---------------------------------------------------------------------------
-- Rules:
-- ------
-- 1) Any DB objects should be created w/o schema prefix,
--    since this file is executed in a separate schema context.
-- 2) There should be no DROP statements in this script, since
--    all objects created in the default schema will be cleaned-up outside.
---------------------------------------------------------------------------
m4_include(`SQLCommon.m4')

---------------------------------------------------------------------------
-- Build vocabulary:
---------------------------------------------------------------------------
CREATE TABLE lda_vocab(wordid INT4, word TEXT);

INSERT INTO lda_vocab VALUES
(0, 'code'), (1, 'data'), (2, 'graph'), (3, 'image'), (4, 'input'), (5,
'layer'), (6, 'learner'), (7, 'loss'), (8, 'model'), (9, 'network'), (10,
'neuron'), (11, 'object'), (12, 'output'), (13, 'rate'), (14, 'set'), (15,
'signal'), (16, 'sparse'), (17, 'spatial'), (18, 'system'), (19, 'training');

---------------------------------------------------------------------------
-- Build training dataset:
---------------------------------------------------------------------------
CREATE TABLE lda_training
(
    docid INT4,
    wordid INT4,
    count INT4
);

INSERT INTO lda_training VALUES
(0, 0, 2),(0, 3, 2),(0, 5, 1),(0, 7, 1),(0, 8, 1),(0, 9, 1),(0, 11, 1),(0, 13,
1), (1, 0, 1),(1, 3, 1),(1, 4, 1),(1, 5, 1),(1, 6, 1),(1, 7, 1),(1, 10, 1),(1,
14, 1),(1, 17, 1),(1, 18, 1), (2, 4, 2),(2, 5, 1),(2, 6, 2),(2, 12, 1),(2, 13,
1),(2, 15, 1),(2, 18, 2), (3, 0, 1),(3, 1, 2),(3, 12, 3),(3, 16, 1),(3, 17,
2),(3, 19, 1), (4, 1, 1),(4, 2, 1),(4, 3, 1),(4, 5, 1),(4, 6, 1),(4, 10, 1),(4,
11, 1),(4, 14, 1),(4, 18, 1),(4, 19, 1), (5, 0, 1),(5, 2, 1),(5, 5, 1),(5, 7,
1),(5, 10, 1),(5, 12, 1),(5, 16, 1),(5, 18, 1),(5, 19, 2), (6, 1, 1),(6, 3,
1),(6, 12, 2),(6, 13, 1),(6, 14, 2),(6, 15, 1),(6, 16, 1),(6, 17, 1), (7, 0,
1),(7, 2, 1),(7, 4, 1),(7, 5, 1),(7, 7, 2),(7, 8, 1),(7, 11, 1),(7, 14, 1),(7,
16, 1), (8, 2, 1),(8, 4, 4),(8, 6, 2),(8, 11, 1),(8, 15, 1),(8, 18, 1),
(9, 0, 1),(9, 1, 1),(9, 4, 1),(9, 9, 2),(9, 12, 2),(9, 15, 1),(9, 18, 1),(9,
19, 1);

CREATE TABLE lda_training_odd_voc_size
(
    docid INT4,
    wordid INT4,
    count INT4
);

INSERT INTO lda_training_odd_voc_size VALUES
(0, 0, 2),(0, 3, 2),(0, 5, 1),(0, 7, 1),(0, 8, 1),(0, 9, 1),(0, 11, 1),(0, 13,
1), (1, 0, 1),(1, 3, 1),(1, 4, 1),(1, 5, 1),(1, 6, 1),(1, 7, 1),(1, 10, 1),(1,
14, 1),(1, 17, 1),(1, 18, 1), (2, 4, 2),(2, 5, 1),(2, 6, 2),(2, 12, 1),(2, 13,
1),(2, 15, 1),(2, 18, 2), (3, 0, 1),(3, 1, 2),(3, 12, 3),(3, 16, 1),(3, 17,
2),(3, 19, 1), (4, 1, 1),(4, 2, 1),(4, 3, 1),(4, 5, 1),(4, 6, 1),(4, 10, 1),(4,
11, 1),(4, 14, 1),(4, 18, 1),(4, 19, 1), (5, 0, 1),(5, 2, 1),(5, 5, 1),(5, 7,
1),(5, 10, 1),(5, 12, 1),(5, 16, 1),(5, 18, 1),(5, 19, 2), (6, 1, 1),(6, 3,
1),(6, 12, 2),(6, 13, 1),(6, 14, 2),(6, 15, 1),(6, 16, 1),(6, 17, 1), (7, 0,
1),(7, 2, 1),(7, 4, 1),(7, 5, 1),(7, 7, 2),(7, 8, 1),(7, 11, 1),(7, 14, 1),(7,
16, 1), (8, 2, 1),(8, 4, 4),(8, 6, 2),(8, 11, 1),(8, 15, 1),(8, 18, 1),
(9, 0, 1),(9, 1, 1),(9, 4, 1),(9, 9, 2),(9, 12, 2),(9, 15, 1),(9, 18, 1),(9,
19, 1),(9, 20, 1);


