Python

Search

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]]))

Comment

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)

Comment

PREVIOUS	NEXT

Code Example
Python :: python nth prime function
Python :: pyautogui press
Python :: pyqt5 close event
Python :: stop program python
Python :: rnadom number python
Python :: python tar a directory
Python :: pandas convert entries in a column after groupby in list
Python :: How to recursively sort the elements of a stack, in Python?
Python :: decision tree regressor
Python :: how to check type inside a list in python
Python :: python get average of list
Python :: initialize dictionary to zero in python
Python :: transpose matrix numpy
Python :: get just filename without extension from the path python
Python :: if elseif in single line python
Python :: function without return python
Python :: palindrome string python
Python :: sort a list numbers in python
Python :: render template in django
Python :: python allowed variable caracters
Python :: 13 pseudo random numbers between 0 to 3 python
Python :: python basic flask app
Python :: pandas drop column in dataframe
Python :: flask error
Python :: how to remove a string inside another string python
Python :: skip element in list comprehension
Python :: Converting objects into integers
Python :: df rename columns
Python :: custom validation in django models
Python :: does jupyter notebook need internet

Search

PYTHON

lda scikit learn

scikit learn lda

ADD CONTENT