求解惩戒线性回归-LARS算法源码

import urllib
import numpy as np
from sklearn import datasets, linear_model
from math import sqrt
import matplotlib.pyplot as plot

#read data into iterable
target_url = "http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv"
data = urllib.request.urlopen(target_url)

xList = []
labels = []
names = []
firstLine = True
for line in data:
    if firstLine:
        names = str(line).strip().split(";")
        firstLine = False
    else:
        row = str(line).strip("\\n'").split(';')
        labels.append(row[-1])
        row.pop()
        floatRow = []
        for num in row:
            if "b'" in num:
                num = num.replace("b'",'')
            floatRow.append(float(num))
        xList.append(floatRow)
nrows = len(xList)
ncols = len(xList[0])
xMeans = np.array(xList).mean(axis=0)