├── .gitignore ├── .idea ├── compiler.xml ├── misc.xml └── modules.xml ├── README.md ├── Spark-MLlib-Tutorial.iml ├── beijing.csv ├── beijing.txt ├── beijing2.txt ├── female.txt ├── gender.txt ├── house.csv ├── iris.data ├── iris.list ├── male.txt ├── neg.txt ├── pos.txt ├── scriptFile.scala ├── src ├── META-INF │ └── MANIFEST.MF ├── WordCount.scala ├── cluster │ ├── Ida │ │ └── Main.scala │ └── kmeans │ │ └── Main.scala ├── gender │ └── Main.scala ├── iris │ └── Main.scala ├── isotonic │ ├── Main.scala │ └── scriptFile.scala ├── linear │ ├── Main.scala │ └── scriptFile.scala ├── pca │ └── Main.scala ├── rs │ └── Main.scala └── sentiment_analysis │ └── Main.scala └── u.data /.gitignore: -------------------------------------------------------------------------------- 1 | # Created by .ignore support plugin (hsz.mobi) 2 | ### Java template 3 | # Compiled class file 4 | *.class 5 | 6 | # Log file 7 | *.log 8 | 9 | # BlueJ files 10 | *.ctxt 11 | 12 | # Mobile Tools for Java (J2ME) 13 | .mtj.tmp/ 14 | 15 | # Package Files # 16 | *.jar 17 | *.war 18 | *.nar 19 | *.ear 20 | *.zip 21 | *.tar.gz 22 | *.rar 23 | 24 | # virtual machine crash logs, see http://www.java.com/en/download/help/error_hotspot.xml 25 | hs_err_pid* 26 | 27 | .idea/ 28 | out/production/Spark-MLlib-Tutorial/META-INF/ 29 | out/production/Spark-MLlib-Tutorial/generated/ 30 | .idea/vcs.xml 31 | .idea/misc.xml 32 | .idea/libraries/activation_1_1_1.xml 33 | .idea/inspectionProfiles/Project_Default.xml 34 | .idea/encodings.xml 35 | .idea/dictionaries/sss.xml 36 | .idea/artifacts/Spark_MLlib_Tutorial_jar.xml 37 | -------------------------------------------------------------------------------- /.idea/compiler.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Spark机器学习实践系列 2 | - [基于Spark的机器学习实践 (一) - 初识机器学习] 3 | - [基于Spark的机器学习实践 (二) - 初识MLlib] 4 | - [基于Spark的机器学习实践 (三) - 实战环境搭建] 5 | - [基于Spark的机器学习实践 (四) - 数据可视化] 6 | - [基于Spark的机器学习实践 (六) - 基础统计模块] 7 | - [基于Spark的机器学习实践 (七) - 回归算法] 8 | - [基于Spark的机器学习实践 (八) - 分类算法] 9 | - [基于Spark的机器学习实践 (九) - 聚类算法] 10 | - [基于Spark的机器学习实践 (十) - 降维算法] 11 | - [基于Spark的机器学习实践(十一) - 文本情感分类项目实战] 12 | - [基于Spark的机器学习实践 (十二) - 推荐系统实战] 13 | 14 | ## 掌握Spark机器学习库 大数据开发技能更进一步 15 | 16 | “大数据时代”已经不是一个新鲜词汇了，随着技术的商业化推广，越来越多的大数据技术已经进入人们的生活。与此同时，大数据技术的相关岗位需求也越来越多，更多的同学希望向大数据方向转型。本课程主要讲解Spark机器学习库，侧重实践的讲解，同时也以浅显易懂的方式介绍机器学习算法的内在原理。学习本教程，可以为想要转型大数据工程师或是入行大数据工作的同学提供实践指导作用。欢迎感兴趣的小伙伴们一起来学习。 17 | 18 | ## 兼顾常见业务场景&算法 整合大数据&机器学习 19 | ![](https://upload-images.jianshu.io/upload_images/16782311-657721947e344fa1.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240) 20 | 21 | ## 更贴近后端开发的讲解 让你迅速掌握Spark机器学习库 22 | 侧重实践 ，解决实际问题； 浅显易懂， 讲述内在原理 23 | ![image.png](https://upload-images.jianshu.io/upload_images/16782311-f2570022ab77dded.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240) 24 | 25 | # 案例+原理+代码 聚焦Spark核心技术 26 | 回归技术本身 揭开代码后面的奥秘 27 | ![](https://upload-images.jianshu.io/upload_images/16782311-9c0378c93851a135.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240) 28 | 29 | ![](https://upload-images.jianshu.io/upload_images/16782311-1e82e7a1f14e3ea8.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240) 30 | 31 | ## X 联系我 32 | 33 | ### [编程严选网](http://www.javaedge.cn/) 34 | -------------------------------------------------------------------------------- /Spark-MLlib-Tutorial.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | -------------------------------------------------------------------------------- /beijing.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Java-Edge/Spark-MLlib-Tutorial/34aa116adc8a07fd8795ee9c11f7ad898f359175/beijing.csv -------------------------------------------------------------------------------- /beijing.txt: -------------------------------------------------------------------------------- 1 | 0.4806,0.4839,0.318,0.4107,0.4835,0.4445,0.3704,0.3389,0.3711,0.2669,0.7317,0.4309,0.7009,0.5725,0.8132,0.5067,0.5415,0.7479,0.6973,0.4422,0.6733,0.6839,0.6653,0.721,0.4888,0.4899,0.5444,0.3932,0.3807,0.7184,0.6648,0.779,0.684,0.3928,0.4747,0.6982,0.3742,0.5112,0.597,0.9132,0.3867,0.5934,0.5279,0.2618,0.8177,0.7756,0.3669,0.5998,0.5271,1.406,0.6919,0.4868,1.1157,0.9332,0.9614,0.6577,0.5573,0.4816,0.9109,0.921 -------------------------------------------------------------------------------- /beijing2.txt: -------------------------------------------------------------------------------- 1 | 2008,2007,2006,2005,2004,2003,2002,2001,2000,1999,1998,1997,1996,1995,1994,1993,1992,1991,1990,1989,1988,1987,1986,1985,1984,1983,1982,1981,1980,1979,1978,1977,1976,1975,1974,1973,1972,1971,1970,1969,1968,1967,1966,1965,1964,1963,1962,1961,1960,1959,1958,1957,1956,1955,1954,1953,1952,1951,1950,1949 2 | 0.4806,0.4839,0.318,0.4107,0.4835,0.4445,0.3704,0.3389,0.3711,0.2669,0.7317,0.4309,0.7009,0.5725,0.8132,0.5067,0.5415,0.7479,0.6973,0.4422,0.6733,0.6839,0.6653,0.721,0.4888,0.4899,0.5444,0.3932,0.3807,0.7184,0.6648,0.779,0.684,0.3928,0.4747,0.6982,0.3742,0.5112,0.597,0.9132,0.3867,0.5934,0.5279,0.2618,0.8177,0.7756,0.3669,0.5998,0.5271,1.406,0.6919,0.4868,1.1157,0.9332,0.9614,0.6577,0.5573,0.4816,0.9109,0.921 -------------------------------------------------------------------------------- /female.txt: -------------------------------------------------------------------------------- 1 | [161.2, 51.6], [167.5, 59.0], [159.5, 49.2], [157.0, 63.0], [155.8, 53.6], 2 | [170.0, 59.0], [159.1, 47.6], [166.0, 69.8], [176.2, 66.8], [160.2, 75.2], 3 | [172.5, 55.2], [170.9, 54.2], [172.9, 62.5], [153.4, 42.0], [160.0, 50.0], 4 | [147.2, 49.8], [168.2, 49.2], [175.0, 73.2], [157.0, 47.8], [167.6, 68.8], 5 | [159.5, 50.6], [175.0, 82.5], [166.8, 57.2], [176.5, 87.8], [170.2, 72.8], 6 | [174.0, 54.5], [173.0, 59.8], [179.9, 67.3], [170.5, 67.8], [160.0, 47.0], 7 | [154.4, 46.2], [162.0, 55.0], [176.5, 83.0], [160.0, 54.4], [152.0, 45.8], 8 | [162.1, 53.6], [170.0, 73.2], [160.2, 52.1], [161.3, 67.9], [166.4, 56.6], 9 | [168.9, 62.3], [163.8, 58.5], [167.6, 54.5], [160.0, 50.2], [161.3, 60.3], 10 | [167.6, 58.3], [165.1, 56.2], [160.0, 50.2], [170.0, 72.9], [157.5, 59.8], 11 | [167.6, 61.0], [160.7, 69.1], [163.2, 55.9], [152.4, 46.5], [157.5, 54.3], 12 | [168.3, 54.8], [180.3, 60.7], [165.5, 60.0], [165.0, 62.0], [164.5, 60.3], 13 | [156.0, 52.7], [160.0, 74.3], [163.0, 62.0], [165.7, 73.1], [161.0, 80.0], 14 | [162.0, 54.7], [166.0, 53.2], [174.0, 75.7], [172.7, 61.1], [167.6, 55.7], 15 | [151.1, 48.7], [164.5, 52.3], [163.5, 50.0], [152.0, 59.3], [169.0, 62.5], 16 | [164.0, 55.7], [161.2, 54.8], [155.0, 45.9], [170.0, 70.6], [176.2, 67.2], 17 | [170.0, 69.4], [162.5, 58.2], [170.3, 64.8], [164.1, 71.6], [169.5, 52.8], 18 | [163.2, 59.8], [154.5, 49.0], [159.8, 50.0], [173.2, 69.2], [170.0, 55.9], 19 | [161.4, 63.4], [169.0, 58.2], [166.2, 58.6], [159.4, 45.7], [162.5, 52.2], 20 | [159.0, 48.6], [162.8, 57.8], [159.0, 55.6], [179.8, 66.8], [162.9, 59.4], 21 | [161.0, 53.6], [151.1, 73.2], [168.2, 53.4], [168.9, 69.0], [173.2, 58.4], 22 | [171.8, 56.2], [178.0, 70.6], [164.3, 59.8], [163.0, 72.0], [168.5, 65.2], 23 | [166.8, 56.6], [172.7, 105.2], [163.5, 51.8], [169.4, 63.4], [167.8, 59.0], 24 | [159.5, 47.6], [167.6, 63.0], [161.2, 55.2], [160.0, 45.0], [163.2, 54.0], 25 | [162.2, 50.2], [161.3, 60.2], [149.5, 44.8], [157.5, 58.8], [163.2, 56.4], 26 | [172.7, 62.0], [155.0, 49.2], [156.5, 67.2], [164.0, 53.8], [160.9, 54.4], 27 | [162.8, 58.0], [167.0, 59.8], [160.0, 54.8], [160.0, 43.2], [168.9, 60.5], 28 | [158.2, 46.4], [156.0, 64.4], [160.0, 48.8], [167.1, 62.2], [158.0, 55.5], 29 | [167.6, 57.8], [156.0, 54.6], [162.1, 59.2], [173.4, 52.7], [159.8, 53.2], 30 | [170.5, 64.5], [159.2, 51.8], [157.5, 56.0], [161.3, 63.6], [162.6, 63.2], 31 | [160.0, 59.5], [168.9, 56.8], [165.1, 64.1], [162.6, 50.0], [165.1, 72.3], 32 | [166.4, 55.0], [160.0, 55.9], [152.4, 60.4], [170.2, 69.1], [162.6, 84.5], 33 | [170.2, 55.9], [158.8, 55.5], [172.7, 69.5], [167.6, 76.4], [162.6, 61.4], 34 | [167.6, 65.9], [156.2, 58.6], [175.2, 66.8], [172.1, 56.6], [162.6, 58.6], 35 | [160.0, 55.9], [165.1, 59.1], [182.9, 81.8], [166.4, 70.7], [165.1, 56.8], 36 | [177.8, 60.0], [165.1, 58.2], [175.3, 72.7], [154.9, 54.1], [158.8, 49.1], 37 | [172.7, 75.9], [168.9, 55.0], [161.3, 57.3], [167.6, 55.0], [165.1, 65.5], 38 | [175.3, 65.5], [157.5, 48.6], [163.8, 58.6], [167.6, 63.6], [165.1, 55.2], 39 | [165.1, 62.7], [168.9, 56.6], [162.6, 53.9], [164.5, 63.2], [176.5, 73.6], 40 | [168.9, 62.0], [175.3, 63.6], [159.4, 53.2], [160.0, 53.4], [170.2, 55.0], 41 | [162.6, 70.5], [167.6, 54.5], [162.6, 54.5], [160.7, 55.9], [160.0, 59.0], 42 | [157.5, 63.6], [162.6, 54.5], [152.4, 47.3], [170.2, 67.7], [165.1, 80.9], 43 | [172.7, 70.5], [165.1, 60.9], [170.2, 63.6], [170.2, 54.5], [170.2, 59.1], 44 | [161.3, 70.5], [167.6, 52.7], [167.6, 62.7], [165.1, 86.3], [162.6, 66.4], 45 | [152.4, 67.3], [168.9, 63.0], [170.2, 73.6], [175.2, 62.3], [175.2, 57.7], 46 | [160.0, 55.4], [165.1, 104.1], [174.0, 55.5], [170.2, 77.3], [160.0, 80.5], 47 | [167.6, 64.5], [167.6, 72.3], [167.6, 61.4], [154.9, 58.2], [162.6, 81.8], 48 | [175.3, 63.6], [171.4, 53.4], [157.5, 54.5], [165.1, 53.6], [160.0, 60.0], 49 | [174.0, 73.6], [162.6, 61.4], [174.0, 55.5], [162.6, 63.6], [161.3, 60.9], 50 | [156.2, 60.0], [149.9, 46.8], [169.5, 57.3], [160.0, 64.1], [175.3, 63.6], 51 | [169.5, 67.3], [160.0, 75.5], [172.7, 68.2], [162.6, 61.4], [157.5, 76.8], 52 | [176.5, 71.8], [164.4, 55.5], [160.7, 48.6], [174.0, 66.4], [163.8, 67.3] 53 | -------------------------------------------------------------------------------- /gender.txt: -------------------------------------------------------------------------------- 1 | 女性 2 | [[161.2, 51.6], [167.5, 59.0], [159.5, 49.2], [157.0, 63.0], [155.8, 53.6], 3 | [170.0, 59.0], [159.1, 47.6], [166.0, 69.8], [176.2, 66.8], [160.2, 75.2], 4 | [172.5, 55.2], [170.9, 54.2], [172.9, 62.5], [153.4, 42.0], [160.0, 50.0], 5 | [147.2, 49.8], [168.2, 49.2], [175.0, 73.2], [157.0, 47.8], [167.6, 68.8], 6 | [159.5, 50.6], [175.0, 82.5], [166.8, 57.2], [176.5, 87.8], [170.2, 72.8], 7 | [174.0, 54.5], [173.0, 59.8], [179.9, 67.3], [170.5, 67.8], [160.0, 47.0], 8 | [154.4, 46.2], [162.0, 55.0], [176.5, 83.0], [160.0, 54.4], [152.0, 45.8], 9 | [162.1, 53.6], [170.0, 73.2], [160.2, 52.1], [161.3, 67.9], [166.4, 56.6], 10 | [168.9, 62.3], [163.8, 58.5], [167.6, 54.5], [160.0, 50.2], [161.3, 60.3], 11 | [167.6, 58.3], [165.1, 56.2], [160.0, 50.2], [170.0, 72.9], [157.5, 59.8], 12 | [167.6, 61.0], [160.7, 69.1], [163.2, 55.9], [152.4, 46.5], [157.5, 54.3], 13 | [168.3, 54.8], [180.3, 60.7], [165.5, 60.0], [165.0, 62.0], [164.5, 60.3], 14 | [156.0, 52.7], [160.0, 74.3], [163.0, 62.0], [165.7, 73.1], [161.0, 80.0], 15 | [162.0, 54.7], [166.0, 53.2], [174.0, 75.7], [172.7, 61.1], [167.6, 55.7], 16 | [151.1, 48.7], [164.5, 52.3], [163.5, 50.0], [152.0, 59.3], [169.0, 62.5], 17 | [164.0, 55.7], [161.2, 54.8], [155.0, 45.9], [170.0, 70.6], [176.2, 67.2], 18 | [170.0, 69.4], [162.5, 58.2], [170.3, 64.8], [164.1, 71.6], [169.5, 52.8], 19 | [163.2, 59.8], [154.5, 49.0], [159.8, 50.0], [173.2, 69.2], [170.0, 55.9], 20 | [161.4, 63.4], [169.0, 58.2], [166.2, 58.6], [159.4, 45.7], [162.5, 52.2], 21 | [159.0, 48.6], [162.8, 57.8], [159.0, 55.6], [179.8, 66.8], [162.9, 59.4], 22 | [161.0, 53.6], [151.1, 73.2], [168.2, 53.4], [168.9, 69.0], [173.2, 58.4], 23 | [171.8, 56.2], [178.0, 70.6], [164.3, 59.8], [163.0, 72.0], [168.5, 65.2], 24 | [166.8, 56.6], [172.7, 105.2], [163.5, 51.8], [169.4, 63.4], [167.8, 59.0], 25 | [159.5, 47.6], [167.6, 63.0], [161.2, 55.2], [160.0, 45.0], [163.2, 54.0], 26 | [162.2, 50.2], [161.3, 60.2], [149.5, 44.8], [157.5, 58.8], [163.2, 56.4], 27 | [172.7, 62.0], [155.0, 49.2], [156.5, 67.2], [164.0, 53.8], [160.9, 54.4], 28 | [162.8, 58.0], [167.0, 59.8], [160.0, 54.8], [160.0, 43.2], [168.9, 60.5], 29 | [158.2, 46.4], [156.0, 64.4], [160.0, 48.8], [167.1, 62.2], [158.0, 55.5], 30 | [167.6, 57.8], [156.0, 54.6], [162.1, 59.2], [173.4, 52.7], [159.8, 53.2], 31 | [170.5, 64.5], [159.2, 51.8], [157.5, 56.0], [161.3, 63.6], [162.6, 63.2], 32 | [160.0, 59.5], [168.9, 56.8], [165.1, 64.1], [162.6, 50.0], [165.1, 72.3], 33 | [166.4, 55.0], [160.0, 55.9], [152.4, 60.4], [170.2, 69.1], [162.6, 84.5], 34 | [170.2, 55.9], [158.8, 55.5], [172.7, 69.5], [167.6, 76.4], [162.6, 61.4], 35 | [167.6, 65.9], [156.2, 58.6], [175.2, 66.8], [172.1, 56.6], [162.6, 58.6], 36 | [160.0, 55.9], [165.1, 59.1], [182.9, 81.8], [166.4, 70.7], [165.1, 56.8], 37 | [177.8, 60.0], [165.1, 58.2], [175.3, 72.7], [154.9, 54.1], [158.8, 49.1], 38 | [172.7, 75.9], [168.9, 55.0], [161.3, 57.3], [167.6, 55.0], [165.1, 65.5], 39 | [175.3, 65.5], [157.5, 48.6], [163.8, 58.6], [167.6, 63.6], [165.1, 55.2], 40 | [165.1, 62.7], [168.9, 56.6], [162.6, 53.9], [164.5, 63.2], [176.5, 73.6], 41 | [168.9, 62.0], [175.3, 63.6], [159.4, 53.2], [160.0, 53.4], [170.2, 55.0], 42 | [162.6, 70.5], [167.6, 54.5], [162.6, 54.5], [160.7, 55.9], [160.0, 59.0], 43 | [157.5, 63.6], [162.6, 54.5], [152.4, 47.3], [170.2, 67.7], [165.1, 80.9], 44 | [172.7, 70.5], [165.1, 60.9], [170.2, 63.6], [170.2, 54.5], [170.2, 59.1], 45 | [161.3, 70.5], [167.6, 52.7], [167.6, 62.7], [165.1, 86.3], [162.6, 66.4], 46 | [152.4, 67.3], [168.9, 63.0], [170.2, 73.6], [175.2, 62.3], [175.2, 57.7], 47 | [160.0, 55.4], [165.1, 104.1], [174.0, 55.5], [170.2, 77.3], [160.0, 80.5], 48 | [167.6, 64.5], [167.6, 72.3], [167.6, 61.4], [154.9, 58.2], [162.6, 81.8], 49 | [175.3, 63.6], [171.4, 53.4], [157.5, 54.5], [165.1, 53.6], [160.0, 60.0], 50 | [174.0, 73.6], [162.6, 61.4], [174.0, 55.5], [162.6, 63.6], [161.3, 60.9], 51 | [156.2, 60.0], [149.9, 46.8], [169.5, 57.3], [160.0, 64.1], [175.3, 63.6], 52 | [169.5, 67.3], [160.0, 75.5], [172.7, 68.2], [162.6, 61.4], [157.5, 76.8], 53 | [176.5, 71.8], [164.4, 55.5], [160.7, 48.6], [174.0, 66.4], [163.8, 67.3] 54 | ] 55 | 56 | 57 | 男性 58 | [[174.0, 65.6], [175.3, 71.8], [193.5, 80.7], [186.5, 72.6], [187.2, 78.8], 59 | [181.5, 74.8], [184.0, 86.4], [184.5, 78.4], [175.0, 62.0], [184.0, 81.6], 60 | [180.0, 76.6], [177.8, 83.6], [192.0, 90.0], [176.0, 74.6], [174.0, 71.0], 61 | [184.0, 79.6], [192.7, 93.8], [171.5, 70.0], [173.0, 72.4], [176.0, 85.9], 62 | [176.0, 78.8], [180.5, 77.8], [172.7, 66.2], [176.0, 86.4], [173.5, 81.8], 63 | [178.0, 89.6], [180.3, 82.8], [180.3, 76.4], [164.5, 63.2], [173.0, 60.9], 64 | [183.5, 74.8], [175.5, 70.0], [188.0, 72.4], [189.2, 84.1], [172.8, 69.1], 65 | [170.0, 59.5], [182.0, 67.2], [170.0, 61.3], [177.8, 68.6], [184.2, 80.1], 66 | [186.7, 87.8], [171.4, 84.7], [172.7, 73.4], [175.3, 72.1], [180.3, 82.6], 67 | [182.9, 88.7], [188.0, 84.1], [177.2, 94.1], [172.1, 74.9], [167.0, 59.1], 68 | [169.5, 75.6], [174.0, 86.2], [172.7, 75.3], [182.2, 87.1], [164.1, 55.2], 69 | [163.0, 57.0], [171.5, 61.4], [184.2, 76.8], [174.0, 86.8], [174.0, 72.2], 70 | [177.0, 71.6], [186.0, 84.8], [167.0, 68.2], [171.8, 66.1], [182.0, 72.0], 71 | [167.0, 64.6], [177.8, 74.8], [164.5, 70.0], [192.0, 101.6], [175.5, 63.2], 72 | [171.2, 79.1], [181.6, 78.9], [167.4, 67.7], [181.1, 66.0], [177.0, 68.2], 73 | [174.5, 63.9], [177.5, 72.0], [170.5, 56.8], [182.4, 74.5], [197.1, 90.9], 74 | [180.1, 93.0], [175.5, 80.9], [180.6, 72.7], [184.4, 68.0], [175.5, 70.9], 75 | [180.6, 72.5], [177.0, 72.5], [177.1, 83.4], [181.6, 75.5], [176.5, 73.0], 76 | [175.0, 70.2], [174.0, 73.4], [165.1, 70.5], [177.0, 68.9], [192.0, 102.3], 77 | [176.5, 68.4], [169.4, 65.9], [182.1, 75.7], [179.8, 84.5], [175.3, 87.7], 78 | [184.9, 86.4], [177.3, 73.2], [167.4, 53.9], [178.1, 72.0], [168.9, 55.5], 79 | [157.2, 58.4], [180.3, 83.2], [170.2, 72.7], [177.8, 64.1], [172.7, 72.3], 80 | [165.1, 65.0], [186.7, 86.4], [165.1, 65.0], [174.0, 88.6], [175.3, 84.1], 81 | [185.4, 66.8], [177.8, 75.5], [180.3, 93.2], [180.3, 82.7], [177.8, 58.0], 82 | [177.8, 79.5], [177.8, 78.6], [177.8, 71.8], [177.8, 116.4], [163.8, 72.2], 83 | [188.0, 83.6], [198.1, 85.5], [175.3, 90.9], [166.4, 85.9], [190.5, 89.1], 84 | [166.4, 75.0], [177.8, 77.7], [179.7, 86.4], [172.7, 90.9], [190.5, 73.6], 85 | [185.4, 76.4], [168.9, 69.1], [167.6, 84.5], [175.3, 64.5], [170.2, 69.1], 86 | [190.5, 108.6], [177.8, 86.4], [190.5, 80.9], [177.8, 87.7], [184.2, 94.5], 87 | [176.5, 80.2], [177.8, 72.0], [180.3, 71.4], [171.4, 72.7], [172.7, 84.1], 88 | [172.7, 76.8], [177.8, 63.6], [177.8, 80.9], [182.9, 80.9], [170.2, 85.5], 89 | [167.6, 68.6], [175.3, 67.7], [165.1, 66.4], [185.4, 102.3], [181.6, 70.5], 90 | [172.7, 95.9], [190.5, 84.1], [179.1, 87.3], [175.3, 71.8], [170.2, 65.9], 91 | [193.0, 95.9], [171.4, 91.4], [177.8, 81.8], [177.8, 96.8], [167.6, 69.1], 92 | [167.6, 82.7], [180.3, 75.5], [182.9, 79.5], [176.5, 73.6], [186.7, 91.8], 93 | [188.0, 84.1], [188.0, 85.9], [177.8, 81.8], [174.0, 82.5], [177.8, 80.5], 94 | [171.4, 70.0], [185.4, 81.8], [185.4, 84.1], [188.0, 90.5], [188.0, 91.4], 95 | [182.9, 89.1], [176.5, 85.0], [175.3, 69.1], [175.3, 73.6], [188.0, 80.5], 96 | [188.0, 82.7], [175.3, 86.4], [170.5, 67.7], [179.1, 92.7], [177.8, 93.6], 97 | [175.3, 70.9], [182.9, 75.0], [170.8, 93.2], [188.0, 93.2], [180.3, 77.7], 98 | [177.8, 61.4], [185.4, 94.1], [168.9, 75.0], [185.4, 83.6], [180.3, 85.5], 99 | [174.0, 73.9], [167.6, 66.8], [182.9, 87.3], [160.0, 72.3], [180.3, 88.6], 100 | [167.6, 75.5], [186.7, 101.4], [175.3, 91.1], [175.3, 67.3], [175.9, 77.7], 101 | [175.3, 81.8], [179.1, 75.5], [181.6, 84.5], [177.8, 76.6], [182.9, 85.0], 102 | [177.8, 102.5], [184.2, 77.3], [179.1, 71.8], [176.5, 87.9], [188.0, 94.3], 103 | [174.0, 70.9], [167.6, 64.5], [170.2, 77.3], [167.6, 72.3], [188.0, 87.3], 104 | [174.0, 80.0], [176.5, 82.3], [180.3, 73.6], [167.6, 74.1], [188.0, 85.9], 105 | [180.3, 73.2], [167.6, 76.3], [183.0, 65.9], [183.0, 90.9], [179.1, 89.1], 106 | [170.2, 62.3], [177.8, 82.7], [179.1, 79.1], [190.5, 98.2], [177.8, 84.1], 107 | [180.3, 83.2], [180.3, 83.2] 108 | ] 109 | 110 | 111 | 112 | 113 | 114 | 115 | -------------------------------------------------------------------------------- /house.csv: -------------------------------------------------------------------------------- 1 | position;square;price;direction;type;name; 2 | 0;190;20000;0;4室2厅2卫;中信城(别墅); 3 | 0;190;20000;0;4室2厅2卫;中信城(别墅); 4 | 5;400;15000;0;4室3厅3卫;融创上城; 5 | 0;500;15000;0;5室3厅2卫;中海莱茵东郡; 6 | 5;500;15000;0;5室3厅4卫;融创上城(别墅); 7 | 