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Given the following initially unsorted list:
[77, 101, 40, 43, 81, 129, 85, 144]
Which sorting algorithm produces the following list at the end of Pass Number 3? Is it Bubble, Insertion or Selection?
[40, 43, 77, 81, 85, 101, 129, 144]
Can someone give me a clue on how I can solve this please.
Insertion sort would change the relative order of at most 3 items in 3 passes resulting in the first 3 items being in order and the rest unchanged. Selection sort would affect only the positions of the first 3 items and the 3 smallest (or greatest) items. Only the bubble sort would swap other items around. The movements of 40 and 129 is a telltale sign that points to a Bubble sort.
Note that this may be a trick question because all numbers that need to be shifted are at most 2 positions off except 2 of them (101 & 129 which are the 2nd and 3rd largest and would end up in their right places after 2 passes). A properly implemented Bubble sort would not get to a 3rd pass. So the answer could be "none of them"
Insertion sort:
def insertion_sort(array):
for i in range(1, len(array)):
key_item = array[i]
j = i - 1
while j >= 0 and array[j] > key_item:
array[j + 1] = array[j]
j -= 1
array[j + 1] = key_item
print("Step",i,":",array)
return array
data=[77, 101, 40, 43, 81, 129, 85, 144]
insertion_sort(data)
Output:
Step 1 : [77, 101, 40, 43, 81, 129, 85, 144]
Step 2 : [40, 77, 101, 43, 81, 129, 85, 144]
Step 3 : [40, 43, 77, 101, 81, 129, 85, 144]
Step 4 : [40, 43, 77, 81, 101, 129, 85, 144]
Step 5 : [40, 43, 77, 81, 101, 129, 85, 144]
Step 6 : [40, 43, 77, 81, 85, 101, 129, 144]
Step 7 : [40, 43, 77, 81, 85, 101, 129, 144]
Bubble sort:
def bubble_sort(array):
n = len(array)
for i in range(n):
already_sorted = True
for j in range(n - i - 1):
if array[j] > array[j + 1]:
array[j], array[j + 1] = array[j + 1], array[j]
already_sorted = False
if already_sorted:
break
print("Step:",n-j-1)
print(array)
return array
data = [77, 101, 40, 43, 81, 129, 85, 144]
bubble_sort(data)
Output:
Step: 1
[77, 40, 43, 81, 101, 85, 129, 144]
Step: 2
[40, 43, 77, 81, 85, 101, 129, 144]
Selection Sort:
def selectionSort(array, size):
for step in range(size):
min_idx = step
for i in range(step + 1, size):
if array[i] < array[min_idx]:
min_idx = i
(array[step], array[min_idx]) = (array[min_idx], array[step])
print("step",step+1,":",end="")
print(array)
data = [77, 101, 40, 43, 81, 129, 85, 144]
size = len(data)
selectionSort(data, size)
Output:
step 1 :[40, 101, 77, 43, 81, 129, 85, 144]
step 2 :[40, 43, 77, 101, 81, 129, 85, 144]
step 3 :[40, 43, 77, 101, 81, 129, 85, 144]
step 4 :[40, 43, 77, 81, 101, 129, 85, 144]
step 5 :[40, 43, 77, 81, 85, 129, 101, 144]
step 6 :[40, 43, 77, 81, 85, 101, 129, 144]
step 7 :[40, 43, 77, 81, 85, 101, 129, 144]
step 8 :[40, 43, 77, 81, 85, 101, 129, 144]
You can also get more guidelines from the link below how to run algorithms:
https://realpython.com/sorting-algorithms-python/
guys. I am now working on a python algorithm and I am new to python. I'd like to generate a list of numbers like 4, 7, 8, 11, 12, 13, 16, 17, 18, 19, 22, 23, 24, 25... with 2 for loops.
I've done some work to find some numbers and I am close to the result I want, which is generate a list contains this numbers
My code is here:
for x in range(0, 6, 1):
start_ind = int(((x+3) * (x+2)) / 2 + 1)
print("start index is ", [start_ind], x)
start_node = node[start_ind]
for y in range(0, x):
ind = start_ind + y + 1
ind_list = node[ind]
index = [ind_list]
print(index)
Node is a list:
node = ['n%d' % i for i in range(0, 36, 1)]
What I received from this code is:
start index is [7] 1
['n8']
start index is [11] 2
['n12']
['n13']
start index is [16] 3
['n17']
['n18']
['n19']
start index is [22] 4
['n23']
['n24']
['n25']
['n26']
start index is [29] 5
['n30']
['n31']
['n32']
['n33']
['n34']
This seems to give the same list: and I think it's much clearer what's happening!
