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

Adjusts the percentage of available threads to use, from 0 to 1. A value of 0 indicates the use of a single thread, while 1 implies the use of all possible current threads. Values outside the range will be ignored and this function heuristically determines 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 in pvals.

Examples

                        >>> res = multinomial(conn_context=cc, n=10, pvals=(0.1, 0.2, 0.3, 0.4), num_random=10)
>>> res.collect()