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try it right now or see Running MLDB for installation details. We’re happy to announce the immediate availability of MLDB version 2016.08.04.0. This release contains 161 new commits, modified 290 files and fatal error movie fixes 82 issues. On top of many bug fixes and performance improvements, here are
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some of the highlights of this release: New DISTINCT ON clause The DISTINCT ON clause can be used to to
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filter out duplicate rows based on the value of an expression. The syntax is as follows: SELECT DISTINCT ON (algorithm, project) algorithm, project, date FROM ml_experiments ORDER BY algorithm, project This will return one row
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per unique value of the columns algorithm and project. See the Select Expression documentation for more details. New try builtin function When an error occurs when processing a query, the whole query fails and no result is returned, even if only a single line caused the error. The new try function is meant to handle this type of situation. The first argument is the expression to try to apply. datacratic crunchbase The optional second argument is what will be returned if an error is encountered. In the example below, since the string foo will not parse as valid JSON, the row expression {'error': 1} will be returned instead: SELECT try(parse_json('foo'), {'error': 1}) AS * Check out the try function documentation for more details. Deep learning Added support for NVIDIA CUDNN, improving the performance of MLDB’s Tensorflow integration on GPUs. This is another step in making MLDB the easiest platform to use to run Tensorflow graphs. Updated pymldb to version 0.7.0 The pymldb library is an open-source pure-Python module which provides a wrapper library that makes it easy to work with MLDB from Python. Version 0.7.0 adds support for passing in a JSON payload in GET requests. This is necessary when passing in big feature vectors to MLDB functions. Check out the Using pymldb Tutorial notebook for more info. Internal hashing is now done using HighwayHash MLDB’s hash functions now use the Highway Tree Hash, which is claimed to be both likely secure and very fast. This will improve the speed of working with large numbers of columns. Other changes and fixes New aggregators: vertical_stddev (alias of stddev) and vertical_variance (alias
the procedure will be available via the REST API type is a string fatal desire lifetime that specified the procedure's type (see below) params is an object mlbd that configures the procedure, and whose contents will vary according to the type Not all three fatal system error of these fields are required in all contexts: one or both of id and type must be specified if only id is specified, MLDB will assume this is http://blog.mldb.ai/blog/posts/2016/08/version-2016.08.04.0/ a pre-existing procedure and will try to load it (an error will ensue if it doesn't already exist) if type is specified, MLDB will assume that the procedure doesn't exist yet and will try to create it (an error will ensue if it already exists) if type is specified without id, an id will be http://mldb.ai/doc/builtin/procedures/ProcedureConfig.md.html auto-generated if type is specified with id, the procedure will be created with the specified id unless a procedure already exists with that id if type is specified, then a corresponding params function must be specified if the type requires it The following types of procedures are available: TypeDescriptionDoc classifier.experimentTrain and test a classifier[doc] classifier.testCalculate the accuracy of a classifier on held-out data[doc] classifier.trainTrain a supervised classifier[doc] export.csvExports a dataset to a target location as a CSV[doc] import.gitImport a Git repository's metadata into MLDB[doc] import.jsonImport a text file with one JSON per line into MLDB[doc] import.sentiwordnetImport a SentiWordNet file into MLDB[doc] import.textImport from a text file, line by line.[doc] import.word2vecImport a word2vec file into MLDB[doc] kmeans.trainSimple clustering algorithm based on cluster centroids in embedding space[doc] meltPerforms a melt operation on a dataset[doc] mongodb.importImport a dataset from MongoDB[doc] permuter.runRun a child procedure with permutations of its configuration[doc] probabilizer.trainTrains a model to calibrate a score into a probability[doc] randomforest.binary.trainTrain a supervised binary random forest[doc] statsTable.b
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