agamemnonc

import numpy as np
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
y = iris.target
lda_clf = LinearDiscriminantAnalysis(solver = 'lsqr').fit(X,y)
post_probs = lda_clf.predict_proba(X)
print(post_probs[51])

agamemnonc

clear

load iris_dataset.mat % Load iris dataset (same order as in sklearn)
X = irisInputs';
[~, y] = max(irisTargets); y = y'; % Transform target 

lda_clf =  fitcdiscr(X,y,'DiscrimType','linear'); % Classifier
[~,post_probs] = predict(lda_clf,X); % Predicted probability for training data
disp(post_probs(52,:)) % Display predicted probability for datum 52