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Copy file name to clipboardExpand all lines: docs/src/index.md
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@@ -193,4 +193,94 @@ println("Acc for train: $acc_train")
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println("Acc for val: $acc_val")
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```
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After you had results from path finding functions like learn_paths or build_paths, you can later save the results into a csv file or into a dataframe which can be loaded into R.
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Examples are like these:
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```julia
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JudiLing.write2csv(
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res_train,
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latin_train,
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cue_obj_train,
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cue_obj_train,
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"latin_train_res.csv",
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grams=3,
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tokenized=false,
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sep_token=nothing,
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start_end_token="#",
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output_sep_token="",
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path_sep_token=":",
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target_col=:Word,
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root_dir=@__DIR__,
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output_dir="latin_out"
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)
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JudiLing.write2csv(
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res_val,
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latin_val,
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cue_obj_train,
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cue_obj_val,
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"latin_val_res.csv",
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grams=3,
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tokenized=false,
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sep_token=nothing,
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start_end_token="#",
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output_sep_token="",
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path_sep_token=":",
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target_col=:Word,
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root_dir=@__DIR__,
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output_dir="latin_out"
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)
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JudiLing.write2csv(
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gpi_train,
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"latin_train_gpi.csv",
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root_dir=@__DIR__,
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output_dir="latin_out"
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)
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JudiLing.write2csv(
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gpi_val,
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"latin_val_gpi.csv",
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root_dir=@__DIR__,
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output_dir="latin_out"
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)
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df_train = JudiLing.write2df(
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res_train,
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latin_train,
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cue_obj_train,
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cue_obj_train,
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"latin_train_res.csv",
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grams=3,
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tokenized=false,
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sep_token=nothing,
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start_end_token="#",
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output_sep_token="",
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path_sep_token=":",
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target_col=:Word
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)
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df_val = JudiLing.write2df(
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res_val,
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latin_val,
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cue_obj_train,
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cue_obj_val,
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"latin_val_res.csv",
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grams=3,
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tokenized=false,
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sep_token=nothing,
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start_end_token="#",
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output_sep_token="",
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path_sep_token=":",
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target_col=:Word
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)
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display(df_train)
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display(df_val)
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# you may want to delete the temp out folder
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path =joinpath(@__DIR__, "latin_out")
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rm(path, force=true, recursive=true)
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```
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You can download and try our completed [script](https://github.com/MegamindHenry/JudiLing.jl/blob/master/examples/latin.jl).
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