Python loadSeasons 예제들

예제 #1

파일: NLTKMaxent.py 프로젝트: csrgxtu/maxent

def seasonMain():
  DIR = '/home/archer/Documents/maxent/data/basketball/leaguerank/'
  seasons = loadSeasons(DIR + 'seasons-18-Nov-2014.txt')
  countTotal = 0
  total = 0

  for season in seasons:
    train = buildTrainingSets(DIR + season + '-train.csv')
    test = buildTestingSets(DIR + season + '-test.csv')
    labels = buildTestingLabels(DIR + season + '-test.csv')
    total = total + len(labels)

    # train
    classifier = nltk.MaxentClassifier.train(train, 'IIS', trace=0, max_iter=1000)
    
    # test
    count = 0
    for i in range(len(labels)):
      pdist = classifier.prob_classify(test[i])
      if pdist.prob('L') >= pdist.prob('W'):
        flag = 'L'
      else:
        flag = 'W'
      
      if flag == labels[i]:
        count = count + 1
        
    print 'INFO: accuracy ', season, " ", float(count)/len(labels)

예제 #2

파일: CVKNN.py 프로젝트: ssj018/maxent

def seasonMain():
    DIR = '/home/archer/Documents/maxent/data/basketball/leaguerank/'
    seasons = loadSeasons(DIR + 'seasons-18-Nov-2014.txt')
    countTotal = 0
    total = 0

    for season in seasons:
        trainData = buildTrainingSets(DIR + season + '-train.csv')
        testData = buildTestingSets(DIR + season + '-test.csv')
        trainLabels = buildTestingLabels(DIR + season + '-train.csv')
        testLabels = buildTestingLabels(DIR + season + '-test.csv')
        total = total + len(testLabels)

        knn = cv2.KNearest()
        knn.train(trainData, trainLabels)

        # Accuracy
        count = 0
        for i in range(len(testLabels)):
            ret, results, neighbours, dist = knn.find_nearest(
                np.array([testData[i]]), 11)
            if results[0][0] == testLabels[i][0]:
                count = count + 1

        countTotal = countTotal + count
        print 'INFO: Accuracy(', season, ')', count / float(len(testLabels))

    print 'INFO: Total Accuracy: ', countTotal / float(total)

예제 #3

파일: CVSVM-V1.0.py 프로젝트: csrgxtu/maxent

def seasonMain():
  DIR = '/home/archer/Documents/maxent/data/basketball/leaguerank/'
  seasons = loadSeasons(DIR + 'seasons-18-Nov-2014.txt')
  countTotal = 0
  total = 0

  for season in seasons:
    trainData = buildTrainingSets(DIR + season + '-train.csv.knn')
    testData = buildTestingSets(DIR + season + '-test.csv.knn')
    trainLabels = buildTestingLabels(DIR + season + '-train.csv.knn')
    testLabels = buildTestingLabels(DIR + season + '-test.csv.knn')
    total = total + len(testLabels)

    svm = cv2.SVM()
    svm.train(trainData, trainLabels, params=svm_params)
    svm.save('svm_data.dat')

    # Accuracy
    count = 0
    for i in range(len(testLabels)):
      ret = svm.predict(np.array([testData[i]]))
      if ret == testLabels[i][0]:
        count = count + 1

    countTotal = countTotal + count
    print 'INFO: Accuracy(', season, ')', count/float(len(testLabels))

  print 'INFO: Total Accuracy: ', countTotal/float(total)

예제 #4

def seasonMain():
    DIR = '/home/archer/Documents/maxent/data/basketball/leaguerank/'
    seasons = loadSeasons(DIR + 'seasons-18-Nov-2014.txt')
    countTotal = 0
    total = 0

    for season in seasons:
        trainData = buildTrainingSets(DIR + season + '-train.csv.knn')
        testData = buildTestingSets(DIR + season + '-test.csv.knn')
        trainLabels = buildTestingLabels(DIR + season + '-train.csv.knn')
        testLabels = buildTestingLabels(DIR + season + '-test.csv.knn')
        total = total + len(testLabels)

        svm = cv2.SVM()
        svm.train(trainData, trainLabels, params=svm_params)
        svm.save('svm_data.dat')

        # Accuracy
        count = 0
        for i in range(len(testLabels)):
            ret = svm.predict(np.array([testData[i]]))
            if ret == testLabels[i][0]:
                count = count + 1

        countTotal = countTotal + count
        print 'INFO: Accuracy(', season, ')', count / float(len(testLabels))

    print 'INFO: Total Accuracy: ', countTotal / float(total)

예제 #5

파일: NLTKMaxent.py 프로젝트: ssj018/maxent

def seasonMain():
    DIR = '/home/archer/Documents/maxent/data/basketball/leaguerank/'
    seasons = loadSeasons(DIR + 'seasons-18-Nov-2014.txt')
    countTotal = 0
    total = 0

    for season in seasons:
        train = buildTrainingSets(DIR + season + '-train.csv')
        test = buildTestingSets(DIR + season + '-test.csv')
        labels = buildTestingLabels(DIR + season + '-test.csv')
        total = total + len(labels)

        # train
        classifier = nltk.MaxentClassifier.train(train,
                                                 'IIS',
                                                 trace=0,
                                                 max_iter=1000)

        # test
        count = 0
        for i in range(len(labels)):
            pdist = classifier.prob_classify(test[i])
            if pdist.prob('L') >= pdist.prob('W'):
                flag = 'L'
            else:
                flag = 'W'

            if flag == labels[i]:
                count = count + 1

        print 'INFO: accuracy ', season, " ", float(count) / len(labels)

예제 #6

파일: CVKNN-V1.0.py 프로젝트: csrgxtu/maxent

def seasonMain():
  DIR = '/home/archer/Documents/maxent/data/basketball/leaguerank/'
  seasons = loadSeasons(DIR + 'seasons-18-Nov-2014.txt')
  countTotal = 0
  total = 0

  for season in seasons:
    trainData = buildTrainingSets(DIR + season + '-train.csv')
    testData = buildTestingSets(DIR + season + '-test.csv')
    trainLabels = buildTestingLabels(DIR + season + '-train.csv')
    testLabels = buildTestingLabels(DIR + season + '-test.csv')
    total = total + len(testLabels)

    knn = cv2.KNearest()
    knn.train(trainData, trainLabels)

    # Accuracy
    count = 0
    for i in range(len(testLabels)):
      ret, results, neighbours, dist = knn.find_nearest(np.array([testData[i]]), 31)
      if results[0][0] == testLabels[i][0]:
        count = count + 1

    countTotal = countTotal + count
    print 'INFO: Accuracy(', season, ')', count/float(len(testLabels))

  print 'INFO: Total Accuracy: ', countTotal/float(total)

예제 #7

파일: GenerateDataForKNN.py 프로젝트: ssj018/maxent

def generateTestDataBySeasons():
  DIR = '/home/archer/Documents/maxent/data/basketball/leaguerank/'
  seasons = loadSeasons(DIR + 'seasons-18-Nov-2014.txt')

  for season in seasons:
    res = generateTestDataBySeason(season)
    outputFile = DIR + season + '-test.csv.knn'
    print 'INFO: ', outputFile
    saveMatrixToFile(outputFile, res)

예제 #8

파일: GenerateDataForKNN.py 프로젝트: ssj018/maxent

def generateTestDataByTeam(teamId):
  DIR = '/home/archer/Documents/maxent/data/basketball/leaguerank/'
  teamIds = loadTeamIds(DIR + 'teamidshortname.csv')
  teamNames = [row[1] for row in loadMatrixFromFile(DIR + 'teamidshortname.csv')]
  seasons = loadSeasons(DIR + 'seasons-18-Nov-2014.txt')
  res = []
  
  for season in seasons:
    mat = loadMatrixFromFile(DIR + season + '.playoff.csv')
    for row in mat:
      if teamNames[teamIds.index(teamId)] not in row[6]:
        continue

