Skip to content
Projects
Groups
Snippets
Help
Loading...
Help
Support
Keyboard shortcuts
?
Submit feedback
Contribute to GitLab
Sign in / Register
Toggle navigation
dream
Project overview
Project overview
Details
Activity
Releases
Repository
Repository
Files
Commits
Branches
Tags
Contributors
Graph
Compare
Issues
1
Issues
1
List
Boards
Labels
Milestones
Merge Requests
0
Merge Requests
0
Analytics
Analytics
Repository
Value Stream
Wiki
Wiki
Members
Members
Collapse sidebar
Close sidebar
Activity
Graph
Create a new issue
Commits
Issue Boards
Open sidebar
nexedi
dream
Commits
852a42c7
Commit
852a42c7
authored
Apr 24, 2015
by
panos
Browse files
Options
Browse Files
Download
Email Patches
Plain Diff
Change the object name from HandleOutliers to DetectOutliers
parent
8f4a0e0a
Changes
1
Hide whitespace changes
Inline
Side-by-side
Showing
1 changed file
with
2 additions
and
2 deletions
+2
-2
dream/KnowledgeExtraction/DetectOutliers.py
dream/KnowledgeExtraction/DetectOutliers.py
+2
-2
No files found.
dream/KnowledgeExtraction/DetectOutliers.py
View file @
852a42c7
...
@@ -22,10 +22,10 @@ Created on 24 Sep 2014
...
@@ -22,10 +22,10 @@ Created on 24 Sep 2014
# along with DREAM. If not, see <http://www.gnu.org/licenses/>.
# along with DREAM. If not, see <http://www.gnu.org/licenses/>.
# ===========================================================================
# ===========================================================================
from
StatisticalMeasures
import
Basic
StatisticalMeasures
from
StatisticalMeasures
import
StatisticalMeasures
#The DetectOuliers object
#The DetectOuliers object
class
HandleOutliers
(
Basic
StatisticalMeasures
):
class
DetectOutliers
(
StatisticalMeasures
):
#Two different approaches to handle the outliers are included in this object,
#Two different approaches to handle the outliers are included in this object,
#the first one delete both the mild and extreme outliers while the second approach delete only the extreme outliers in the given data set
#the first one delete both the mild and extreme outliers while the second approach delete only the extreme outliers in the given data set
def
DeleteOutliers
(
self
,
mylist
):
#Delete the ouliers (both mild and extreme) in a given data set
def
DeleteOutliers
(
self
,
mylist
):
#Delete the ouliers (both mild and extreme) in a given data set
...
...
Write
Preview
Markdown
is supported
0%
Try again
or
attach a new file
Attach a file
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment