logistic regression（Python实现以及MATLAB实现）
Linear Regression解决的是连续的预测和拟合问题，而Logistic Regression解决的是离散的分类问题。两种方式，但本质殊途同归，两者都可以算是指数函数族的特例。
在分类问题中，y取值在{0,1}之间，因此，上述的Liear Regression显然不适应。
Sigmoid函数范围在[0,1]之间，参数 θ 只不过控制曲线的陡峭程度。以0.5为截点，>0.5则y值为1，< 0.5则y值为0，这样就实现了两类分类的效果。
Python版：
import numpy as np
import matplotlib.pyplot as plt
a = np.loadtxt('ex1.txt')
print(a.shape)
X=a[:,0:2]
y=a[:,2:3]
m=y.shape[0]
l=X.shape[1]
print(m,l)
X0=np.ones(m)
X_m=X
x1=X[:,0:1]
x2=X[:,1:2]
pos0=np.where(y==0)
pos1=np.where(y==1)
posf0=pos0[0]
posf1=pos1[0]
def featureNormalize(X,l):
X_norm=X
mu=np.mean(X,0)
print(mu)
sigma=np.std(X)
X_norm=(X-mu)/sigma
print(X_norm.shape)
return X_norm
X_norm=featureNormalize(X,l)
X=X_norm
x11=X[:,0:1]
x21=X[:,1:2]
print(x11)
X=np.c_[X0,X]
n=X.shape[1]
print(y.shape)
print(n)
print(type(X))
theta=np.zeros((n,1))
alpha=0.003
iterations=5000
def gradientdescentlogistic(theta,alpha,iterations,X,y,m):
J_h=np.zeros((iterations,1))
for i in range (0,iterations):
h_x=1/(1+np.exp(-np.dot(X,theta)))
theta=theta-alpha*np.dot(X.transpose(),(h_x-y))
J=-sum(y*np.log(h_x)+(1-y)*np.log(1-h_x))/m
J_h[i,:]=J
return theta,J_h
(theta,J_h)=gradientdescentlogistic(theta,alpha,iterations,X,y,m)
print(theta)
print(J_h)
#定义决策边界函数：
def plot_decision_boundary(x11,x21):
x1_m=np.array([(x11.min()),(x11.max())])
posx1m=np.where(x11==x1_m)
x2_m=x21[posx1m[0],:]
#x2_m=np.array([(x21.min()),(x21.max())])
x_m=np.c_[x1_m,x2_m]
print(x_m)
x_mf=np.c_[np.ones(2),x_m]
print(x_mf)
h_hat=1/(1+np.exp(-np.dot(x_mf,theta)))
x11_m=np.array([(x11.min()-3),(x11.max()+3)])
plt.plot(x11_m,h_hat)
plot_decision_boundary(x11,x21)
plt.scatter(x1[posf0],x2[posf0],marker='o',color='r',s=20)
plt.scatter(x1[posf1],x2[posf1],marker='o',color='b',s=20)
plt.show()
x2=np.arange(iterations)
plt.plot(x2,J_h)
plt.title("costfunction")
plt.xlabel("iterations")
plt.ylabel("J(θ)")
plt.show()
Matlab版：
clear ; close all; clc
data = load('ex1.txt');
X = data(:, [1, 2]); y = data(:, 3); % 成绩和录取结果
plotData(X, y);
hold on;
% Labels and Legend
xlabel('Exam 1 score')
ylabel('Exam 2 score')
% Specified in plot order
legend('Admitted', 'Not admitted')
[m, n] = size(X);
X = [ones(m, 1) X];
% Initialize fitting parameters
initial_theta = zeros(n + 1, 1);
% Compute and display initial cost and gradient
[cost, grad] = costFunction(initial_theta, X, y);
options = optimset('GradObj', 'on', 'MaxIter', 400);
[theta, cost] = ...
fminunc(@(t)(costFunction(t, X, y)), initial_theta, options);
plotDecisionBoundary(theta, X, y);
function plotDecisionBoundary(theta, X, y)
plotData(X(:,2:3), y);
hold on
if size(X, 2) <= 3
plot_x = [min(X(:,2))-2, max(X(:,2))+2];
plot_y = (-1./theta(3)).*(theta(2).*plot_x + theta(1));
plot(plot_x, plot_y)
legend('Admitted', 'Not admitted', 'Decision Boundary')
axis([30, 100, 30, 100])
else
u = linspace(-1, 1.5, 50);
v = linspace(-1, 1.5, 50);
z = zeros(length(u), length(v));
% Evaluate z = theta*x over the grid
for i = 1:length(u)
for j = 1:length(v)
z(i,j) = mapFeature(u(i), v(j))*theta;
end
end
z = z'; % important to transpose z before calling contour
contour(u, v, z, [0, 0], 'LineWidth', 2)
end
hold off
end
function [J, grad] = costFunction(theta, X, y)
m = length(y); % number of training examples
J = 0;
grad = zeros(size(theta));
J= -1 * sum( y .* log( sigmoid(X*theta) ) + (1 - y ) .* log( (1 - sigmoid(X*theta)) ) ) / m ;
grad = ( X' * (sigmoid(X*theta) - y ) )/ m ;
function plotData(X, y)
figure; hold on;
pos = find(y == 1); neg = find(y == 0);
plot(X(pos, 1), X(pos, 2), 'k+','LineWidth', 2, 'MarkerSize', 7);
plot(X(neg, 1), X(neg, 2), 'ko', 'MarkerFaceColor', 'y','MarkerSize', 7);
hold off;
end
本代码中用到的数据集：https://download.****.net/download/qq_20406597/10375205