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I'm a beginner in swift (probably) and I am learning how to use arrays I was trying to make a for-in loop with a loop amount of 1...array.count, but instead I get an error of:
Fatal error: Index out of range Current stack trace: 0
libswiftCore.so 0x00007f0f71f0aea0
swift_reportError + 50 1 libswiftCore.so
0x00007f0f71f7c0c0 swift_stdlib_reportFatalError + 69 2
libswiftCore.so 0x00007f0f71e775d7 +
3347927 3 libswiftCore.so 0x00007f0f71c94d80
fatalErrorMessage(::file:line:flags:) + 19 4 libswiftSwiftOnoneSupport.so 0x00007f0f755c7ad0 specialized
Array.subscript.getter + 85 6 swift
0x00000000004f23c9 + 992201 7 swift
0x00000000004f6a40 + 1010240 8 swift
0x00000000004e62ef + 942831 9 swift
0x00000000004d5093 + 872595 10 swift
0x00000000004d0e4e + 855630 11 swift
0x0000000000473c16 + 474134 12 libc.so.6
0x00007f0f73771ab0 __libc_start_main + 231 13 swift
0x000000000047387a + 473210 Stack dump:
0. Program arguments: /usr/bin/swift -frontend -interpret Forecast.swift -disable-objc-interop -module-name Forecast
/usr/bin/swift[0x4521834] /usr/bin/swift[0x451f48e]
/usr/bin/swift[0x4521c48]
/lib/x86_64-linux-gnu/libpthread.so.0(+0x128a0)[0x7f0f7532b8a0]
/usr/lib/swift/linux/libswiftCore.so(+0x3315d7)[0x7f0f71e775d7]
/usr/lib/swift/linux/libswiftCore.so($ss18_fatalErrorMessage__4file4line5flagss5NeverOs12StaticStringV_A2HSus6UInt32VtF+0x13)[0x7f0f71c94d93]
/usr/lib/swift/linux/libswiftSwiftOnoneSupport.so($sSayxSicigSi_Tg5+0x55)[0x7f0f755c7b25]
[0x7f0f7575d315] /usr/bin/swift[0x4f23c9] /usr/bin/swift[0x4f6a40]
/usr/bin/swift[0x4e62ef] /usr/bin/swift[0x4d5093]
/usr/bin/swift[0x4d0e4e] /usr/bin/swift[0x473c16]
/lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xe7)[0x7f0f73771b97]
/usr/bin/swift[0x47387a]
what do I do? Here is my code, it is for CodeAcademy:
var temperature: [Int] = [66, 68, 72, 76, 80, 82, 85, 85, 84, 82, 81, 78, 74, 73, 72, 71, 70, 69, 68, 65, 63, 62, 61, 63]
// Write your code below 🌴
for i in 1...temperature.count{
print(temperature[i])
}
I presume your goal is to print all the temperature elements. Change
for i in 1...temperature.count {
To
for i in 0..<temperature.count {
Array indexes start at zero and end at one less than the count. The ..< operator handles this nicely. Or, even better, say
for i in temperature.indices {
One more option is to use forEach:
array.forEach { print($0) }
use for i in temperature. this is basic array iteration, keep in mind :D
var temperature: [Int] = [66, 68, 72, 76, 80, 82, 85, 85, 84, 82, 81, 78, 74, 73, 72, 71, 70, 69, 68, 65, 63, 62, 61, 63]
// Write your code below 🌴
for i in 1...temperature.count{
print(temperature[i-1])
}
You are getting this error because temperature[temperature.count] doesn't exist. You need to limit it till temperature[temperature.count-1] and the index should start from 1 instead of 0.
Alternatively
for i in 0...(temperature.count-1){
print(temperature[i])
}
I have a 3d numpy array and want to generate a secondary array consisting of the minimum of each value and the values in the 10 rows directly above and 10 rows directly below (i.e each entry is the minimum value from 21 values) for each 2d array.
I've been trying to use 'numpy.clip' to deal with the edges of the array - here the range of values which the minimum is taken from should simply reduce to 10 at the values on the top/bottom of the array. I think something like 'scipy.signal.argrelmin' seems to be what I'm after.
Here's my code so far, definitely not the best way to go about it:
import numpy as np
array_3d = np.random.random_integers(50, 80, (3, 50, 18))
minimums = np.zeros(array_3d.shape)
for array_2d_index in range(len(array_3d)):
for row_index in range(len(array_3d[array_2d_index])):
for col_index in range(len(array_3d[array_2d_index][row_index])):
minimums[array_2d_index][row_index][col_index] = min(array_3d[array_2d_index][np.clip(row_index-10, 0, 49):np.clip(row_index+10, 0, 49)][col_index])
The main issue I think is that this is taking the minimum from the columns either side of each entry instead of the rows, which has been giving index errors.
