[英]gradient descent using python numpy matrix class
我正在嘗試在python中實現單變量梯度下降算法。 我嘗試了很多不同的方法,但沒有任何效果。 以下是我嘗試過的一個示例。 我究竟做錯了什么? 提前致謝!!!
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
一點更新; 我以前曾嘗試以一種更加“向量化”的方式進行操作,如下所示:
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)
gammaMat = concatenate((sumDeriv1,sumDeriv2),axis = 0)
coeff = self.alpha*(1.0/m)
updateMatrix = gammaMat*coeff
print updateMatrix, gammaMat
jcost = self.computeCost(x,y,m)
print jcost
tempTheta = self.theta
tempTheta = self.theta - updateMatrix
self.theta = tempTheta
return self.theta
這導致θ為[[-0.86221218],[0.88827876]]。
您有兩個問題,都與浮點有關:
1.像這樣初始化theta矩陣:
self.theta = matrix([[0.0],[0.0]])
2.更改更新行,將(1/m)
替換為(1.0/m)
:
tempTheta[0] = self.theta[0] - self.alpha*(1.0/m)*sumDeriv1
tempTheta[1] = self.theta[1] - self.alpha*(1.0/m)*sumDeriv2
無關緊要的是:您的tempTheta
變量是不必要的。
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