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DB Tsai updated SPARK-7262:
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Issue Type: New Feature (was: Bug)
> LogisticRegression with L1/L2 (elastic net) using OWLQN in new ML packag
DB Tsai created SPARK-7262:
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Summary: LogisticRegression with L1/L2 (elastic net) using OWLQN
in new ML package
Key: SPARK-7262
URL: https://issues.apache.org/jira/browse/SPARK-7262
Project: Spark
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DB Tsai updated SPARK-7222:
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Issue Type: Improvement (was: Documentation)
> Added mathematical derivation in comment and compressed the mo
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DB Tsai updated SPARK-7222:
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Summary: Added mathematical derivation in comment and compressed the model
to LinearRegression with ElasticNet
DB Tsai created SPARK-7222:
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Summary: Added mathematical derivation in comment to
LinearRegression with ElasticNet
Key: SPARK-7222
URL: https://issues.apache.org/jira/browse/SPARK-7222
Project: Spark
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DB Tsai updated SPARK-7222:
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Description: Added detailed mathematical derivation of how scaling and
LeastSquaresAggregator work. Also refac
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DB Tsai closed SPARK-7191.
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Resolution: Not A Problem
sorry, it's my rebase issue. not a bug.
> SharedParamsCodeGen doesn't import org.apac
DB Tsai created SPARK-7191:
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Summary: SharedParamsCodeGen doesn't import
org.apache.spark.util.Utils
Key: SPARK-7191
URL: https://issues.apache.org/jira/browse/SPARK-7191
Project: Spark
Issue Type:
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DB Tsai updated SPARK-7191:
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Description: When we run `build/sbt "mllib/runMain
org.apache.spark.ml.param.shared.SharedParamsCodeGen"`, the
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DB Tsai commented on SPARK-2505:
For example, in GLMNET package, it allows users to regula
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https://issues.apache.org/jira/browse/SPARK-6683?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14395147#comment-14395147
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DB Tsai edited comment on SPARK-6683 at 4/3/15 10:11 PM:
-
PS, do w
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DB Tsai commented on SPARK-6683:
PS, do we still want to make it work properly with gradie
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DB Tsai commented on SPARK-6683:
The squared error will have slightly more work than logis
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DB Tsai edited comment on SPARK-6683 at 4/3/15 9:44 PM:
The square
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DB Tsai commented on SPARK-6683:
I think we should hide the scaling api, and the bottom li
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DB Tsai commented on SPARK-6683:
I have this implemented in our lab including
handling the
DB Tsai created SPARK-6141:
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Summary: Upgrade Breeze to 0.11 to fix convergence bug
Key: SPARK-6141
URL: https://issues.apache.org/jira/browse/SPARK-6141
Project: Spark
Issue Type: Bug
Com
DB Tsai created SPARK-5253:
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Summary: LinearRegression with L1/L2 (elastic net) using OWLQN in
new ML pacakge
Key: SPARK-5253
URL: https://issues.apache.org/jira/browse/SPARK-5253
Project: Spark
Is
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DB Tsai commented on SPARK-5207:
[~mengxr]'s idea sounds great for me. Specifically, let's
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https://issues.apache.org/jira/browse/SPARK-5128?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14267392#comment-14267392
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DB Tsai commented on SPARK-5128:
https://github.com/apache/spark/pull/3915/commits
> Add
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DB Tsai commented on SPARK-5127:
Not an issue in binary logistic regression. Problem only
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DB Tsai closed SPARK-5127.
