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wiseodd / natural_grad.py
Created March 13, 2018 19:36
Natural Gradient Descent for Logistic Regression
import numpy as np
from sklearn.utils import shuffle
# Data comes from y = f(x) = [2, 3].x + [5, 7]
X0 = np.random.randn(100, 2) - 1
X1 = np.random.randn(100, 2) + 1
X = np.vstack([X0, X1])
t = np.vstack([np.zeros([100, 1]), np.ones([100, 1])])