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I have this python script that helps me with generating random combinations based upon odds, it could be used for harness racing when we have to pick the winning horse in 7 consecutive races.
Now I would like to refine this script a bit more to my wishes if possible. As already said the script now spits out random numbers based upon odds. Now I would like that to be done in a bit more distinguished manner. I don't want combinations with too many numbers corresponding with low odds. In other words, I want the script to select randomly 4 of the 7 races and come back with a random number with higher odds (+20%) for each of the 4 races. For the 3 remaining races it must give me numbers each with less than a 10% probability.
This all goes way beyond my own python knowledge so I was hoping to receive some help here.
Thx in advance for all useful input.
import bpy
import random
scene = bpy.context.scene
def get_random_value(race):
"""
Function which returns a String containing random values based on the given probability distribution
"""
# Generates an Array [1,2,3,...,amount_of_race_participants]
numberList = [x+1 for x in range(len(race))]
temp = ' '
for x in random.choices(numberList, weights=race, k=1):
temp = temp + str(x) + ","
temp1 = temp[0:-1] + ''
return temp1
def generate_text(iteration, race):
"""
Creates a Text Object with random content and a location based on the input value
Also gives race odds to the get_random_value function, which is used for the probability distribution
"""
# Create Curve with text content
font_curve = bpy.data.curves.new(type="FONT",name="Font Curve")
font_curve.body = get_random_value(race)
# Put Curve into Object and link it to the scene
font_obj = bpy.data.objects.new(name="Font Object", object_data=font_curve)
scene.collection.objects.link(font_obj)
# Set location of Object
font_obj.location = (0,-iteration,0)
return font_obj
def generate_n_texts():
"""
Creates a variable amount of Texts objects and returns them
"""
races = [
[5, 58, 4, 4, 8, 3, 3, 2, 5, 1, 6, 1 ],
[3, 4, 6, 5, 19, 4, 4, 2, 2, 21, 11, 19 ],
[12, 3, 6, 14, 6, 8, 4, 3, 18, 2, 1, 23 ],
[15, 4, 23, 4, 29, 4, 3, 5, 8, 5 ],
[9, 2, 12, 3, 4, 43, 5, 4, 3, 7, 1, 7 ],
[3, 3, 2, 4, 3, 61, 3, 2, 7, 11, 1 ],
[10, 3, 13, 31, 6, 14, 6, 9, 4, 2, 2 ]
]
font_objs = []
# enumerate goes through the array race and gives us an element and its index
for iteration, race in enumerate(races):
font_objs.append((generate_text(iteration, race), race))
# Font Objects includes tuples of font objects and their respective probability distribution
# like this [(obj, race), (obj, race), ...]
return font_objs
# Generate font_objs list before definint recalculate_text() so the function can use the list
font_objs = generate_n_texts()
def recalculate_text(scene):
"""
Sets the bodies of the font_objs list to a random number
"""
#Remove seed to get a different number for every frame, even when revisiting a frame
random.seed(scene.frame_current)
for obj, race in font_objs:
obj.data.body = get_random_value(race)
def register():
bpy.app.handlers.frame_change_post.append(recalculate_text)
def unregister():
bpy.app.handlers.frame_change_post.remove(recalculate_text)
register()
I have a dictionary containing a variable number of numpy arrays (all same length), each array is stored in its respective key.
For each index I want to replace the value in one of the arrays by a newly calculated value. (This is a very simplyfied version what I'm actually doing.)
The problem is that when I try this as shown below, the value at the current index of every array in the dictionary is replaced, not just the one I specify.
Sorry if the formatting of the example code is confusing, it's my first question here (Don't quite get how to show the line example_dict["key1"][idx] = idx+10 properly indented in the next line of the for loop...).
>>> import numpy as np
>>> example_dict = dict.fromkeys(["key1", "key2"], np.array(range(10)))
>>> example_dict["key1"]
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> example_dict["key2"]
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> for idx in range(10):
example_dict["key1"][idx] = idx+10
>>> example_dict["key1"]
array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19])
>>> example_dict["key2"]
array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19])
I expected the loop to only access the array in example_dict["key1"], but somehow the same operation is applied to the array stored in example_dict["key2"] as well.
>>> hex(id(example_dict["key1"]))
'0x26a543ea990'
>>> hex(id(example_dict["key2"]))
'0x26a543ea990'
example_dict["key1"] and example_dict["key2"] are pointing at the same address. To fix this, you can use a dict comprehension.
import numpy
keys = ["key1", "key2"]
example_dict = {key: numpy.array(range(10)) for key in keys}
I have a list of lists. The lists within these list look like the following:
[0,2,5,8,7,12,16,18], [0,9,18,23,5,8,15,16], [1,3,4,17,19,6,13,23],
[9,22,21,10,11,20,14,15], [2,8,23,0,7,16,9,15], [0,5,8,7,9,11,20,16]
Every small list has 8 values from 0-23 and there are no value repeats within a small list.
