我已经编写了一些初学者代码来使用正规方程计算简单线性模型的系数。Python/Numpy中的正常方程实现
# Modules
import numpy as np
# Loading data set
X, y = np.loadtxt('ex1data3.txt', delimiter=',', unpack=True)
data = np.genfromtxt('ex1data3.txt', delimiter=',')
def normalEquation(X, y):
m = int(np.size(data[:, 1]))
# This is the feature/parameter (2x2) vector that will
# contain my minimized values
theta = []
# I create a bias_vector to add to my newly created X vector
bias_vector = np.ones((m, 1))
# I need to reshape my original X(m,) vector so that I can
# manipulate it with my bias_vector; they need to share the same
# dimensions.
X = np.reshape(X, (m, 1))
# I combine these two vectors together to get a (m, 2) matrix
X = np.append(bias_vector, X, axis=1)
# Normal Equation:
# theta = inv(X^T * X) * X^T * y
# For convenience I create a new, tranposed X matrix
X_transpose = np.transpose(X)
# Calculating theta
theta = np.linalg.inv(X_transpose.dot(X))
theta = theta.dot(X_transpose)
theta = theta.dot(y)
return theta
p = normalEquation(X, y)
print(p)
使用小数据集在这里找到:
http://www.lauradhamilton.com/tutorial-linear-regression-with-octave
我取得共同efficients:[-0.34390603; 0.2124426]使用上面的代码而不是:[24.9660; 3.3058]。任何人都可以帮助澄清我哪里错了?
你有你的周围,从例子中的错路X和Y!如果我扭转他们,我会得到你建议的答案 – jeremycg