[jira] [Commented] (IGNITE-9283) [ML] Add Discrete Cosine preprocessor
[ https://issues.apache.org/jira/browse/IGNITE-9283?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16897035#comment-16897035 ] Aleksey Zinoviev commented on IGNITE-9283: -- [~ilantukh] Great, will make the review on the next week and leave the comments on github. thank you > [ML] Add Discrete Cosine preprocessor > - > > Key: IGNITE-9283 > URL: https://issues.apache.org/jira/browse/IGNITE-9283 > Project: Ignite > Issue Type: Sub-task > Components: ml >Reporter: Aleksey Zinoviev >Assignee: Ilya Lantukh >Priority: Major > Time Spent: 10m > Remaining Estimate: 0h > > Add [https://en.wikipedia.org/wiki/Discrete_cosine_transform] > Please look at the MinMaxScaler or Normalization packages in preprocessing > package. > Add classes if required > 1) Preprocessor > 2) Trainer > 3) custom PartitionData if shuffling is a step of algorithm > > Requirements for successful PR: > # PartitionedDataset usage > # Trainer-Model paradigm support > # Tests for Model and for Trainer (and other stuff) > # Example of usage with small, but famous dataset like IRIS, Titanic or > House Prices > # Javadocs/codestyle according guidelines > > -- This message was sent by Atlassian JIRA (v7.6.14#76016)
[jira] [Commented] (IGNITE-9283) [ML] Add Discrete Cosine preprocessor
[ https://issues.apache.org/jira/browse/IGNITE-9283?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16896482#comment-16896482 ] Ilya Lantukh commented on IGNITE-9283: -- [~zaleslaw] Please review https://github.com/apache/ignite/pull/6735. > [ML] Add Discrete Cosine preprocessor > - > > Key: IGNITE-9283 > URL: https://issues.apache.org/jira/browse/IGNITE-9283 > Project: Ignite > Issue Type: Sub-task > Components: ml >Reporter: Aleksey Zinoviev >Assignee: Ilya Lantukh >Priority: Major > Time Spent: 10m > Remaining Estimate: 0h > > Add [https://en.wikipedia.org/wiki/Discrete_cosine_transform] > Please look at the MinMaxScaler or Normalization packages in preprocessing > package. > Add classes if required > 1) Preprocessor > 2) Trainer > 3) custom PartitionData if shuffling is a step of algorithm > > Requirements for successful PR: > # PartitionedDataset usage > # Trainer-Model paradigm support > # Tests for Model and for Trainer (and other stuff) > # Example of usage with small, but famous dataset like IRIS, Titanic or > House Prices > # Javadocs/codestyle according guidelines > > -- This message was sent by Atlassian JIRA (v7.6.14#76016)
[jira] [Commented] (IGNITE-9283) [ML] Add Discrete Cosine preprocessor
[ https://issues.apache.org/jira/browse/IGNITE-9283?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16887951#comment-16887951 ] Aleksey Zinoviev commented on IGNITE-9283: -- [~ilantukh] Happy to hear that, mention me to review it when PR will be prepared > [ML] Add Discrete Cosine preprocessor > - > > Key: IGNITE-9283 > URL: https://issues.apache.org/jira/browse/IGNITE-9283 > Project: Ignite > Issue Type: Sub-task > Components: ml >Reporter: Aleksey Zinoviev >Assignee: Ilya Lantukh >Priority: Major > > Add [https://en.wikipedia.org/wiki/Discrete_cosine_transform] > Please look at the MinMaxScaler or Normalization packages in preprocessing > package. > Add classes if required > 1) Preprocessor > 2) Trainer > 3) custom PartitionData if shuffling is a step of algorithm > > Requirements for successful PR: > # PartitionedDataset usage > # Trainer-Model paradigm support > # Tests for Model and for Trainer (and other stuff) > # Example of usage with small, but famous dataset like IRIS, Titanic or > House Prices > # Javadocs/codestyle according guidelines > > -- This message was sent by Atlassian JIRA (v7.6.14#76016)