val=4
result=[]
for i in range(1,7):
for j in range(val,val+i):
val = val+1
result.append(j)
val = j+3
print(result)
Do not think you need a loop for this, let alone two:
import numpy as np
dif = np.ones(100, dtype = np.int32)
dif[np.cumsum(np.arange(14))] = 3
(1+np.cumsum(dif)).tolist()
output
[4, 7, 8, 11, 12, 13, 16, 17, 18, 19, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33, 34, 37, 38, 39, 40, 41, 42, 43, 46, 47, 48, 49, 50, 51, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 121, 122, 123, 124, 125, 126, 127, 128, 129]
ind_list = []
start_ind = 4
for x in range(0, 6):
ind_list.append(start_ind)
for y in range(1, x+1):
ind_list.append(start_ind + y)
start_ind = ind_list[len(ind_list)-1]+3
print(ind_list)
You could probably use this. the print function works fine, the list I assume works fairly well for the numbers provided. It appends the new number at the beginning of the loop, with a cotinually longer loop each time for x. I'm assuming the number sequence is 4, 4+3, 4+3+1, 4+3+1+3, 4+3+1+3+1, 4+3+1+3+1+1, 4+3+1+3+1+1+3, ....
I have a code which generates random number and put them in a list. The total of the values of these number must follow a defined value (in this case 6066). The numbers in the list also have to be a certain amount, meaning that i want 95 numbers to be generated randomly into a list, and the total of the values of these 95 numbers in the list is equals to 6066.
The code :
import random
def num(n, total):
dividers = sorted(random.sample(range(1, total), n - 1))
j= [a - b for a, b in zip(dividers + [total], [0] + dividers)]
return j
i=num(95,6066)
print (i)
The problem im facing is that i do not want any of the values of the 95 numbers in the list to exceed 85. How do i do this?
I have tried:
import random
def num(n, total):
dividers = sorted(random.sample(range(1, total), n - 1))
j= [a - b for a, b in zip(dividers + [total], [0] + dividers)]
for k in j:
if k>85:
j.remove(k)
return j
i=num(95,6066)
print (i)
But this only removes the number which are more than 85 from the list, i need to have 95 numbers in the list and total up to 6066
One solution will be to consider the problem like you are trying to distribute 6066 items between 95 buckets and each one has a capacity of 85, so you just loop over the items and each time choose a bucket that is not already full.
Here is a simple implementation. It won't be particularly fast, but it avoids the need to backtrack because there is no possibility of violating the rules (total sum incorrect or individual value exceeds the maximum).
Note that other solutions that are equally valid within your rules may have a different probability distribution, but you have not said anything about what probability distribution you require.
import random
def num(n, total, maxv):
if total > n * maxv:
raise ValueError("incompatible requirements")
vals = [0 for _ in range(n)]
not_full = list(range(n))
for _ in range(total):
index = random.choice(not_full)
vals[index] += 1
if vals[index] == maxv:
not_full.remove(index)
return vals
answer = num(95, 6066, 85)
print(answer)
print(max(answer))
print(sum(answer))
Gives:
[59, 59, 73, 63, 77, 58, 54, 71, 73, 67, 69, 67, 58, 79, 63, 59, 80, 58, 77, 64, 62, 64, 54, 50, 64, 72, 62, 69, 81, 61, 63, 50, 65, 56, 60, 51, 59, 61, 63, 56, 67, 69, 69, 64, 85, 66, 74, 66, 63, 63, 63, 68, 84, 66, 53, 82, 59, 66, 63, 58, 67, 58, 59, 58, 69, 56, 63, 61, 73, 58, 65, 60, 61, 53, 68, 51, 58, 57, 67, 60, 65, 73, 63, 59, 62, 49, 66, 59, 64, 56, 69, 58, 61, 67, 74]
85
6066
I've got a pandas DataFrame with a column, containing images as numpy 2D arrays.
I need to have a Series or DataFrame with their histograms, again in a single column, in parallel with dask.
Sample code:
import numpy as np
import pandas as pd
import dask.dataframe as dd
def func(data):
result = np.histogram(data.image.ravel(), bins=128)[0]
return result
n = 10
df = pd.DataFrame({'image': [(np.random.random((60, 24)) * 255).astype(np.uint8) for i in np.arange(n)],
'n1': np.arange(n),
'n2': np.arange(n) * 2,
'n3': np.arange(n) * 4
}
)
print 'DataFrame\n', df
hists = pd.Series([func(r[1]) for r in df.iterrows()])
# MAX_PROCESSORS = 4
# ddf = dd.from_pandas(df, npartitions=MAX_PROCESSORS)
# hists = ddf.apply(func, axis=1, meta=pd.Series(name='data', dtype=np.ndarray)).compute()
print 'Histograms \n', hists
Desired output
DataFrame
image n1 n2 n3
0 [[51, 254, 167, 61, 230, 135, 40, 194, 101, 24... 0 0 0
1 [[178, 130, 204, 196, 80, 97, 61, 51, 195, 38,... 1 2 4
2 [[122, 126, 47, 31, 208, 130, 85, 189, 57, 227... 2 4 8
3 [[185, 141, 206, 233, 9, 157, 152, 128, 129, 1... 3 6 12
4 [[131, 6, 95, 23, 31, 182, 42, 136, 46, 118, 2... 4 8 16
5 [[111, 89, 173, 139, 42, 131, 7, 9, 160, 130, ... 5 10 20
6 [[197, 223, 15, 40, 30, 210, 145, 182, 74, 203... 6 12 24
7 [[161, 87, 44, 198, 195, 153, 16, 195, 100, 22... 7 14 28
8 [[0, 158, 60, 217, 164, 109, 136, 237, 49, 25,... 8 16 32
9 [[222, 64, 64, 37, 142, 124, 173, 234, 88, 40,... 9 18 36
Histograms
0 [81, 87, 80, 94, 99, 79, 86, 90, 90, 113, 96, ...
