A c++ code for calculating pi value. Reply

Finally I am successful in calculating pi value — less than 0.3% error, by using random number generation. Although my computer needs some fixation on its compiler or path definition etc, there are very good online compilers which helps in testing and running c++ codes: try the given link.

OUTPUT
Computing the value of pi using std::rand()
Enter number of trials: 10000
Enter number of random (x,y) points per trial: 10
pi = 3.14376 +- 0.00519107
average – exact = 0.00216735
CPU time = 0.004027 secs

Here is the code I found by searching a good deal on the web. Yes I did tinker around but only because my own compiler (Turbo C++ on windows 10, 64 bits) was throwing some exceptions on the included headers.

#include
#include
#include
#include
//#include
using namespace std;

double pi_estimate(const unsigned long points) More…

How information can be viral ! Reply

Why information is more and more viral, if its got more statistics? Because it sits on more and more number of servers which are being accessed in a randomized fashion there is increase in the statistics and thus possible viral tendencies.

When you are reading an article on a large computer system, lets say a NY times article another person isn’t accessing the same servers, given a particular server can be accessed lest say by only 100 people. Thus NY times has to buy more number of computer servers and more bandwidth etc. More…

A new optimization parameter in a statistical sample ! Reply

It reflects the quality scope of the citations. Its the total percentage of a citation that goes into defining a particular citation index. Let me call it q-index therefore (q for quality)

See this example.

My h-ind is 60. So total (minimum) citation it accounts for is 60*60 = 3600. My total citation is 12215. So my q-ind is 3600/12215 = 29.47% Or 29.47% of my total citation were important for this parameter. Hence my q-index is 29.47. In this way if someone has 500 total citation with h-index 60, he has a much better q-index than mine, because more of his paper are highly cited More…

Uncertainty Principle Again. Reply

2. The object can be a large object, eg say something whose picture you are taking. But as explained above its not the energy of the object (or momentum) which is directly coming into the problem. That would be an added degree of concern if the object is moving with certain velocity, a reason why pictures are blurred. Because motion of objects introduces additional energy-time-momentum-position variables and their corresponding uncertainties. For the argument of the above problem one can imagine the large sized object, lets say a bird, is standing still on a tree while its picture is being taken. In that case if the wavelength of the light [few 100 nano meters = 1/10th of a micrometer] is used (eg in a digital-camera) the corresponding accuracy of the light will be less than micrometers. You can take a very sharp picture of the bird, which is lets say 6 inch long. But when you zoom in to a large degree, the inaccuracies will show up. [in this case how to see a micrometer level image? Is a computer sufficient to show us the uncertain edges of the pixels?] If the wavelength (here visible light) is so small, evidently by de-Broglie relationship, momentum or energy of such light is very large. But its not as large to disturb the feelings of the bird. The bird doesn’t have a problem with visible light, and such energy does not disturb its position or energy or any thing so to say. So while Quantum Mechanics is valid, we are accustomed to say this is a classical mechanics situation. To say QM is invalid is incorrect. To say QM is understood to be valid is a knowledgeable position. More…

Why Japanese is the most elaborate Language? Reply

Couple years ago, for this reason, not the kai reason, but any phonetic-word-attempt which gave me tons of kanjis, for same phonetics, I said, Japanese Language has so many synonyms, (perhaps this is anti-synonym, but in my defense, I don’t remember exact word I used, its a memory retrieval, you see) and still Japanese Language unites them all and makes into one language; we can also do this for Indian language.
Indian (Language) would be far easier (than the complexity of Japanese) in that, given, lets say, jack-fruit will have 8 different words only, across India, then, we have to put them all into one syllable-alphabet unit (that can be newly defined, because now we have at-least 20 ways to define) eg Hindi: kathar/kathal, Odi: panas (Telugu also Panas, I know, I asked this to my friend 15 years ago, he was from AP, his name Venkateswaralu Goruganthi, funny fella with thilak on his head, see since when I am doing my language research?) I don’t know more language, lets say: Gujurati: xyz.

Then take all Indian Language and list these into one unit. That unit would be given a special phonetic tag. (Because we don’t have any) The reverse of this has been accomplished by Japanese. By having each meaning for a single phonetic or syllable unit as a kanji (or pictorial unit) they have unified language. Back then I said, 20 some synonyms, (so many = 20) Lets count today.
As if a coincidence I found 20. Although, there would be, some redundant issues here (eg 買い and 買 ) and a few more may be left. (eg I remember, now I know some Japanese Language by memory: kai = write, kai in kaishi = begin). Lets see what we found in Japanese for kai; each of the following is kai, with different meaning. Only one is (sa)kai and couple are not known (just kai, in 2nd line), from where I am searching. Lets then, give their meaning.

会 回 海 買い 貝 階 解 界 介 下位 峡 怪 甲斐 飼い 買 櫂 快 歌意 交い 科医

Meeting Time Sea Buy Shellfish Floor Solution Field Through Subordinate Isthmuses Mystery Worth Domestic Purchase Paddle Comfort Kai Kai Family-physician More…

Whats the population; if male and female literacy is given as percentage. Reply

May I add one more small note, see how easy it becomes to understand an “order of estimate”; A good student, that is one with a good maths background, should immediately pick up, population ~ 1.19 million. 61.26 % male. 58.04 % female. (Not only literate but total male and female). Then even, one question can be asked, what is male-female disparity, in terms of their population. (That is, without regard, to any further attributes, such as literacy numbers, or purchasing power distributions etc, which are btw non-existent variables in India, because research in India means Governmental Apathy.)

Its a slightly tricky question, if you already note, there is a mistake in the above, The % is not scaled to 100. Its an over-estimation by a factor of 1.193, and the really smart student recognizes this, (s)he doesn’t go and change all calculations. See how all numbers came just from the first few digits of the given numbers; 11,92,948 >> 1.19 million, vs over estimation factor; 1.193 (or less precise 1.19). Male: 6,12,597 >> overestimated percentage: 61.26. Female; 5,80,351 >> overestimated percentage: 58.04.

You would know they are over-estimated, because these two numbers, male and female population, while exclusive parameters, hence must add up to normal: 100 or 1.00, added up to 61.26+58.04 = 119.30, or (61.26+58.04)/100 = 119.30/100 = 1.193, do you see how easily, without doing any further adding etc, I caught the actual overestimation factor, above, to be 1.193? Cool Huh? Just from the first few numbers. If maths runs in your mind, you can do all these, if it doesn’t, but you have the right numbers, you will be led to believe nobody would catch your mistakes, and lie about the numbers. Possible. Just from the numbers as are stated, we can, catch the inconsistency, thats why maths education is important. In-fact, I committed the mistakes and wanted to catch the inconsistency, and from the calculations gradually caught it, so a more consistent picture was envisaged. More…