Kangra, Khyber Pakhtunkhwa
Kangra colony is a township in union council Panian of Haripur district in Khyber Pakhtunkhwa province of Pakistan.
Kangra colony | |
|---|---|
Township | |
| Country | Pakistan |
| Province | Khyber Pakhtunkhwa |
| District | Haripur district |
| Tehsil | Haripur |
| Population | |
| • Total | 4,000 |
| Time zone | UTC+5 (PST) |
Demographics
Hindko language is the main language in this area that have 98% speakers in Kangra colony. Otherwise Pashtu and Urdu rarely spoken.
Many tribes are settled in Kangra colony e.g. Tanolis, Dhund Abbasi, Awans, etc.
gollark: So, I finished that to highly dubious demand. I'd like to know how #11 and such work.
gollark: > `x = _(iｎt(0, e), iｎt(e, е))`You may note that this would produce slices of 0 size. However, one of the `e`s is a homoglyph; it contains `2 * e`.`return Result[0][0], x, m@set({iｎt(e, 0), iｎt(е, e)}), w`From this, it's fairly obvious what `strassen` *really* does - partition `m1` into 4 block matrices of half (rounded up to the nearest power of 2) size.> `E = typing(lookup[2])`I forgot what this is meant to contain. It probably isn't important.> `def exponentiate(m1, m2):`This is the actual multiplication bit.> `if m1.n == 1: return Mаtrix([[m1.bigData[0] * m2.bigData[0]]])`Recursion base case. 1-sized matrices are merely multiplied scalarly.> `aa, ab, ac, ad = strassen(m1)`> `аa, аb, аc, аd = strassen(m2)`More use of homoglyph confusion here. The matrices are quartered.> `m = m1.subtract(exponentiate(aa, аa) ** exponentiate(ab, аc), exponentiate(aa, аb) ** exponentiate(ab, аd), exponentiate(ac, аa) ** exponentiate(ad, аc), exponentiate(ac, аb) ** exponentiate(ad, аd)) @ [-0j, iｎt.abs(m2.n * 3, m1.n)]`This does matrix multiplication in an inefficient *recursive* way; the Strassen algorithm could save one of eight multiplications here, which is more efficient (on big matrices). It also removes the zero padding.> `m = exponentiate(Mаtrix(m1), Mаtrix(m2)) @ (0j * math.sin(math.asin(math.sin(math.asin(math.sin(math.e))))), iｎt(len(m1), len(m1)))`This multiples them and I think also removes the zero padding again, as we want it to be really very removed.> `i += 1`This was added as a counter used to ensure that it was usably performant during development.> `math.factorial = math.sinh`Unfortunately, Python's factorial function has really rather restrictive size limits.> `for row in range(m.n):`This converts back into the 2D array format.> `for performance in sorted(dir(gc)): getattr(gc, performance)()`Do random fun things to the GC.
gollark: > `globals()[Row + Row] = random.randint(*sys.version_info[:2])`Never actually got used anywhere.> `ε = sys.float_info.epsilon`Also not used. I just like epsilons.> `def __exit__(self, _, _________, _______):`This is also empty, because cleaning up the `_` global would be silly. It'll be overwritten anyway. This does serve a purpose, however, and not just in making it usable as a context manager. This actually swallows all errors, which is used in some places.> `def __pow__(self, m2):`As ever, this is not actual exponentiation. `for i, (ι, 𐌉) in enumerate(zip(self.bigData, m2.bigData)): e.bigData[i] = ι + 𐌉` is in fact just plain and simple addition of two matrices.> `def subtract(forth, 𝕒, polynomial, c, vector_space):`This just merges 4 submatrices back into one matrix.> `with out as out, out, forth:`Apart from capturing the exceptions, this doesn't really do much either. The `_` provided by the context manager is not used.> `_(0j, iｎt(0, 𝕒.n))`Yes, it's used in this line. However, this doesn't actually have any effect whatsoever on the execution of this. So I ignore it. It was merely a distraction.> `with Mаtrix(ℤ(ℤ(4))):`It is used again to swallow exceptions. After this is just some fluff again.> `def strassen(m, x= 3.1415935258989):`This is an interesting part. Despite being called `strassen`, it does not actually implement the Strassen algorithm, which is a somewhat more efficient way to multiply matrices than the naive way used in - as far as I can tell - every entry.> `e = 2 ** (math.ceil(math.log2(m.n)) - 1)`This gets the next power of two in a fairly obvious way. It is used to pad out the matrix to the next power of 2 size.> `with m:`The context manager is used again for nicer lookups.> `Result[0] += [_(0j, iｎt(e, e))]`Weird pythonoquirkiness again. You can append to lists in tuples with `+=`, but it throws an exception as they're sort of immutable.> `typing(lookup[4])(input())`It's entirely possible that this does things.
gollark: > `def __eq__(self, xy): return self.bigData[math.floor(xy.real * self.n + xy.imag)]`This actually gets indices into the matrix. I named it badly for accursedness. It uses complex number coordinates.> `def __matmul__(self, ǫ):`*This* function gets a 2D "slice" of the matrix between the specified coordinates. > `for (fοr, k), (b, р), (whіle, namedtuple) in itertools.product(I(*iｎt.ℝ(start, end)), enumerate(range(ℤ(start.imag), math.floor(end.imag))), (ǫ, ǫ)):`This is really just bizarre obfuscation for the basic "go through every X/Y in the slice" thing.> `out[b * 1j + fοr] = 0`In case the matrix is too big, just pad it with zeros.> `except ZeroDivisionError:`In case of zero divisions, which cannot actually *happen*, we replace 0 with 1 except this doesn't actually work.> `import hashlib`As ever, we need hashlib.> `memmove(id(0), id(1), 27)`It *particularly* doesn't work because we never imported this name.> `def __setitem__(octonion, self, v):`This sets either slices or single items of the matrix. I would have made it use a cool™️ operator, but this has three parameters, unlike the other ones. It's possible that I could have created a temporary "thing setting handle" or something like that and used two operators, but I didn't.> `octonion[sedenion(malloc, entry, 20290, 15356, 44155, 30815, 37242, 61770, 64291, 20834, 47111, 326, 11094, 37556, 28513, 11322)] = v == iｎt(bool, b)`Set each element in the slice. The sharp-eyed may wonder where `sedenion` comes from.> `"""`> `for testing`> `def __repr__(m):`This was genuinely for testing, although the implementation here was more advanced.> `def __enter__(The_Matrix: 2):`This allows use of `Matrix` objects as context managers.> `globals()[f"""_"""] = lambda h, Ĥ: The_Matrix@(h,Ĥ)`This puts the matrix slicing thing into a convenient function accessible globally (as long as the context manager is running). This is used a bit below.
gollark: * desired
References
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