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spark-streaming-mqtt_2.10 from group org.apache.spark (version 1.6.3)

Group: org.apache.spark Artifact: spark-streaming-mqtt_2.10
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1 downloads
Artifact spark-streaming-mqtt_2.10
Group org.apache.spark
Version 1.6.3
Last update 03. November 2016
Newest version Yes
Organization not specified
URL http://spark.apache.org/
License not specified
Dependencies amount 1
Dependencies org.eclipse.paho.client.mqttv3,
There are maybe transitive dependencies!

spark-streaming-kafka_2.11 from group org.apache.spark (version 1.6.3)

Group: org.apache.spark Artifact: spark-streaming-kafka_2.11
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56 downloads
Artifact spark-streaming-kafka_2.11
Group org.apache.spark
Version 1.6.3
Last update 03. November 2016
Newest version Yes
Organization not specified
URL http://spark.apache.org/
License not specified
Dependencies amount 1
Dependencies kafka_2.11,
There are maybe transitive dependencies!

spark-streaming-mqtt_2.11 from group org.apache.spark (version 1.6.3)

Group: org.apache.spark Artifact: spark-streaming-mqtt_2.11
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2 downloads
Artifact spark-streaming-mqtt_2.11
Group org.apache.spark
Version 1.6.3
Last update 02. November 2016
Newest version Yes
Organization not specified
URL http://spark.apache.org/
License not specified
Dependencies amount 1
Dependencies org.eclipse.paho.client.mqttv3,
There are maybe transitive dependencies!

spark-streaming-twitter_2.10 from group org.apache.spark (version 1.6.3)

Group: org.apache.spark Artifact: spark-streaming-twitter_2.10
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38 downloads
Artifact spark-streaming-twitter_2.10
Group org.apache.spark
Version 1.6.3
Last update 02. November 2016
Newest version Yes
Organization not specified
URL http://spark.apache.org/
License not specified
Dependencies amount 1
Dependencies twitter4j-stream,
There are maybe transitive dependencies!

spark-streaming-twitter_2.11 from group org.apache.spark (version 1.6.3)

Group: org.apache.spark Artifact: spark-streaming-twitter_2.11
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25 downloads
Artifact spark-streaming-twitter_2.11
Group org.apache.spark
Version 1.6.3
Last update 02. November 2016
Newest version Yes
Organization not specified
URL http://spark.apache.org/
License not specified
Dependencies amount 1
Dependencies twitter4j-stream,
There are maybe transitive dependencies!

spark-streaming-zeromq_2.10 from group org.apache.spark (version 1.6.3)

Group: org.apache.spark Artifact: spark-streaming-zeromq_2.10
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0 downloads
Artifact spark-streaming-zeromq_2.10
Group org.apache.spark
Version 1.6.3
Last update 02. November 2016
Newest version Yes
Organization not specified
URL http://spark.apache.org/
License not specified
Dependencies amount 1
Dependencies akka-zeromq_2.10,
There are maybe transitive dependencies!

spark-streaming-kafka_2.10 from group org.apache.spark (version 1.6.3)

Group: org.apache.spark Artifact: spark-streaming-kafka_2.10
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467 downloads
Artifact spark-streaming-kafka_2.10
Group org.apache.spark
Version 1.6.3
Last update 02. November 2016
Newest version Yes
Organization not specified
URL http://spark.apache.org/
License not specified
Dependencies amount 1
Dependencies kafka_2.10,
There are maybe transitive dependencies!

spark-streaming-zeromq_2.11 from group org.apache.spark (version 1.6.3)

Group: org.apache.spark Artifact: spark-streaming-zeromq_2.11
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0 downloads
Artifact spark-streaming-zeromq_2.11
Group org.apache.spark
Version 1.6.3
Last update 02. November 2016
Newest version Yes
Organization not specified
URL http://spark.apache.org/
License not specified
Dependencies amount 1
Dependencies akka-zeromq_2.11,
There are maybe transitive dependencies!

multiLayerPerceptrons from group nz.ac.waikato.cms.weka (version 1.0.10)

This package currently contains classes for training multilayer perceptrons with one hidden layer, where the number of hidden units is user specified. MLPClassifier can be used for classification problems and MLPRegressor is the corresponding class for numeric prediction tasks. The former has as many output units as there are classes, the latter only one output unit. Both minimise a penalised squared error with a quadratic penalty on the (non-bias) weights, i.e., they implement "weight decay", where this penalised error is averaged over all training instances. The size of the penalty can be determined by the user by modifying the "ridge" parameter to control overfitting. The sum of squared weights is multiplied by this parameter before added to the squared error. Both classes use BFGS optimisation by default to find parameters that correspond to a local minimum of the error function. but optionally conjugated gradient descent is available, which can be faster for problems with many parameters. Logistic functions are used as the activation functions for all units apart from the output unit in MLPRegressor, which employs the identity function. Input attributes are standardised to zero mean and unit variance. MLPRegressor also rescales the target attribute (i.e., "class") using standardisation. All network parameters are initialised with small normally distributed random values.

Group: nz.ac.waikato.cms.weka Artifact: multiLayerPerceptrons
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10 downloads
Artifact multiLayerPerceptrons
Group nz.ac.waikato.cms.weka
Version 1.0.10
Last update 31. October 2016
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/multiLayerPerceptrons
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

ptstemmer-weka-package from group com.github.fracpete (version 2.0.0)

Contains the PTStemmer developed by Pedro Oliveira (http://code.google.com/p/ptstemmer/).

Group: com.github.fracpete Artifact: ptstemmer-weka-package
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0 downloads
Artifact ptstemmer-weka-package
Group com.github.fracpete
Version 2.0.0
Last update 29. October 2016
Newest version Yes
Organization University of Waikato, Hamilton, NZ
URL https://github.com/fracpete/ptstemmer-weka-package
License GNU General Public License 3
Dependencies amount 2
Dependencies weka-dev, ptstemmer,
There are maybe transitive dependencies!



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