What I need now are the three lists which have the values 0-23 stored. It is possible that there are a couple of combinations to accomplish it but I do only need one.
In this particular case the output would be:
[0,2,5,8,7,12,16,18], [1,3,4,17,19,6,13,23], [9,22,21,10,11,20,14,15]
I thought to do something with the order but I'm not a python pro so it is hard for me to handle all the lists within the list (to compare all).
Thanks for your help.
The following appears to work:
from itertools import combinations, chain
lol = [[0,2,5,8,7,12,16,18], [0,9,18,23,5,8,15,16], [1,3,4,17,19,6,13,23], [9,22,21,10,11,20,14,15], [2,8,23,0,7,16,9,15], [0,5,8,7,9,11,20,16]]
for p in combinations(lol, 3):
if len(set((list(chain.from_iterable(p))))) == 24:
print(p)
break # if only one is required
This displays the following:
([0, 2, 5, 8, 7, 12, 16, 18], [1, 3, 4, 17, 19, 6, 13, 23], [9, 22, 21, 10, 11, 20, 14, 15])
If it will always happen that 3 list will form numbers from 0-23, and you only want first list, then this can be done by creating combinations of length 3, and then set intersection:
>>> li = [[0,2,5,8,7,12,16,18], [0,9,18,23,5,8,15,16], [1,3,4,17,19,6,13,23], [9,22,21,10,11,20,14,15], [2,8,23,0,7,16,9,15], [0,5,8,7,9,11,20,16]]
>>> import itertools
>>> for t in itertools.combinations(li, 3):
... if not set(t[0]) & set(t[1]) and not set(t[0]) & set(t[2]) and not set(t[1]) & set(t[2]):
... print t
... break
([0, 2, 5, 8, 7, 12, 16, 18], [1, 3, 4, 17, 19, 6, 13, 23], [9, 22, 21, 10, 11, 20, 14, 15])
Let's do a recursive solution.
We need a list of lists that contain these values:
target_set = set(range(24))
This is a function that recursively tries to find a list of lists that match exactly that set:
def find_covering_lists(target_set, list_of_lists):
if not target_set:
# Done
return []
if not list_of_lists:
# Failed
raise ValueError()
# Two cases -- either the first element works, or it doesn't
try:
first_as_set = set(list_of_lists[0])
if first_as_set <= target_set:
# If it's a subset, call this recursively for the rest
return [list_of_lists[0]] + find_covering_lists(
target_set - first_as_set, list_of_lists[1:])
except ValueError:
pass # The recursive call failed to find a solution
# If we get here, the first element failed.
return find_covering_lists(target_set, list_of_lists[1:])
Greetings stackoverflow friends. I've decided to get a little wild this evening and party with for loops to iterate through a list I have created.
It appears the party has been pooped on, though, as the manner through which I would like to create a range is not readily apparent, neither through research nor playing around, and proving bothersome
The Desire: I would like to create a range of numbers much in a similar way that a range is usually created... by specifying range(start, stop, step) but with the minor alteration that I may additionally specify a step 'sweep' value such that range performed more like range(start, stop, step:sweep)
That is to say, if the glorious function above existed it could be used as following;
range(0,16,3:5)
# [0,3,4,5,8,9,10,13,14,15]
Another example!
range(0,24,2:9)
# [0,2,3,4,5,6,7,8,9,11,12,13,14,15,16,17,18,20,21,22,23]
Yet another!
range(0,24,3:9)
# [0,3,4,5,6,7,8,9,12,13,14,15,16,17,18,21,22,23]
Last one.
swept_range(5,20,3,4)
# [7, 8, 11, 12, 15, 16, 19]
In English, I desire a simple way to create a range of ordered numbers holding on to every Nth through Nth + D number group where D is some positive number.
I've looked at slices to no avail.
I know MATLAB can succinctly do this but wasn't sure this exists in Python - does anyone?
How about this generator, using modular arithmetic:
def swept_range(start, stop, step=1, sweep=1):
for i in range(start, stop):
if not 0 < i % sweep < step:
yield i
You could also use a list comprehension, if you need a sequence, rather than an iterator:
def swept_range(start, stop, step=1, sweep=1):
return [i for i in range(start, stop) if not 0 < i % sweep < step]
def yrange(st, sp, N, D):
return [st] + [j for i in range(st,sp,D) for j in range(i+N,i+D+1) if j < sp]
print yrange(0, 16, 3, 5)
# [0, 3, 4, 5, 8, 9, 10, 13, 14, 15]
print yrange(0, 24, 2, 9)
# [0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23]
print yrange(0, 24, 3, 9)
# [0, 3, 4, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 17, 18, 21, 22, 23]
def srange(start, stop, step=1, sweep=0):
if sweep < 0 :
raise Exception("sweep needs to be positive.")
