We learn to predict the labels from feature vectors using the Logistic Regression algorithm. In this example, we take a dataset of labels and feature vectors. MLlib also provides tools such as ML Pipelines for building workflows, CrossValidator for tuning parameters,Īnd model persistence for saving and loading models. These algorithms cover tasks such as feature extraction, classification, regression, clustering, MLlib, Spark’s Machine Learning (ML) library, provides many distributed ML algorithms. save ( "s3a://." ) Machine learning example show () // Saves countsByAge to S3 in the JSON format. The fraction should be / 4, so we use this to get our estimate. We pick random points in the unit square ((0, 0) to (1,1)) and see how many fall in the unit circle. This code estimates by 'throwing darts' at a circle. printSchema () // Counts people by age DataFrame countsByAge = df. Spark can also be used for compute-intensive tasks. load () // Looks the schema of this DataFrame. String url = "jdbc:mysql://yourIP:yourPort/test?user=yourUsername password=yourPassword" DataFrame df = sqlContext. Creates a DataFrame based on a table named "people" // stored in a MySQL database. In this example, we read a table stored in a database and calculate the number of people for every age.įinally, we save the calculated result to S3 in the format of JSON.Ī simple MySQL table "people" is used in the example and this table has two columns, count () // Fetches the MySQL errors as an array of strings errors. count () // Counts errors mentioning MySQL errors. like ( "%ERROR%" )) // Counts all the errors errors. Firstly import math module Create function to calculate factorial Create function to calculate Pi by Ramanujans Formula Initialise sum0, n0, imath.sqrt. createDataFrame ( rowRDD, schema ) DataFrame errors = df. createStructType ( fields ) DataFrame df = sqlContext. StringType, true )) StructType schema = DataTypes. map ( RowFactory: : create ) List fields = Arrays. textFile ( "hdfs://." ) JavaRDD rowRDD = textFile. Yee & Shigeru Kondo were able to set a record of 10 Trillion 50 Digits of Pi using y-cruncher under a 2 x Intel Xeon X5680 3.33 GHz - (12 physical cores, 24 hyperthreaded) computer on Octo Super PI is much slower than these other programs, and utilizes inferior algorithms to them.// Creates a DataFrame having a single column named "line" JavaRDD textFile = sc. Last but not least, while SuperPi is unable to calculate more than 32 million digits, and Alexander J. Other multithreaded programs include: Hyper PI, IntelBurnTest, Prime95, Montecarlo superPI, OCCT or y-cruncher. Therefore, wPrime has been developed to support multiple threaded calculations to be run at the same time so one can test stability on multi-core machines. Super PI is single threaded, so its relevance as a measure of performance in the current era of multi-core processors is diminishing quickly. Super PI utilizes x87 floating point instructions which are supported on all x86 and x86-64 processors, current versions which also support the lower precision Streaming SIMD Extensions vector instructions. However, other methods exist of producing inaccurate or fake time results, raising questions about the program's future as an overclocking benchmark. Attempts to counter the fraudulent results resulted in a modified version of Super PI, with a checksum to validate the results. The competitive nature of achieving the best Super PI calculation times led to fraudulent Super PI results, reporting calculation times faster than normal. Super PI is popular in the overclocking community, both as a benchmark to test the performance of these systems and as a stress test to check that they are still functioning correctly. It uses Gauss–Legendre algorithm and is a Windows port of the program used by Yasumasa Kanada in 1995 to compute pi to 2 32 digits. Super PI is a computer program that calculates pi to a specified number of digits after the decimal point-up to a maximum of 32 million. Super PI finishing a calculation of 1,048,576, or 2 20 digits of pi
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