1;320;15000;1;1室1厅1卫;长江花园; 8 | 0;143;12000;0;3室2厅2卫;融创上城; 9 | 0;200;10000;0;4室3厅2卫;中海莱茵东郡(别墅); 10 | 0;207;9000;0;4室3厅4卫;中海莱茵东郡; 11 | 0;130;8500;0;3室2厅2卫;伟峰东第; 12 | 5;150;7000;0;3室2厅2卫;融创上城; 13 | 2;178;6000;0;4室2厅2卫;鸿城国际花园; 14 | 5;190;6000;0;3室2厅2卫;亚泰豪苑C栋; 15 | 1;150;6000;0;5室1厅2卫;通安新居A区; 16 | 2;165;6000;0;3室2厅2卫;万科惠斯勒小镇; 17 | 0;64;5500;0;1室1厅1卫;保利中央公园; 18 | 2;105;5500;0;2室2厅1卫;虹馆; 19 | 1;160;5300;0;3室2厅1卫;昊源高格蓝湾; 20 | 2;170;5100;0;4室2厅2卫;亚泰鼎盛国际; 21 | 0;155;5000;0;3室2厅2卫;中海水岸馨都; 22 | 5;128;5000;0;4室2厅1卫;长影世纪村; 23 | 0;145;4500;0;3室2厅2卫;富奥临河湾; 24 | 2;92;4200;0;3室2厅1卫;御翠豪庭尚府一期; 25 | 0;75;4100;0;1室1厅1卫;恒大雅苑; 26 | 5;105;4000;0;2室1厅1卫;南湖名家; 27 | 2;93;4000;0;3室2厅1卫;御翠豪庭尚府一期; 28 | 5;121;4000;0;3室1厅1卫;万达广场; 29 | 0;104;4000;0;2室1厅1卫;棠棣; 30 | 1;135;4000;0;3室2厅2卫;万科蓝山; 31 | 5;98;4000;0;2室2厅1卫;华亿红府; 32 | 0;128;3800;0;3室2厅1卫;复地哥德堡森林; 33 | 2;154;3700;0;3室2厅2卫;长春明珠; 34 | 0;100;3700;0;2室2厅1卫;翡翠花溪; 35 | 4;66;3700;0;1室1厅1卫;长客厂南; 36 | 2;106;3600;0;2室2厅1卫;虹馆; 37 | 5;132;3500;0;3室2厅1卫;大禹城邦; 38 | 1;135;3500;0;3室2厅1卫;昊源高格蓝湾; 39 | 0;130;3500;0;3室2厅1卫;中海国际社区(AJ区); 40 | 2;70;3500;0;2室1厅1卫;中信御园; 41 | 0;145;3500;0;3室2厅2卫;万盛东城; 42 | 5;132;3500;0;3室2厅2卫;大禹城邦; 43 | 5;100;3300;0;2室2厅1卫;国信南湖公馆; 44 | 5;75;3200;0;1室1厅1卫;南湖祥水湾; 45 | 0;120;3200;0;3室3厅2卫;复地哥德堡森林; 46 | 2;95;3200;0;3室2厅1卫;万科金域长春; 47 | 0;106;3200;0;2室2厅1卫;富腾天下城; 48 | 5;118;3200;0;3室2厅2卫;融创上城(别墅); 49 | 1;130;3200;0;3室2厅1卫;万龙名城; 50 | 0;137;3200;0;3室2厅2卫;中海国际社区(AJ区); 51 | 0;160;3000;0;3室2厅2卫;富奥花园C区; 52 | 1;88;3000;0;2室2厅1卫;万科蓝山; 53 | 2;90;3000;0;2室1厅1卫;秋实e景二期; 54 | 0;95;3000;0;2室2厅1卫;环球凯旋公馆; 55 | 4;122;3000;0;3室2厅1卫;御景名家A区; 56 | 5;60;3000;0;2室1厅1卫;桂林小区; 57 | 0;102;3000;0;2室2厅1卫;东方万达城; 58 | 5;75;3000;0;2室2厅1卫;华亿红府; 59 | 1;95;3000;0;2室2厅1卫;昊源高格蓝湾; 60 | 2;87;3000;0;2室2厅1卫;保利林语; 61 | 0;92;2950;0;2室2厅1卫;浦东新城; 62 | 2;85;2900;0;2室2厅1卫;秋实e景二期; 63 | 0;85;2900;0;3室2厅2卫;开发区六小区; 64 | 2;89;2800;0;2室2厅1卫;豪苑翡翠城; 65 | 2;127;2800;0;3室2厅2卫;国税繁荣小区; 66 | 0;97;2800;0;2室2厅1卫;万科城; 67 | 0;148;2800;0;3室2厅2卫;富奥花园C区; 68 | 0;92;2800;0;2室2厅1卫;华润凯旋门; 69 | 5;120;2800;0;3室2厅1卫;西康小区; 70 | 0;89;2800;0;2室2厅1卫;万科城; 71 | 0;148;2800;0;3室2厅2卫;富奥花园C区; 72 | 0;70;2800;0;2室1厅1卫;复地哥德堡森林; 73 | 2;92;2800;0;2室2厅1卫;保利金香槟; 74 | 0;135;2800;0;3室2厅1卫;净月富奥A区; 75 | 2;89;2800;0;2室2厅1卫;豪苑翡翠城; 76 | 0;110;2700;0;2室2厅1卫;临河风景; 77 | 0;88;2700;0;1室1厅1卫;中海净月华庭; 78 | 5;95;2700;0;2室2厅1卫;大禹城邦; 79 | 0;90;2700;0;2室1厅1卫;阳光城; 80 | 0;89;2700;0;2室2厅1卫;中海国际社区(AJ区); 81 | 2;110;2700;0;2室2厅1卫;长春明珠; 82 | 0;90;2700;0;2室1厅1卫;万科城; 83 | 1;81;2700;0;2室2厅1卫;昊源高格蓝湾; 84 | 1;117;2600;0;2室2厅1卫;长江花园; 85 | 5;75;2600;0;2室1厅1卫;一品红城; 86 | 0;86;2600;0;2室2厅1卫;中海净月华庭; 87 | 0;90;2600;0;2室2厅1卫;倚澜观邸(二期); 88 | 4;85;2600;0;2室1厅1卫;新城吾悦广场; 89 | 5;80;2600;0;2室2厅1卫;清河街道万宝社区; 90 | 0;92;2600;1;2室2厅1卫;吉大菲尔瑞特; 91 | 5;50;2600;0;1室1厅1卫;华亿红府; 92 | 2;100;2600;0;2室2厅1卫;长春明珠; 93 | 2;114;2600;0;3室1厅1卫;长春明珠; 94 | 0;110;2600;0;2室2厅1卫;富奥花园C区; 95 | 0;110;2600;0;2室2厅1卫;阳光帝景; 96 | 4;92;2600;0;2室2厅1卫;融和嘉苑; 97 | 0;96;2500;0;2室2厅1卫;华荣泰时代; 98 | 1;92;2500;0;2室2厅1卫;亚泰樱花苑; 99 | 3;105;2500;0;2室2厅1卫;证大光明城一期; 100 | 5;120;2500;0;3室2厅1卫;地矿花园; 101 | 5;70;2500;0;2室1厅1卫;长影世纪村; 102 | 5;94;2500;0;3室1厅1卫;东安开运福里; 103 | 0;90;2500;0;2室1厅1卫;华润凯旋门; 104 | 0;90;2500;0;2室2厅1卫;西湖一号; 105 | 0;60;2500;0;2室1厅1卫;力旺康景; 106 | 0;122;2500;0;2室1厅1卫;园丁花园; 107 | 5;68;2500;0;3室1厅1卫;桂林胡同; 108 | 4;146;2500;0;3室2厅2卫;万福小区; 109 | 5;80;2500;0;2室2厅1卫;工程学院家属楼; 110 | 2;95;2500;0;2室2厅1卫;好景山庄; 111 | 0;110;2500;0;2室2厅1卫;金叶嘉园; 112 | 0;110;2500;0;3室2厅1卫;嘉惠红树湾; 113 | 0;98;2500;0;2室2厅1卫;华荣泰时代; 114 | 0;89;2500;0;2室2厅1卫;万科城; 115 | 4;104;2500;0;2室2厅1卫;先行名苑; 116 | 1;75;2500;0;2室1厅1卫;万龙名城; 117 | 0;110;2500;0;2室2厅1卫;富奥花园C区; 118 | 2;120;2500;0;3室2厅1卫;净水山庄; 119 | 0;100;2500;0;3室2厅1卫;万科城; 120 | 0;90;2500;0;2室2厅2卫;中海国际社区(E区); 121 | 0;102;2500;1;2室2厅1卫;远洋戛纳小镇; 122 | 5;100;2500;0;3室1厅1卫;湖光小区; 123 | 0;80;2500;0;2室2厅1卫;万科城; 124 | 5;124;2500;0;3室2厅1卫;湖西小区; 125 | 0;89;2500;0;2室2厅1卫;保利罗兰香谷; 126 | 0;110;2400;0;2室2厅1卫;富奥花园C区; 127 | 0;80;2400;1;1室1厅1卫;华荣泰时代; 128 | 1;100;2400;0;2室2厅1卫;万龙名城; 129 | 2;80;2400;0;2室1厅1卫;信达东湾半岛A区; 130 | 0;98;2400;0;2室2厅1卫;高新怡众名城; 131 | 0;87;2300;0;2室2厅1卫;锦绣东南; 132 | 5;78;2300;0;2室1厅1卫;长影世纪村; 133 | 1;100;2300;0;3室2厅2卫;蔚蓝国际; 134 | 2;99;2300;0;2室2厅1卫;长春明珠; 135 | 2;45;2300;1;1室1厅1卫;天伦中央广场; 136 | 0;80;2300;0;2室1厅1卫;环球凯旋公馆; 137 | 5;45;2300;4;1室1厅1卫;大禹南湖首府; 138 | 0;99;2300;0;2室2厅1卫;蓝调倾城; 139 | 0;89;2300;0;2室2厅1卫;力旺格林春天; 140 | 0;50;2300;0;1室1厅1卫;GTC环球贸易中心; 141 | 5;64;2300;0;1室0厅1卫;富苑盛世城; 142 | 0;50;2300;0;1室1厅1卫;垠禄新界; 143 | 0;96;2300;0;2室1厅1卫;首地首城; 144 | 5;55;2300;4;1室0厅1卫;富苑盛世城; 145 | 0;100;2300;0;2室2厅1卫;假日名都; 146 | 1;50;2300;0;2室0厅1卫;万龙名城; 147 | 1;72;2300;0;2室1厅1卫;万龙名城; 148 | 2;75;2300;0;2室1厅1卫;绿地新里中央公馆C区; 149 | 5;100;2300;0;2室2厅1卫;富豪花园; 150 | 5;100;2200;0;2室1厅1卫;慈光世纪居; 151 | 2;108;2200;0;2室2厅1卫;创新花园; 152 | 3;84;2200;0;2室1厅1卫;证大光明城一期; 153 | 5;45;2200;4;1室1厅1卫;万达广场; 154 | 5;80;2200;0;3室1厅1卫;地震局宿舍; 155 | 2;25;2200;0;1室1厅1卫;亚泰鼎盛国际; 156 | 0;100;2200;0;2室1厅1卫;东润枫景; 157 | 1;96;2200;0;2室2厅1卫;汉森金烁广场; 158 | 2;84;2200;0;2室1厅1卫;信达东湾半岛(第三区); 159 | 0;120;2200;0;3室2厅1卫;新星宇和邑(A区); 160 | 0;90;2200;0;2室2厅1卫;中国铁建国际花园(一期); 161 | 1;93;2200;1;3室1厅1卫;奥体玉园; 162 | 0;90;2200;0;2室2厅1卫;中国铁建国际花园(一期); 163 | 1;80;2200;0;1室1厅1卫;蓝色港湾(二期); 164 | 5;120;2200;0;3室1厅1卫;大兴小区; 165 | 2;62;2200;0;1室1厅1卫;绿地新里中央公馆C区; 166 | 5;84;2200;0;2室1厅1卫;长久家苑2区; 167 | 1;54;2200;0;1室1厅1卫;蓝色港湾(一期); 168 | 2;25;2200;0;1室1厅1卫;亚泰鼎盛国际; 169 | 2;50;2100;0;2室1厅1卫;天伦中央; 170 | 0;83;2100;0;2室1厅1卫;超达家园; 171 | 0;60;2100;0;1室1厅1卫;棠棣; 172 | 0;90;2100;0;2室2厅1卫;万龙丽水湾; 173 | 2;46;2100;1;1室1厅1卫;天伦中央; 174 | 1;80;2100;0;2室1厅1卫;万龙名城; 175 | 5;56;2100;0;1室0厅1卫;富苑盛世城; 176 | 5;70;2100;0;2室1厅1卫;桂林小区; 177 | 5;74;2100;0;2室2厅1卫;东安开运福里; 178 | 2;80;2000;1;1室1厅1卫;万晟现代城B区; 179 | 3;98;2000;0;2室2厅1卫;卓扬中华城; 180 | 0;90;2000;0;2室2厅1卫;恒大帝景; 181 | 0;80;2000;0;2室2厅1卫;天旗凤凰城; 182 | 5;80;2000;0;3室1厅1卫;南湖新村; 183 | 1;90;2000;0;2室2厅1卫;吉星花园; 184 | 1;63;2000;1;1室1厅1卫;万科蓝山; 185 | 5;80;2000;0;2室1厅1卫;司法厅宿舍; 186 | 3;90;2000;0;2室2厅1卫;东田青年城; 187 | 0;103;2000;0;2室2厅1卫;咖啡小镇; 188 | 0;70;2000;0;2室1厅1卫;东城国际花园; 189 | 0;74;2000;0;2室1厅1卫;嘉柏湾; 190 | 5;45;2000;1;1室1厅1卫;大禹南湖首府; 191 | 2;62;2000;0;1室1厅1卫;亚泰鼎盛国际; 192 | 5;85;2000;0;2室2厅1卫;威尼斯花园; 193 | 4;96;2000;0;2室2厅1卫;丰和西郡; 194 | 1;60;2000;0;1室1厅1卫;昊源高格蓝湾; 195 | 0;118;2000;0;2室2厅1卫;金色世界湾; 196 | 5;91;2000;0;3室2厅1卫;南湖新村; 197 | 1;96;1950;1;2室2厅1卫;亚泰杏花苑; 198 | 0;72;1900;0;2室1厅1卫;远创樾府; 199 | 2;62;1900;0;2室1厅1卫;东北师范大学第一教职工...; 200 | 5;42;1900;0;1室1厅1卫;阳光城; 201 | 5;88;1900;0;2室2厅1卫;星宇华宇花园; 202 | 0;65;1900;0;2室1厅1卫;东城国际花园; 203 | 5;60;1900;0;2室1厅1卫;南湖祥水湾; 204 | 4;104;1900;0;2室1厅1卫;万福小区; 205 | 0;60;1900;0;2室1厅1卫;金色世界湾; 206 | 0;75;1900;0;2室1厅1卫;新星宇和邑(A区); 207 | 5;57;1850;0;1室1厅1卫;万达广场; 208 | 5;36;1805;4;1室0厅1卫;世纪鸿源; 209 | 0;89;1800;0;2室1厅1卫;远创樾府; 210 | 0;58;1800;0;1室1厅1卫;天茂城中央; 211 | 5;45;1800;1;1室0厅1卫;大禹南湖首府; 212 | 5;45;1800;0;1室1厅1卫;大禹南湖首府; 213 | 0;90;1800;0;2室2厅1卫;七小区; 214 | 0;64;1800;0;2室1厅1卫;金色世界湾; 215 | 0;100;1800;0;2室1厅1卫;中海紫御华府; 216 | 0;74;1800;0;2室1厅1卫;远创樾府; 217 | 0;50;1800;0;1室1厅1卫;中国铁建国际花园(一期); 218 | 4;90;1800;0;2室1厅1卫;水利家园; 219 | 2;62;1800;0;1室1厅1卫;万晟现代城; 220 | 5;62;1800;0;2室1厅1卫;桂林小区; 221 | 0;70;1800;0;1室1厅1卫;安华美郡; 222 | 5;80;1800;0;2室1厅1卫;交通小区; 223 | 0;58;1800;0;2室1厅1卫;隆泰富苑; 224 | 5;49;1800;0;1室1厅1卫;富苑盛世城; 225 | 2;63;1800;0;2室1厅1卫;永春居住区; 226 | 5;98;1800;0;2室1厅1卫;和光小区; 227 | 2;50;1800;0;1室1厅1卫;天伦中央广场; 228 | 3;88;1800;0;2室1厅1卫;沈铁盛华庭; 229 | 2;49;1800;0;1室1厅1卫;天伦中央; 230 | 1;88;1800;0;3室1厅1卫;澳海梦想城; 231 | 1;55;1800;0;1室1厅1卫;昊源高格蓝湾; 232 | 0;45;1800;0;1室1厅1卫;首地首城; 233 | 0;98;1800;0;2室2厅1卫;环球凯旋公馆; 234 | 1;75;1750;0;2室2厅1卫;德展长春印; 235 | 0;45;1700;0;1室1厅1卫;中海国际社区(AJ区); 236 | 4;60;1700;0;2室0厅1卫;先行名苑; 237 | 5;40;1700;0;1室1厅1卫;桂林小区; 238 | 0;45;1700;4;1室1厅1卫;剑桥园西区; 239 | 2;60;1700;0;2室1厅1卫;航空家园; 240 | 0;65;1700;0;1室1厅1卫;安华美郡; 241 | 2;50;1700;4;1室1厅1卫;东康小区; 242 | 0;83;1700;0;2室2厅1卫;东皇君园一期; 243 | 0;86;1700;0;2室2厅1卫;中意之尊; 244 | 0;55;1700;0;1室1厅1卫;天茂城中央; 245 | 5;50;1700;0;1室1厅1卫;安达天下; 246 | 0;89;1700;0;2室2厅1卫;领秀蓝珀湖; 247 | 0;56;1700;0;1室1厅1卫;高新怡众名城; 248 | 0;58;1700;0;1室1厅1卫;昆玉府; 249 | 4;76;1700;0;2室1厅1卫;丰和西郡; 250 | 5;66;1700;0;2室1厅1卫;西昌小区西区; 251 | 5;63;1700;1;2室1厅1卫;四分局吉大家属楼; 252 | 4;80;1700;0;2室1厅1卫;中新花园; 253 | 0;60;1700;0;1室1厅1卫;天茂城中央; 254 | 0;76;1700;0;2室1厅1卫;领秀蓝珀湖; 255 | 4;77;1700;0;2室1厅1卫;丰和西郡; 256 | 5;56;1700;0;2室0厅1卫;领秀朝阳; 257 | 4;55;1700;0;1室1厅1卫;北方尚品; 258 | 0;53;1666;0;1室1厅1卫;伟业星城; 259 | 2;60;1666;0;2室1厅1卫;龙泰富苑; 260 | 2;49;1666;0;1室1厅1卫;天伦中央; 261 | 5;65;1650;0;2室1厅1卫;安达小区; 262 | 2;40;1650;0;1室0厅1卫;中金名筑; 263 | 0;47;1650;0;1室1厅1卫;天茂城中央; 264 | 0;105;1650;0;2室1厅1卫;雍景嘉苑; 265 | 5;85;1650;0;2室1厅1卫;信义小区; 266 | 2;87;1650;0;2室2厅1卫;工大新村武威路; 267 | 0;49;1650;0;1室1厅1卫;天盛名都; 268 | 2;49;1600;1;1室1厅1卫;万晟现代城; 269 | 2;46;1600;1;1室1厅1卫;天伦中央; 270 | 2;80;1600;1;2室1厅1卫;净水山庄; 271 | 0;60;1600;0;1室1厅1卫;华盛碧水云天一期; 272 | 1;60;1600;0;1室1厅1卫;松苑小区; 273 | 0;46;1600;0;1室1厅1卫;新星宇左岸小区; 274 | 2;80;1600;0;2室1厅1卫;师大东电社区; 275 | 2;55;1600;0;1室1厅1卫;龙泰富苑; 276 | 0;63;1600;0;2室1厅1卫;天茂城中央; 277 | 1;60;1600;0;1室2厅1卫;金色橄榄城二期; 278 | 0;50;1600;0;2室1厅1卫;澳海澜庭; 279 | 4;45;1600;1;1室1厅1卫;中冶新奥蓝城三期; 280 | 3;70;1600;0;2室1厅1卫;万龙第十城; 281 | 5;55;1600;0;1室0厅1卫;富苑盛世城; 282 | 0;88;1600;0;2室1厅1卫;澳海澜苑; 283 | 0;55;1600;0;1室1厅1卫;华盛碧水云天一期; 284 | 0;58;1600;0;2室1厅1卫;像素公馆; 285 | 1;68;1600;0;2室1厅1卫;亚泰樱花苑; 286 | 1;88;1600;0;2室1厅1卫;二道晨宇小区(一期); 287 | 0;50;1600;0;2室2厅2卫;保利拉菲公馆C区; 288 | 5;50;1600;1;1室1厅1卫;西中华小区; 289 | 3;91;1600;0;2室2厅1卫;上台花园A区; 