Would appreciate any advice, thanks.
Approach #1
Here's one approach with np.lib.stride_tricks.as_strided -
def strided_3D_axis1(array_3d, L):
s0,s1,s2 = array_3d.strides
strided = np.lib.stride_tricks.as_strided
m,n,r = array_3d.shape
nL = n-L+1
return strided(array_3d, (m,nL,L,r),(s0,s1,s1,s2))
out = strided_3D_axis1(array_3d, L=21).min(axis=-2)
Sample run -
1) Input :
In [179]: array_3d
Out[179]:
array([[[73, 65, 51, 76, 59],
[74, 57, 75, 53, 70],
[60, 74, 52, 54, 60],
[54, 52, 62, 75, 50],
[68, 56, 68, 63, 77]],
[[62, 70, 60, 79, 74],
[70, 68, 50, 74, 57],
[63, 57, 69, 65, 54],
[63, 63, 68, 58, 60],
[70, 66, 65, 78, 78]]])
2) Strided view :
In [180]: strided_3D_axis1(array_3d, L=3)
Out[180]:
array([[[[73, 65, 51, 76, 59],
[74, 57, 75, 53, 70],
[60, 74, 52, 54, 60]],
[[74, 57, 75, 53, 70],
[60, 74, 52, 54, 60],
[54, 52, 62, 75, 50]],
[[60, 74, 52, 54, 60],
[54, 52, 62, 75, 50],
[68, 56, 68, 63, 77]]],
[[[62, 70, 60, 79, 74],
[70, 68, 50, 74, 57],
[63, 57, 69, 65, 54]],
[[70, 68, 50, 74, 57],
[63, 57, 69, 65, 54],
[63, 63, 68, 58, 60]],
[[63, 57, 69, 65, 54],
[63, 63, 68, 58, 60],
[70, 66, 65, 78, 78]]]])
3) Strided view based min :
In [181]: strided_3D_axis1(array_3d, L=3).min(axis=-2)
Out[181]:
array([[[60, 57, 51, 53, 59],
[54, 52, 52, 53, 50],
[54, 52, 52, 54, 50]],
[[62, 57, 50, 65, 54],
[63, 57, 50, 58, 54],
[63, 57, 65, 58, 54]]])
Approach #2
Here's another with broadcasting upon creating all sliding indices along the second axis -
array_3d[:,np.arange(array_3d.shape[1]-L+1)[:,None] + range(L)].min(-2)
Approach #3
Here's another using Scipy's 1D minimum filter -
from scipy.ndimage.filters import minimum_filter1d as minf
L = 21
hL = (L-1)//2
out = minf(array_3d,L,axis=1)[:,hL:-hL]
Runtime test -
In [231]: array_3d = np.random.randint(50, 80, (3, 50, 18))
In [232]: %timeit strided_3D_axis1(array_3d, L=21).min(axis=-2)
10000 loops, best of 3: 54.2 µs per loop
In [233]: %timeit array_3d[:,np.arange(array_3d.shape[1]-L+1)[:,None] + range(L)].min(-2)
10000 loops, best of 3: 81.3 µs per loop
In [234]: L = 21
...: hL = (L-1)//2
...:
In [235]: %timeit minf(array_3d,L,axis=1)[:,hL:-hL]
10000 loops, best of 3: 32 µs per loop
I need to create and work with lists with 2**30 elements, but It's to slow. Is there any form to increase the speed?
My code:
sup = []
for i in range(2**30):
sup.append([i,pow(y,i,N)])
pow(y,i,n) == y**i*mod(N), modular exponentiation
I tried to use list comprehensions but isn't enough.
Different approach: why do you want to store those numbers in a list?
You have your formula right there; whenever some piece of code needs sup[i]; you just compute pow(y,i,N).
In other words: instead of storing values within a list; just compute them when you need them.
Edit: as it seems that you have good reasons to store that data in an array; I would then say: use the appropriate tool then.
Meaning: instead of doing computing intense things directly with python, you rather look into the numpy framework. That framework is designed for exactly such purposes. Beyond that, I would also look in the way you are storing/preparing your data. Example: you mention to later look for identical entries in that array. I am wondering if that would meant you should use a dictionary instead of a list; or did you really intend do check 2**30 entries each time you look for equal pow values?