      if row[0] == 'W':
        WIN = 1
      else:
        WIN = 0
     
      if 'vs.' in row[6]:
        HOME = 1
      else:
        HOME = 0

      season = row[3]
      #heightTotal, weightTotal, ageTotal, expTotal = loadMatrixFromFile(DIR + teamId + '.' + season + '.player.csv.processed.total')[0]
      #heightTotal, weightTotal, ageTotal, expTotal = loadMatrixFromFile(DIR + teamId + '.' + season + '.player.csv.processed.avg')[0]
      heightTotal, weightTotal, ageTotal, expTotal = loadMatrixFromFile(DIR + teamId + '.' + season + '.player.csv.processed.norm')[0]

      leagueranks = loadMatrixFromFile(DIR + season + '.l')[0]
      leaguerank = leagueranks[teamNames.index(row[6][0:3])]

      vsTeamId = teamIds[teamNames.index(row[6][-3:])]
      #vsHeightTotal, vsWeightTotal, vsAgeTotal, vsExpTotal = loadMatrixFromFile(DIR + vsTeamId + '.' + season + '.player.csv.processed.total')[0]
      #vsHeightTotal, vsWeightTotal, vsAgeTotal, vsExpTotal = loadMatrixFromFile(DIR + vsTeamId + '.' + season + '.player.csv.processed.avg')[0]
      vsHeightTotal, vsWeightTotal, vsAgeTotal, vsExpTotal = loadMatrixFromFile(DIR + vsTeamId + '.' + season + '.player.csv.processed.norm')[0]
      vsLeaguerank = leagueranks[teamIds.index(vsTeamId)]

      tmp = []
      tmp.append(HOME)
      tmp.append(heightTotal)
      tmp.append(weightTotal)
      tmp.append(ageTotal)
      tmp.append(expTotal)
      tmp.append(leaguerank)

      tmp.append(vsHeightTotal)
      tmp.append(vsWeightTotal)
      tmp.append(vsAgeTotal)
      tmp.append(vsExpTotal)
      tmp.append(vsLeaguerank)

      tmp.append(WIN)
      
      res.append(tmp)
  return res

예제 #9

def main():
    DIR = '/home/archer/Documents/maxent/data/basketball/leaguerank/'
    seasons = loadSeasons(DIR + 'seasons-18-Nov-2014.txt')
    teamIds = loadTeamIds(DIR + 'teamidshortname.csv')
    seasonTypes = ['Regular Season', 'Playoffs']
    # print seasons
    # return
    for team in teamIds:
        for season in seasons:
            #for seasonType in seasonTypes:
            seasonType = 'Regular Season'
            n = NBAStatsTeamPlayerExtractor(team, season, seasonType)
            outputFile = DIR + team + '.' + season + '.player.csv'
            print 'INFO: Processing ', outputFile
            mat = n.getStats()
            if mat == False:
                saveMatrixToFile(outputFile, [])
            else:
                saveMatrixToFile(outputFile, mat)

예제 #10

파일: NBAStatsTeamPlayerExtractorMain.py 프로젝트: csrgxtu/maxent

def main():
  DIR = '/home/archer/Documents/maxent/data/basketball/leaguerank/'
  seasons = loadSeasons(DIR + 'seasons-18-Nov-2014.txt')
  teamIds = loadTeamIds(DIR + 'teamidshortname.csv')
  seasonTypes = ['Regular Season', 'Playoffs']
  # print seasons
  # return
  for team in teamIds:
    for season in seasons:
      #for seasonType in seasonTypes:
      seasonType = 'Regular Season'
      n = NBAStatsTeamPlayerExtractor(team, season, seasonType)
      outputFile = DIR + team + '.' + season + '.player.csv'
      print 'INFO: Processing ', outputFile
      mat = n.getStats()
      if mat == False:
        saveMatrixToFile(outputFile, [])
      else:
        saveMatrixToFile(outputFile, mat)