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Resolution: Not a Problem
> Fixed overflow when there are outliers in data in Logistic Regression
>
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DB Tsai updated SPARK-5127:
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Description:
gradientMultiplier = (1.0 / (1.0 + math.exp(margin))) - label
However, the first part of gradien
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DB Tsai updated SPARK-5127:
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Description:
gradientMultiplier = (1.0 / (1.0 + math.exp(margin))) - label
However, the first part of gradien
DB Tsai created SPARK-5127:
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Summary: Fixed overflow when there are outliers in data in
Logistic Regression
Key: SPARK-5127
URL: https://issues.apache.org/jira/browse/SPARK-5127
Project: Spark
Issu
DB Tsai created SPARK-4972:
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Summary: Updated the scala doc for lasso and ridge regression for
the change of LeastSquaresGradient
Key: SPARK-4972
URL: https://issues.apache.org/jira/browse/SPARK-4972
Project
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https://issues.apache.org/jira/browse/SPARK-4907?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14258981#comment-14258981
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DB Tsai commented on SPARK-4907:
[~sowen] It seems that the existing document has 1/2 fact
DB Tsai created SPARK-4907:
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Summary: Inconsistent loss and gradient in LeastSquaresGradient
compared with R
Key: SPARK-4907
URL: https://issues.apache.org/jira/browse/SPARK-4907
Project: Spark
Iss
DB Tsai created SPARK-4887:
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Summary: Fix a bad unittest in LogisticRegressionSuite
Key: SPARK-4887
URL: https://issues.apache.org/jira/browse/SPARK-4887
Project: Spark
Issue Type: Bug
Com
DB Tsai created SPARK-4717:
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Summary: Optimize BLAS library to avoid de-reference multiple
times in loop
Key: SPARK-4717
URL: https://issues.apache.org/jira/browse/SPARK-4717
Project: Spark
Issue T
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DB Tsai updated SPARK-4708:
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Summary: Make k-mean runs two/three times faster with dense/sparse sample
(was: k-mean runs two/three times f
DB Tsai created SPARK-4708:
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Summary: k-mean runs two/three times faster with dense/sparse
sample
Key: SPARK-4708
URL: https://issues.apache.org/jira/browse/SPARK-4708
Project: Spark
Issue Type: Im
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DB Tsai updated SPARK-4708:
---
Component/s: MLlib
> Make k-mean runs two/three times faster with dense/sparse sample
> --
DB Tsai created SPARK-4611:
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Summary: Implement the efficient vector norm
Key: SPARK-4611
URL: https://issues.apache.org/jira/browse/SPARK-4611
Project: Spark
Issue Type: Improvement
Compo
DB Tsai created SPARK-4596:
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Summary: Refactorize Normalizer to make code cleaner
Key: SPARK-4596
URL: https://issues.apache.org/jira/browse/SPARK-4596
Project: Spark
Issue Type: Improvement
DB Tsai created SPARK-4581:
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Summary: Refactorize StandardScaler to improve the transformation
performance
Key: SPARK-4581
URL: https://issues.apache.org/jira/browse/SPARK-4581
Project: Spark
Issue
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https://issues.apache.org/jira/browse/SPARK-4431?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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DB Tsai updated SPARK-4431:
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Description:
Previously, we were using Breeze's activeIterator to access the non-zero
elements
in dense/spar
DB Tsai created SPARK-4431:
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Summary: Implement efficient activeIterator for dense and sparse
vector
Key: SPARK-4431
URL: https://issues.apache.org/jira/browse/SPARK-4431
Project: Spark
Issue Type:
DB Tsai created SPARK-4129:
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Summary: Performance tuning in MultivariateOnlineSummarizer
Key: SPARK-4129
URL: https://issues.apache.org/jira/browse/SPARK-4129
Project: Spark
Issue Type: Improvement
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https://issues.apache.org/jira/browse/SPARK-2493?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14170080#comment-14170080
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DB Tsai commented on SPARK-2493:
sbt gen-idea will add extra meso dependency other than me
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DB Tsai closed SPARK-2493.
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Resolution: Won't Fix
> SBT gen-idea doesn't generate correct Intellij project
> ---
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DB Tsai commented on SPARK-3630:
I think there is something else going wrong in the curren
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https://issues.apache.org/jira/browse/SPARK-1239?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14163423#comment-14163423
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DB Tsai commented on SPARK-1239:
+1, we run into this issue as well.
> Don't fetch all ma
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DB Tsai commented on SPARK-3630:
I think there are some issues in the shuffle manger with
DB Tsai created SPARK-3832:
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Summary: Upgrade Breeze dependency to 0.10
Key: SPARK-3832
URL: https://issues.apache.org/jira/browse/SPARK-3832
Project: Spark
Issue Type: Task
Components: ML
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DB Tsai edited comment on SPARK-3630 at 10/7/14 2:07 PM:
-
We also
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DB Tsai edited comment on SPARK-3630 at 10/7/14 2:08 PM:
-
We also
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DB Tsai edited comment on SPARK-3630 at 10/7/14 2:07 PM:
-
We also
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DB Tsai commented on SPARK-3630:
We also see similar issue when we perform map -> reduceBy
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DB Tsai closed SPARK-3317.