CREATE TABLE lda_testing
(
    docid INT4,
    wordid INT4,
    count INT4
)
m4_ifdef(`__GREENPLUM__',`DISTRIBUTED BY (docid)');

INSERT INTO lda_testing VALUES
(0, 0, 2),(0, 8, 1),(0, 9, 1),(0, 10, 1),(0, 12, 1),(0, 15, 2),(0, 18, 1),(0,
19, 1), (1, 0, 1),(1, 2, 1),(1, 5, 1),(1, 7, 1),(1, 12, 2),(1, 13, 1),(1, 16,
1),(1, 17, 1),(1, 18, 1), (2, 0, 1),(2, 1, 1),(2, 2, 1),(2, 3, 1),(2, 4, 1),(2,
5, 1),(2, 6, 1),(2, 12, 1),(2, 14, 1),(2, 18, 1), (3, 2, 2),(3, 6, 2),(3, 7,
1),(3, 9, 1),(3, 11, 2),(3, 14, 1),(3, 15, 1), (4, 1, 1),(4, 2, 2),(4, 3,
1),(4, 5, 2),(4, 6, 1),(4, 11, 1),(4, 18, 2);

---------------------------------------------------------------------------
-- Test
---------------------------------------------------------------------------
SELECT lda_train(
    'lda_training',
    'lda_model',
    'lda_output_data',
    20, 5, 2, 10, 0.01);

SELECT lda_predict(
    'lda_testing',
    'lda_model',
    'lda_pred');

SELECT lda_predict(
    'lda_testing',
    'lda_model',
    'lda_pred_2',
    5);

SELECT lda_get_perplexity(
    'lda_model',
    'lda_pred');

SELECT __lda_util_index_sort(array[1, 4, 2, 3]);
SELECT __lda_util_transpose(array[[1, 2, 3],[4, 5, 6]]);
SELECT assert(count(*) = 2, 'Wrong answer: __lda_util_unnest_transpose()')
FROM
(
    SELECT __lda_util_unnest_transpose(array[1, 2, 0, 4, 5, 0]::bigint[], 3, 2)
) subq;
SELECT assert(count(*) = 3, 'Wrong answer: __lda_util_unnest()')
FROM
(
    SELECT __lda_util_unnest(array[1, 2, 0, 4, 5, 0]::bigint[], 3, 2)
) subq;
SELECT __lda_util_norm_with_smoothing(array[1, 4, 2, 3], 0.1);

-- no words hit ceiling
SELECT
    assert(
        __lda_check_count_ceiling(
            array[0, 0, 0, 0]::bigint[],
            2,
            2)
        IS NULL,
        '__lda_check_count_ceiling should return NULL for [0, 0, 0, 0]');
-- length: 1
SELECT
    assert(
        __lda_check_count_ceiling(
            array[-1, -1, -1, -1]::bigint[],
            2,
            2)
        IS NOT NULL,
        '__lda_check_count_ceiling should not return NULL for [-1, -1, -1, -1]');

SELECT lda_get_topic_desc(
    'lda_model',
    'lda_vocab',
    'topic_word_desc',
    5);

SELECT lda_get_topic_word_count(
    'lda_model',
    'topic_word_count');

SELECT lda_get_word_topic_count(
    'lda_model',
    'topic_word_count_2');

SELECT *
FROM __lda_util_norm_vocab('lda_vocab', 'norm_lda_vocab');

SELECT *
FROM __lda_util_norm_dataset('lda_testing', 'norm_lda_vocab',
'norm_lda_data');

SELECT *
FROM __lda_util_conorm_data('lda_testing', 'lda_vocab',
'norm_lda_data_2', 'norm_lda_vocab_2');

-- both voc_size and topic_num are odd or even
SELECT lda_train(
    'lda_training_odd_voc_size',
    'lda_model_odd_voc_size_even_topic_num',
    'lda_output_odd_voc_size_even_topic_num',
    21, 6, 2, 3, 0.01);

SELECT lda_train(
    'lda_training_odd_voc_size',
    'lda_model_odd_voc_size_odd_topic_num',
    'lda_output_odd_voc_size_odd_topic_num',
    21, 5, 2, 3, 0.01);

SELECT lda_train(
    'lda_training',
    'lda_model_even_voc_size_even_topic_num',
    'lda_output_even_voc_size_even_topic_num',
    20, 6, 2, 3, 0.01);