290 | 1;60;1600;0;1室1厅1卫;长江花园; 291 | 5;85;1600;0;2室1厅1卫;易安花园; 292 | 2;96;1600;0;2室1厅1卫;八一水韵城D区; 293 | 0;52;1600;0;1室1厅1卫;新星宇左岸小区; 294 | 1;60;1600;0;2室1厅1卫;丽景秀苑; 295 | 5;43;1600;0;1室0厅1卫;富苑盛世城; 296 | 3;60;1600;0;2室1厅1卫;北安路2021号小区; 297 | 5;60;1600;0;2室1厅1卫;科技花园; 298 | 4;95;1600;0;2室2厅1卫;隆都翡翠湾; 299 | 4;75;1600;0;2室1厅1卫;大禹华邦; 300 | 3;85;1600;0;2室1厅1卫;豪邦四季经典; 301 | 5;50;1600;1;1室0厅1卫;万达广场; 302 | 0;50;1600;0;1室1厅1卫;高新怡众名城; 303 | 1;75;1600;0;2室1厅1卫;德展长春印; 304 | 5;57;1600;1;2室1厅1卫;大禹南湖首府; 305 | 4;60;1600;0;1室1厅1卫;交通指挥中心标点小区; 306 | 0;75;1600;0;1室1厅1卫;众诚一品东南; 307 | 0;85;1600;0;2室2厅1卫;东方1号; 308 | 3;58;1600;4;1室0厅1卫;万达公寓; 309 | 4;71;1600;0;2室1厅1卫;吉林省第二建筑公司第二...; 310 | 3;93;1600;0;2室1厅1卫;金苹果家园; 311 | 5;54;1600;0;2室1厅1卫;领秀朝阳; 312 | 5;42;1600;3;1室1厅1卫;金坐标; 313 | 5;80;1600;1;2室2厅1卫;永昌路小区; 314 | 5;50;1600;0;2室0厅1卫;大禹城邦; 315 | 3;92;1550;0;2室2厅1卫;兆丰凯旋明珠; 316 | 1;52;1550;4;1室1厅1卫;上东街区; 317 | 2;65;1500;0;2室1厅1卫;园东小区; 318 | 2;70;1500;0;2室1厅1卫;四五小区; 319 | 0;35;1500;3;1室1厅1卫;倚澜观邸(二期); 320 | 2;70;1500;0;3室1厅1卫;桃源春晖小区; 321 | 3;98;1500;0;2室1厅;沈铁北部湾(B区); 322 | 1;51;1500;0;2室1厅1卫;吉盛花园; 323 | 5;60;1500;0;2室1厅1卫;中医学院宿舍; 324 | 1;60;1500;0;2室1厅1卫;亚泰杏花苑; 325 | 1;50;1500;0;1室1厅1卫;阳光帝景; 326 | 1;41;1500;0;1室1厅1卫;御景名都B区; 327 | 0;52;1500;0;1室1厅1卫;绿地长春上海城C区; 328 | 5;40;1500;1;1室0厅1卫;大禹城邦; 329 | 5;65;1500;1;2室1厅0卫;西昌小区东区; 330 | 4;121;1500;0;2室1厅1卫;弘海小区; 331 | 3;60;1500;0;2室1厅1卫;康泰小区(D区); 332 | 5;42;1500;0;1室0厅1卫;金坐标; 333 | 2;60;1500;0;2室1厅1卫;【首租】长春大街二院自...; 334 | 2;70;1500;0;2室1厅1卫;邮政宿舍; 335 | 1;76;1500;0;2室1厅1卫;香水湾; 336 | 4;70;1500;0;3室1厅1卫;西延小区; 337 | 4;54;1500;0;1室1厅1卫;西城国际公馆; 338 | 1;48;1500;0;1室1厅1卫;昊源高格蓝湾; 339 | 3;54;1500;0;1室1厅1卫;龙泰檀香苑; 340 | 2;74;1500;0;2室1厅1卫;净水山庄; 341 | 5;40;1500;0;1室1厅1卫;桂林小区; 342 | 1;55;1500;0;1室1厅1卫;武夷嘉园; 343 | 1;65;1500;0;2室1厅1卫;热电新村二区; 344 | 3;75;1500;0;2室1厅1卫;钻石礼都西区; 345 | 0;48;1500;0;1室1厅1卫;西湖一号; 346 | 2;40;1500;0;1室1厅1卫;中海金域中央; 347 | 0;58;1500;0;1室1厅1卫;新星宇和邑(C区); 348 | 4;70;1500;4;2室1厅1卫;诚悦家园; 349 | 0;80;1500;0;2室1厅1卫;福临花园; 350 | 0;60;1500;0;1室1厅1卫;安华美郡; 351 | 0;35;1500;0;1室0厅1卫;倚澜观邸(二期); 352 | 0;60;1500;0;1室1厅1卫;金色橄榄城; 353 | 2;80;1500;1;2室1厅1卫;林业厅宿舍; 354 | 5;60;1500;0;2室1厅1卫;气象宿舍小区; 355 | 2;72;1500;0;2室1厅1卫;电力七小区; 356 | 1;59;1500;0;1室1厅1卫;松苑小区; 357 | 0;62;1500;0;1室1厅1卫;天茂城中央; 358 | 1;54;1500;0;1室1厅1卫;蔚蓝国际; 359 | 2;80;1500;0;3室1厅1卫;东岭解困小区; 360 | 0;50;1500;0;1室1厅1卫;大唐东方盛世; 361 | 0;49;1500;0;2室1厅1卫;金色世界湾; 362 | 0;48;1500;4;1室1厅1卫;月伴林湾; 363 | 0;65;1500;0;2室1厅1卫;开发区六小区; 364 | 2;70;1500;0;2室1厅1卫;永长小区; 365 | 1;98;1500;0;2室2厅1卫;松苑小区; 366 | 0;50;1500;0;1室1厅1卫;力旺格林春天; 367 | 0;45;1500;0;1室0厅1卫;倚澜观邸(一期); 368 | 0;72;1500;0;2室1厅1卫;东皇波尔的家; 369 | 5;74;1500;0;2室1厅1卫;华侨新村; 370 | 2;40;1500;0;1室1厅1卫;水利宿舍(人民大街); 371 | 3;53;1500;0;1室1厅1卫;恒大城; 372 | 3;50;1400;0;2室1厅1卫;华大天朗国际A区; 373 | 5;60;1400;0;1室1厅1卫;青云小区; 374 | 3;55;1400;0;1室0厅1卫;万达公寓; 375 | 1;64;1400;0;2室1厅1卫;亚泰桂花苑; 376 | 4;59;1400;0;1室1厅1卫;成城蓉桥壹号; 377 | 2;55;1400;0;1室1厅1卫;重庆小区; 378 | 2;70;1400;0;2室1厅1卫;平阳小区; 379 | 0;1400;1400;0;1室1厅1卫;力旺康景; 380 | 0;30;1400;1;1室1厅1卫;天茂城中央; 381 | 3;60;1400;0;2室1厅1卫;旭阳家园二期; 382 | 5;63;1400;0;2室1厅1卫;地矿花园; 383 | 4;50;1400;0;1室1厅1卫;大众花园三期; 384 | 2;54;1400;0;1室1厅1卫;卫星花园; 385 | 1;80;1400;0;2室1厅1卫;吉盛小区; 386 | 2;60;1400;1;2室1厅1卫;三四小区; 387 | 5;62;1400;0;2室1厅1卫;同光路丰顺街; 388 | 5;65;1400;0;2室1厅1卫;南湖新村; 389 | 0;57;1400;0;1室1厅1卫;远创樾府; 390 | 5;50;1400;0;1室1厅1卫;金坐标; 391 | 0;40;1400;0;1室1厅1卫;倚澜观邸(二期); 392 | 3;90;1377;0;2室2厅1卫;基隆家园; 393 | 0;46;1350;1;1室0厅1卫;恒盛豪庭; 394 | 0;70;1300;0;1室1厅1卫;恒盛豪庭; 395 | 3;60;1300;0;1室1厅1卫;华大天朗国际; 396 | 0;43;1300;4;1室1厅1卫;恒盛豪庭; 397 | 0;70;1300;0;2室1厅1卫;乐东拖拉机宿舍; 398 | 4;65;1300;0;2室1厅1卫;娜奇美商住综合楼; 399 | 1;53;1300;0;2室1厅1卫;汉森金烁广场; 400 | 1;63;1300;0;2室0厅1卫;盈嘉大厦; 401 | 2;50;1300;0;2室1厅1卫;卫星花园; 402 | 5;25;1300;0;6室0厅6卫;富豪花园; 403 | 4;60;1300;1;2室1厅1卫;旺达小区; 404 | 2;40;1300;0;1室1厅1卫;卉香花园; 405 | 5;68;1300;0;2室1厅1卫;昌平铁路小区; 406 | 4;70;1300;0;2室1厅1卫;长客厂南B区; 407 | 0;50;1300;0;1室1厅1卫;恒盛豪庭; 408 | 1;51;1300;0;2室1厅1卫;蓝色港湾(一期); 409 | 5;70;1300;0;2室1厅1卫;长春光机与物理研究所第...; 410 | 0;20;1300;0;4室1厅4卫;澳洲城; 411 | 5;45;1300;0;1室1厅1卫;典石广场; 412 | 2;60;1300;0;2室1厅1卫;星河湾; 413 | 4;50;1300;1;1室1厅1卫;万盛理想国红堡; 414 | 1;56;1300;0;2室1厅1卫;荣乐家园; 415 | 0;56;1300;0;1室1厅1卫;假日名都; 416 | 0;69;1300;1;2室1厅1卫;34街区; 417 | 2;61;1300;1;2室1厅1卫;南关区政府家属楼; 418 | 3;56;1300;0;2室1厅1卫;钻石礼都西区; 419 | 4;50;1300;0;1室1厅1卫;万盛理想国红堡; 420 | 0;61;1260;0;1室2厅1卫;大众欢乐颂; 421 | 0;35;1250;1;1室0厅1卫;倚澜观邸(一期); 422 | 4;46;1250;0;1室0厅1卫;万盛理想国; 423 | 4;60;1200;0;2室1厅1卫;生物宿舍; 424 | 0;42;1200;3;1室1厅1卫;佳泰缔景城; 425 | 1;60;1200;0;2室1厅1卫;滨河东区; 426 | 1;60;1200;0;1室1厅1卫;东逸美郡; 427 | 0;20;1200;0;6室0厅7卫;22街区; 428 | 2;50;1200;0;1室1厅1卫;卫星花园; 429 | 1;53;1200;0;2室1厅1卫;昊源高格蓝郡; 430 | 4;47;1200;0;1室1厅1卫;万鑫花园; 431 | 0;30;1200;0;8室1厅6卫;GTC环球贸易中心; 432 | 2;55;1200;0;2室1厅1卫;星河湾; 433 | 0;56;1200;0;1室1厅1卫;佳泰缔景城; 434 | 0;72;1200;1;1室2厅1卫;大众欢乐颂; 435 | 5;5233;1200;4;1室1厅1卫;轻铁湖光花园(二期); 436 | 3;60;1200;0;2室1厅1卫;美景天城B区; 437 | 1;50;1200;0;1室1厅1卫;亚泰花园桃花苑; 438 | 4;41;1200;1;1室1厅1卫;大禹华邦; 439 | 3;63;1200;0;2室1厅1卫;地铁名典海棠苑; 440 | 0;122;1200;0;3室2厅2卫;中海蘭庭; 441 | 2;70;1200;1;3室1厅1卫;二三小区; 442 | 3;75;1146;0;2室1厅1卫;基隆家园; 443 | 1;55;1100;0;2室1厅1卫;新开河小区; 444 | 3;65;1100;0;2室1厅1卫;团山小区; 445 | 5;62;1100;0;2室1厅1卫;省委党校宿舍; 446 | 1;56;1100;0;1室1厅1卫;吉盛小区; 447 | 5;25;1100;0;8室2厅3卫;南关区自强街道重庆路北...; 448 | 1;76;1100;0;2室1厅1卫;金色8里城二期; 449 | 1;60;1100;0;1室1厅1卫;长电紫盈花城; 450 | 4;62;1100;0;2室1厅1卫;普阳小区; 451 | 1;70;1100;0;2室1厅1卫;金色8里城二期; 452 | 3;68;1100;0;2室1厅1卫;华大城; 453 | 0;40;1100;0;1室1厅1卫;47街区; 454 | 0;22;1100;0;1室0厅1卫;红苹果家园; 455 | 2;40;1100;0;1室1厅1卫;电力三小区; 456 | 5;15;1100;2;4室2厅4卫;星宇名家; 457 | 2;38;1100;4;1室1厅1卫;奥莱公寓; 458 | 2;50;1000;0;2室1厅1卫;解民物业小区; 459 | 2;50;1000;1;1室1厅1卫;永春居住区; 460 | 0;40;1000;0;1室1厅1卫;一汽43街区; 461 | 0;40;1000;0;1室1厅1卫;39街区; 462 | 0;60;1000;0;2室1厅1卫;1街区; 463 | 0;25;1000;0;6室1厅6卫;诺睿德翰府; 464 | 2;49;1000;1;2室1厅1卫;信达东湾半岛第四区; 465 | 3;55;1000;0;2室1厅1卫;旭阳家园; 466 | 0;58;1000;0;2室1厅1卫;荣鼎康城; 467 | 1;50;1000;0;1室1厅1卫;昊源高格蓝郡; 468 | 5;120;1000;0;3室2厅2卫;豪邦缇香公馆; 469 | 1;50;1000;1;1室1厅1卫;亚泰杏花苑; 470 | 4;65;1000;0;2室1厅1卫;一汽20街区; 471 | 1;41;1000;0;1室1厅1卫;通安小区; 472 | 0;45;1000;0;1室1厅1卫;33街区; 473 | 5;62;1000;0;1室2厅1卫;青云小区; 474 | 0;15;1000;0;1室1厅1卫;阳光城; 475 | 3;54;965;0;2室1厅1卫;柳影家园; 476 | 5;37;950;0;1室1厅1卫;铁路小区; 477 | 5;20;930;0;1室0厅1卫;富苑华城; 478 | 2;25;900;0;2室2厅1卫;华侨休闲广场; 479 | 3;46;900;1;1室1厅1卫;康泰乐园; 480 | 0;25;900;0;4室2厅2卫;中海水岸春城; 481 | 0;18;900;0;4室2厅2卫;枫林园; 482 | 5;30;900;0;1室1厅1卫;德昌小区; 483 | 0;45;900;0;1室1厅1卫;金祥奥邻郡; 484 | 5;25;900;2;1室1厅1卫;康达小区; 485 | 5;25;900;0;1室1厅1卫;富豪花园; 486 | 1;55;850;0;1室1厅1卫;吉星花园; 487 | 2;42;850;0;1室1厅1卫;龙泰富苑; 488 | 5;18;850;0;4室1厅1卫;国联小区; 489 | 2;18;850;2;2室1厅1卫;新城雅苑; 490 | 0;20;850;2;2室1厅1卫;经开五区; 491 | 0;35;850;0;5室1厅2卫;海口花园; 492 | 1;42;800;0;1室1厅1卫;万科蓝山; 493 | 5;25;800;1;7室1厅1卫;红旗街西四胡同; 494 | 3;50;800;0;1室1厅1卫;温馨花园; 495 | 5;40;800;2;4室1厅1卫;红旗街西三胡同; 496 | 0;20;800;0;6室1厅1卫;临河风景苑; 497 | 4;40;800;0;1室1厅1卫;一汽20街区; 498 | 5;40;800;2;3室1厅1卫;信义新区; 499 | 0;47;800;2;2室1厅1卫;经开四区; 500 | 5;60;800;0;2室2厅2卫;清华小区; 501 | 5;28;800;2;7室2厅2卫;得昌小区; 502 | 3;15;800;0;8室1厅2卫;长春银座; 503 | 5;28;800;2;7室2厅2卫;得昌小区; 504 | 0;15;800;0;7室1厅1卫;伟业星城; 505 | 0;20;800;0;3室2厅2卫;福临家园; 506 | 5;25;800;0;3室1厅1卫;长春光机与物理研究所第...; 507 | 0;17;800;0;5室0厅2卫;新星宇和邑(C区); 508 | 0;30;750;0;5室1厅1卫;巴赫丽舍; 509 | 2;20;750;2;3室1厅1卫;东师家园净月住宅(一区); 510 | 5;30;750;2;3室1厅1卫;华侨新村; 511 | 5;16;750;0;8室1厅2卫;桂林小区; 512 | 5;15;750;2;4室1厅1卫;信义新区; 513 | 5;30;750;2;3室1厅1卫;华侨新村; 514 | 0;80;750;2;2室1厅1卫;经开八区; 515 | 5;17;750;0;8室1厅2卫;桂林小区; 516 | 0;16;750;2;2室1厅1卫;新开河小区南区; 517 | 0;20;750;0;6室1厅2卫;国信嘉邑; 518 | 0;20;750;0;1室0厅1卫;新星宇和邑(A区); 519 | 5;25;750;2;2室1厅1卫;东安开运福里; 520 | 5;20;750;0;6室1厅2卫;得易居; 521 | 1;60;750;0;2室1厅1卫;滨河东区; 522 | 5;30;700;2;3室1厅1卫;长久家苑2区; 523 | 5;30;700;2;3室1厅1卫;红旗街长影宿舍; 524 | 2;25;700;2;5室1厅2卫;我的家园; 525 | 1;20;700;0;3室1厅1卫;吉森春城; 526 | 4;80;700;2;3室1厅1卫;朝阳小区; 527 | 5;30;700;2;4室1厅1卫;富锦路天宝街; 528 | 0;12;700;0;6室2厅2卫;枫林园; 529 | 2;50;700;0;2室1厅1卫;中环十二区; 530 | 3;64;700;0;1室1厅1卫;郡望安石; 531 | 1;26;700;0;1室1厅1卫;吉盛小区; 532 | 0;75;700;2;2室1厅1卫;50街区; 533 | 1;53;700;2;2室1厅1卫;丽景秀苑; 534 | 5;15;700;2;4室1厅2卫;中山小区; 535 | 5;30;700;2;3室1厅1卫;开运街三院附近; 536 | 5;15;700;0;5室1厅1卫;北安路与清明街交汇; 537 | 2;30;700;2;4室1厅1卫;民康路; 538 | 5;22;700;0;4室1厅1卫;西安大路与康平街交汇; 539 | 0;20;700;2;5室1厅2卫;益田枫露二期; 540 | 2;25;700;2;5室1厅2卫;我的家园; 541 | 2;70;700;2;3室1厅1卫;二三小区; 542 | 1;20;700;0;9室1厅1卫;乐群小区; 543 | 5;15;700;0;9室1厅9卫;车城名仕花园; 544 | 2;20;650;0;9室2厅3卫;岳阳富苑; 545 | 2;85;650;2;3室1厅1卫;师大东电社区; 546 | 0;15;650;2;4室2厅2卫;超达家园; 547 | 1;51;650;0;1室1厅1卫;亚泰花园桃花苑; 548 | 5;30;600;0;1室1厅1卫;星宇名家; 549 | 5;20;600;0;4室1厅1卫;信义小区; 550 | 5;20;600;0;4室1厅1卫;万达广场; 551 | 3;18;600;2;5室2厅2卫;友谊花园; 552 | 5;20;600;0;4室1厅1卫;德昌小区; 553 | 2;80;600;0;3室2厅1卫;滨河小区; 554 | 5;18;600;0;4室1厅1卫;西朝阳南胡同万宝街交汇; 555 | 5;20;600;2;6室2厅1卫;时代家园; 556 | 5;20;600;3;1室1厅1卫;英海小区; 557 | 1;15;600;2;2室1厅1卫;葡萄牙小镇; 558 | 0;10;600;2;9室2厅2卫;世纪花园小区; 559 | 0;10;600;2;9室2厅2卫;世纪花园小区; 560 | 1;50;600;0;1室1厅1卫;御景名都A区; 561 | 2;18;600;0;4室2厅2卫;中环十一区; 562 | 0;30;550;0;3室1厅1卫;君地天城; 563 | 1;158;550;0;7室1厅2卫;天富家园; 564 | 5;17;550;0;9室1厅1卫;清华苑; 565 | 5;17;550;0;9室1厅1卫;新宇富贵苑; 566 | 0;15;500;2;5室1厅1卫;长春开发区三区; 567 | 2;14;500;0;6室1厅1卫;吉顺小区; 568 | 2;15;500;0;8室2厅3卫;鑫隆家园; 569 | 5;10;500;0;5室2厅2卫;富民小区; 570 | 5;100;500;2;6室1厅1卫;大兴路万宝街; 571 | 5;12;500;0;9室1厅1卫;重庆路万达对面; 572 | 5;20;500;2;4室1厅1卫;红旗街; 573 | 5;12;500;0;6室1厅2卫;万达广场; 574 | 5;12;500;1;8室1厅2卫;同仁书店; 575 | 