[jira] [Created] (SPARK-10356) MLlib: Normalization should use absolute values
Carsten Schnober created SPARK-10356:
Summary: MLlib: Normalization should use absolute values
Key: SPARK-10356
URL: https://issues.apache.org/jira/browse/SPARK-10356
Project: Spark
Issue Type: Bug
Components: MLlib
Affects Versions: 1.4.1
Reporter: Carsten Schnober
The normalizer does not handle vectors with negative values properly. It can be
tested with the following code
{{
val normalized = new Normalizer(1.0).transform(v: Vector)
normalizer.toArray.sum == 1.0
}}
This yields true if all values in Vector v are positive, but false when v
contains one or more negative values. This is because the values in v are taken
immediately without applying {{abs()}},
This (probably) does not occur for {{p=2.0}} because the values are squared and
hence positive anyway.
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[jira] [Updated] (SPARK-10356) MLlib: Normalization should use absolute values
[
https://issues.apache.org/jira/browse/SPARK-10356?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Carsten Schnober updated SPARK-10356:
-
Description:
The normalizer does not handle vectors with negative values properly. It can be
tested with the following code
{code}
val normalized = new Normalizer(1.0).transform(v: Vector)
normalizer.toArray.sum == 1.0
{code}
This yields true if all values in Vector v are positive, but false when v
contains one or more negative values. This is because the values in v are taken
immediately without applying {{abs()}},
This (probably) does not occur for {{p=2.0}} because the values are squared and
hence positive anyway.
was:
The normalizer does not handle vectors with negative values properly. It can be
tested with the following code
{{val normalized = new Normalizer(1.0).transform(v: Vector)
normalizer.toArray.sum == 1.0}}
This yields true if all values in Vector v are positive, but false when v
contains one or more negative values. This is because the values in v are taken
immediately without applying {{abs()}},
This (probably) does not occur for {{p=2.0}} because the values are squared and
hence positive anyway.
MLlib: Normalization should use absolute values
---
Key: SPARK-10356
URL: https://issues.apache.org/jira/browse/SPARK-10356
Project: Spark
Issue Type: Bug
Components: MLlib
Affects Versions: 1.4.1
Reporter: Carsten Schnober
Labels: easyfix
Original Estimate: 2h
Remaining Estimate: 2h
The normalizer does not handle vectors with negative values properly. It can
be tested with the following code
{code}
val normalized = new Normalizer(1.0).transform(v: Vector)
normalizer.toArray.sum == 1.0
{code}
This yields true if all values in Vector v are positive, but false when v
contains one or more negative values. This is because the values in v are
taken immediately without applying {{abs()}},
This (probably) does not occur for {{p=2.0}} because the values are squared
and hence positive anyway.
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[jira] [Updated] (SPARK-10356) MLlib: Normalization should use absolute values
[
https://issues.apache.org/jira/browse/SPARK-10356?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Carsten Schnober updated SPARK-10356:
-
Description:
The normalizer does not handle vectors with negative values properly. It can be
tested with the following code
{{val normalized = new Normalizer(1.0).transform(v: Vector)
normalizer.toArray.sum == 1.0}}
This yields true if all values in Vector v are positive, but false when v
contains one or more negative values. This is because the values in v are taken
immediately without applying {{abs()}},
This (probably) does not occur for {{p=2.0}} because the values are squared and
hence positive anyway.
was:
The normalizer does not handle vectors with negative values properly. It can be
tested with the following code
{{
val normalized = new Normalizer(1.0).transform(v: Vector)
normalizer.toArray.sum == 1.0
}}
This yields true if all values in Vector v are positive, but false when v
contains one or more negative values. This is because the values in v are taken
immediately without applying {{abs()}},
This (probably) does not occur for {{p=2.0}} because the values are squared and
hence positive anyway.
MLlib: Normalization should use absolute values
---
Key: SPARK-10356
URL: https://issues.apache.org/jira/browse/SPARK-10356
Project: Spark
Issue Type: Bug
Components: MLlib
Affects Versions: 1.4.1
Reporter: Carsten Schnober
Labels: easyfix
Original Estimate: 2h
Remaining Estimate: 2h
The normalizer does not handle vectors with negative values properly. It can
be tested with the following code
{{val normalized = new Normalizer(1.0).transform(v: Vector)
normalizer.toArray.sum == 1.0}}
This yields true if all values in Vector v are positive, but false when v
contains one or more negative values. This is because the values in v are
taken immediately without applying {{abs()}},
This (probably) does not occur for {{p=2.0}} because the values are squared
and hence positive anyway.
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[jira] [Comment Edited] (SPARK-10356) MLlib: Normalization should use absolute values
[
https://issues.apache.org/jira/browse/SPARK-10356?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14721502#comment-14721502
]
Carsten Schnober edited comment on SPARK-10356 at 8/30/15 12:00 PM:
According to https://en.wikipedia.org/wiki/Norm_%28mathematics%29#p-norm, each
value's absolute value should be used to compute the norm:
{code}
||x||_p := (sum(|x|^p)^1/p
{code}
For p = 1, this results in:
{code}
||x||_1 := sum(|x|)
{code}
I suppose the issue is thus actually located in the norm() method.
was (Author: carschno):
According to
[[Wikipedia][https://en.wikipedia.org/wiki/Norm_%28mathematics%29#p-norm], each
value's absolute value should be used to compute the norm:
{||x||_p := (sum(|x|^p)^1/p}
For p = 1, this results in:
{||x||_1 := sum(|x|)}
I suppose the issue is thus actually located in the {norm()} method.
MLlib: Normalization should use absolute values
---
Key: SPARK-10356
URL: https://issues.apache.org/jira/browse/SPARK-10356
Project: Spark
Issue Type: Bug
Components: MLlib
Affects Versions: 1.4.1
Reporter: Carsten Schnober
Labels: easyfix
Original Estimate: 2h
Remaining Estimate: 2h
The normalizer does not handle vectors with negative values properly. It can
be tested with the following code
{code}
val normalized = new Normalizer(1.0).transform(v: Vector)
normalizer.toArray.sum == 1.0
{code}
This yields true if all values in Vector v are positive, but false when v
contains one or more negative values. This is because the values in v are
taken immediately without applying {{abs()}},
This (probably) does not occur for {{p=2.0}} because the values are squared
and hence positive anyway.
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[jira] [Commented] (SPARK-10356) MLlib: Normalization should use absolute values
[
https://issues.apache.org/jira/browse/SPARK-10356?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14721502#comment-14721502
]
Carsten Schnober commented on SPARK-10356:
--
According to
[[Wikipedia][https://en.wikipedia.org/wiki/Norm_%28mathematics%29#p-norm], each
value's absolute value should be used to compute the norm:
{||x||_p := (sum(|x|^p)^1/p}
For p = 1, this results in:
{||x||_1 := sum(|x|)}
I suppose the issue is thus actually located in the {norm()} method.
MLlib: Normalization should use absolute values
---
Key: SPARK-10356
URL: https://issues.apache.org/jira/browse/SPARK-10356
Project: Spark
Issue Type: Bug
Components: MLlib
Affects Versions: 1.4.1
Reporter: Carsten Schnober
Labels: easyfix
Original Estimate: 2h
Remaining Estimate: 2h
The normalizer does not handle vectors with negative values properly. It can
be tested with the following code
{code}
val normalized = new Normalizer(1.0).transform(v: Vector)
normalizer.toArray.sum == 1.0
{code}
This yields true if all values in Vector v are positive, but false when v
contains one or more negative values. This is because the values in v are
taken immediately without applying {{abs()}},
This (probably) does not occur for {{p=2.0}} because the values are squared
and hence positive anyway.
--
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