python 矩阵乘法梯度下降_使用python numpy矩阵类的梯度下降
我试图用python实现一元梯度下降算法。我尝试了很多不同的方法，但都没用。下面是我尝试过的一个例子。我做错什么了？提前谢谢！！！在from numpy import *
class LinearRegression:
def __init__(self,data_file):
self.raw_data_ref = data_file
self.theta = matrix([[0],[0]])
self.iterations = 1500
self.alpha = 0.001
def format_data(self):
data = loadtxt(self.raw_data_ref, delimiter = ',')
dataMatrix = matrix(data)
x = dataMatrix[:,0]
y = dataMatrix[:,1]
m = y.shape[0]
vec = mat(ones((m,1)))
x = concatenate((vec,x),axis = 1)
return [x, y, m]
def computeCost(self, x, y, m):
predictions = x*self.theta
squaredErrorsMat = power((predictions-y),2)
sse = squaredErrorsMat.sum(axis = 0)
cost = sse/(2*m)
return cost
def descendGradient(self, x, y, m):
for i in range(self.iterations):
predictions = x*self.theta
errors = predictions - y
sumDeriv1 = (multiply(errors,x[:,0])).sum(axis = 0)
sumDeriv2 = (multiply(errors,x[:,1])).sum(axis = 0)
print self.computeCost(x,y,m)
tempTheta = self.theta
tempTheta[0] = self.theta[0] - self.alpha*(1/m)*sumDeriv1
tempTheta[1] = self.theta[1] - self.alpha*(1/m)*sumDeriv2
self.theta[0] = tempTheta[0]
self.theta[1] = tempTheta[1]
return self.theta
regressor = LinearRegression('ex1data1.txt')
output = regressor.format_data()
regressor.descendGradient(output[0],output[1],output[2])
print regressor.theta
有一点更新；我以前尝试用一种更“矢量化”的方式来实现，比如：
^{pr2}$
这导致θ为[[-0.86221218]，[0.88827876]]。在