b1de080823e41b921bec2949db2b6c3cb1f1d5ef,examples/plot_lda.py,,,#,43

Before Change


y = data["y"]

// create a pipeline for LDA transformation of the feature representation
feed = Pipeline([("segment", Segment()),
                 ("features", SegFeatures(features = base_features()))])
est = LinearDiscriminantAnalysis(n_components=2)
pipe = SegPipe(feed, est)

pipe.fit(X,y)

After Change


y = data["y"]

// create a pipeline for LDA transformation of the feature representation
est = Pipeline([ ("ftr", sgl.FeatureRep()),
    ("lda", LinearDiscriminantAnalysis(n_components=2))])

pipe = sgl.SegPipe(est)
Italian Trulli
In pattern: SUPERPATTERN

Frequency: 3

Non-data size: 9

Instances


Project Name: dmbee/seglearn
Commit Name: b1de080823e41b921bec2949db2b6c3cb1f1d5ef
Time: 2018-03-11
Author: david.mo.burns@gmail.com
File Name: examples/plot_lda.py
Class Name:
Method Name:


Project Name: dmbee/seglearn
Commit Name: b1de080823e41b921bec2949db2b6c3cb1f1d5ef
Time: 2018-03-11
Author: david.mo.burns@gmail.com
File Name: examples/plot_feature_rep.py
Class Name:
Method Name:


Project Name: dmbee/seglearn
Commit Name: b1de080823e41b921bec2949db2b6c3cb1f1d5ef
Time: 2018-03-11
Author: david.mo.burns@gmail.com
File Name: examples/plot_model_selection1.py
Class Name:
Method Name: