By Karl Lieberherr
This ebook offers a whole, well-designed technique for adaptive programming in C++ and instruments for assisting the technique. and as the method is programming-language autonomous, any programmer drawn to writing cleanser, extra versatile OBJECT-ORIENTED code may still learn this publication. we are hoping the paintings awarded during this booklet becomes one of many construction blocks for a brand new development in object-oriented programming, relocating past item encapsulation to supply new abstraction instruments for the interplay between gadgets.
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Extra resources for Adaptive object-oriented software: the Demeter method
Terminology Extended learning classifier systems XCS components Application to portfolio management The XCS core data XCS rules Covering An implementation example Benefits and limitations of learning classifier systems Summary 12. Scalable Frameworks An overview Scala Object creation Streams Parallel collections Processing a parallel collection The benchmark framework Performance evaluation Scalability with Actors The Actor model Partitioning Beyond actors – reactive programming Akka Master-workers Exchange of messages Worker actors The workflow controller The master actor Master with routing Distributed discrete Fourier transform Limitations Futures The Actor life cycle Blocking on futures Handling future callbacks Putting it all together Apache Spark Why Spark?
To function effectively, every member of the team must nevertheless have a general working knowledge of all three areas. To give a more concrete overview of the workflow in a data science project, let's imagine that we are trying to write an application that analyzes the public perception of a political campaign. This is what the data science pipeline might look like: Obtaining data: This might involve extracting information from text files, polling a sensor network or querying a web API. Data ingestion: Data often comes from many different sources and might be unstructured or semi-structured.
Hello World! Modeling A model by any other name Model versus design Selecting features Extracting features Defining a methodology Monadic data transformation Error handling Explicit models Implicit models A workflow computational model Supporting mathematical abstractions Step 1 – variable declaration Step 2 – model definition Step 3 – instantiation Composing mixins to build a workflow Understanding the problem Defining modules Instantiating the workflow Modularization Profiling data Immutable statistics Z-Score and Gauss Assessing a model Validation Key quality metrics F-score for binomial classification F-score for multinomial classification Cross-validation One-fold cross validation K-fold cross validation Bias-variance decomposition Overfitting Summary 3.