 7 part is easy, 111, but how do we do .3? 111.1 is 7.5, too big, 111.01 is 7.25, getting closer. Turns out, 111.010011 is the "next closest smaller number", 7.296875, and when we try to fill in the missing .003125 eventually we find out that it's just 111.010011001100110011... forever, not representable in our chosen encoding in a finite bit string.
The problem is that floating point is inaccurate. You can solve it by using Rational, BigDecimal or just plain integers (for example if you want to store money you can store the number of cents as an int instead of the number of dollars as a float).
BigDecimal can accurately store any number that has a finite number of digits in base 10 and rounds numbers that don't (so three thirds aren't one whole).
Rational can accurately store any rational number and can't store irrational numbers at all.
That is a common error from how float point numbers are represented in memory.
Use BigDecimal if you need exact results.
result=BigDecimal.new("7.3")-BigDecimal("7.2")
puts "%2.2f" % result
It is interesting to note that a number that has few decimals in one base may typically have a very large number of decimals in another. For instance, it takes an infinite number of decimals to express 1/3 (=0.3333...) in the base 10, but only one decimal in the base 3. Similarly, it takes many decimals to express the number 1/10 (=0.1) in the base 2.
Since you are doing floating point math then the number returned is what your computer uses for precision.
If you want a closer answer, to a set precision, just multiple the float by that (such as by 100), convert it to an int, do the math, then divide.
There are other solutions, but I find this to be the simplest since rounding always seems a bit iffy to me.
This has been asked before here, you may want to look for some of the answers given before, such as this one:
Dealing with accuracy problems in floating-point numbers