[jira] [Commented] (IGNITE-9283) [ML] Add Discrete Cosine preprocessor
[ https://issues.apache.org/jira/browse/IGNITE-9283?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16887941#comment-16887941 ] Ilya Lantukh commented on IGNITE-9283: -- [~zaleslaw] , I will prepare a PR in a few days. > [ML] Add Discrete Cosine preprocessor > - > > Key: IGNITE-9283 > URL: https://issues.apache.org/jira/browse/IGNITE-9283 > Project: Ignite > Issue Type: Sub-task > Components: ml >Reporter: Aleksey Zinoviev >Assignee: Ilya Lantukh >Priority: Major > > Add [https://en.wikipedia.org/wiki/Discrete_cosine_transform] > Please look at the MinMaxScaler or Normalization packages in preprocessing > package. > Add classes if required > 1) Preprocessor > 2) Trainer > 3) custom PartitionData if shuffling is a step of algorithm > > Requirements for successful PR: > # PartitionedDataset usage > # Trainer-Model paradigm support > # Tests for Model and for Trainer (and other stuff) > # Example of usage with small, but famous dataset like IRIS, Titanic or > House Prices > # Javadocs/codestyle according guidelines > > -- This message was sent by Atlassian JIRA (v7.6.14#76016)
[jira] [Commented] (IGNITE-9283) [ML] Add Discrete Cosine preprocessor
[ https://issues.apache.org/jira/browse/IGNITE-9283?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16886097#comment-16886097 ] Aleksey Zinoviev commented on IGNITE-9283: -- [~ilantukh] are you going to make this task? > [ML] Add Discrete Cosine preprocessor > - > > Key: IGNITE-9283 > URL: https://issues.apache.org/jira/browse/IGNITE-9283 > Project: Ignite > Issue Type: Sub-task > Components: ml >Reporter: Aleksey Zinoviev >Assignee: Ilya Lantukh >Priority: Major > > Add [https://en.wikipedia.org/wiki/Discrete_cosine_transform] > Please look at the MinMaxScaler or Normalization packages in preprocessing > package. > Add classes if required > 1) Preprocessor > 2) Trainer > 3) custom PartitionData if shuffling is a step of algorithm > > Requirements for successful PR: > # PartitionedDataset usage > # Trainer-Model paradigm support > # Tests for Model and for Trainer (and other stuff) > # Example of usage with small, but famous dataset like IRIS, Titanic or > House Prices > # Javadocs/codestyle according guidelines > > -- This message was sent by Atlassian JIRA (v7.6.14#76016)
[jira] [Commented] (IGNITE-9283) [ML] Add Discrete Cosine preprocessor
[ https://issues.apache.org/jira/browse/IGNITE-9283?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16701911#comment-16701911 ] Yury Babak commented on IGNITE-9283: Usefull paper: https://www.researchgate.net/publication/224112688_On_the_Use_of_Distributed_DCT_in_Speaker_Identification > [ML] Add Discrete Cosine preprocessor > - > > Key: IGNITE-9283 > URL: https://issues.apache.org/jira/browse/IGNITE-9283 > Project: Ignite > Issue Type: Sub-task > Components: ml >Reporter: Aleksey Zinoviev >Assignee: Ilya Lantukh >Priority: Major > > Add [https://en.wikipedia.org/wiki/Discrete_cosine_transform] > Please look at the MinMaxScaler or Normalization packages in preprocessing > package. > Add classes if required > 1) Preprocessor > 2) Trainer > 3) custom PartitionData if shuffling is a step of algorithm > > Requirements for successful PR: > # PartitionedDataset usage > # Trainer-Model paradigm support > # Tests for Model and for Trainer (and other stuff) > # Example of usage with small, but famous dataset like IRIS, Titanic or > House Prices > # Javadocs/codestyle according guidelines > > -- This message was sent by Atlassian JIRA (v7.6.3#76005)
