I have this query:
SELECT * FROM PATIENT_MED_SPEC
which gives this result:
NUM_MALADE| NUM_MED| SPECIALITE
----------+--------+------------
3 53 Traumatologue
3 85 Anesthésiste
3 126 Radiologue
6 34 Pneumologue
6 85 Anesthésiste
6 114 Traumatologue
6 135 Anesthésiste
13 4 Orthopédiste
13 8 Cardiologue
13 114 Traumatologue
21 19 Traumatologue
21 64 Radiologue
21 135 Anesthésiste
23 4 Orthopédiste
23 8 Cardiologue
23 88 Cardiologue
And I want to get the result below using count and group by clauses
NUM_MALADE| count_MED| count_SPEc
----------+----------+------------
3 3 3
6 4 3
13 3 3
21 3 3
23 3 2
i.e for every MALADE (patient) we count the MEDECIN's
(doctors) number and the number of SPECIALITES concerned
You could use COUNT DISTINCT:
SELECT MALADE, COUNT(*) AS count_MED, COUNT(DISTINCT SPECIALITE) AS num_SPEC
FROM PATIENT_MED_SPEC
GROUP BY MALADE
Related
I have the following matrix:
L = [3 6 18 92 2
2 24 39 59 3];
I intend to enter the first row of matrix L into the 2nd row of the following matrix:
X = [2 7 43 52 1
4 21 14 97 4
3 17 27 85 5];
And the result should be:
Xnew = [2 7 43 52 1
3 6 18 92 2
4 21 14 97 4
3 17 27 85 5];
How can I do that in Julia?
This is a way to do it:
julia> #views [X[1:1, :]; L[1:1, :]; X[2:end, :]]
4×5 Matrix{Int64}:
2 7 43 52 1
3 6 18 92 2
4 21 14 97 4
3 17 27 85 5
You could get the same without #views but it would be less efficient as it would create intermediate copies of data.
Is there a way to sum pairwise in Octave?
If for example, I have a 10-row by 4 column. I want a new 10 row by 2 column, where each column is the sum of the pairs.
ex.
[ 1 2 3 4
2 3 4 5
...
]
=> [ 3 7
5 9
...
]
I know how to accomplish this using for loops and accumarray etc, but I'm just not sure if there's a way to do it that is completely vectorized.
Here are a few more options.
Given:
a = reshape(1:40, 10, 4)
a =
1 11 21 31
2 12 22 32
3 13 23 33
4 14 24 34
5 15 25 35
6 16 26 36
7 17 27 37
8 18 28 38
9 19 29 39
10 20 30 40
Keep it simple
b = [sum(a(:,1:2),2) sum(a(:,3:4),2)]
b =
12 52
14 54
16 56
18 58
20 60
22 62
24 64
26 66
28 68
30 70
Squeeze a little
b = squeeze(sum(reshape(a, [], 2, 2), 2))
b =
12 52
14 54
16 56
18 58
20 60
22 62
24 64
26 66
28 68
30 70
Or, my personal favorite...
Mathemagic
b = a * [1 1 0 0; 0 0 1 1].'
b =
12 52
14 54
16 56
18 58
20 60
22 62
24 64
26 66
28 68
30 70
Perhaps someone comes with a better idea:
a = [1 2 3 4; 2 3 4 5]
b = reshape (sum (reshape (a.', 2, [])), [], rows(a)).'
gives
b =
3 7
5 9
I have some data:
P = [3 10 25 32 43 1 3
6 12 35 39 49 4 9
2 9 23 36 47 2 9
...
7 20 35 42 44 3 7
15 18 19 41 42 4 6
10 18 32 35 46 3 10];
Data is always between 1 and 50.
I am selecting left 5 columns and 2 right columns:
L=P(:,1:5);
R=P(:,6:7);
I am counting occurrences:
a=tabul(L);
b=tabul(R);
In this moment, in a I am getting:
50. 3.
49. 4.
48. 3.
which tells me, that value 50 occurs 3 times, 49 occurs 4 times and so on.
What I need now is sort matrix a by second column but the first column should be arranged with the second column values. So it would look like this:
49. 4.
50. 3.
48. 3.