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Resolution: Won't Fix
> The loss of regularization in Updater should use the oldWeights
> --
DB Tsai created SPARK-3317:
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Summary: The loss of regularization in Updater should use the
oldWeights
Key: SPARK-3317
URL: https://issues.apache.org/jira/browse/SPARK-3317
Project: Spark
Issue Type
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https://issues.apache.org/jira/browse/SPARK-2979?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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DB Tsai updated SPARK-2979:
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Summary: Improve the convergence rate by minimizing the condition number in
LOR with LBFGS (was: Improve the
DB Tsai created SPARK-2979:
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Summary: Improve the convergence rate by minimize the condition
number in LOR with LBFGS
Key: SPARK-2979
URL: https://issues.apache.org/jira/browse/SPARK-2979
Project: Spark
DB Tsai created SPARK-2934:
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Summary: Adding LogisticRegressionWithLBFGS for training with
LBFGS Optimizer
Key: SPARK-2934
URL: https://issues.apache.org/jira/browse/SPARK-2934
Project: Spark
Iss
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https://issues.apache.org/jira/browse/SPARK-2599?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14070929#comment-14070929
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DB Tsai commented on SPARK-2599:
I'm the original guy implementing `almostEquals` for my u
DB Tsai created SPARK-2505:
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Summary: Weighted Regularizer
Key: SPARK-2505
URL: https://issues.apache.org/jira/browse/SPARK-2505
Project: Spark
Issue Type: New Feature
Components: MLlib
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DB Tsai closed SPARK-1451.
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Resolution: Duplicate
Duplicate of SPARK-2309
> Multinomial Logistic Regression Support
>
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DB Tsai updated SPARK-2309:
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Affects Version/s: 1.1.0
> Generalize the binary logistic regression into multinomial logistic regression
> -
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DB Tsai updated SPARK-2479:
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Description:
Floating point math is not exact, and most floating-point numbers end up being
slightly impreci
DB Tsai created SPARK-2493:
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Summary: SBT gen-idea doesn't generate correct Intellij project
Key: SPARK-2493
URL: https://issues.apache.org/jira/browse/SPARK-2493
Project: Spark
Issue Type: Sub-task
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DB Tsai updated SPARK-2479:
---
Description:
Due to rounding errors, most floating-point numbers end up being slightly
imprecise. As long as
DB Tsai created SPARK-2479:
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Summary: Comparing floating-point numbers using relative error in
UnitTests
Key: SPARK-2479
URL: https://issues.apache.org/jira/browse/SPARK-2479
Project: Spark
Issue T
DB Tsai created SPARK-2477:
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Summary: Using appendBias for adding intercept in
GeneralizedLinearAlgorithm
Key: SPARK-2477
URL: https://issues.apache.org/jira/browse/SPARK-2477
Project: Spark
Issue
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https://issues.apache.org/jira/browse/SPARK-2413?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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DB Tsai closed SPARK-2413.
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> Upgrade junit_xml_listener to 0.5.1
> ---
>
> Key: SPARK-2413
>
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DB Tsai closed SPARK-2281.
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Resolution: Not a Problem
> Simplify the duplicate code in Gradient.scala
> ---
DB Tsai created SPARK-2413:
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Summary: Upgrade junit_xml_listener to 0.5.1
Key: SPARK-2413
URL: https://issues.apache.org/jira/browse/SPARK-2413
Project: Spark
Issue Type: Improvement
Rep
DB Tsai created SPARK-2309:
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Summary: Generalize the binary logistic regression into
multinomial logistic regression
Key: SPARK-2309
URL: https://issues.apache.org/jira/browse/SPARK-2309
Project: Spark
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DB Tsai updated SPARK-2309:
---
Description:
Currently, there is no multi-class classifier in mllib. Logistic regression can
be extended to
DB Tsai created SPARK-2281:
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Summary: Simplify the duplicate code in Gradient.scala
Key: SPARK-2281
URL: https://issues.apache.org/jira/browse/SPARK-2281
Project: Spark
Issue Type: Improvement
DB Tsai created SPARK-2272:
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Summary: Feature scaling which standardizes the range of
independent variables or features of data.