예제 #11

파일: CVKNN-V2.0.py 프로젝트: ssj018/maxent

def main():
    DIR = '/home/archer/Documents/maxent/data/basketball/leaguerank/'
    seasons = loadSeasons(DIR + 'seasons-18-Nov-2014.txt')
    total = 0
    count = 0
    trainData = []
    trainLabels = []
    testData = []
    testLabels = []

    for season in seasons:
        tmpTrainData = buildTrainingSets(DIR + season +
                                         '-train.csv.knn').tolist()
        tmpTrainLabels = buildTestingLabels(DIR + season +
                                            '-train.csv').tolist()
        tmpTestData = buildTestingSets(DIR + season + '-test.csv').tolist()
        tmpTestLabels = buildTestingLabels(DIR + season + '-test.csv').tolist()

        trainData.extend(tmpTrainData)
        trainLabels.extend(tmpTrainLabels)
        testData.extend(tmpTestData)
        testLabels.extend(tmpTestLabels)

    trainData = np.array(trainData).astype(np.float32)
    trainLabels = np.array(trainLabels).astype(np.float32)
    testData = np.array(testData).astype(np.float32)
    testLabels = np.array(testLabels).astype(np.float32)
    total = len(testLabels)

    knn = cv2.KNearest()
    knn.train(trainData, trainLabels)

    for i in range(len(testLabels)):
        ret, results, neighbours, dist = knn.find_nearest(
            np.array([testData[i]]), 31)
        if results[0][0] == testLabels[i][0]:
            count = count + 1

    print 'INFO: Total Accuracy: ', count / float(total)

예제 #12

def main():
    DIR = '/home/archer/Documents/maxent/data/basketball/leaguerank/'
    seasons = loadSeasons(DIR + 'seasons-18-Nov-2014.txt')
    total = 0
    count = 0
    trainData = []
    trainLabels = []
    testData = []
    testLabels = []

    for season in seasons:
        tmpTrainData = buildTrainingSets(DIR + season +
                                         '-train.csv.knn').tolist()
        tmpTrainLabels = buildTestingLabels(DIR + season +
                                            '-train.csv').tolist()
        tmpTestData = buildTestingSets(DIR + season + '-test.csv').tolist()
        tmpTestLabels = buildTestingLabels(DIR + season + '-test.csv').tolist()

        trainData.extend(tmpTrainData)
        trainLabels.extend(tmpTrainLabels)
        testData.extend(tmpTestData)
        testLabels.extend(tmpTestLabels)

    trainData = np.array(trainData).astype(np.float32)
    trainLabels = np.array(trainLabels).astype(np.float32)
    testData = np.array(testData).astype(np.float32)
    testLabels = np.array(testLabels).astype(np.float32)
    total = len(testLabels)

    svm = cv2.SVM()
    svm.train(trainData, trainLabels, params=svm_params)
    svm.save('svm_data.dat')

    for i in range(len(testLabels)):
        ret = svm.predict(np.array([testData[i]]))
        if results[0][0] == testLabels[i][0]:
            count = count + 1

    print 'INFO: Total Accuracy: ', count / float(total)

예제 #13

파일: CVSVM-V1.0.py 프로젝트: csrgxtu/maxent

def main():
  DIR = '/home/archer/Documents/maxent/data/basketball/leaguerank/'
  seasons = loadSeasons(DIR + 'seasons-18-Nov-2014.txt')
  total = 0
  count = 0
  trainData = []
  trainLabels = []
  testData = []
  testLabels = []

  for season in seasons:
    tmpTrainData = buildTrainingSets(DIR + season + '-train.csv.knn').tolist()
    tmpTrainLabels = buildTestingLabels(DIR + season + '-train.csv').tolist()
    tmpTestData = buildTestingSets(DIR + season + '-test.csv').tolist()
    tmpTestLabels = buildTestingLabels(DIR + season + '-test.csv').tolist()
    
    trainData.extend(tmpTrainData)
    trainLabels.extend(tmpTrainLabels)
    testData.extend(tmpTestData)
    testLabels.extend(tmpTestLabels)

  trainData = np.array(trainData).astype(np.float32)
  trainLabels = np.array(trainLabels).astype(np.float32)
  testData = np.array(testData).astype(np.float32)
  testLabels = np.array(testLabels).astype(np.float32)
  total = len(testLabels)

  svm = cv2.SVM()
  svm.train(trainData, trainLabels, params=svm_params)
  svm.save('svm_data.dat')

  for i in range(len(testLabels)):
    ret = svm.predict(np.array([testData[i]]))
    if results[0][0] == testLabels[i][0]:
      count = count + 1
  
  print 'INFO: Total Accuracy: ', count/float(total)

