Lda scikit learn

import numpy as np
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
y = np.array([1, 1, 1, 2, 2, 2])
clf = LinearDiscriminantAnalysis()
clf.fit(X, y)

print(clf.predict([[-0.8, -1]]))

Source: scikit-learn.org

Scikit learn lda

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
lda = LDA(n_components = 2)
X_train = lda.fit_transform(X_train, y_train)
X_test = lda.transform(X_test)

Similar codes

2 votes234 views1 answer
Set http response content type django
Python
1 vote213 views1 answer
Conda install lxml
Python
0 vote225 views1 answer
Python loop through files in directory recursively
Python
0 vote245 views1 answer
Compute the 2d histogram of x and y.
Python
0 vote216 views2 answers
Rolling window pandas
Python
0 vote256 views1 answer
RuntimeError: Error in qhull Delaunay triangulation calculation: singular input data (exitcode=2); use python verbose option (-v) to see original qhull error.
Python
0 vote210 views2 answers
Ocr image table to excel python
Python
1 vote210 views1 answer
Minute range python
Python
1 vote220 views1 answer
Pandas .nlargest
Python
0 vote233 views6 answers
ORing two cv mat objects
Python