Going by your comment and complementing the answer of GhostCat, go directly for the data you are looking for, for example like this
>>> from collections import defaultdict
>>> y = 2
>>> N = 10
>>> data = defaultdict(list)
>>> for i in range(100):
data[pow(y,i,N)].append(i)
>>> for x in data.items():
x
(8, [3, 7, 11, 15, 19, 23, 27, 31, 35, 39, 43, 47, 51, 55, 59, 63, 67, 71, 75, 79, 83, 87, 91, 95, 99])
(1, [0])
(2, [1, 5, 9, 13, 17, 21, 25, 29, 33, 37, 41, 45, 49, 53, 57, 61, 65, 69, 73, 77, 81, 85, 89, 93, 97])
(4, [2, 6, 10, 14, 18, 22, 26, 30, 34, 38, 42, 46, 50, 54, 58, 62, 66, 70, 74, 78, 82, 86, 90, 94, 98])
(6, [4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96])
>>>
or more specifically, as you need a random sample go for it from the start and don't waste time producing a gazillion stuff you would not need, for example
>>> import random
>>> random_data = defaultdict(list)
>>> for i in random.sample(range(2**30), 20):
random_data[pow(2,i,10)].append(i)
>>> for x in random_data.items():
x
(8, [633728687, 357300263, 208747091, 456291987, 1028949643, 23961003, 750842555])
(2, [602395153, 215460881, 144481457, 829193705])
(4, [752840814, 26689262])
(6, [423520476, 969809132, 326786996, 736424520, 929123176, 865279408, 338237708])
>>>
and depending of what you do with those i later on, you can instead try a more mathematical approach to uncover the underplaying patter that produce an i for which yi mod N is the same and that way you can produce as many i as you need for that particular modular class.
Which for this example is easy, it is
2i = 8 (mod 10) for all i=3 (mod 4) -> range(3,2**30,4)
2i = 2 (mod 10) for all i=1 (mod 4) -> range(1,2**30,4)
2i = 4 (mod 10) for all i=2 (mod 4) -> range(2,2**30,4)
2i = 6 (mod 10) for all i=0 (mod 4) -> range(4,2**30,4)
2i = 1 (mod 10) for i=0
I am trying to optimizing the Kasumi crypto algorithm written in C.
There are S-box which uses to encrypt the data. which I am representing as an array in which is huge:
int S7[128] = {
54, 50, 62, 56, 22, 34, 94, 96, 38, 6, 63, 93, 2, 18,123, 33,
55,113, 39,114, 21, 67, 65, 12, 47, 73, 46, 27, 25,111,124, 81,
53, 9,121, 79, 52, 60, 58, 48,101,127, 40,120,104, 70, 71, 43,
20,122, 72, 61, 23,109, 13,100, 77, 1, 16, 7, 82, 10,105, 98,
117,116, 76, 11, 89,106, 0,125,118, 99, 86, 69, 30, 57,126, 87,
112, 51, 17, 5, 95, 14, 90, 84, 91, 8, 35,103, 32, 97, 28, 66,
102, 31, 26, 45, 75, 4, 85, 92, 37, 74, 80, 49, 68, 29,115, 44,
64,107,108, 24,110, 83, 36, 78, 42, 19, 15, 41, 88,119, 59, 3
};
int S9[512] = {
167,239,161,379,391,334, 9,338, 38,226, 48,358,452,385, 90,397,
183,253,147,331,415,340, 51,362,306,500,262, 82,216,159,356,177,
175,241,489, 37,206, 17, 0,333, 44,254,378, 58,143,220, 81,400,
95, 3,315,245, 54,235,218,405,472,264,172,494,371,290,399, 76,
165,197,395,121,257,480,423,212,240, 28,462,176,406,507,288,223,
501,407,249,265, 89,186,221,428,164, 74,440,196,458,421,350,163,
232,158,134,354, 13,250,491,142,191, 69,193,425,152,227,366,135,
344,300,276,242,437,320,113,278, 11,243, 87,317, 36, 93,496, 27,
487,446,482, 41, 68,156,457,131,326,403,339, 20, 39,115,442,124,
475,384,508, 53,112,170,479,151,126,169, 73,268,279,321,168,364,
363,292, 46,499,393,327,324, 24,456,267,157,460,488,426,309,229,
439,506,208,271,349,401,434,236, 16,209,359, 52, 56,120,199,277,
465,416,252,287,246, 6, 83,305,420,345,153,502, 65, 61,244,282,
173,222,418, 67,386,368,261,101,476,291,195,430, 49, 79,166,330,
280,383,373,128,382,408,155,495,367,388,274,107,459,417, 62,454,
132,225,203,316,234, 14,301, 91,503,286,424,211,347,307,140,374,
35,103,125,427, 19,214,453,146,498,314,444,230,256,329,198,285,
50,116, 78,410, 10,205,510,171,231, 45,139,467, 29, 86,505, 32,
72, 26,342,150,313,490,431,238,411,325,149,473, 40,119,174,355,
185,233,389, 71,448,273,372, 55,110,178,322, 12,469,392,369,190,
1,109,375,137,181, 88, 75,308,260,484, 98,272,370,275,412,111,
336,318, 4,504,492,259,304, 77,337,435, 21,357,303,332,483, 18,
47, 85, 25,497,474,289,100,269,296,478,270,106, 31,104,433, 84,
414,486,394, 96, 99,154,511,148,413,361,409,255,162,215,302,201,
266,351,343,144,441,365,108,298,251, 34,182,509,138,210,335,133,
311,352,328,141,396,346,123,319,450,281,429,228,443,481, 92,404,
