search for: exapmpl

Hello, how is posibble to search only parts of words for exapmple if i have in index "Lazy fog was jumping over microstadio." I want this one will return for following search: "micro" -- Posted via http://www.ruby-forum.com/.

...ch can circumvent the "zombie" processes you mentioned - that I think arise from overly complex parallel nesting and excessivememory utilization. If the presence/absence indicators are ultimately sparse (many zeros and few ones), consider storing the result in a sparse matrix format (for exapmple- `Matrix` package in R). Instead of creating thousands of columns as dense integers, using a sparse matrix representation should dramatically reduce memory. After processing the data into a sparse format, you can then save it in a suitable file format and only convert to a dense format if absolute...

...ch can circumvent the "zombie" processes you mentioned - that I think arise from overly complex parallel nesting and excessivememory utilization. If the presence/absence indicators are ultimately sparse (many zeros and few ones), consider storing the result in a sparse matrix format (for exapmple- `Matrix` package in R). Instead of creating thousands of columns as dense integers, using a sparse matrix representation should dramatically reduce memory. After processing the data into a sparse format, you can then save it in a suitable file format and only convert to a dense format if absolute...

...umvent the "zombie" processes you mentioned - that I think arise from overly complex parallel nesting and excessivememory utilization. > > If the presence/absence indicators are ultimately sparse (many zeros and few ones), consider storing the result in a sparse matrix format (for exapmple- `Matrix` package in R). Instead of creating thousands of columns as dense integers, using a sparse matrix representation should dramatically reduce memory. After processing the data into a sparse format, you can then save it in a suitable file format and only convert to a dense format if absolute...

...ch can circumvent the "zombie" processes you mentioned - that I think arise from overly complex parallel nesting and excessivememory utilization. If the presence/absence indicators are ultimately sparse (many zeros and few ones), consider storing the result in a sparse matrix format (for exapmple- `Matrix` package in R). Instead of creating thousands of columns as dense integers, using a sparse matrix representation should dramatically reduce memory. After processing the data into a sparse format, you can then save it in a suitable file format and only convert to a dense format if absolute...

..."zombie" processes you mentioned - that I think arise from overly complex parallel nesting and excessivememory utilization. > > > > If the presence/absence indicators are ultimately sparse (many zeros and few ones), consider storing the result in a sparse matrix format (for exapmple- `Matrix` package in R). Instead of creating thousands of columns as dense integers, using a sparse matrix representation should dramatically reduce memory. After processing the data into a sparse format, you can then save it in a suitable file format and only convert to a dense format if absolute...

...ch can circumvent the "zombie" processes you mentioned - that I think arise from overly complex parallel nesting and excessivememory utilization. If the presence/absence indicators are ultimately sparse (many zeros and few ones), consider storing the result in a sparse matrix format (for exapmple- `Matrix` package in R). Instead of creating thousands of columns as dense integers, using a sparse matrix representation should dramatically reduce memory. After processing the data into a sparse format, you can then save it in a suitable file format and only convert to a dense format if absolute...

...bie" processes you mentioned - that I think arise from overly complex parallel nesting and excessivememory utilization. > > > > > > If the presence/absence indicators are ultimately sparse (many zeros and few ones), consider storing the result in a sparse matrix format (for exapmple- `Matrix` package in R). Instead of creating thousands of columns as dense integers, using a sparse matrix representation should dramatically reduce memory. After processing the data into a sparse format, you can then save it in a suitable file format and only convert to a dense format if absolute...

Hi R users. Apologies for the lack of concrete examples because the dataset is large, and it being so I believe is the issue. I multiple, very large datasets for which I need to generate 0/1 absence/presence columns Some include over 200M rows, with two columns that need presence/absence columns based on the strings contained within them, as an example, one set has ~29k unique values and the