STEPPING = 0
SWEEPING = 1
state = STEPPING
next = start
res = []
while next < stop:
res.append(next)
#ignores state if sweep is 0
if state == STEPPING or sweep == 0 :
state = SWEEPING
next = next + step
elif state == SWEEPING :
next = next + 1
if next % sweep == 0:
state = STEPPING
return res
I need a number of unique random permutations of a list without replacement, efficiently. My current approach:
total_permutations = math.factorial(len(population))
permutation_indices = random.sample(xrange(total_permutations), k)
k_permutations = [get_nth_permutation(population, x) for x in permutation_indices]
where get_nth_permutation does exactly what it sounds like, efficiently (meaning O(N)). However, this only works for len(population) <= 20, simply because 21! is so mindblowingly long that xrange(math.factorial(21)) won't work:
OverflowError: Python int too large to convert to C long
Is there a better algorithm to sample k unique permutations without replacement in O(N)?
Up to a certain point, it's unnecessary to use get_nth_permutation to get permutations. Just shuffle the list!
>>> import random
>>> l = range(21)
>>> def random_permutations(l, n):
... while n:
... random.shuffle(l)
... yield list(l)
... n -= 1
...
>>> list(random_permutations(l, 5))
[[11, 19, 6, 10, 0, 3, 12, 7, 8, 16, 15, 5, 14, 9, 20, 2, 1, 13, 17, 18, 4],
[14, 8, 12, 3, 5, 20, 19, 13, 6, 18, 9, 16, 2, 10, 4, 1, 17, 15, 0, 7, 11],
[7, 20, 3, 8, 18, 17, 4, 11, 15, 6, 16, 1, 14, 0, 13, 5, 10, 9, 2, 19, 12],
[10, 14, 5, 17, 8, 15, 13, 0, 3, 16, 20, 18, 19, 11, 2, 9, 6, 12, 7, 4, 1],
[1, 13, 15, 18, 16, 6, 19, 8, 11, 12, 10, 20, 3, 4, 17, 0, 9, 5, 2, 7, 14]]
The odds are overwhelmingly against duplicates appearing in this list for len(l) > 15 and n < 100000, but if you need guarantees, or for lower values of len(l), just use a set to record and skip duplicates if that's a concern (though as you've observed in your comments, if n gets close to len(l)!, this will stall). Something like:
def random_permutations(l, n):
pset = set()
while len(pset) < n:
random.shuffle(l)
pset.add(tuple(l))
return pset
However, as len(l) gets longer and longer, random.shuffle becomes less reliable, because the number of possible permutations of the list increases beyond the period of the random number generator! So not all permutations of l can be generated that way. At that point, not only do you need to map get_nth_permutation over a sequence of random numbers, you also need a random number generator capable of producing every random number between 0 and len(l)! with relatively uniform distribution. That might require you to find a more robust source of randomness.
However, once you have that, the solution is as simple as Mark Ransom's answer.
To understand why random.shuffle becomes unreliable for large len(l), consider the following. random.shuffle only needs to pick random numbers between 0 and len(l) - 1. But it picks those numbers based on its internal state, and it can take only a finite (and fixed) number of states. Likewise, the number of possible seed values you can pass to it is finite. This means that the set of unique sequences of numbers it can generate is also finite; call that set s. For len(l)! > len(s), some permutations can never be generated, because the sequences that correspond to those permutations aren't in s.
What are the exact lengths at which this becomes a problem? I'm not sure. But for what it's worth, the period of the mersenne twister, as implemented by random, is 2**19937-1. The shuffle docs reiterate my point in a general way; see also what Wikipedia has to say on the matter here.
Instead of using xrange simply keep generating random numbers until you have as many as you need. Using a set makes sure they're all unique.
permutation_indices = set()
while len(permutation_indices) < k:
permutation_indices.add(random.randrange(total_permutations))
I had one implementation of nth_permutation (not sure from where I got it) which I modified for your purpose. I believe this would be fast enough to suit your need
>>> def get_nth_permutation(population):
total_permutations = math.factorial(len(population))
while True:
temp_population = population[:]
n = random.randint(1,total_permutations)
size = len(temp_population)
def generate(s,n,population):
for x in range(s-1,-1,-1):
fact = math.factorial(x)
d = n/fact
n -= d * fact
yield temp_population[d]
temp_population.pop(d)
next_perm = generate(size,n,population)
yield [e for e in next_perm]
>>> nth_perm = get_nth_permutation(range(21))
>>> [next(nth_perm) for k in range(1,10)]
You seem to be searching for the Knuth Shuffle! Good luck!
You could use itertools.islice instead of xrange():
CPython implementation detail: xrange() is intended to be simple and
fast Implementations may impose restrictions to achieve this. The C
implementation of Python restricts all arguments to native C longs
(“short” Python integers), and also requires that the number of
elements fit in a native C long. If a larger range is needed, an
alternate version can be crafted using the itertools module:
islice(count(start, step), (stop-start+step-1+2*(step<0))//step).