Created
February 6, 2018 05:31
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from sklearn import datasets | |
import pandas as pd | |
import numpy as np | |
from matplotlib import pyplot as plt | |
from numpy.linalg import inv | |
iris = datasets.load_iris() | |
X = iris.data[:100, :] | |
y = iris.target[:100].reshape((100, -1)) | |
def logit(x): | |
return 1. / (1 + np.exp(-x)) | |
m, n = X.shape | |
alpha = 0.0065 | |
theta_g = np.random.random((n, 1)) | |
maxCycles = 30 | |
J = pd.Series(np.arange(maxCycles, dtype=float)) | |
for i in range(maxCycles): | |
h = logit(np.dot(X, theta_g)) | |
J[i] = -(1 / 100.) * np.sum(y * np.log(h) + (1 - y) * np.log(1 - h)) | |
error = h - y | |
grad = np.dot(X.T, error) | |
theta_g -= alpha * grad | |
print theta_g | |
J.plot() | |
plt.show() | |
theta_n = np.random.random((n, 1)) | |
maxCycles = 1 | |
C = pd.Series(np.arange(maxCycles, dtype=float)) | |
for i in range(maxCycles): | |
h = logit(np.dot(X, theta_n)) | |
C[i] = -(1 / 100.) * np.sum(y * np.log(h) + (1 - y) * np.log(1 - h)) | |
error = h - y | |
grad = np.dot(X.T, error) | |
A = h * (1 - h) * np.eye(len(X)) | |
H = np.mat(X.T) * A * np.mat(X) | |
theta_n -= inv(H) * grad | |
print theta_n | |
C.plot() | |
plt.show() |
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