1 [93, 76, 103, 83, 76, 101, 85, 83, 96, 92, 87,...
2 [84, 93, 87, 113, 83, 83, 89, 89, 114, 92, 86,...
3 [98, 101, 95, 111, 77, 92, 106, 72, 91, 100, 9...
4 [95, 96, 87, 82, 89, 87, 99, 82, 70, 93, 76, 9...
5 [77, 94, 95, 85, 82, 90, 77, 92, 87, 89, 94, 7...
6 [73, 86, 81, 91, 91, 82, 96, 94, 112, 95, 74, ...
7 [88, 89, 87, 88, 76, 95, 96, 98, 108, 96, 92, ...
8 [83, 84, 76, 88, 96, 112, 89, 80, 93, 94, 98, ...
9 [91, 78, 85, 98, 105, 75, 83, 66, 79, 86, 109,...
You can see commented lines, calling dask.DataFrame.apply. If I have uncommented them, I've got the exception dask.async.ValueError: Shape of passed values is (3, 128), indices imply (3, 4)
And here is the exception stack:
File "C:\Anaconda\envs\MBA\lib\site-packages\dask\base.py", line 94, in compute
(result,) = compute(self, traverse=False, **kwargs)
File "C:\Anaconda\envs\MBA\lib\site-packages\dask\base.py", line 201, in compute
results = get(dsk, keys, **kwargs)
File "C:\Anaconda\envs\MBA\lib\site-packages\dask\threaded.py", line 76, in get
**kwargs)
File "C:\Anaconda\envs\MBA\lib\site-packages\dask\async.py", line 500, in get_async
raise(remote_exception(res, tb))
dask.async.ValueError: Shape of passed values is (3, 128), indices imply (3, 4)
How can I overcome it?
My goal is to process this data frame in parallel.
map_partitions was the answer. After several days of experiments and time measurements, I've come to the following code. It gives 2-4 times speedup compared to list comprehensions or generator expressions wrapping pandas.DataFrame.itertuples
def func(data):
filtered = # filter data.image
result = np.histogram(filtered)
return result
def func_partition(data, additional_args):
result = data.apply(func, args=(bsifilter, ), axis=1)
return result
if __name__ == '__main__':
dask.set_options(get=dask.multiprocessing.get)
n = 30000
df = pd.DataFrame({'image': [(np.random.random((180, 64)) * 255).astype(np.uint8) for i in np.arange(n)],
'n1': np.arange(n),
'n2': np.arange(n) * 2,
'n3': np.arange(n) * 4
}
)
ddf = dd.from_pandas(df, npartitions=MAX_PROCESSORS)
dhists = ddf.map_partitions(func_partition, bfilter, meta=pd.Series(dtype=np.ndarray))
print 'Delayed dhists = \n', dhists
hists = pd.Series(dhists.compute())
I have a python list question:
Input:
l=[2, 5, 6, 7, 10, 11, 12, 19, 20, 26, 28, 33, 34, 45, 46, 47, 50, 57, 59, 64, 67, 77, 79, 87, 93, 97, 106, 110, 111, 113, 115, 120, 125, 126, 133, 135, 142, 148, 160, 166, 169, 176, 202, 228, 234, 253, 274, 365, 433, 435, 436, 468, 476, 529, 570, 575, 577, 581, 614, 766, 813, 944, 1058, 1079, 1245, 1363, 1389, 1428, 1758, 2129, 2336, 2402, 2405, 2576, 3013, 3993, 7687, 8142, 8455, 8456]
Now I want to write mark the numbers in a [0]*10000 list, such that the beginning is like:
Output:
lp=[0,1,0,0,1,...]
The second and fifth elements are marked since they appeared in the input.
lp = [0] * 10000
for index in l:
lp[index - 1] = 1
You could use the following list comprehension
lp = [1 if i in l else 0 for i in range(1, 10001)]
Though I'd recommend since l could be long that you convert it to a set first
set_l = set(l)
lp = [1 if i in set_l else 0 for i in range(1, 10001)]