机器学习源代码.zip

#coding = utf-8 __author__ = 'wuhaohan' import urllib2 import sys import numpy as np #read data from uci data repository target_url = ("https://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/sonar/sonar.all-data") data = urllib2.urlopen(target_url) #arrange data into list for labels and list of lists for attributes xList = [] labels = [] for line in data: #split on comma row = line.strip().split(",") xList.append(row) nrow = len(xList) #行 ncol = len(xList[1])#列 type = [0]*3 colCounts = [] #generate summary statistics for column 3 (e.g.) col = 3 colData = [] for row in xList: colData.append(float(row[col])) colArray = np.array(colData)#是否多次一举 colMean = np.mean(colArray) colsd = np.std(colArray) sys.stdout.write("Mean = " + '\t' + str(colMean) + '\t\t' + "Standard Deviation = " + '\t ' + str(colsd) + "\n") #calculate quantile boundaries ntiles = 4 percentBdry = [] for i in range(ntiles+1): percentBdry.append(np.percentile(colArray, i*(100)/ntiles)) sys.stdout.write("\nBoundaries for 4 Equal Percentiles \n") print(percentBdry) sys.stdout.write(" \n") #run again with 10 equal intervals ntiles = 10 percentBdry = [] for i in range(ntiles+1): percentBdry.append(np.percentile(colArray, i*(100)/ntiles)) sys.stdout.write("Boundaries for 10 Equal Percentiles \n") print(percentBdry) sys.stdout.write(" \n") #The last column contains categorical variables col = 60 colData = [] for row in xList: colData.append(row[col]) unique = set(colData)#删除重复数据 sys.stdout.write("Unique Label Values \n") print(unique) #count up the number of elements having each value catDict = dict(zip(list(unique),range(len(unique)))) catCount = [0]*2 for elt in colData: catCount[catDict[elt]] += 1 sys.stdout.write("\nCounts for Each Value of Categorical Label \n") print(list(unique)) print(catCount)