485,422,248,297, 23,213,130,466, 22,217,283, 70,294,360,419,127,
312,377, 7,468,194, 2,117,295,463,258,224,447,247,187, 80,398,
284,353,105,390,299,471,470,184, 57,200,348, 63,204,188, 33,451,
97, 30,310,219, 94,160,129,493, 64,179,263,102,189,207,114,402,
438,477,387,122,192, 42,381, 5,145,118,180,449,293,323,136,380,
43, 66, 60,455,341,445,202,432, 8,237, 15,376,436,464, 59,461
};
During the encryption we are accessing this array very frequently.
One optimization which I had done moving this array from header file to local function so that some cache miss will not happened.
Any suggestion to more optimize this either by changing this array to any other data structure?
that array is not huge. a typical L1 cache is at least 10s of kB (that's the total memory on, say, an apple ii). and moving the array from a header to a function is not going to change cache locality.
storing it in the appropriate form (as in comments) may make sense (it's going to fit in l1 cache, but if you have other data, perhaps used by another thread, there's more chance of it staying there) - there's no need for more than 2 bytes per value (but i have no idea if that introduces extra cost compared to using native size ints).
if this is really critical, you should look at the code generated and optimize that.
First of all, make sure you declare those arrays as const, so that the compiler knows they'll never change.
Second, as Oli Charlesworth suggests in the comments, you don't really need a full int to store each value. The elements of the S7 and S9 arrays are 7-bit and 9-bit unsigned integers, so either of int8_t or uint8_t should be enough for S7, and either of int16_t or uint16_t for S9. (You may want to benchmark whether there's any difference between using signed or unsigned types, although I wouldn't really expect any.)
Finally, if you really want to get rid of the arrays entirely, it's also possible to implement the KASUMI S-boxes directly without any lookup tables, using bit operations (specifically, AND and XOR). For details, see pages 13–16 of the KASUMI specification. However, I strongly suspect that this will not be useful for a software implementation, unless you're using bit-slicing to encrypt many blocks in parallel.
This question already has answers here:
Closed 10 years ago.
Possible Duplicate:
Why do I always get the same sequence of random numbers with rand()?
I've been experimenting with generating random numbers in C, and I've come across something weird. I don't know if it's only on my compiler but whenever I try to generate a pseudo-random number with the rand() function, it returns a very predictable number — the number generated with the parameter before plus 3.125 to be exact. It's hard to explain but here's an example.
srand(71);
int number = rand();
printf("%d", number);
This returns 270.
srand(72);
int number = rand();
printf("%d", number);
This returns 273.
srand(73);
int number = rand();
printf("%d", number);
This returns 277.
srand(74);
int number = rand();
printf("%d", number);
This returns 280.
Every eighth number is 4 higher. Otherwise it's 3.
This can't possibly be right. Is there something wrong with my compiler?
Edit: I figured it out — I created a function where I seed only once, then I loop the rand() and it generates random numbers. Thank you all!
The confusion here is about how pseudorandom number generators work.
Pseudorandom number generators like C's rand work by having a number representing the current 'state'. Every time the rand function is called, some deterministic computations are done on the 'state' number to produce the next 'state' number. Thus, if the generator is given the same input (the same 'state'), it will produce the same output.
So, when you seed the generator with srand(74), it will always generate the same string of numbers, every time. When you seed the generator with srand(75), it will generate a different string of numbers, etc.
The common way to ensure different output each time is to always provide a different seed, usually done by seeding the generator with the current time in seconds/milliseconds, e.g. srand(time(NULL)).
EDIT: Here is a Python session demonstrating this behavior. It is entirely expected.
