llx1026的博客

import numpyfrom sklearn import datasets, linear_modelfrom math import sqrtimport matplotlib.pyplot as pltimport urllib.requestdef xattrSelect(x, idxSet): xOut = [] for row in x:

target = [1.5, 2.1, 3.3, -4.7, -2.3, 0.75]prediction = [0.5, 1.5, 2.1, -2.2, 0.1, -0.5]error = []for i in range(len(target)): error.append(target[i] - prediction[i])print("Errors: ", error

import urllib.requestfrom pylab import *from math import sqrtimport matplotlib.pyplot as plottarget_url = ("http://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data")data =

import urllib.requestimport matplotlib.pyplot as plotfrom math import sqrt, cos, logtarget_url = ("http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv")data

import urllib.requestimport sysfrom math import sqrtimport matplotlib.pyplot as plottarget_url = ("http://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/sonar/

import urllib.requestimport numpyfrom sklearn import datasets, linear_modelfrom math import sqrtimport matplotlib.pyplot as plotdef S(z, gamma): if gamma >= abs(z): return 0.0 re

import urllib.requestimport numpyfrom sklearn import datasets, linear_modelfrom math import sqrtimport matplotlib.pyplot as plot#从网页中读取数据target_url = ("http://archive.ics.uci.edu/ml/machine-lea

import urllib.requestimport numpyfrom sklearn import datasets, linear_modelfrom sklearn.metrics import roc_curve, aucimport pylab as plttarget_url = ("http://archive.ics.uci.edu/ml/machine-learn

import urllib.requestimport numpyfrom sklearn import datasets, linear_modelfrom math import sqrtimport matplotlib.pyplot as plttarget_url = ("http://archive.ics.uci.edu/ml/machine-learning-datab

from sklearn import datasets, linear_modelfrom sklearn.metrics import roc_curve, aucimport pylab as pldef confusionMatrix(predicted, actual, threshold): if len(predicted) != len(actual):