There is much debate about unsigned. Without going too much into subjective territory, consider the following: What matters is not whether the value returned from rand() cannot be negative. What matters is that rand() returns a value of a certain type and that type determines what you can do with that value. rand() never returns … Read more
std::random_device may be expensive to create. It may open a device feeding it data, and that takes some time. It can also be expensive to call because it requires entropy to deliver a truly random number, not a pseudo random number. uniform_int_distribution is never expensive to create. It’s designed to be cheap. It does have … Read more
You can use Random.Next(int maxValue): Return: A 32-bit signed integer greater than or equal to zero, and less than maxValue; that is, the range of return values ordinarily includes zero but not maxValue. However, if maxValue equals zero, maxValue is returned. var r = new Random(); // print random integer >= 0 and < 100 … Read more
Use lists unless you want some very specific features that are in the C array libraries. python really has three primitive data structures tuple = (‘a’,’b’,’c’) list = [‘a’,’b’,’c’] dict = {‘a’:1, ‘b’: true, ‘c’: “name”} list.append(‘d’) #will add ‘d’ to the list list[0] #will get the first item ‘a’ list.insert(i, x) # Insert an … Read more
IMHO – the following is completely redundant: f.write(struct.pack(“=I”,random.randint(0,sys.maxint*2+1))) There’s absolutely no need to use struct.pack, just do something like: import os fileSizeInBytes = 1024 with open(‘output_filename’, ‘wb’) as fout: fout.write(os.urandom(fileSizeInBytes)) # replace 1024 with a size in kilobytes if it is not unreasonably large Then, if you need to re-use the file for reading integers, … Read more
Basic improvements, sets and local names Use a set, not a list, and testing for uniqueness is much faster; set membership testing takes constant time independent of the set size, while lists take O(N) linear time. Use a set comprehension to produce a series of keys at a time to avoid having to look up … Read more
You can use np.random.choice on an index array and apply it to both arrays: idx = np.random.choice(np.arange(len(x)), 1000, replace=False) x_sample = x[idx] y_sample = y[idx]
A random sample like this returns list of unique items of sequence. Don’t confuse this with random integers in the range. >>> import random >>> random.sample(range(30), 4) [3, 1, 21, 19]
If we can find one example where it does, the answer to your question is “yes”. #include <stdio.h> #include <stdlib.h> int main(int argc, char* argv[]) { unsigned int i; for(i = 0; ; i++) { int r = rand(); if (r == rand()) { printf(“Oops. rand() = %d; i = %d\n”, r, i); break; } … Read more