>>> import random
If we seed the generator with the same number, it will always output the same sequence:
>>> random.seed(500)
>>> [random.randint(0, 100) for _ in xrange(20)]
[80, 95, 58, 25, 76, 37, 80, 34, 57, 79, 1, 33, 40, 29, 92, 6, 45, 31, 13, 11]
>>> random.seed(500)
>>> [random.randint(0, 100) for _ in xrange(20)]
[80, 95, 58, 25, 76, 37, 80, 34, 57, 79, 1, 33, 40, 29, 92, 6, 45, 31, 13, 11]
>>> random.seed(500)
>>> [random.randint(0, 100) for _ in xrange(20)]
[80, 95, 58, 25, 76, 37, 80, 34, 57, 79, 1, 33, 40, 29, 92, 6, 45, 31, 13, 11]
If we give it a different seed, even a slightly different one, the numbers will be totally different from the old seed, yet still the same if the same (new) seed is used:
>>> random.seed(501)
>>> [random.randint(0, 100) for _ in xrange(20)]
[64, 63, 24, 81, 33, 36, 72, 35, 95, 46, 37, 2, 76, 21, 46, 68, 47, 96, 39, 36]
>>> random.seed(501)
>>> [random.randint(0, 100) for _ in xrange(20)]
[64, 63, 24, 81, 33, 36, 72, 35, 95, 46, 37, 2, 76, 21, 46, 68, 47, 96, 39, 36]
>>> random.seed(501)
>>> [random.randint(0, 100) for _ in xrange(20)]
[64, 63, 24, 81, 33, 36, 72, 35, 95, 46, 37, 2, 76, 21, 46, 68, 47, 96, 39, 36]
How do we make our program have different behavior each time? If we supply the same seed, it will always behave the same. We can use the time.time() function, which will yield a different number each time we call it:
>>> import time
>>> time.time()
1347917648.783
>>> time.time()
1347917649.734
>>> time.time()
1347917650.835
So if we keep re-seeding it with a call to time.time(), we will get a different sequence of numbers each time, because the seed is different each time:
>>> random.seed(time.time())
>>> [random.randint(0, 100) for _ in xrange(20)]
[60, 75, 60, 26, 19, 70, 12, 87, 58, 2, 79, 74, 1, 79, 4, 39, 62, 20, 28, 19]
>>> random.seed(time.time())
>>> [random.randint(0, 100) for _ in xrange(20)]
[98, 45, 85, 1, 67, 25, 30, 88, 17, 93, 44, 17, 94, 23, 98, 32, 35, 90, 56, 35]
>>> random.seed(time.time())
>>> [random.randint(0, 100) for _ in xrange(20)]
[44, 17, 10, 98, 18, 6, 17, 15, 60, 83, 73, 67, 18, 2, 40, 76, 71, 63, 92, 5]
Of course, even better than constantly re-seeding it is to seed it once and keep going from there:
>>> random.seed(time.time())
>>> [random.randint(0, 100) for _ in xrange(20)]
[94, 80, 63, 66, 31, 94, 74, 15, 20, 29, 76, 90, 50, 84, 43, 79, 50, 18, 58, 15]
>>> [random.randint(0, 100) for _ in xrange(20)]
[30, 53, 75, 19, 35, 11, 73, 88, 3, 67, 55, 43, 37, 91, 66, 0, 9, 4, 41, 49]
>>> [random.randint(0, 100) for _ in xrange(20)]
[69, 7, 25, 68, 39, 57, 72, 51, 33, 93, 81, 89, 44, 61, 78, 77, 43, 10, 33, 8]
Every invocation of rand() returns the next number in a predefined sequence where the starting number is the seed supplied to srand(). That' why it's called a pseudo-random number generator, and not a random number generator.
rand() is implemented by a pseudo random number generator.
The distribution of numbers generated by consecutive calls to rand() have the properties of being random numbers, but the order is pre-determined.
The 'start' number is determined by the seed that you provide.
You should give a PRNG a single seed only. Providing it with multiple seeds can radically alter the randomness of the generator. In addition, providing it the same seed over and over removes all randomness.
Generating a "random" number regardless of the implementation is dependent on a divergent infinite sequence. The infinite sequence is generated using the seed of the random function and it is actually pseudo random because of its nature. This would explain to you why your number is actually very dependent on the seed that you give the function.
In some implementations the sequence is only one and the seed is the starting member of the sequence. In others there are difference sequences depending on the seed. If a seed is not provided then the seed is determined by the internal "clock".
The number is truncated when using an upper and lower bounds for your random number by respectively doing randValue % upperBound and randValue + lowerBound. Random implementation is very similar to Hash Functions. Depending on architecture the upper bound of the random value is set depending on what it the largest integer/double that it can carry out if not set lower by the user.