How can I sort matrix a this way (later I will sort b the same way)?
I was trying something like:
[a,idx]=gsort(a(:,2),"g","d");
a=a(idx,:);
but this not does what I need.
It does not work because you are overwriting a in the gsort call although you just need the index here. The following does what you want:
[dummy,idx]=gsort(a(:,2),"g","d");
a=a(idx,:);
I have a table with a list of numbers. Each number belongs to an entity.
Entity Number
1 1
1 2
1 3
1 4
...
1 20
2 21
2 22
2 23
1 24
2 25
2 26
2 30
2 31
2 32
2 33
The goal is to list the numbers, grouped by the entities as ranges (min-max pairs).
I need to find a way to group the above table as:
Entity Min Max
1 1 20
2 21 23
1 24 24
2 25 26
2 30 33
I've succesfully done this in my education, but I always found it hard and can't remember how the algorithm was done
This looks similar to SQL Data Range Min Max category
and TSQL Select Min & Max row when grouping
When does the quicksort algorithm take O(n^2) time?
Quicksort works by taking a pivot, then putting all the elements lower than that pivot on one side and all the higher elements on the other; it then recursively sorts the two sub groups in the same way (all the way down until everything is sorted.) Now if you pick the worst pivot each time (the highest or lowest element in the list) you'll only have one group to sort, with everything in that group other than the original pivot that you picked. This in essence gives you n groups that each need to be iterated through n times, hence the O(n^2) complexity.
The most common reason for this occurring is if the pivot is chosen to be the first or last element in the list in the quicksort implementation. For unsorted lists this is just as valid as any other, however for sorted or nearly sorted lists (which occur quite commonly in practice) this is very likely to give you the worst case scenario. This is why all half-decent implementations tend to take a pivot from the centre of the list.
There are modifications to the standard quicksort algorithm to avoid this edge case - one example is the dual-pivot quicksort that was integrated into Java 7.
In short, Quicksort for sorting an array lowest element first works like this:
Choose a pivot element
Presort array, such that all elements smaller than the pivot are on the left side
Recursively do step 1. and 2. for the left side and the right side
Ideally, you would want a pivot element that partitions the sequence in two equally long subsequences but this is not so easy.
There are different schemes for choosing the pivot element. Early versions just took the leftmost element. In the worst case, the pivot element will always be the lowest element of the current range.
Leftmost element is pivot
In this case it can be easily thought out that the worst case is an monotonic increasing array:
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Rightmost element is pivot
Similarly, when choosing the rightmost element the worst case will be a decreasing sequence.
20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0
Center element is pivot
One possible remedy for the worst-case for presorted arrays, is to use the center element (or slightly left of center if the sequence is of even length). Then, the worst case would be quite more exotic. It can be constructed by modifying the Quicksort algorithm to set the array elements corresponding to the currently selected pivot element to a monotonic increasing value. I.e. we know the first pivot is the center, so the center must be the lowest value, e.g. 0. Next it gets swapped to the leftmost, i.e. the leftmost value is now in the center and would be the next pivot element, so it must be 1. Now, we can already guess that the array would look like this:
1 ? ? 0 ? ? ?