3;10;500;1;9室0厅3卫;站前科技城新装修;实墙... 576 | 0;15;500;2;1室1卫;金泰小区; 577 | 0;12;500;0;6室1厅1卫;汉森华尔兹; 578 | 5;15;500;0;5室1厅1卫;粮食局宿舍; 579 | 0;30;500;2;3室1厅1卫;德惠市西三道街丰泽苑宾...; 580 | 0;30;500;1;2室0厅1卫;5街区; 581 | 0;20;500;0;7室0厅2卫;中海国际社区(C区); 582 | 5;20;500;2;1室1厅1卫;西安小区; 583 | 0;23;500;0;2室1厅1卫;安华美郡; 584 | 3;15;500;4;1室0厅1卫;远东批发一期; 585 | 1;12;500;3;2室1厅1卫;吉盛花园小区A区; 586 | 0;23;500;0;2室1厅1卫;宜家7080; 587 | 3;15;500;1;3室2厅3卫;远东批发一期; 588 | 0;100;500;0;3室2厅1卫;东方1号; 589 | 3;10;500;0;6室1厅1卫;铁路小区; 590 | 2;10;450;0;7室1厅1卫;金色世界湾; 591 | 1;10;450;1;9室2厅2卫;德展长春印; 592 | 5;25;450;1;5室2厅2卫;桂林小区; 593 | 5;8;450;2;8室0厅1卫;安华卓展; 594 | 1;10;450;1;9室2厅2卫;德展长春印; 595 | 1;31;420;0;3室2厅2卫;清华苑; 596 | 3;10;400;0;3室1厅1卫;站前银座; 597 | 2;10;400;0;9室1厅1卫;亚泰大街与四道街交汇; 598 | 5;12;400;0;7室1厅2卫;欧亚商都小区; 599 | 0;10;400;0;9室1厅2卫;冶金宿舍小区; 600 | 2;9;400;2;6室1厅1卫;重庆小区; 601 | 5;10;400;0;9室1厅3卫;隆礼嘉园; 602 | 0;128;400;0;8室1厅1卫;经开四区; 603 | 0;15;400;0;9室1厅1卫;工大家园; 604 | 3;10;400;0;3室1厅1卫;站前银座; 605 | 5;10;400;0;4室1厅1卫;太阳现代居; 606 | 5;100;400;2;6室1厅1卫;红旗街欧亚商都; 607 | 3;15;400;0;7室7厅1卫;四平路西三条; 608 | 5;10;400;0;9室2厅2卫;星宇名家; 609 | 2;8;400;1;9室1厅1卫;鑫隆家园; 610 | 0;10;350;0;1室1厅1卫;海口花园; 611 | 5;13;350;2;6室1厅2卫;国际大厦b座; 612 | 5;15;350;0;9室2厅3卫;中山小区; 613 | 3;9;350;0;1室0厅1卫;蓝天佳苑; 614 | 0;8;350;4;4室2厅1卫;临河风景; 615 | 5;150;350;0;6室2厅1卫;富贵名苑; 616 | 5;76;300;0;3室1厅1卫;国联小区; 617 | 2;8;300;0;8室1厅1卫;平阳小区; 618 | 2;76;300;0;3室1厅1卫;国联小区; 619 | 1;15;300;0;6室1厅2卫;清华苑; 620 | 3;100;300;0;3室1厅1卫;站前金街; 621 | 5;10;300;0;9室1厅1卫;世纪兴嘉园; 622 | 5;9;300;0;4室2厅1卫;活力城亚泰富源卓展百货...; 623 | 5;10;300;0;9室1厅1卫;世纪兴嘉园; 624 | 5;120;280;0;4室1厅1卫;朝阳区医院对面; 625 | 3;100;240;0;2室1厅1卫;太平洋大厦; 626 | 3;20;150;3;4室1厅1卫;沈铁盛华庭; 627 | -------------------------------------------------------------------------------- /iris.data: -------------------------------------------------------------------------------- 1 | 5.1,3.5,1.4,0.2,Iris-setosa 2 | 4.9,3.0,1.4,0.2,Iris-setosa 3 | 4.7,3.2,1.3,0.2,Iris-setosa 4 | 4.6,3.1,1.5,0.2,Iris-setosa 5 | 5.0,3.6,1.4,0.2,Iris-setosa 6 | 5.4,3.9,1.7,0.4,Iris-setosa 7 | 4.6,3.4,1.4,0.3,Iris-setosa 8 | 5.0,3.4,1.5,0.2,Iris-setosa 9 | 4.4,2.9,1.4,0.2,Iris-setosa 10 | 4.9,3.1,1.5,0.1,Iris-setosa 11 | 5.4,3.7,1.5,0.2,Iris-setosa 12 | 4.8,3.4,1.6,0.2,Iris-setosa 13 | 4.8,3.0,1.4,0.1,Iris-setosa 14 | 4.3,3.0,1.1,0.1,Iris-setosa 15 | 5.8,4.0,1.2,0.2,Iris-setosa 16 | 5.7,4.4,1.5,0.4,Iris-setosa 17 | 5.4,3.9,1.3,0.4,Iris-setosa 18 | 5.1,3.5,1.4,0.3,Iris-setosa 19 | 5.7,3.8,1.7,0.3,Iris-setosa 20 | 5.1,3.8,1.5,0.3,Iris-setosa 21 | 5.4,3.4,1.7,0.2,Iris-setosa 22 | 5.1,3.7,1.5,0.4,Iris-setosa 23 | 4.6,3.6,1.0,0.2,Iris-setosa 24 | 5.1,3.3,1.7,0.5,Iris-setosa 25 | 4.8,3.4,1.9,0.2,Iris-setosa 26 | 5.0,3.0,1.6,0.2,Iris-setosa 27 | 5.0,3.4,1.6,0.4,Iris-setosa 28 | 5.2,3.5,1.5,0.2,Iris-setosa 29 | 5.2,3.4,1.4,0.2,Iris-setosa 30 | 4.7,3.2,1.6,0.2,Iris-setosa 31 | 4.8,3.1,1.6,0.2,Iris-setosa 32 | 5.4,3.4,1.5,0.4,Iris-setosa 33 | 5.2,4.1,1.5,0.1,Iris-setosa 34 | 5.5,4.2,1.4,0.2,Iris-setosa 35 | 4.9,3.1,1.5,0.1,Iris-setosa 36 | 5.0,3.2,1.2,0.2,Iris-setosa 37 | 5.5,3.5,1.3,0.2,Iris-setosa 38 | 4.9,3.1,1.5,0.1,Iris-setosa 39 | 4.4,3.0,1.3,0.2,Iris-setosa 40 | 5.1,3.4,1.5,0.2,Iris-setosa 41 | 5.0,3.5,1.3,0.3,Iris-setosa 42 | 4.5,2.3,1.3,0.3,Iris-setosa 43 | 4.4,3.2,1.3,0.2,Iris-setosa 44 | 5.0,3.5,1.6,0.6,Iris-setosa 45 | 5.1,3.8,1.9,0.4,Iris-setosa 46 | 4.8,3.0,1.4,0.3,Iris-setosa 47 | 5.1,3.8,1.6,0.2,Iris-setosa 48 | 4.6,3.2,1.4,0.2,Iris-setosa 49 | 5.3,3.7,1.5,0.2,Iris-setosa 50 | 5.0,3.3,1.4,0.2,Iris-setosa 51 | 7.0,3.2,4.7,1.4,Iris-versicolor 52 | 6.4,3.2,4.5,1.5,Iris-versicolor 53 | 6.9,3.1,4.9,1.5,Iris-versicolor 54 | 5.5,2.3,4.0,1.3,Iris-versicolor 55 | 6.5,2.8,4.6,1.5,Iris-versicolor 56 | 5.7,2.8,4.5,1.3,Iris-versicolor 57 | 6.3,3.3,4.7,1.6,Iris-versicolor 58 | 4.9,2.4,3.3,1.0,Iris-versicolor 59 | 6.6,2.9,4.6,1.3,Iris-versicolor 60 | 5.2,2.7,3.9,1.4,Iris-versicolor 61 | 5.0,2.0,3.5,1.0,Iris-versicolor 62 | 5.9,3.0,4.2,1.5,Iris-versicolor 63 | 6.0,2.2,4.0,1.0,Iris-versicolor 64 | 6.1,2.9,4.7,1.4,Iris-versicolor 65 | 5.6,2.9,3.6,1.3,Iris-versicolor 66 | 6.7,3.1,4.4,1.4,Iris-versicolor 67 | 5.6,3.0,4.5,1.5,Iris-versicolor 68 | 5.8,2.7,4.1,1.0,Iris-versicolor 69 | 6.2,2.2,4.5,1.5,Iris-versicolor 70 | 5.6,2.5,3.9,1.1,Iris-versicolor 71 | 5.9,3.2,4.8,1.8,Iris-versicolor 72 | 6.1,2.8,4.0,1.3,Iris-versicolor 73 | 6.3,2.5,4.9,1.5,Iris-versicolor 74 | 6.1,2.8,4.7,1.2,Iris-versicolor 75 | 6.4,2.9,4.3,1.3,Iris-versicolor 76 | 6.6,3.0,4.4,1.4,Iris-versicolor 77 | 6.8,2.8,4.8,1.4,Iris-versicolor 78 | 6.7,3.0,5.0,1.7,Iris-versicolor 79 | 6.0,2.9,4.5,1.5,Iris-versicolor 80 | 5.7,2.6,3.5,1.0,Iris-versicolor 81 | 5.5,2.4,3.8,1.1,Iris-versicolor 82 | 5.5,2.4,3.7,1.0,Iris-versicolor 83 | 5.8,2.7,3.9,1.2,Iris-versicolor 84 | 6.0,2.7,5.1,1.6,Iris-versicolor 85 | 5.4,3.0,4.5,1.5,Iris-versicolor 86 | 6.0,3.4,4.5,1.6,Iris-versicolor 87 | 6.7,3.1,4.7,1.5,Iris-versicolor 88 | 6.3,2.3,4.4,1.3,Iris-versicolor 89 | 5.6,3.0,4.1,1.3,Iris-versicolor 90 | 5.5,2.5,4.0,1.3,Iris-versicolor 91 | 5.5,2.6,4.4,1.2,Iris-versicolor 92 | 6.1,3.0,4.6,1.4,Iris-versicolor 93 | 5.8,2.6,4.0,1.2,Iris-versicolor 94 | 5.0,2.3,3.3,1.0,Iris-versicolor 95 | 5.6,2.7,4.2,1.3,Iris-versicolor 96 | 5.7,3.0,4.2,1.2,Iris-versicolor 97 | 5.7,2.9,4.2,1.3,Iris-versicolor 98 | 6.2,2.9,4.3,1.3,Iris-versicolor 99 | 5.1,2.5,3.0,1.1,Iris-versicolor 100 | 5.7,2.8,4.1,1.3,Iris-versicolor 101 | 6.3,3.3,6.0,2.5,Iris-virginica 102 | 5.8,2.7,5.1,1.9,Iris-virginica 103 | 7.1,3.0,5.9,2.1,Iris-virginica 104 | 6.3,2.9,5.6,1.8,Iris-virginica 105 | 6.5,3.0,5.8,2.2,Iris-virginica 106 | 7.6,3.0,6.6,2.1,Iris-virginica 107 | 4.9,2.5,4.5,1.7,Iris-virginica 108 | 7.3,2.9,6.3,1.8,Iris-virginica 109 | 6.7,2.5,5.8,1.8,Iris-virginica 110 | 7.2,3.6,6.1,2.5,Iris-virginica 111 | 6.5,3.2,5.1,2.0,Iris-virginica 112 | 6.4,2.7,5.3,1.9,Iris-virginica 113 | 6.8,3.0,5.5,2.1,Iris-virginica 114 | 5.7,2.5,5.0,2.0,Iris-virginica 115 | 5.8,2.8,5.1,2.4,Iris-virginica 116 | 6.4,3.2,5.3,2.3,Iris-virginica 117 | 6.5,3.0,5.5,1.8,Iris-virginica 118 | 7.7,3.8,6.7,2.2,Iris-virginica 119 | 7.7,2.6,6.9,2.3,Iris-virginica 120 | 6.0,2.2,5.0,1.5,Iris-virginica 121 | 6.9,3.2,5.7,2.3,Iris-virginica 122 | 5.6,2.8,4.9,2.0,Iris-virginica 123 | 7.7,2.8,6.7,2.0,Iris-virginica 124 | 6.3,2.7,4.9,1.8,Iris-virginica 125 | 6.7,3.3,5.7,2.1,Iris-virginica 126 | 7.2,3.2,6.0,1.8,Iris-virginica 127 | 6.2,2.8,4.8,1.8,Iris-virginica 128 | 6.1,3.0,4.9,1.8,Iris-virginica 129 | 6.4,2.8,5.6,2.1,Iris-virginica 130 | 7.2,3.0,5.8,1.6,Iris-virginica 131 | 7.4,2.8,6.1,1.9,Iris-virginica 132 | 7.9,3.8,6.4,2.0,Iris-virginica 133 | 6.4,2.8,5.6,2.2,Iris-virginica 134 | 6.3,2.8,5.1,1.5,Iris-virginica 135 | 6.1,2.6,5.6,1.4,Iris-virginica 136 | 7.7,3.0,6.1,2.3,Iris-virginica 137 | 6.3,3.4,5.6,2.4,Iris-virginica 138 | 6.4,3.1,5.5,1.8,Iris-virginica 139 | 6.0,3.0,4.8,1.8,Iris-virginica 140 | 6.9,3.1,5.4,2.1,Iris-virginica 141 | 6.7,3.1,5.6,2.4,Iris-virginica 142 | 6.9,3.1,5.1,2.3,Iris-virginica 143 | 5.8,2.7,5.1,1.9,Iris-virginica 144 | 6.8,3.2,5.9,2.3,Iris-virginica 145 | 6.7,3.3,5.7,2.5,Iris-virginica 146 | 6.7,3.0,5.2,2.3,Iris-virginica 147 | 6.3,2.5,5.0,1.9,Iris-virginica 148 | 6.5,3.0,5.2,2.0,Iris-virginica 149 | 6.2,3.4,5.4,2.3,Iris-virginica 150 | 5.9,3.0,5.1,1.8,Iris-virginica 151 | 152 | -------------------------------------------------------------------------------- /iris.list: -------------------------------------------------------------------------------- 1 | [5.1,3.5,1.4,0.2,0], 2 | [4.9,3.0,1.4,0.2,0], 3 | [4.7,3.2,1.3,0.2,0], 4 | [4.6,3.1,1.5,0.2,0], 5 | [5.0,3.6,1.4,0.2,0], 6 | [5.4,3.9,1.7,0.4,0], 7 | [4.6,3.4,1.4,0.3,0], 8 | [5.0,3.4,1.5,0.2,0], 9 | [4.4,2.9,1.4,0.2,0], 10 | [4.9,3.1,1.5,0.1,0], 11 | [5.4,3.7,1.5,0.2,0], 12 | [4.8,3.4,1.6,0.2,0], 13 | [4.8,3.0,1.4,0.1,0], 14 | [4.3,3.0,1.1,0.1,0], 15 | [5.8,4.0,1.2,0.2,0], 16 | [5.7,4.4,1.5,0.4,0], 17 | [5.4,3.9,1.3,0.4,0], 18 | [5.1,3.5,1.4,0.3,0], 19 | [5.7,3.8,1.7,0.3,0], 20 | [5.1,3.8,1.5,0.3,0], 21 | [5.4,3.4,1.7,0.2,0], 22 | [5.1,3.7,1.5,0.4,0], 23 | [4.6,3.6,1.0,0.2,0], 24 | [5.1,3.3,1.7,0.5,0], 25 | [4.8,3.4,1.9,0.2,0], 26 | [5.0,3.0,1.6,0.2,0], 27 | [5.0,3.4,1.6,0.4,0], 28 | [5.2,3.5,1.5,0.2,0], 29 | [5.2,3.4,1.4,0.2,0], 30 | [4.7,3.2,1.6,0.2,0], 31 | [4.8,3.1,1.6,0.2,0], 32 | [5.4,3.4,1.5,0.4,0], 33 | [5.2,4.1,1.5,0.1,0], 34 | [5.5,4.2,1.4,0.2,0], 35 | [4.9,3.1,1.5,0.1,0], 36 | [5.0,3.2,1.2,0.2,0], 37 | [5.5,3.5,1.3,0.2,0], 38 | [4.9,3.1,1.5,0.1,0], 39 | [4.4,3.0,1.3,0.2,0], 40 | [5.1,3.4,1.5,0.2,0], 41 | [5.0,3.5,1.3,0.3,0], 42 | [4.5,2.3,1.3,0.3,0], 43 | [4.4,3.2,1.3,0.2,0], 44 | [5.0,3.5,1.6,0.6,0], 45 | [5.1,3.8,1.9,0.4,0], 46 | [4.8,3.0,1.4,0.3,0], 47 | [5.1,3.8,1.6,0.2,0], 48 | [4.6,3.2,1.4,0.2,0], 49 | [5.3,3.7,1.5,0.2,0], 50 | [5.0,3.3,1.4,0.2,0], 51 | [7.0,3.2,4.7,1.4,1], 52 | [6.4,3.2,4.5,1.5,1], 53 | [6.9,3.1,4.9,1.5,1], 54 | [5.5,2.3,4.0,1.3,1], 55 | [6.5,2.8,4.6,1.5,1], 56 | [5.7,2.8,4.5,1.3,1], 57 | [6.3,3.3,4.7,1.6,1], 58 | [4.9,2.4,3.3,1.0,1], 59 | [6.6,2.9,4.6,1.3,1], 60 | [5.2,2.7,3.9,1.4,1], 61 | [5.0,2.0,3.5,1.0,1], 62 | [5.9,3.0,4.2,1.5,1], 63 | [6.0,2.2,4.0,1.0,1], 64 | [6.1,2.9,4.7,1.4,1], 65 | [5.6,2.9,3.6,1.3,1], 66 | [6.7,3.1,4.4,1.4,1], 67 | [5.6,3.0,4.5,1.5,1], 68 | [5.8,2.7,4.1,1.0,1], 69 | [6.2,2.2,4.5,1.5,1], 