multinomial
- hana_ml.algorithms.pal.random.multinomial(conn_context, n, pvals, num_random=100, seed=None, thread_ratio=None)
Draw samples from a multinomial distribution.
- Parameters:
- conn_contextConnectionContext
Database connection object.
- nint
Number of trials.
- pvalstuple of float and int
Success fractions of each category.
- num_randomint, optional
Specifies the number of random data to be generated.
Defaults to 100.
- seedint, optional
Indicates the seed used to initialize the random number generator:
0: Uses the system time.
Not 0: Uses the specified seed.
Note
When multithreading is enabled, the random number sequences of different runs might be different even if the SEED value remains the same.
Defaults to 0.
- thread_ratiofloat, optional
Controls the proportion of available threads to use.
The value range is from 0 to 1, where 0 indicates a single thread, and 1 indicates up to all available threads.
Values between 0 and 1 will use that percentage of available threads.
Values outside the range [0, 1] tell PAL to heuristically determine the number of threads to use.
Defaults to 0.
- Returns:
- DataFrame
Dataframe containing the generated random samples, structured as follows:
ID, type INTEGER, ID column.
Generated random number columns, named by appending index number (starting from 1 to length of pvals) to
Random_P, type DOUBLE. There will be as many columns here as there are values inpvals.
Examples
Draw samples from a multinomial distribution.
>>> res = multinomial(conn_context=cc, n=10, pvals=(0.1, 0.2, 0.3, 0.4), num_random=10) >>> res.collect() ID RANDOM_P1 RANDOM_P2 RANDOM_P3 RANDOM_P4 0 0 1.0 2.0 2.0 5.0 1 1 1.0 2.0 3.0 4.0 2 2 0.0 0.0 8.0 2.0 3 3 0.0 2.0 1.0 7.0 4 4 1.0 1.0 4.0 4.0 5 5 1.0 1.0 4.0 4.0 6 6 1.0 2.0 3.0 4.0 7 7 1.0 4.0 2.0 3.0 8 8 1.0 2.0 3.0 4.0 9 9 4.0 1.0 1.0 4.0