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CarlitosDev / nextDoor

Next Door neighbours Python implementation
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Can you see this issue using api.github.com? #1

Open CarlitosDev opened 5 years ago

CarlitosDev commented 5 years ago

You're actually reading the issue now...

NATHANIGOWTHAMI commented 3 years ago

Thank u for the work.may i know what was the algorithms used in this project .

CarlitosDev commented 3 years ago

Thank u for the work.may i know what was the algorithms used in this project .

Hi Nathani,

The algorithm is an ensemble of self-weighted k-nearest neighbours. You can find the mathematical description in our paper in Section III. And the entry point in the code

How are you planning to use it? Let me know if I can be of any help

Carlos

NATHANIGOWTHAMI commented 3 years ago

sir but u used some kpi

On Thu, 3 Dec 2020 at 17:57, Carlos Aguilar notifications@github.com wrote:

Thank u for the work.may i know what was the algorithms used in this project .

Hi Nathani,

The algorithm is an ensemble of self-weighted k-nearest neighbours. You can find the mathematical description in our paper in Section III https://ieeexplore.ieee.org/document/8727882. And the entry point in the code https://github.com/CarlitosDev/nextDoor/blob/a1d116b1d5ea39c4cfd223adfd2600c3a992e704/nextDoorForecasterV2.py#L104

How are you planning to use it? Let me know if I can be of any help

Carlos

— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/CarlitosDev/nextDoor/issues/1#issuecomment-737917013, or unsubscribe https://github.com/notifications/unsubscribe-auth/APH2ZXTEXLNL2IG3XEDI6WTSS572RANCNFSM4HZBPINA .

NATHANIGOWTHAMI commented 3 years ago

ohe algorithm. sir actually we are dng term paper by taking your ieee paper as reference i am new to machine learning sir

On Thu, 3 Dec 2020 at 17:59, Nathani Gowthami nathanigowthami6@gmail.com wrote:

sir but u used some kpi

On Thu, 3 Dec 2020 at 17:57, Carlos Aguilar notifications@github.com wrote:

Thank u for the work.may i know what was the algorithms used in this project .

Hi Nathani,

The algorithm is an ensemble of self-weighted k-nearest neighbours. You can find the mathematical description in our paper in Section III https://ieeexplore.ieee.org/document/8727882. And the entry point in the code https://github.com/CarlitosDev/nextDoor/blob/a1d116b1d5ea39c4cfd223adfd2600c3a992e704/nextDoorForecasterV2.py#L104

How are you planning to use it? Let me know if I can be of any help

Carlos

— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/CarlitosDev/nextDoor/issues/1#issuecomment-737917013, or unsubscribe https://github.com/notifications/unsubscribe-auth/APH2ZXTEXLNL2IG3XEDI6WTSS572RANCNFSM4HZBPINA .

CarlitosDev commented 3 years ago

ohe algorithm. sir actually we are dng term paper by taking your ieee paper as reference i am new to machine learning sir On Thu, 3 Dec 2020 at 17:59, Nathani Gowthami nathanigowthami6@gmail.com wrote: … sir but u used some kpi On Thu, 3 Dec 2020 at 17:57, Carlos Aguilar @.***> wrote: > Thank u for the work.may i know what was the algorithms used in this > project . > > Hi Nathani, > > The algorithm is an ensemble of self-weighted k-nearest neighbours. You > can find the mathematical description in our paper in Section III > https://ieeexplore.ieee.org/document/8727882. And the entry point in > the code > https://github.com/CarlitosDev/nextDoor/blob/a1d116b1d5ea39c4cfd223adfd2600c3a992e704/nextDoorForecasterV2.py#L104 > > How are you planning to use it? Let me know if I can be of any help > > Carlos > > — > You are receiving this because you commented. > Reply to this email directly, view it on GitHub > <#1 (comment)>, > or unsubscribe > https://github.com/notifications/unsubscribe-auth/APH2ZXTEXLNL2IG3XEDI6WTSS572RANCNFSM4HZBPINA > . >

Welcome to ML then ;) You can do OHE directly in Pandas as pd.get_dummies(df[varName], columns=varName, prefix=varName) where varName is the categorical variable that you want to encode.

NATHANIGOWTHAMI commented 3 years ago

Thanks for the tip!

On Thu, 3 Dec 2020 at 18:07, Carlos Aguilar notifications@github.com wrote:

ohe algorithm. sir actually we are dng term paper by taking your ieee paper as reference i am new to machine learning sir On Thu, 3 Dec 2020 at 17:59, Nathani Gowthami nathanigowthami6@gmail.com wrote: … <#m-1134551124792968154> sir but u used some kpi On Thu, 3 Dec 2020 at 17:57, Carlos Aguilar @.***> wrote: > Thank u for the work.may i know what was the algorithms used in this > project . > > Hi Nathani, > > The algorithm is an ensemble of self-weighted k-nearest neighbours. You > can find the mathematical description in our paper in Section III > https://ieeexplore.ieee.org/document/8727882. And the entry point in > the code > https://github.com/CarlitosDev/nextDoor/blob/a1d116b1d5ea39c4cfd223adfd2600c3a992e704/nextDoorForecasterV2.py#L104

How are you planning to use it? Let me know if I can be of any help > > Carlos > > — > You are receiving this because you commented. > Reply to this email directly, view it on GitHub > <#1 (comment) https://github.com/CarlitosDev/nextDoor/issues/1#issuecomment-737917013>, or unsubscribe > https://github.com/notifications/unsubscribe-auth/APH2ZXTEXLNL2IG3XEDI6WTSS572RANCNFSM4HZBPINA . >

Welcome to ML then ;) You can do OHE directly in Pandas as pd.get_dummies(df[varName], columns=varName, prefix=varName) where varName is the categorical variable that you want to encode.

— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/CarlitosDev/nextDoor/issues/1#issuecomment-737927717, or unsubscribe https://github.com/notifications/unsubscribe-auth/APH2ZXUWKG3AGXXOGNOGHMLSS6A77ANCNFSM4HZBPINA .