Here is the C++ code for the modified Quicksort to generate a worst sequence:
// g++ -std=c++11 worstCaseQuicksort.cpp && ./a.out
#include <algorithm> // swap
#include <iostream>
#include <vector>
#include <numeric> // iota
int main( void )
{
std::vector<int> v(20); /**< will hold the worst case later */
/* p basically saves the indices of what was the initial position of the
* elements of v. As they get swapped around by Quicksort p becomes a
* permutation */
auto p = v;
std::iota( p.begin(), p.end(), 0 );
/* in the worst case we need to work on v.size( sequences, because
* the initial sequence is always split after the first element */
for ( auto i = 0u; i < v.size(); ++i )
{
/* i can be interpreted as:
* - subsequence starting index
* - current minimum value, if we start at 0 */
/* note thate in the last step iPivot == v.size()-1 */
auto const iPivot = ( v.size()-1 + i )/2;
v[ p[ iPivot ] ] = i;
std::swap( p[ iPivot ], p[i] );
}
for ( auto x : v ) std::cout << " " << x;
}
The result:
0
0 1
1 0 2
2 0 1 3
1 3 0 2 4
4 2 0 1 3 5
1 5 3 0 2 4 6
4 2 6 0 1 3 5 7
1 5 3 7 0 2 4 6 8
8 2 6 4 0 1 3 5 7 9
1 9 3 7 5 0 2 4 6 8 10
6 2 10 4 8 0 1 3 5 7 9 11
1 7 3 11 5 9 0 2 4 6 8 10 12
10 2 8 4 12 6 0 1 3 5 7 9 11 13
1 11 3 9 5 13 7 0 2 4 6 8 10 12 14
8 2 12 4 10 6 14 0 1 3 5 7 9 11 13 15
1 9 3 13 5 11 7 15 0 2 4 6 8 10 12 14 16
16 2 10 4 14 6 12 8 0 1 3 5 7 9 11 13 15 17
1 17 3 11 5 15 7 13 9 0 2 4 6 8 10 12 14 16 18
10 2 18 4 12 6 16 8 14 0 1 3 5 7 9 11 13 15 17 19
1 11 3 19 5 13 7 17 9 15 0 2 4 6 8 10 12 14 16 18 20
16 2 12 4 20 6 14 8 18 10 0 1 3 5 7 9 11 13 15 17 19 21
1 17 3 13 5 21 7 15 9 19 11 0 2 4 6 8 10 12 14 16 18 20 22
12 2 18 4 14 6 22 8 16 10 20 0 1 3 5 7 9 11 13 15 17 19 21 23
1 13 3 19 5 15 7 23 9 17 11 21 0 2 4 6 8 10 12 14 16 18 20 22 24
There is order in this. The right side is just increments of two starting with zero. The left side also has an order. Let's format the left side for the 73 element long worst case sequence nicely using Ascii art:
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
------------------------------------------------------------------------------------------------------------
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
37 39 41 43 45 47 49 51 53
55 57 59 61 63
65 67
69
71
The header is the element index. In the first row numbers starting from 1 and increasing by 2 are given to every 2nd element. In the second row the same is done to every 4th element, in the 3rd row numbers are assigned to every 8th element and so on. In this case the first value to be written in the i-th row is at index 2^i-1, but for certain lengths this looks a tad different.
The resulting structure is reminiscent to an inverted binary tree whose nodes are labeled bottom-up starting from the leaves.
Median of leftmost, center and rightmost elements is pivot
Another way is to use the median of the leftmost, the center and the rightmost element. In this case the worst case can only be, that the w.l.o.g. left subsequence is of length 2 (not just length 1 like in the examples above). Also we assume that the rightmost value will always be the highest of the median-of-three. This also means it is the highest of all values. Making adjustments in the program above, we now have this:
auto p = v;
std::iota( p.begin(), p.end(), 0 );
auto i = 0u;
for ( ; i < v.size(); i+=2 )
{
auto const iPivot0 = i;
auto const iPivot1 = ( i + v.size()-1 )/2;
v[ p[ iPivot1 ] ] = i+1;
v[ p[ iPivot0 ] ] = i;
std::swap( p[ iPivot1 ], p[i+1] );
}
if ( v.size() > 0 && i == v.size() )
v[ v.size()-1 ] = i-1;
The generated sequences are:
0
0 1
0 1 2
0 1 2 3
0 2 1 3 4
