Created:
| Last Modified:
I have added a few lines of code that will eliminate the duplicate combinations. In addition, I would suggest that you look into
Tables=>Join
at the Cartesian join capability.
It produces the all possible combinations much faster than JSL code can do it.
Names Default To Here( 1 );
New Table( "Test",
Add Rows( 5 ),
New Column( "API 10", Numeric, "Continuous", Format( "Best", 15 ), Set Values( [4238939261, 4238939263, 4238939262, 4238939328, 4238939329] ) ),
);
Test = Data Table( "Test" );
APIs = Test:API 10 << get values;
working = New Table( "TestAPIComb", New Column( "API10" ), New Column( "API102" ), add rows( N Items( APIs ) ^ 2 ) );
APIs2 = Test:API 10 << get values;
theRow = 0;
For( APICNT = 1, APICNT <= N Items( APIs ), APICNT++,
For( API2CNT = 1, API2CNT <= N Items( APIs2 ), API2CNT++,
theRow++;
:API10[theRow] = APIs[APICNT];
:API102[theRow] = APIs[API2CNT];
)
);
dt = Data Table( "TestAPIcomb" );
// Arange data so order is the same for all rows
For( i = 1, i <= N Rows( dt ), i++,
If( :API102[i] > :API10[i],
hold = :API10[i];
:API10[i] = :API102[i];
:API102[i] = hold;
)
);
// Find the duplicates
dt << Select Duplicate Rows( :API10, :API102 );
// Delete the duplicates
dt << delete rows;
Most likely there are quite many ways of doing this (the cartesian join @txnelson is good starting point). Here is one option using just matrix calculations:
Names Default To Here(1);
// input list
m = [4238939261, 4238939263, 4238939262, 4238939328, 4238939329];
n_items = N Items(m);
// combination count
comb_count = Summation(i = 1, n_items - 1, i++);
m = Sort Ascending(m);
first_col = [];
second_col = [];
For(i = 2, i <= n_items, i++,
first_col ||= Repeat(m[i - 1], n_items - i + 1)`;
second_col ||= m[Index(i, n_items)]`;
);
dt = New Table("Combinations",
Add Rows(comb_count),
New Column("FirstCol", Numeric, Continuous, Values(first_col)),
New Column("SecondCol", Numeric, Continuous, Values(second_col)),
);
As to performance.
The code below generates the almost 2 million row table of pairs from a set of 2000 in a little under 6 seconds, which is a bit less than half as long as looping took. I don't know whether such performance is suitable for your purposes. Hopefully it is a feasible option for you.
Names Default To Here( 1 );
letters = Words( "abcdefghijklmnopqrstuvwxyz", "" );
// create a large list of random strings of length 10. use an AA to remove (unlikely) duplicates
lis = Associative Array( Repeat( {Concat Items( letters[J( 10, 1, Random Integer( 26 ) )], "" )}, 2e3 ) ) << get keys;
t1 = HP Time();
// looping solution
dt = new table( "xx", add rows (2e6), << new column ("X", character), <<new column ("y", character));
n = nitems(lis);
r=0;
for each ( { i }, 1::(n-1),
for each( { j }, ( i + 1):: n,
r++;
dt[ r, 1] = lis[ i ]; dt[ r, 2 ] = lis [ j ]
)
);
t2 = HP Time();
// nChooseK matrix solution
dt = As Table( NChooseK Matrix( N Items( lis ), 2 ), <<column names( {"x", "y"} ) );
dt << New Column( "first", character, <<set values( lis[dt:x << get values] ) );
dt << New Column( "second", character, <<set values( lis[dt:y << get values] ) );
dt << delete columns( 1 :: 2 );
t3 = HP Time();
Print( Eval Insert( "Create table of pairs from loop: ^(t2-t1)/1e6^ seconds." ) );
Print( Eval Insert( "Create table of pairs from nChooseK matrix: ^(t3-t2)/1e6^ seconds." ) );
I should also mention that the examples I used were with strings. If you are using numbers, you can just use straight matrix ops, which are pretty fast... the 2 million row table is generated in 0.3 seconds, and the 26choose5 table in 0.04 seconds.
Names Default To Here(1);
nums = J(2e3, 1, randominteger(1e9));
t1 = hptime();
astable(shape(nums[nChoosekMatrix( 2e3, 2 )], ., 2));
t2 = hptime();
Print( Eval Insert( "Create table of 2 numbers chosen from 2000: ^(t2-t1)/1e6^ seconds." ) );
// 26 choose 5
nums = J(26, 1, randominteger(1e9));
t3 = hptime();
astable(shape(nums[nChoosekMatrix( 26, 5 )], ., 5));
t4 = hptime();
Print( Eval Insert( "Create table of 5 numbers chosen from 26: ^(t4-t3)/1e6^ seconds." ) );
