8000 final fix for v0.2.2 by Shusei-E · Pull Request #130 · keyATM/keyATM · GitHub
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final fix for v0.2.2 #130

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Merged
merged 4 commits into from
Jun 3, 2020
Merged

final fix for v0.2.2 #130

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055b6dd
update link
Shusei-E Jun 2, 2020
3bb3aa6
update LDA
Shusei-E Jun 2, 2020
d663b72
updates for LDA modeles
Shusei-E Jun 2, 2020
6c8b6b8
update news
Shusei-E Jun 3, 2020
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4 changes: 2 additions & 2 deletions NEWS.md
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
# keyATM 0.2.2
### Major changes
* Updated for dplyr 1.0.0
* Update for dplyr 1.0.0
* Update tests

# keyATM 0.2.1
Expand Down Expand Up @@ -31,7 +31,7 @@
* Organize functions into a package
* Add keyATM Label
* Replace `hashmap` with `fastmap`
* Thank you [Santiago Olivella](http://santiagoolivella.info/) for finding several bugs!
* Thank you [Santiago Olivella](https://github.com/solivella) for finding several bugs!

# keyATM 0.0.7
### Major changes
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42 changes: 21 additions & 21 deletions docs/articles/pkgdown_files/Weighted_LDA.html
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2 changes: 1 addition & 1 deletion docs/news/news-0.1.html
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4 changes: 2 additions & 2 deletions docs/news/news-0.2.html
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1 change: 1 addition & 0 deletions src/LDA_base.cpp
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Expand Up @@ -13,6 +13,7 @@ void LDAbase::read_data_common()
regular_k = model["no_keyword_topics"];
model_fit = model["model_fit"];

keyword_k = 0;
num_topics = regular_k;

// document-related constants
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6 changes: 3 additions & 3 deletions tests/testthat/test-weightedLDA.R
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Expand Up @@ -21,8 +21,8 @@ test_that("weightedLDA base", {
expect_error(plot_pi(base))

skip_on_os("linux") ; skip_on_cran()
expect_equal(base$model_fit$Perplexity[3], 1978.213, tolerance = 0.0001)
expect_equal(top_words(base)[3, 1], "library")
expect_equal(base$model_fit$Perplexity[3], 1981.469, tolerance = 0.0001)
expect_equal(top_words(base)[3, 1], "end")
expect_equal(base$pi, NULL)
})

Expand Down Expand Up @@ -58,6 +58,6 @@ test_that("weightedLDA dynamic", {
expect_error(plot_pi(dyn))

skip_on_os("linux") ; skip_on_cran()
expect_equal(dyn$model_fit$Perplexity[2], 2098.058, tolerance = 0.0001)
expect_equal(dyn$model_fit$Perplexity[2], 2087.217, tolerance = 0.0001)
expect_equal(top_words(dyn)[3, 1], "commission")
})
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