70 | [5.6,2.5,3.9,1.1,1], 71 | [5.9,3.2,4.8,1.8,1], 72 | [6.1,2.8,4.0,1.3,1], 73 | [6.3,2.5,4.9,1.5,1], 74 | [6.1,2.8,4.7,1.2,1], 75 | [6.4,2.9,4.3,1.3,1], 76 | [6.6,3.0,4.4,1.4,1], 77 | [6.8,2.8,4.8,1.4,1], 78 | [6.7,3.0,5.0,1.7,1], 79 | [6.0,2.9,4.5,1.5,1], 80 | [5.7,2.6,3.5,1.0,1], 81 | [5.5,2.4,3.8,1.1,1], 82 | [5.5,2.4,3.7,1.0,1], 83 | [5.8,2.7,3.9,1.2,1], 84 | [6.0,2.7,5.1,1.6,1], 85 | [5.4,3.0,4.5,1.5,1], 86 | [6.0,3.4,4.5,1.6,1], 87 | [6.7,3.1,4.7,1.5,1], 88 | [6.3,2.3,4.4,1.3,1], 89 | [5.6,3.0,4.1,1.3,1], 90 | [5.5,2.5,4.0,1.3,1], 91 | [5.5,2.6,4.4,1.2,1], 92 | [6.1,3.0,4.6,1.4,1], 93 | [5.8,2.6,4.0,1.2,1], 94 | [5.0,2.3,3.3,1.0,1], 95 | [5.6,2.7,4.2,1.3,1], 96 | [5.7,3.0,4.2,1.2,1], 97 | [5.7,2.9,4.2,1.3,1], 98 | [6.2,2.9,4.3,1.3,1], 99 | [5.1,2.5,3.0,1.1,1], 100 | [5.7,2.8,4.1,1.3,1], 101 | [6.3,3.3,6.0,2.5,2], 102 | [5.8,2.7,5.1,1.9,2], 103 | [7.1,3.0,5.9,2.1,2], 104 | [6.3,2.9,5.6,1.8,2], 105 | [6.5,3.0,5.8,2.2,2], 106 | [7.6,3.0,6.6,2.1,2], 107 | [4.9,2.5,4.5,1.7,2], 108 | [7.3,2.9,6.3,1.8,2], 109 | [6.7,2.5,5.8,1.8,2], 110 | [7.2,3.6,6.1,2.5,2], 111 | [6.5,3.2,5.1,2.0,2], 112 | [6.4,2.7,5.3,1.9,2], 113 | [6.8,3.0,5.5,2.1,2], 114 | [5.7,2.5,5.0,2.0,2], 115 | [5.8,2.8,5.1,2.4,2], 116 | [6.4,3.2,5.3,2.3,2], 117 | [6.5,3.0,5.5,1.8,2], 118 | [7.7,3.8,6.7,2.2,2], 119 | [7.7,2.6,6.9,2.3,2], 120 | [6.0,2.2,5.0,1.5,2], 121 | [6.9,3.2,5.7,2.3,2], 122 | [5.6,2.8,4.9,2.0,2], 123 | [7.7,2.8,6.7,2.0,2], 124 | [6.3,2.7,4.9,1.8,2], 125 | [6.7,3.3,5.7,2.1,2], 126 | [7.2,3.2,6.0,1.8,2], 127 | [6.2,2.8,4.8,1.8,2], 128 | [6.1,3.0,4.9,1.8,2], 129 | [6.4,2.8,5.6,2.1,2], 130 | [7.2,3.0,5.8,1.6,2], 131 | [7.4,2.8,6.1,1.9,2], 132 | [7.9,3.8,6.4,2.0,2], 133 | [6.4,2.8,5.6,2.2,2], 134 | [6.3,2.8,5.1,1.5,2], 135 | [6.1,2.6,5.6,1.4,2], 136 | [7.7,3.0,6.1,2.3,2], 137 | [6.3,3.4,5.6,2.4,2], 138 | [6.4,3.1,5.5,1.8,2], 139 | [6.0,3.0,4.8,1.8,2], 140 | [6.9,3.1,5.4,2.1,2], 141 | [6.7,3.1,5.6,2.4,2], 142 | [6.9,3.1,5.1,2.3,2], 143 | [5.8,2.7,5.1,1.9,2], 144 | [6.8,3.2,5.9,2.3,2], 145 | [6.7,3.3,5.7,2.5,2], 146 | [6.7,3.0,5.2,2.3,2], 147 | [6.3,2.5,5.0,1.9,2], 148 | [6.5,3.0,5.2,2.0,2], 149 | [6.2,3.4,5.4,2.3,2], 150 | [5.9,3.0,5.1,1.8,2]] 151 | -------------------------------------------------------------------------------- /male.txt: -------------------------------------------------------------------------------- 1 | [174.0, 65.6], [175.3, 71.8], [193.5, 80.7], [186.5, 72.6], [187.2, 78.8], 2 | [181.5, 74.8], [184.0, 86.4], [184.5, 78.4], [175.0, 62.0], [184.0, 81.6], 3 | [180.0, 76.6], [177.8, 83.6], [192.0, 90.0], [176.0, 74.6], [174.0, 71.0], 4 | [184.0, 79.6], [192.7, 93.8], [171.5, 70.0], [173.0, 72.4], [176.0, 85.9], 5 | [176.0, 78.8], [180.5, 77.8], [172.7, 66.2], [176.0, 86.4], [173.5, 81.8], 6 | [178.0, 89.6], [180.3, 82.8], [180.3, 76.4], [164.5, 63.2], [173.0, 60.9], 7 | [183.5, 74.8], [175.5, 70.0], [188.0, 72.4], [189.2, 84.1], [172.8, 69.1], 8 | [170.0, 59.5], [182.0, 67.2], [170.0, 61.3], [177.8, 68.6], [184.2, 80.1], 9 | [186.7, 87.8], [171.4, 84.7], [172.7, 73.4], [175.3, 72.1], [180.3, 82.6], 10 | [182.9, 88.7], [188.0, 84.1], [177.2, 94.1], [172.1, 74.9], [167.0, 59.1], 11 | [169.5, 75.6], [174.0, 86.2], [172.7, 75.3], [182.2, 87.1], [164.1, 55.2], 12 | [163.0, 57.0], [171.5, 61.4], [184.2, 76.8], [174.0, 86.8], [174.0, 72.2], 13 | [177.0, 71.6], [186.0, 84.8], [167.0, 68.2], [171.8, 66.1], [182.0, 72.0], 14 | [167.0, 64.6], [177.8, 74.8], [164.5, 70.0], [192.0, 101.6], [175.5, 63.2], 15 | [171.2, 79.1], [181.6, 78.9], [167.4, 67.7], [181.1, 66.0], [177.0, 68.2], 16 | [174.5, 63.9], [177.5, 72.0], [170.5, 56.8], [182.4, 74.5], [197.1, 90.9], 17 | [180.1, 93.0], [175.5, 80.9], [180.6, 72.7], [184.4, 68.0], [175.5, 70.9], 18 | [180.6, 72.5], [177.0, 72.5], [177.1, 83.4], [181.6, 75.5], [176.5, 73.0], 19 | [175.0, 70.2], [174.0, 73.4], [165.1, 70.5], [177.0, 68.9], [192.0, 102.3], 20 | [176.5, 68.4], [169.4, 65.9], [182.1, 75.7], [179.8, 84.5], [175.3, 87.7], 21 | [184.9, 86.4], [177.3, 73.2], [167.4, 53.9], [178.1, 72.0], [168.9, 55.5], 22 | [157.2, 58.4], [180.3, 83.2], [170.2, 72.7], [177.8, 64.1], [172.7, 72.3], 23 | [165.1, 65.0], [186.7, 86.4], [165.1, 65.0], [174.0, 88.6], [175.3, 84.1], 24 | [185.4, 66.8], [177.8, 75.5], [180.3, 93.2], [180.3, 82.7], [177.8, 58.0], 25 | [177.8, 79.5], [177.8, 78.6], [177.8, 71.8], [177.8, 116.4], [163.8, 72.2], 26 | [188.0, 83.6], [198.1, 85.5], [175.3, 90.9], [166.4, 85.9], [190.5, 89.1], 27 | [166.4, 75.0], [177.8, 77.7], [179.7, 86.4], [172.7, 90.9], [190.5, 73.6], 28 | [185.4, 76.4], [168.9, 69.1], [167.6, 84.5], [175.3, 64.5], [170.2, 69.1], 29 | [190.5, 108.6], [177.8, 86.4], [190.5, 80.9], [177.8, 87.7], [184.2, 94.5], 30 | [176.5, 80.2], [177.8, 72.0], [180.3, 71.4], [171.4, 72.7], [172.7, 84.1], 31 | [172.7, 76.8], [177.8, 63.6], [177.8, 80.9], [182.9, 80.9], [170.2, 85.5], 32 | [167.6, 68.6], [175.3, 67.7], [165.1, 66.4], [185.4, 102.3], [181.6, 70.5], 33 | [172.7, 95.9], [190.5, 84.1], [179.1, 87.3], [175.3, 71.8], [170.2, 65.9], 34 | [193.0, 95.9], [171.4, 91.4], [177.8, 81.8], [177.8, 96.8], [167.6, 69.1], 35 | [167.6, 82.7], [180.3, 75.5], [182.9, 79.5], [176.5, 73.6], [186.7, 91.8], 36 | [188.0, 84.1], [188.0, 85.9], [177.8, 81.8], [174.0, 82.5], [177.8, 80.5], 37 | [171.4, 70.0], [185.4, 81.8], [185.4, 84.1], [188.0, 90.5], [188.0, 91.4], 38 | [182.9, 89.1], [176.5, 85.0], [175.3, 69.1], [175.3, 73.6], [188.0, 80.5], 39 | [188.0, 82.7], [175.3, 86.4], [170.5, 67.7], [179.1, 92.7], [177.8, 93.6], 40 | [175.3, 70.9], [182.9, 75.0], [170.8, 93.2], [188.0, 93.2], [180.3, 77.7], 41 | [177.8, 61.4], [185.4, 94.1], [168.9, 75.0], [185.4, 83.6], [180.3, 85.5], 42 | [174.0, 73.9], [167.6, 66.8], [182.9, 87.3], [160.0, 72.3], [180.3, 88.6], 43 | [167.6, 75.5], [186.7, 101.4], [175.3, 91.1], [175.3, 67.3], [175.9, 77.7], 44 | [175.3, 81.8], [179.1, 75.5], [181.6, 84.5], [177.8, 76.6], [182.9, 85.0], 45 | [177.8, 102.5], [184.2, 77.3], [179.1, 71.8], [176.5, 87.9], [188.0, 94.3], 46 | [174.0, 70.9], [167.6, 64.5], [170.2, 77.3], [167.6, 72.3], [188.0, 87.3], 47 | [174.0, 80.0], [176.5, 82.3], [180.3, 73.6], [167.6, 74.1], [188.0, 85.9], 48 | [180.3, 73.2], [167.6, 76.3], [183.0, 65.9], [183.0, 90.9], [179.1, 89.1], 49 | [170.2, 62.3], [177.8, 82.7], [179.1, 79.1], [190.5, 98.2], [177.8, 84.1], 50 | [180.3, 83.2], [180.3, 83.2] 51 | -------------------------------------------------------------------------------- /scriptFile.scala: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Java-Edge/Spark-MLlib-Tutorial/34aa116adc8a07fd8795ee9c11f7ad898f359175/scriptFile.scala -------------------------------------------------------------------------------- /src/META-INF/MANIFEST.MF: -------------------------------------------------------------------------------- 1 | Manifest-Version: 1.0 2 | Main-Class: WordCount 3 | 4 | -------------------------------------------------------------------------------- /src/WordCount.scala: -------------------------------------------------------------------------------- 1 | import org.apache.spark.SparkContext 2 | 3 | /** 4 | * @author JavaEdge 5 | * @date 2019-04-09 6 | * 7 | */ 8 | object WordCount { 9 | 10 | def main(args: Array[String]): Unit = { 11 | val sc = new SparkContext("local", "WordCount") 12 | 13 | val file = sc.textFile("/Volumes/doc/spark-2.4.1-bin-hadoop2.7/LICENSE") 14 | 15 | // 先分割成单词数组，然后合并，再与1形成KV映射 16 | val result = file.flatMap(_.split(" ")).map((_, 1)).reduceByKey((a, b) => a + b).sortBy(_._2) 17 | result.foreach(println(_)) 18 | 19 | } 20 | 21 | } 22 | -------------------------------------------------------------------------------- /src/cluster/Ida/Main.scala: -------------------------------------------------------------------------------- 1 | package cluster.Ida 2 | 3 | import org.apache.spark.SparkConf 4 | import org.apache.spark.ml.clustering.LDA 5 | import org.apache.spark.ml.feature.VectorAssembler 6 | import org.apache.spark.sql.SparkSession 7 | 8 | import scala.util.Random 9 | 10 | /** 11 | * @author JavaEdge 12 | * @date 2019-04-17 13 | * 14 | */ 15 | object Main { 16 | def main(args: Array[String]): Unit = { 17 | val conf = new SparkConf().setMaster("local").setAppName("iris") 18 | val spark = SparkSession.builder().config(conf).getOrCreate() 19 | 20 | // 加载数据 21 | val file = spark.read.format("csv").load("iris.data") 22 | file.show() 23 | 24 | import spark.implicits._ 25 | val random = new Random() 26 | val data = file.map(row => { 27 | val label = row.getString(4) match { 28 | case "Iris-setosa" => 0 29 | case "Iris-versicolor" => 1 30 | case "Iris-virginica" => 2 31 | } 32 | 33 | (row.getString(0).toDouble, 34 | row.getString(1).toDouble, 35 | row.getString(2).toDouble, 36 | row.getString(3).toDouble, 37 | label, 38 | random.nextDouble()) 39 | }).toDF("_c0", "_c1", "_c2", "_c3", "label", "rand").sort("rand") 40 | val assembler = new VectorAssembler() 41 | .setInputCols(Array("_c0", "_c1", "_c2", "_c3")) 42 | .setOutputCol("features") 43 | 44 | val dataset = assembler.transform(data) 45 | val Array(train, test) = dataset.randomSplit(Array(0.8, 0.2)) 46 | train.show() 47 | 48 | // 训练一个LDA模型 49 | val lda = new LDA().setFeaturesCol("features").setK(3).setMaxIter(40) 50 | val model = lda.fit(train) 51 | 52 | // 展示结果 53 | val prediction = model.transform(train) 54 | prediction.show() 55 | 56 | val ll = model.logLikelihood(train) 57 | val lp = model.logPerplexity(train) 58 | 59 | // Describe topics. 