0 2 1 3 4 5
0 4 2 1 3 5 6
0 4 2 1 3 5 6 7
0 4 2 6 1 3 5 7 8
0 4 2 6 1 3 5 7 8 9
0 8 2 6 4 1 3 5 7 9 10
0 8 2 6 4 1 3 5 7 9 10 11
0 6 2 10 4 8 1 3 5 7 9 11 12
0 6 2 10 4 8 1 3 5 7 9 11 12 13
0 10 2 8 4 12 6 1 3 5 7 9 11 13 14
0 10 2 8 4 12 6 1 3 5 7 9 11 13 14 15
0 8 2 12 4 10 6 14 1 3 5 7 9 11 13 15 16
0 8 2 12 4 10 6 14 1 3 5 7 9 11 13 15 16 17
0 16 2 10 4 14 6 12 8 1 3 5 7 9 11 13 15 17 18
0 16 2 10 4 14 6 12 8 1 3 5 7 9 11 13 15 17 18 19
0 10 2 18 4 12 6 16 8 14 1 3 5 7 9 11 13 15 17 19 20
0 10 2 18 4 12 6 16 8 14 1 3 5 7 9 11 13 15 17 19 20 21
0 16 2 12 4 20 6 14 8 18 10 1 3 5 7 9 11 13 15 17 19 21 22
0 16 2 12 4 20 6 14 8 18 10 1 3 5 7 9 11 13 15 17 19 21 22 23
0 12 2 18 4 14 6 22 8 16 10 20 1 3 5 7 9 11 13 15 17 19 21 23 24
Pseudorandom element with random seed 0 is pivot
The worst case sequences for center element and median-of-three look already pretty random, but in order to make Quicksort even more robust the pivot element can be chosen randomly. If the random sequence used is at least reproducible on every Quicksort run, then we can also construct a worst case sequence for that. We only have to adjust the iPivot = line in the first program, e.g. to:
srand(0); // you shouldn't use 0 as a seed
for ( auto i = 0u; i < v.size(); ++i )
{
auto const iPivot = i + rand() % ( v.size() - i );
[...]
The generated sequences are:
0
1 0
1 0 2
2 3 1 0
1 4 2 0 3
5 0 1 2 3 4
6 0 5 4 2 1 3
7 2 4 3 6 1 5 0
4 0 3 6 2 8 7 1 5
2 3 6 0 8 5 9 7 1 4
3 6 2 5 7 4 0 1 8 10 9
8 11 7 6 10 4 9 0 5 2 3 1
0 12 3 10 6 8 11 7 2 4 9 1 5
9 0 8 10 11 3 12 4 6 7 1 2 5 13
2 4 14 5 9 1 12 6 13 8 3 7 10 0 11
3 15 1 13 5 8 9 0 10 4 7 2 6 11 12 14
11 16 8 9 10 4 6 1 3 7 0 12 5 14 2 15 13
6 0 15 7 11 4 5 14 13 17 9 2 10 3 12 16 1 8
8 14 0 12 18 13 3 7 5 17 9 2 4 15 11 10 16 1 6
3 6 16 0 11 4 15 9 13 19 7 2 10 17 12 5 1 8 18 14
6 0 14 9 15 2 8 1 11 7 3 19 18 16 20 17 13 12 10 4 5
14 16 7 9 8 1 3 21 5 4 12 17 10 19 18 15 6 0 11 2 13 20
1 2 22 11 16 9 10 14 12 6 17 0 5 20 4 21 19 8 3 7 18 15 13
22 1 15 18 8 19 13 0 14 23 9 12 10 5 11 21 6 4 17 2 16 7 3 20
2 19 17 6 10 13 11 8 0 16 12 22 4 18 15 20 3 24 21 7 5 14 9 1 23
So how to check whether those sequences are correct?
Measure time it took for the sequences. Plot time over the sequence length N. If the curve scales with O(N^2) instead of O(N log(N)), then these are indeed worst case sequences.
Adjust a correct Quicksort to give debug output about the subsequence lengths and/or the chosen pivot elements. One of the subsequences should always be of length 1 (or 2 for median-of-three). The chosen pivot elements printed should be increasing.
Getting a pivot equal to the lowest or highest number, should also trigger the worst case scenario of O(n2).
Different implementations of quicksort have different datasets required to give it a worstcase runtime. It depends on where the algorithm selects it's pivot-element.
And also as Ghpst said, selecting the biggest or smallest number would give you a worstcase.
If I remember correctly quicksort normally uses a random element for pivot to minimize the chance of getting a worstcase.
I think if the array is in revrse order then it will be worst case for pivot the last element of that array
The factors that contribute to the worst-case scenario of quicksort are as follows:
Worst case occurs when the subarrays are completely unbalanced
The worst case occurs when there are 0 elements in one subarray and n-1 elements in the other.
In other words, the worst-case running time of quicksort occurs when Quicksort takes in a sorted array (in decreasing order), to be on the time complexity of O(n^2).