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)
Xtr, ytr = pipe.transform(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)
X2, y2 = pipe.fit_transform(X, y)
plot_embedding(X2, y2.astype(int), data["y_labels"])
plt.show()

Instances