60 | val topics = model.describeTopics(3) 61 | prediction.select("label", "topicDistribution").show(false) 62 | println("The topics described by their top-weighted terms:") 63 | topics.show(false) 64 | println(s"The lower bound on the log likelihood of the entire corpus: $ll") 65 | println(s"The upper bound on perplexity: $lp") 66 | } 67 | } -------------------------------------------------------------------------------- /src/cluster/kmeans/Main.scala: -------------------------------------------------------------------------------- 1 | package cluster.kmeans 2 | 3 | import org.apache.spark.SparkConf 4 | import org.apache.spark.ml.clustering.KMeans 5 | import org.apache.spark.ml.feature.VectorAssembler 6 | import org.apache.spark.sql.SparkSession 7 | 8 | import scala.util.Random 9 | 10 | /** 11 | * @author JavaEdge 12 | * @date 2019-04-17 13 | * 14 | */ 15 | object Main { 16 | def main(args: Array[String]): Unit = { 17 | val conf = new SparkConf().setMaster("local").setAppName("iris") 18 | val spark = SparkSession.builder().config(conf).getOrCreate() 19 | 20 | val file = spark.read.format("csv").load("iris.data") 21 | file.show() 22 | 23 | import spark.implicits._ 24 | val random = new Random() 25 | val data = file.map(row => { 26 | val label = row.getString(4) match { 27 | case "Iris-setosa" => 0 28 | case "Iris-versicolor" => 1 29 | case "Iris-virginica" => 2 30 | } 31 | 32 | (row.getString(0).toDouble, 33 | row.getString(1).toDouble, 34 | row.getString(2).toDouble, 35 | row.getString(3).toDouble, 36 | label, 37 | random.nextDouble()) 38 | }).toDF("_c0", "_c1", "_c2", "_c3", "label", "rand").sort("rand") 39 | 40 | val assembler = new VectorAssembler() 41 | .setInputCols(Array("_c0", "_c1", "_c2", "_c3")) 42 | .setOutputCol("features") 43 | 44 | // 分割 45 | val dataset = assembler.transform(data) 46 | val Array(train, test) = dataset.randomSplit(Array(0.8, 0.2)) 47 | train.show() 48 | 49 | // kmeans 算法 50 | val kmeans = new KMeans().setFeaturesCol("features").setK(3).setMaxIter(20) 51 | val model = kmeans.fit(train) 52 | 53 | model.transform(train).show() 54 | 55 | } 56 | } 57 | -------------------------------------------------------------------------------- /src/gender/Main.scala: -------------------------------------------------------------------------------- 1 | package gender 2 | 3 | import org.apache.spark.SparkConf 4 | import org.apache.spark.ml.classification.DecisionTreeClassifier 5 | import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator 6 | import org.apache.spark.ml.feature.VectorAssembler 7 | import org.apache.spark.sql.SparkSession 8 | 9 | import scala.util.Random 10 | 11 | /** 12 | * @author JavaEdge 13 | * @date 2019-04-16 14 | * 15 | */ 16 | object Main { 17 | def main(args: Array[String]): Unit = { 18 | val conf = new SparkConf().setAppName("gender").setMaster("local") 19 | val session = SparkSession.builder().config(conf).getOrCreate() 20 | val sc = session.sparkContext 21 | 22 | val pattern = (filename: String, category: Int) => { 23 | val patternString = "\\[(.*?)\\]".r 24 | val rand = new Random() 25 | sc.textFile(filename) 26 | .flatMap(text => patternString.findAllIn(text.replace(" ", ""))) 27 | .map(text => { 28 | val pairwise = text.substring(1, text.length - 1).split(",") 29 | // 无return的最后一行为返回值 30 | (pairwise(0).toDouble, pairwise(1).toDouble, category, rand.nextDouble()) 31 | }) 32 | } 33 | val male = pattern("male.txt", 1) 34 | val female = pattern("female.txt", 2) 35 | 36 | // 转换成DataFrame,而不是RDD 37 | val maleDF = session 38 | .createDataFrame(male) 39 | .toDF("height", "weight", "category", "rand") 40 | val femaleDF = session 41 | .createDataFrame(female) 42 | .toDF("height", "weight", "category", "rand") 43 | // 合并数据集 44 | val dataset = maleDF.union(femaleDF).sort("rand") 45 | // 开始训练 46 | val assembler = new VectorAssembler() 47 | .setInputCols(Array("height", "weight")) 48 | .setOutputCol("features") 49 | 50 | val transformedDataset = assembler.transform(dataset) 51 | transformedDataset.show() 52 | val Array(train, test) = transformedDataset.randomSplit(Array(0.8, 0.2)) 53 | 54 | // 决策树算法 55 | val classifier = new DecisionTreeClassifier() 56 | .setFeaturesCol("features") 57 | .setLabelCol("category") 58 | val model = classifier.fit(train) 59 | val result = model.transform(test) 60 | result.show() 61 | 62 | val evaluator = new MulticlassClassificationEvaluator() 63 | .setLabelCol("category") 64 | .setPredictionCol("prediction") 65 | .setMetricName("accuracy") 66 | val accuracy = evaluator.evaluate(result) 67 | println(s"""accuracy is $accuracy""") 68 | } 69 | } 70 | -------------------------------------------------------------------------------- /src/iris/Main.scala: -------------------------------------------------------------------------------- 1 | package iris 2 | 3 | import org.apache.spark.SparkConf 4 | import org.apache.spark.ml.classification.DecisionTreeClassifier 5 | import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator 6 | import org.apache.spark.ml.feature.VectorAssembler 7 | import org.apache.spark.sql.SparkSession 8 | 9 | import scala.util.Random 10 | 11 | /** 12 | * @author JavaEdge 13 | * @date 2019-04-15 14 | * 15 | */ 16 | object Main { 17 | def main(args: Array[String]): Unit = { 18 | 19 | val conf = new SparkConf().setMaster("local").setAppName("iris") 20 | val spark = SparkSession.builder().config(conf).getOrCreate() 21 | spark.sparkContext.setLogLevel("WARN") 22 | 23 | val file = spark.read.format("csv").load("iris.data") 24 | 25 | import spark.implicits._ 26 | val random = new Random() 27 | val data = file.map(row => { 28 | val label = row.getString(4) match { 29 | case "Iris-setosa" => 0 30 | case "Iris-versicolor" => 1 31 | case "Iris-virginica" => 2 32 | } 33 | 34 | (row.getString(0).toDouble, 35 | row.getString(1).toDouble, 36 | row.getString(2).toDouble, 37 | row.getString(3).toDouble, 38 | label, 39 | random.nextDouble()) 40 | }).toDF("_c0", "_c1", "_c2", "_c3", "label", "rand").sort("rand") 41 | 42 | val assembler = new VectorAssembler().setInputCols(Array("_c0", "_c1", "_c2", "_c3")).setOutputCol("features") 43 | 44 | val dataset = assembler.transform(data) 45 | val Array(train, test) = dataset.randomSplit(Array(0.8, 0.2)) 46 | 47 | val dt = new DecisionTreeClassifier().setFeaturesCol("features").setLabelCol("label") 48 | val model = dt.fit(train) 49 | val result = model.transform(test) 50 | result.show() 51 | val evaluator = new MulticlassClassificationEvaluator() 52 | .setLabelCol("label") 53 | .setPredictionCol("prediction") 54 | .setMetricName("accuracy") 55 | val accuracy = evaluator.evaluate(result) 56 | println(s"""accuracy is $accuracy""") 57 | } 58 | } 59 | 60 | 61 | -------------------------------------------------------------------------------- /src/isotonic/Main.scala: -------------------------------------------------------------------------------- 1 | package isotonic 2 | 3 | import org.apache.spark.ml.feature.VectorAssembler 4 | import org.apache.spark.ml.regression.IsotonicRegression 5 | import org.apache.spark.sql.SparkSession 6 | import org.apache.spark.{SparkConf, SparkContext} 7 | 8 | import scala.util.Random 9 | 10 | /** 11 | * @author JavaEdge 12 | * @date 2019-04-15 13 | * 14 | */ 15 | object Main { 16 | def main(args: Array[String]): Unit = { 17 | val conf = new SparkConf().setAppName("linear").setMaster("local") 18 | val sc = new SparkContext(conf) 19 | val spark = SparkSession.builder().config(conf).getOrCreate() 20 | 21 | val file = spark.read.format("csv").option("sep", ";").option("header", "true").load("house.csv") 22 | import spark.implicits._ 23 | //打乱顺序 24 | val rand = new Random() 25 | val data = file.select("square", "price").map( 26 | row => (row.getAs[String](0).toDouble, row.getString(1).toDouble, rand.nextDouble())) 27 | .toDF("square", "price", "rand").sort("rand") //强制类型转换过程 28 | 29 | val ass = new VectorAssembler().setInputCols(Array("square")).setOutputCol("features") 30 | val dataset = ass.transform(data) //特征包装 31 | val Array(train, test) = dataset.randomSplit(Array(0.8, 0.2)) //拆分成训练数据集和测试数据集 32 | 33 | val isotonic = new IsotonicRegression().setFeaturesCol("features").setLabelCol("price") 34 | val model = isotonic.fit(train) 35 | model.transform(test).show() 36 | } 37 | 38 | } 39 | 40 | -------------------------------------------------------------------------------- /src/isotonic/scriptFile.scala: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Java-Edge/Spark-MLlib-Tutorial/34aa116adc8a07fd8795ee9c11f7ad898f359175/src/isotonic/scriptFile.scala -------------------------------------------------------------------------------- /src/linear/Main.scala: -------------------------------------------------------------------------------- 1 | import org.apache.spark.ml.classification.LogisticRegression 2 | import org.apache.spark.ml.feature.VectorAssembler 3 | import org.apache.spark.sql.SparkSession 4 | import org.apache.spark.