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Optimize print in high dimension matrix #120

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sepandhaghighi opened this issue Dec 8, 2018 · 8 comments
Closed

Optimize print in high dimension matrix #120

sepandhaghighi opened this issue Dec 8, 2018 · 8 comments
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@sepandhaghighi @alirezazolanvari
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enhancement New feature or request
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PYCM 1.9

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@sepandhaghighi
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@sepandhaghighi sepandhaghighi self-assigned this Dec 8, 2018
@sepandhaghighi sepandhaghighi added the enhancement New feature or request label Dec 8, 2018
sepandhaghighi added a commit that referenced this issue Dec 17, 2018
@sepandhaghighi
fix : matrix_params_calc optimized #120
6903451
sepandhaghighi added a commit that referenced this issue Dec 17, 2018
@sepandhaghighi
fix : normalized_matrix_calc optimized #120
b43fba0
sepandhaghighi added a commit that referenced this issue Dec 17, 2018
@sepandhaghighi
fix : normalized_matrix_calc optimized #120
265f40e
sepandhaghighi added a commit that referenced this issue Dec 17, 2018
@sepandhaghighi
fix : vector_filter optimized #120
8770ead
@alirezazolanvari
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High dimensional CMs can be demonstrated through a CSV file.

@sepandhaghighi sepandhaghighi added this to the PYCM 1.9 milestone Jan 2, 2019
@sepandhaghighi
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Hi @alirezazolanvari
Please create a warning message for high dimension matrix print and add to pycm_param.py line 25 in branch dev.
And also tell me what is your opinion about this warning threshold? (10< classes ? 5< classes ? ...)

Note : Please referenced issues in your commits (commit -m "message #120")

@sepandhaghighi
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sepandhaghighi commented Jan 8, 2019 •
edited

High dimensional CMs can be demonstrated through a CSV file.

@alirezazolanvari
About this option :
I think there is no need to add additional method and we can save matrix by save_csv in a separate file called filename_matrix.csv OK ?

@alirezazolanvari
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Hi @alirezazolanvari
Please create a warning message for high dimension matrix print and add to pycm_param.py line 25 in branch dev.
And also tell me what is your opinion about this warning threshold? (10< classes ? 5< classes ? ...)

Note : Please referenced issues in your commits (commit -m "message #120")

Hi @sepandhaghighi
I just try to find an appropriate threshold for the number of classes and I think up to 10 classes will be demonstrated properly in all types of reports.

alirezazolanvari pushed a commit that referenced this issue Jan 20, 2019
message #120
6b4ef08
@alirezazolanvari
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High dimensional CMs can be demonstrated through a CSV file.

@alirezazolanvari
About this option :
I think there is no need to add additional method and we can save matrix by save_csv in a separate file called filename_matrix.csv OK ?

At the warning message the save_csv method is going to be suggested.

@sepandhaghighi
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@alirezazolanvari Thanks for your efforts ;-)
I will update save_csv in next few days

sepandhaghighi added a commit that referenced this issue Jan 22, 2019
@sepandhaghighi
fix : save_csv updated #120
a46cb49
sepandhaghighi added a commit that referenced this issue Jan 22, 2019
@sepandhaghighi
fix : testcases updated for save_csv update #120
7002f69
sepandhaghighi added a commit that referenced this issue Jan 22, 2019
@sepandhaghighi
doc : save_csv docstring updated #120
2a9e36b
sepandhaghighi added a commit that referenced this issue Jan 22, 2019
@sepandhaghighi
fix : normalize flag added to save_csv #120
93d8a15
sepandhaghighi added a commit that referenced this issue Jan 22, 2019
@sepandhaghighi
fix : High dimension matrix warning added #120
124f2b8
sepandhaghighi added a commit that referenced this issue Jan 22, 2019
@sepandhaghighi
doc : Document updated for save_csv method #120
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sepandhaghighi commented Jan 22, 2019 •
edited

@alirezazolanvari

Please test warning for high dimension matrix and save_csv method with 2 new arguments : save_matrix & normalize for saving matrix in CSV file and give me some feedback ;-)

save_csv method document : https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/dev/Document/Document.ipynb#CSV

@alirezazolanvari
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@sepandhaghighi
All properties look good. The warning is also checked.
Thanks for your effort.

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@alirezazolanvari alirezazolanvari

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