def ridgeRegres(numIter=10):
#loadData
X, y = lr.loadData(filepath)
m, n = np.shape(X)
errorMat = np.zeros((numIter, 30))
#共进行numIter次岭回归，选取误差最小的
for i in range(numIter):
X1 = X.copy()
y1 = y.copy()
index = range(m)
np.random.shuffle(index)
#打乱
X1 = X1[index, :]
y1 = y1[index, :]
xtrain = X1[0:int(m * 0.9), :]
ytrain = y1[0:int(m * 0.9), :]
xtest = X1[int(m * 0.9):, :]
ytest = y1[int(m * 0.9):, :]
#每次岭回归尝试30次lambda值，选取最小误差的w做为此次岭回归的得到的w
wMat = rr.ridgeTest(xtrain, ytrain)
#测试集正则化
xmean = np.mean(xtrain, 0)
xvar = np.var(xtrain, 0)
ymean = np.mean(y, 0)
xtest = (xtest - xmean) / xvar
ytest = ytest - ymean
ypredictMat = xtest * wMat.T
#记录本次循环下不同lambda值对应的误差
for j in range(30):
errorMat[i, j] = rr.squareError(ypredictMat[:, j], ytest)
#找到平均误差最小的w
meanError = np.mat(np.mean(errorMat, 0))
index = np.argmin(meanError, 1)[0, 0]
#index = np.argsort(meanError,1)[0,0];
bestw = wMat[index, :]
#w还原以和线性回归的w进行比较
meanX = np.mean(X, 0)
varX = np.var(X, 0)
meany = np.mean(y, 0)
unw = bestw / varX
const = -np.mat(meanX) * np.mat(unw).T + meany
return np.column_stack((const, unw)).T
return w;
def squareError(ypredict,y):
m,n = shape(y);
return (ypredict - y).T*(ypredict - y)/m;
def ridgeTest(X,y):
m,n = shape(X);
numTest = 30;#total 30 different lambda
wMat = zeros((numTest,n));
for i in range(numTest):
wMat[i,:] = (ridgeRegres(X,y,exp(i-10))).T;#尝试不同的lambda
return wMat;
if __name__ =='__main__':
X,y = lr.loadData('/usr/local/py_workspace/machine-learning/data/8/abalone.txt');
wMat = ridgeTest(X[0:99],y[0:99]);
#cross validation
croX = X[100:199];
croY = y[100:199];
sqError = croX*wMat.T;
for i in range (30):
error = squareError(sqError[:,i],croY);
print error;#find the lambda with the minmum square error
#graphy with different lambda
fig = plt.figure();
ax = fig.add_subplot(111);
ax.plot(wMat);
plt.show();
def linRegres():
X, y = lr.loadData(filepath)
m, n = np.shape(X)
X = np.column_stack((np.ones((m, 1)), X))
return lr.compW(X, y)