{SparkConf, SparkContext} 5 | 6 | import scala.util.Random 7 | 8 | /** 9 | * @author JavaEdge 10 | * @date 2019-04-14 11 | * 12 | */ 13 | object Main { 14 | def main(args: Array[String]): Unit = { 15 | val conf = new SparkConf().setAppName("linear").setMaster("local") 16 | val sc = new SparkContext(conf) 17 | val spark = SparkSession.builder().config(conf).getOrCreate() 18 | 19 | // 加载文件 20 | val file = spark.read.format("csv").option("sep", ";").option("header", "true").load("house.csv") 21 | import spark.implicits._ 22 | // 开始shuffle 23 | // 打乱顺序 24 | val rand = new Random() 25 | val data = file.select("square", "price").map( 26 | row => (row.getAs[String](0).toDouble, row.getString(1).toDouble, rand.nextDouble())) 27 | .toDF("square", "price", "rand").sort("rand") //强制类型转换过程 28 | 29 | // Dataset(Double, Double) 30 | // Dataframe = Dataset(Row) 31 | 32 | val ass = new VectorAssembler().setInputCols(Array("square")).setOutputCol("features") 33 | val dataset = ass.transform(data) //特征包装 34 | 35 | // 训练集， 测试集 36 | val Array(train, test) = dataset.randomSplit(Array(0.8, 0.2)) // 拆分成训练数据集和测试数据集 37 | 38 | val lr = new LogisticRegression().setLabelCol("price").setFeaturesCol("features") 39 | .setRegParam(0.3).setElasticNetParam(0.8).setMaxIter(10) 40 | val model = lr.fit(train) 41 | 42 | model.transform(test).show() 43 | val s = model.summary.totalIterations 44 | println(s"iter: ${s}") 45 | } 46 | } 47 | -------------------------------------------------------------------------------- /src/linear/scriptFile.scala: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Java-Edge/Spark-MLlib-Tutorial/34aa116adc8a07fd8795ee9c11f7ad898f359175/src/linear/scriptFile.scala -------------------------------------------------------------------------------- /src/pca/Main.scala: -------------------------------------------------------------------------------- 1 | package pca 2 | 3 | import org.apache.spark.SparkConf 4 | import org.apache.spark.ml.classification.DecisionTreeClassifier 5 | import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator 6 | import org.apache.spark.ml.feature.{PCA, VectorAssembler} 7 | import org.apache.spark.sql.SparkSession 8 | 9 | import scala.util.Random 10 | 11 | /** 12 | * @author JavaEdge 13 | * @date 2019-04-15 14 | * 15 | */ 16 | object Main { 17 | def main(args: Array[String]): Unit = { 18 | 19 | val conf = new SparkConf().setMaster("local").setAppName("iris") 20 | val spark = SparkSession.builder().config(conf).getOrCreate() 21 | spark.sparkContext.setLogLevel("WARN") ///日志级别 22 | 23 | val file = spark.read.format("csv").load("iris.data") 24 | //file.show() 25 | 26 | import spark.implicits._ 27 | val random = new Random() 28 | val data = file.map(row => { 29 | val label = row.getString(4) match { 30 | case "Iris-setosa" => 0 31 | case "Iris-versicolor" => 1 32 | case "Iris-virginica" => 2 33 | } 34 | 35 | (row.getString(0).toDouble, 36 | row.getString(1).toDouble, 37 | row.getString(2).toDouble, 38 | row.getString(3).toDouble, 39 | label, 40 | random.nextDouble()) 41 | }).toDF("_c0", "_c1", "_c2", "_c3", "label", "rand").sort("rand") //.where("label = 1 or label = 0") 42 | 43 | val assembler = new VectorAssembler().setInputCols(Array("_c0", "_c1", "_c2", "_c3")).setOutputCol("features") 44 | 45 | val pca = new PCA() 46 | .setInputCol("features") 47 | .setOutputCol("features2") 48 | .setK(3) 49 | val dataset = assembler.transform(data) 50 | val pcaModel = pca.fit(dataset) 51 | val dataset2 = pcaModel.transform(dataset) 52 | val Array(train, test) = dataset2.randomSplit(Array(0.8, 0.2)) 53 | 54 | val dt = new DecisionTreeClassifier().setFeaturesCol("features2").setLabelCol("label") 55 | val model = dt.fit(train) 56 | val result = model.transform(test) 57 | result.show(false) 58 | val evaluator = new MulticlassClassificationEvaluator() 59 | .setLabelCol("label") 60 | .setPredictionCol("prediction") 61 | .setMetricName("accuracy") 62 | val accuracy = evaluator.evaluate(result) 63 | println(s"""accuracy is $accuracy""") 64 | } 65 | } 66 | 67 | -------------------------------------------------------------------------------- /src/rs/Main.scala: -------------------------------------------------------------------------------- 1 | package rs 2 | 3 | import org.apache.spark.SparkConf 4 | import org.apache.spark.ml.evaluation.RegressionEvaluator 5 | import org.apache.spark.ml.recommendation.ALS 6 | import org.apache.spark.ml.recommendation.ALS.Rating 7 | import org.apache.spark.sql.SparkSession 8 | 9 | /** 10 | * @author JavaEdge 11 | * @date 2019-04-20 12 | * 13 | */ 14 | object Main { 15 | def main(args: Array[String]): Unit = { 16 | val conf = new SparkConf().setMaster("local").setAppName("RS") 17 | val spark = SparkSession.builder().config(conf).getOrCreate() 18 | spark.sparkContext.setLogLevel("WARN") 19 | 20 | val parseRating = (string: String) => { 21 | // 分割 22 | val stringArray = string.split("\t") 23 | // 包装 24 | Rating(stringArray(0).toInt, stringArray(1).toInt, stringArray(2).toFloat) 25 | } 26 | 27 | import spark.implicits._ 28 | val data = spark.read.textFile("u.data") 29 | // 分割 30 | .map(parseRating) 31 | // 转换成DataFrame 32 | .toDF("userID", "itemID", "rating") 33 | // data.show(false) 34 | val Array(traing, test) = data.randomSplit(Array(0.8, 0.2)) 35 | 36 | val als = new ALS() 37 | .setMaxIter(20) 38 | .setUserCol("userID") 39 | .setItemCol("itemID") 40 | .setRatingCol("rating") 41 | // 正则化参数 42 | .setRegParam(0.01) 43 | 44 | val model = als.fit(traing) 45 | // 冷启动策略 46 | model.setColdStartStrategy("drop") 47 | 48 | val predictions = model.transform(test) 49 | // 根据(userID,itemID)预测rating 50 | // predictions.show(false) 51 | 52 | 53 | // MovieLens数据集(学术界可靠的一种数据集) 给196号用户推荐10部电影 54 | val users = spark.createDataset(Array(196)).toDF("userID") 55 | users.show(false) 56 | model.recommendForUserSubset(users, 10).show(false) // 想一想工业实践该怎么结合这段代码？ 57 | 58 | //模型评估 59 | val evaluator = new RegressionEvaluator() 60 | .setMetricName("rmse") 61 | .setLabelCol("rating") 62 | .setPredictionCol("prediction") 63 | val rmse = evaluator.evaluate(predictions) 64 | println(s"Root-mean-square error is $rmse \n") 65 | 66 | //Spark机器学习模型的持久化 67 | //模型保存 68 | //model.save("./xxx") 69 | //模型加载 70 | //val model = ALS.load("xxxx") 71 | 72 | 73 | } 74 | } 75 | -------------------------------------------------------------------------------- /src/sentiment_analysis/Main.scala: -------------------------------------------------------------------------------- 1 | package sentiment_analysis 2 | 3 | 4 | import org.apache.spark.SparkConf 5 | import org.apache.spark.ml.classification.NaiveBayes 6 | import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator 7 | import org.apache.spark.ml.feature.{HashingTF, IDF} 8 | import org.apache.spark.sql.SparkSession 9 | 10 | import scala.util.Random 11 | 12 | /** 13 | * @author JavaEdge 14 | * @date 2019-04-19 15 | * 16 | */ 17 | object Main { 18 | def main(args: Array[String]): Unit = { 19 | val conf = new SparkConf().setMaster("local").setAppName("SA") 20 | val spark = SparkSession.builder().config(conf).getOrCreate() 21 | spark.sparkContext.setLogLevel("WARN") ///日志级别 22 | 23 | import spark.implicits._ 24 | val rand = new Random() 25 | 26 | val neg = spark.read.textFile("neg.txt").map( 27 | line => { 28 | // 分词 29 | (line.split(" ").filter(!_.equals(" ")), 0, rand.nextDouble()) 30 | }).toDF("words", "value", "random") 31 | 32 | // ============== 数据的预处理 start ================ 33 | val pos = spark.read.textFile("pos.txt").map( 34 | line => { 35 | (line.split(" ").filter(!_.equals(" ")), 1, rand.nextDouble()) 36 | }).toDF("words", "value", "random") //思考：这里把inner function提出重用来如何操作 37 | 38 | // 合并并乱序 39 | val data = neg.union(pos).sort("random") //思考：为什么不用join 40 | data.show(false) 41 | println(neg.count(), data.count()) //合并 42 | // ============== 数据的预处理 end ================ 43 | 44 | // 文本特征抽取(TF-IDF) 45 | val hashingTf = new HashingTF() 46 | .setInputCol("words") 47 | .setOutputCol("hashing") 48 | .transform(data) 49 | val idfModel = new IDF() 50 | .setInputCol("hashing") 51 | .setOutputCol("tfidf") 52 | .fit(hashingTf) 53 | val transformedData = idfModel 54 | .transform(hashingTf) 55 | val Array(training, test) = transformedData 56 | .randomSplit(Array(0.7, 0.3)) 57 | 58 | // 根据抽取到的文本特征，使用分类器进行分类，这是一个二分类问题 59 | // 分类器是可替换的 60 | val bayes = new NaiveBayes() 61 | .setFeaturesCol("tfidf") //X 62 | .setLabelCol("value") //y 0:消极,1:积极 63 | .fit(training) 64 | // 交叉验证 65 | val result = bayes.transform(test) 66 | result.show(false) 67 | 68 | // 评估模型的准确率 69 | val evaluator = new MulticlassClassificationEvaluator() 70 | .setLabelCol("value") 71 | .setPredictionCol("prediction") 72 | .setMetricName("accuracy") 73 | val accuracy = evaluator.evaluate(result) 74 | println(s"""accuracy is $accuracy""") 75 | 76 | //重构思考： 77 | //尝试用pipeline重构代码 78 | //尝试用模型预测随便属于一句话的情感，例如： 79 | //You are a bad girl,I hate you. ^_^ 80 | } 81 | } 82 | --------------------------------------------------------------------------------