Key: SPARK-2272
URL: https://issues.apache.org/jira/browse/SPARK-2272
Project:
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DB Tsai commented on SPARK-2100:
Thanks. I think there are some dependency issue we have,
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DB Tsai edited comment on SPARK-2100 at 6/16/14 9:50 PM:
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[~sowen]
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DB Tsai commented on SPARK-2100:
[~sowen] You are right. The servlet api is pulled by jett
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DB Tsai commented on SPARK-2100:
Jar file conflict. If we only include spark-core in our t
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DB Tsai updated SPARK-2100:
---
Description:
Since we want to use Spark hadoop APIs in local mode for design time to explore
the first coupl
DB Tsai created SPARK-2100:
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Summary: Allow users to disable Jetty Spark UI in local mode
Key: SPARK-2100
URL: https://issues.apache.org/jira/browse/SPARK-2100
Project: Spark
Issue Type: Improvement
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DB Tsai updated SPARK-1969:
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Description:
Basically, it moves the private ColumnStatisticsAggregator class from RowMatrix
to public avail
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DB Tsai updated SPARK-1969:
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Description:
It basically moved the private ColumnStatisticsAggregator class from RowMatrix
to public availa
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DB Tsai updated SPARK-1969:
---
Description:
Basically, it moves the private ColumnStatisticsAggregator class from RowMatrix
to public avail
DB Tsai created SPARK-1969:
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Summary: Online Summarizer for mean, variance, min, max, and
quartile
Key: SPARK-1969
URL: https://issues.apache.org/jira/browse/SPARK-1969
Project: Spark
Issue Type: N
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https://issues.apache.org/jira/browse/SPARK-1870?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14002597#comment-14002597
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DB Tsai commented on SPARK-1870:
This is not Yarn issue, and it's classloader issue. It ha
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DB Tsai updated SPARK-1516:
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Assignee: (was: DB Tsai)
> Yarn Client should not call System.exit, should throw exception instead.
> ---
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DB Tsai reassigned SPARK-1516:
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Assignee: DB Tsai
> Yarn Client should not call System.exit, should throw exception instead.
>
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DB Tsai updated SPARK-1457:
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Assignee: (was: DB Tsai)
> Change APIs for training algorithms to take optimizer as parameter
>
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DB Tsai reassigned SPARK-1457:
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Assignee: DB Tsai
> Change APIs for training algorithms to take optimizer as parameter
> -
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DB Tsai updated SPARK-1516:
---
Assignee: (was: DB Tsai)
> Yarn Client should not call System.exit, should throw exception instead.
> ---
DB Tsai created SPARK-1516:
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Summary: Yarn Client should not call System.exit, should throw
exception instead.
Key: SPARK-1516
URL: https://issues.apache.org/jira/browse/SPARK-1516
Project: Spark
I
DB Tsai created SPARK-1457:
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Summary: Change APIs for training algorithms to take optimizer as
parameter
Key: SPARK-1457
URL: https://issues.apache.org/jira/browse/SPARK-1457
Project: Spark
Issue
DB Tsai created SPARK-1451:
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Summary: Multinomial Logistic Regression Support
Key: SPARK-1451
URL: https://issues.apache.org/jira/browse/SPARK-1451
Project: Spark
Issue Type: New Feature
C
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DB Tsai commented on SPARK-1157:
PR: https://github.com/apache/spark/pull/353
> L-BFGS Op
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DB Tsai updated SPARK-1157:
---
Description:
L-BFGS (Limited-memory BFGS) is an optimization algorithm like BFGS which uses
an approximation
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https://issues.apache.org/jira/browse/SPARK-1401?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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DB Tsai closed SPARK-1401.
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Resolution: Duplicate
Fix Version/s: 0.9.1
> Use mapParitions instead of map to avoid creating expensive
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https://issues.apache.org/jira/browse/SPARK-1401?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13958438#comment-13958438
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DB Tsai commented on SPARK-1401:
In SPARK-1212, this issue is addressed by aggregate. Gonn
DB Tsai created SPARK-1401:
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Summary: Use mapParitions instead of map to avoid creating
expensive object in GradientDescent optimizer
Key: SPARK-1401
URL: https://issues.apache.org/jira/browse/SPARK-1401
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