小编Vig*_*ati的帖子

我是hadoop分布式文件系统的新手,我已经在我的机器上完成了hadoop单节点的安装.但是之后当我要将数据上传到hdfs时,它会给出一条错误消息Permission Denied.

来自终端的消息带命令:

hduser@ubuntu:/usr/local/hadoop$ hadoop fs -put /usr/local/input-data/ /input
put: /usr/local/input-data (Permission denied)

hduser@ubuntu:/usr/local/hadoop$ 

使用sudo并在sudouser中添加hduser后:

hduser@ubuntu:/usr/local/hadoop$ sudo bin/hadoop fs -put /usr/local/input-data/ /inwe
put: org.apache.hadoop.security.AccessControlException: Permission denied: user=root, access=WRITE, inode="":hduser:supergroup:rwxr-xr-x

hduser@ubuntu:/usr/local/hadoop$ 

从(http://girlincomputerscience.blogspot.com/2010/11/apache-mahout.html)安装mahout之后.如何运行mahout算法,从那里我可以获得最受欢迎的mahout新手的简单教程....

提前致谢.

我已经使用Scikit学习建立了预测模型。我已经用Flask，joblib部署了此模型。每当我预测加载的模型有新的传入请求时，它在控制台上的打印消息中都会显示有关内核的总时间。

现在，我要禁用此打印。当模型预测新的传入数据时，我如何抑制给定消息。

[Parallel(n_jobs=24)]: Done 117 out of 174 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=24)]: Done 121 out of 179 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=24)]: Done 122 out of 181 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=24)]: Done 123 out of 183 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=24)]: Done 125 out of 185 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=24)]: Done 127 out of 188 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=24)]: Done 128 out of 190 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=24)]: …

我编写了java代码,用于从file中生成搜索到的数据的json.但是它没有生成精确的JsonArray.就像是

[{"item":"1617"},{"item":"1617"}]

代替

[{"item":"747"},{"item":"1617"}].

这里1617是从文件中获取的最后一项.

JSONArray ja = new JSONArray();
JSONObject jo = new JSONObject();

while (products.readRecord())
{
    String productID = products.get("user");
    int j = Integer.parseInt(productID);
    if(j == userId) {
        itemid = products.get("item");
        jo.put("item",itemid);
        ja.add(jo);
    }
}  

out.println(ja);
products.close();

我想将基于Hadoop的Mahout推荐器与Apache Hive结合起来.所以我生成的建议书直接存储在我的Hive Tables中.任何人都知道类似的教程吗？

我在Ubuntu 12.o4客户端操作系统上安装了Scala,sbt和hadoop 1.0.3.随着链接的参考 - http://docs.sigmoidanalytics.com/index.php/How_to_Install_Spark_on_Ubuntu-12.04,我尝试构建Spark并获得与预留空间相关的错误.

这是我想要运行的:

hduser@vignesh-desktop:/usr/local/spark-1.1.0$ SPARK_HADOOP_VERSION=1.1.0 sbt/sbt assembly

输出有以下错误:

Using /usr/lib/jvm/java-6-openjdk-i386/ as default JAVA_HOME.
Note, this will be overridden by -java-home if it is set.
Error occurred during initialization of VM
Could not reserve enough space for object heap
Could not create the Java virtual machine.

我是R的新手,当我要使用glm()来估计一个逻辑模型时,它不是预测响应,而是为我的预测函数的每个输入调用预测函数(如1)提供一个非实际输出.

Code:

    ex2data1R <- read.csv("/media/ex2data1R.txt")
    x <-ex2data1R$x
    y <-ex2data1R$y
    z <-ex2data1R$z

    logisticmodel <- glm(z~x+y,family=binomial(link = "logit"),data=ex2data1R)
    newdata = data.frame(x=c(10),y=(10))
    predict(logisticmodel, newdata, type="response") 

Output:
> predict(logisticmodel, newdata, type="response") 
           1 
1.181875e-11 

Data(ex2data1R.txt) :
"x","y","z"
34.62365962451697,78.0246928153624,0
30.28671076822607,43.89499752400101,0
35.84740876993872,72.90219802708364,0
60.18259938620976,86.30855209546826,1
79.0327360507101,75.3443764369103,1
45.08327747668339,56.3163717815305,0
61.10666453684766,96.51142588489624,1
75.02474556738889,46.55401354116538,1
76.09878670226257,87.42056971926803,1
84.43281996120035,43.53339331072109,1
95.86155507093572,38.22527805795094,0
75.01365838958247,30.60326323428011,0
82.30705337399482,76.48196330235604,1
69.36458875970939,97.71869196188608,1
39.53833914367223,76.03681085115882,0
53.9710521485623,89.20735013750205,1
69.07014406283025,52.74046973016765,1
67.94685547711617,46.67857410673128,0
70.66150955499435,92.92713789364831,1
76.97878372747498,47.57596364975532,1
67.37202754570876,42.83843832029179,0
89.67677575072079,65.79936592745237,1
50.534788289883,48.85581152764205,0
34.21206097786789,44.20952859866288,0
77.9240914545704,68.9723599933059,1
62.27101367004632,69.95445795447587,1
80.1901807509566,44.82162893218353,1
93.114388797442,38.80067033713209,0
61.83020602312595,50.25610789244621,0
38.78580379679423,64.99568095539578,0
61.379289447425,72.80788731317097,1
85.40451939411645,57.05198397627122,1
52.10797973193984,63.12762376881715,0
52.04540476831827,69.43286012045222,1
40.23689373545111,71.16774802184875,0
54.63510555424817,52.21388588061123,0
33.91550010906887,98.86943574220611,0
64.17698887494485,80.90806058670817,1
74.78925295941542,41.57341522824434,0
34.1836400264419,75.2377203360134,0
83.90239366249155,56.30804621605327,1
51.54772026906181,46.85629026349976,0
94.44336776917852,65.56892160559052,1
82.36875375713919,40.61825515970618,0
51.04775177128865,45.82270145776001,0
62.22267576120188,52.06099194836679,0
77.19303492601364,70.45820000180959,1
97.77159928000232,86.7278223300282,1 …