예제 #14

파일: NLTKNaiveBayes.py 프로젝트: ssj018/maxent

def main():
    DIR = '/home/archer/Documents/Python/maxent/data/basketball/leaguerank/'
    seasons = loadSeasons(DIR + 'seasons-18-Nov-2014.txt')
    countTotal = 0
    total = 0

    for season in seasons:
        train = buildTrainingSets(DIR + season + '-train.csv')
        test = buildTestingSets(DIR + season + '-test.csv')
        labels = buildTestingLabels(DIR + season + '-test.csv')
        total = total + len(labels)

        classifier = NaiveBayesClassifier.train(train)
        res = classifier.batch_classify(test)

        # accuracy
        count = 0
        for i in range(len(res)):
            if labels[i] == res[i]:
                count = count + 1

        countTotal = countTotal + count
        print 'INFO: Accuracy(', season, ')', count / float(len(res))
    print 'INFO: Total Accuracy: ', countTotal / float(total)

예제 #15

파일: CVKNN-V1.0.py 프로젝트: csrgxtu/maxent

def main():
  DIR = '/home/archer/Documents/maxent/data/basketball/leaguerank/'
  seasons = loadSeasons(DIR + 'seasons-18-Nov-2014.txt')
  total = 0
  count = 0
  trainData = []
  trainLabels = []
  testData = []
  testLabels = []

  for season in seasons:
    tmpTrainData = buildTrainingSets(DIR + season + '-train.csv').tolist()
    tmpTrainLabels = buildTestingLabels(DIR + season + '-train.csv').tolist()
    tmpTestData = buildTestingSets(DIR + season + '-test.csv').tolist()
    tmpTestLabels = buildTestingLabels(DIR + season + '-test.csv').tolist()
    
    trainData.extend(tmpTrainData)
    trainLabels.extend(tmpTrainLabels)
    testData.extend(tmpTestData)
    testLabels.extend(tmpTestLabels)

  trainData = np.array(trainData).astype(np.float32)
  trainLabels = np.array(trainLabels).astype(np.float32)
  testData = np.array(testData).astype(np.float32)
  testLabels = np.array(testLabels).astype(np.float32)
  total = len(testLabels)

  knn = cv2.KNearest()
  knn.train(trainData, trainLabels)

  for i in range(len(testLabels)):
    ret, results, neighbours, dist = knn.find_nearest(np.array([testData[i]]), 31)
    if results[0][0] == testLabels[i][0]:
      count = count + 1
  
  print 'INFO: Total Accuracy: ', count/float(total)

예제 #16

#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Author: Archer
# Date: 05/Jun/2015
# File: InsertPlayer.py
# Desc: insert into NBA.Player table
#
# Produced By CSRGXTU
import MySQLdb as mdb
import sys
from Utility import loadSeasons, loadTeamIds, loadMatrixFromFile

basePath = '/home/archer/Documents/Python/maxent/data/basketball/leaguerank/'

seasons = loadSeasons(basePath + 'seasons-18-Nov-2014.txt')
teamIds = loadTeamIds(basePath + 'teamidname-18-Nov-2014.csv')


def insertPlayer(cur):
    for team in teamIds:
        sql = "select TeamID from Team where StatsID = '%s'" % team
        cur.execute(sql)
        TeamID = cur.fetchone()[0]

        for season in seasons:
            sql = "select SeasonID from Season where Season = '%s' and Season_SeasonTypeID = 2" % season
            cur.execute(sql)
            SeasonID = cur.fetchone()[0]

            matrix = loadMatrixFromFile(basePath + team + '.' + season +

예제 #17

파일: InsertBIDS.py 프로젝트: csrgxtu/BeautifulReading

#!/usr/bin/env python
# coding=utf8
# Author: Archer Reilly
# File: InsertBIDS.py
# Desc: Insert bid into a article in bookshelf.article according to the id
#
# Produced By BR
from Utility import loadSeasons
from pymongo import MongoClient
from bson.objectid import ObjectId

# load bids
BIDS = loadSeasons('./BIDS.txt')

# connect to mongodb
client = MongoClient('mongodb://*****:*****@192.168.200.22:27017/bookshelf')
#client = MongoClient('mongodb://192.168.100.2:27017/bookshelf')
db = client['bookshelf']
collection = db['article']

ID = '5761557e05cf2806003e1367'

for bid in BIDS:
    # coll.update({'ref': ref}, {'$push': {'tags': new_tag}})
    print 'Append bid to article.related_books', bid
    collection.update({'_id': ObjectId(ID)}, {'$push': {'related_books': bid}})

예제 #18

파일: BaiduOCR.py 프로젝트: csrgxtu/Russel

import ast
from bs4 import BeautifulSoup
import time
from Utility import loadSeasons, appendstr2fileutf8

url = 'http://apis.baidu.com/idl_baidu/baiduocrpay/idlocrpaid'
data = {}
data['fromdevice'] = "pc"
data['clientip'] = "192.168.100.3"
data['detecttype'] = "LocateRecognize"
data['languagetype'] = "CHN_ENG"
data['imagetype'] = "1"

# first, open names.txt
# for each name, build a csv row and store it
names = loadSeasons('./names.txt')
for name in names:
    time.sleep(2)
    file_object = open('/bookdata/liqiang/Downloads/books/' + name, 'rb')
    try:
         tmp = file_object.read( )
    finally:
         file_object.close( )
    data['image'] = base64.b64encode(tmp)

    decoded_data = urllib.urlencode(data)
    req = urllib2.Request(url, data = decoded_data)

    req.add_header("Content-Type", "application/x-www-form-urlencoded")
    req.add_header("apikey", "150281dc441994b2d21ddb0e57a9bd48")

예제 #19

파일: GetBIDs.py 프로젝트: csrgxtu/BeautifulReading

#!/usr/bin/env python
# coding=utf8
# Author: Archer Reilly
# File: GetBIDs.py
# Date: 14/6/2016
# Desc: Get bids from database according to isbn
#
# Produced By BR
from Utility import loadSeasons
from pymongo import MongoClient

# load isbns
ISBNS = loadSeasons('./isbns.txt')
# print ISBNS[0]

# connect to mongodb
client = MongoClient('mongodb://*****:*****@192.168.200.20:27017/bookshelf')
db = client['bookshelf']
collection = db['bookful']

for isbn in ISBNS:
    res = collection.find_one({'$or': [{'isbn10': isbn}, {'isbn13': isbn}]})
    if res:
        if res.has_key('bid'):
            print isbn, res['bid']

예제 #20

파일: NBAStatsTeamPlayerDataProcessor.py 프로젝트: ssj018/maxent

#!/usr/bin/env python
# coding = utf-8
# Author: Archer Reilly
# Date: 24/DEC/2014
# File: NBAStatsTeamPlayerDataProcessor.py
# Desc: the data downloaded from net isnt good, so need this
#   file process it before used in models
#
# Produced By CSRGXTU
from Utility import loadMatrixFromFile, saveMatrixToFile, readmatricefromfile, loadSeasons, loadTeamIds, saveLstToFile

DIR = '/home/archer/Documents/maxent/data/basketball/leaguerank/'
seasons = loadSeasons(DIR + 'seasons-18-Nov-2014.txt')
teamIds = loadTeamIds(DIR + 'teamidshortname.csv')


# noneWithAVG
# replace None with average value
#
# @param teamId
# @param season
# @return res list(list)
def noneWithAVG(teamId, season):
    DIR = '/home/archer/Documents/maxent/data/basketball/leaguerank/'
    mat = loadMatrixFromFile(DIR + teamId + "." + season + ".player.csv")
    if len(mat) == 0:
        return [[]]

    heights = []
    weights = []
    ages = []

예제 #21

파일: GenerateHtml.py 프로젝트: csrgxtu/Russel

#!/usr/bin/env python
# coding=utf8
# Author: Archer Reilly
# File: GenerateHtml.py
# Desc: Generate a html file from results from Baidu OCR
# Date: 25/Apr/2016
#
# Produced By BR
from Utility import loadSeasons

lines = loadSeasons('./dest.csv')

for line in lines:
    print '<img src="http://192.168.100.2:8082/imgs/' + line.split(',')[0] + '">' +  line.split(',')[1] + '</img><input type="checkbox" onclick="check(this)"><input type="checkbox" onclick="remove(this)"><br/>'

예제 #22

        tmpLst.append(ranking['OPPG'])

        tmpLst.append(profile['Height'])
        tmpLst.append(profile['Weight'])
        tmpLst.append(profile['Age'])

        appendlst2file(tmpLst, dataFile)


if __name__ == '__main__':
    # first, load team id
    teamIds = loadTeamIds(
        '../../data/basketball/leaguerank/teamidname-18-Nov-2014.csv')

    # second, load seasons
    seasons = loadSeasons(
        '../../data/basketball/leaguerank/seasons-18-Nov-2014.txt')

    # seasonTypes
    seasonTypes = ['Playoffs']

    # leagueId
    leagueId = "00"
    """
  for teamId in teamIds:
    dataFile = '../../data/basketball/leaguerank/' + teamId + '.playoff.csv'
    for t in seasonTypes:
      for s in seasons:
        print "Processing " + teamId + " " + s + " " + t,
        run(teamId, s, t, leagueId, dataFile)
        print "  Done"
  """

예제 #23

파일: NBAStatsTeamPlayerDataProcessor.py 프로젝트: csrgxtu/maxent

#!/usr/bin/env python
# coding = utf-8
# Author: Archer Reilly
# Date: 24/DEC/2014
# File: NBAStatsTeamPlayerDataProcessor.py
# Desc: the data downloaded from net isnt good, so need this
#   file process it before used in models
#
# Produced By CSRGXTU
from Utility import loadMatrixFromFile, saveMatrixToFile, readmatricefromfile, loadSeasons, loadTeamIds, saveLstToFile

DIR = '/home/archer/Documents/maxent/data/basketball/leaguerank/'
seasons = loadSeasons(DIR + 'seasons-18-Nov-2014.txt')
teamIds = loadTeamIds(DIR + 'teamidshortname.csv')

# noneWithAVG
# replace None with average value
#
# @param teamId
# @param season
# @return res list(list)
def noneWithAVG(teamId, season):
  DIR = '/home/archer/Documents/maxent/data/basketball/leaguerank/'
  mat = loadMatrixFromFile(DIR + teamId + "." + season + ".player.csv")
  if len(mat) == 0:
    return [[]]

  heights = []
  weights = []
  ages = []
  exps = []

예제 #24

    tmpLst.append(ranking['RPG'])
    tmpLst.append(ranking['APG'])
    tmpLst.append(ranking['OPPG'])

    tmpLst.append(profile['Height'])
    tmpLst.append(profile['Weight'])
    tmpLst.append(profile['Age'])

    appendlst2file(tmpLst, dataFile)

if __name__ == '__main__':
  # first, load team id
  teamIds = loadTeamIds('../../data/basketball/teamidname-18-Nov-2014.csv')

  # second, load seasons
  seasons = loadSeasons('../../data/basketball/seasons.txt')

  # seasonTypes
  seasonTypes = ['Regular Season']

  # leagueId
  leagueId = "00"

  # for teamId in teamIds:
  #   dataFile = '../../data/basketball/' + teamId + '.csv'
  #   for t in seasonTypes:
  #     for s in seasons:
  #       print "Processing " + teamId + " " + s + " " + t,
  #       run(teamId, s, t, leagueId, dataFile)
  #       print "  Done"

예제 #25

파일: NBAStatsExtractorMainPlayOff.py 프로젝트: csrgxtu/maxent

    tmpLst.append(ranking['RPG'])
    tmpLst.append(ranking['APG'])
    tmpLst.append(ranking['OPPG'])

    tmpLst.append(profile['Height'])
    tmpLst.append(profile['Weight'])
    tmpLst.append(profile['Age'])

    appendlst2file(tmpLst, dataFile)

if __name__ == '__main__':
  # first, load team id
  teamIds = loadTeamIds('../../data/basketball/leaguerank/teamidname-18-Nov-2014.csv')

  # second, load seasons
  seasons = loadSeasons('../../data/basketball/leaguerank/seasons-18-Nov-2014.txt')

  # seasonTypes
  seasonTypes = ['Playoffs']

  # leagueId
  leagueId = "00"

  """
  for teamId in teamIds:
    dataFile = '../../data/basketball/leaguerank/' + teamId + '.playoff.csv'
    for t in seasonTypes:
      for s in seasons:
        print "Processing " + teamId + " " + s + " " + t,
        run(teamId, s, t, leagueId, dataFile)
        print "  Done"

예제 #26

#
# Author: Archer
# File: InsertTeamStats.py
# Date: 05/Jun/2015
# Desc: insert NBA.TeamStats table
#
# Produced By CSRGXTU
import MySQLdb as mdb
import sys
from Utility import loadMatrixFromFile, loadSeasons, loadTeamIds

teamIds = loadTeamIds(
    '/home/archer/Documents/Python/maxent/data/basketball/leaguerank/teamidshortname.csv'
)
seasons = loadSeasons(
    '/home/archer/Documents/Python/maxent/data/basketball/leaguerank/seasons-18-Nov-2014.txt'
)
TeamID2TeamShortNames = loadMatrixFromFile(
    '/home/archer/Documents/Python/maxent/data/basketball/leaguerank/TeamID2TeamShortName.csv'
)


def findId(shortName):
    for row in TeamID2TeamShortNames:
        if row[1] == shortName:
            return row[0]
    return False


def isHome(matchUpString):
    if '@' in matchUpString:

예제 #27

파일: InsertSeason.py 프로젝트: csrgxtu/maxent

#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Author: Archer
# File: InsertSeason.py
# Date: 05/Jun/2015
# Desc: insert data into the NBA.Season table
#
# Produced By CSRGXTU
import MySQLdb as mdb
import sys
from Utility import loadSeasons

seasons = loadSeasons('/home/archer/Documents/Python/maxent/data/basketball/seasons-18-Nov-2014.txt')
seasonTypeIDs = [1, 2, 3, 4]

con = mdb.connect('localhost', 'root', 'root', 'NBA')

with con:
    cur = con.cursor()
    for id in seasonTypeIDs:
        for s in seasons:
            sql = "insert into Season (\
                    Season_SeasonTypeID,\
                    Season,\
                    CreatedBy,\
                    CreatedTime) value (\
                    '%d', '%s', '%s', '%s')" %\
                    (id, s, 'archer', '2015-06-05 09:56:00')
            cur.execute(sql)

예제 #28

import ast
from bs4 import BeautifulSoup
import time
from Utility import loadSeasons, appendstr2fileutf8

url = 'http://apis.baidu.com/idl_baidu/baiduocrpay/idlocrpaid'
data = {}
data['fromdevice'] = "pc"
data['clientip'] = "192.168.100.3"
data['detecttype'] = "LocateRecognize"
data['languagetype'] = "CHN_ENG"
data['imagetype'] = "1"

# first, open names.txt
# for each name, build a csv row and store it
names = loadSeasons('./names.txt')
for name in names:
    time.sleep(2)
    file_object = open('/bookdata/liqiang/Downloads/books/' + name, 'rb')
    try:
        tmp = file_object.read()
    finally:
        file_object.close()
    data['image'] = base64.b64encode(tmp)

    decoded_data = urllib.urlencode(data)
    req = urllib2.Request(url, data=decoded_data)

    req.add_header("Content-Type", "application/x-www-form-urlencoded")
    req.add_header("apikey", "150281dc441994b2d21ddb0e57a9bd48")

예제 #29

# coding=utf8
#
# Author: Archer Reilly
# Date: 18/Apr/2016
# File: GetCutResults.py
# Desc: get cut results of a image by accessing the api and store it
# in a csv file
#
# Produced By BR
import unirest
from Utility import loadSeasons, appendstr2file

INPUT = '../data/names.txt'
OUTPUT = '../data/results.csv'
# API = 'https://dev-riowechat.beautifulreading.com/cutbook/'
# API = 'http://localhost:8090/cutbook/'
API = 'http://192.168.100.2:8090/cutbook/'

names = loadSeasons(INPUT)
for name in names:
    url = API + name
    unirest.timeout(120)
    response = unirest.get(url)
    if response.code == 200:
        print response.body
        row = name + ',' + str(response.body[u'data'])
        appendstr2file(row, OUTPUT)
    else:
        row = name + ',' + str(0)
        appendstr2file(row, OUTPUT)