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Haskell data analysis cookbook : explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes /
Step-by-step recipes filled with practical code samples and engaging examples demonstrate Haskell in practice, and then the concepts behind the code. This book shows functional developers and analysts how to leverage their existing knowledge of Haskell specifically for high-quality data analysis. A...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham [England] :
Packt Publishing,
2014.
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| Colección: | Quick answers to common problems.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: The Hunt for Data; Introduction; Harnessing data from various sources; Accumulating text data from a file path; Catching I/O code faults; Keeping and representing data from a CSV file; Examining a JSON file with the aeson package; Reading an XML file using the HXT package; Capturing table rows from an HTML page; Understanding how to perform HTTP GET requests; Learning how to perform HTTP POST requests; Traversing online directories for data
- Using MongoDB queries in HaskellReading from a remote MongoDB server; Exploring data from a SQLite database; Chapter 2: Integrity and Inspection; Introduction; Trimming excess whitespace; Ignoring punctuation and specific characters; Coping with unexpected or missing input; Validating records by matching regular expressions; Lexing and parsing an e-mail address; Deduplication of nonconflicting data items; Deduplication of conflicting data items; Implementing a frequency table using Data.List; Implementing a frequency table using Data.MultiSet; Computing the Manhattan distance
- Computing the Euclidean distanceComparing scaled data using the Pearson correlation coefficient; Comparing sparse data using cosine similarity; Chapter 3: The Science of Words; Introduction; Displaying a number in another base; Reading a number from another base; Searching for a substring using Data.ByteString; Searching a string using the Boyer-Moore-Horspool algorithm; Searching a string using the Rabin-Karp algorithm; Splitting a string on lines, words, or arbitrary tokens; Finding the longest common subsequence; Computing a phonetic code; Computing the edit distance
- Computing the Jaro-Winkler distance between two stringsFinding strings within one-edit distance; Fixing spelling mistakes; Chapter 4: Data Hashing; Introduction; Hashing a primitive data type; Hashing a custom data type; Running popular cryptographic hash functions; Running a cryptographic checksum on a file; Performing fast comparisons between data types; Using a high-performance hash table; Using Google's CityHash hash functions for strings; Computing a Geohash for location coordinates; Using a bloom filter to remove unique items; Running MurmurHash, a simple but speedy hashing algorithm
- Measuring image similarity with perceptual hashesChapter 5: The Dance with Trees; Introduction; Defining a binary tree data type; Defining a rose tree (multiway tree) data type; Traversing a tree depth-first; Traversing a tree breadth-first; Implementing a Foldable instance for a tree; Calculating the height of a tree; Implementing a binary search tree data structure; Verifying the order property of a binary search tree; Using a self-balancing tree; Implementing a min-heap data structure; Encoding a string using a Huffman tree; Decoding a Huffman code; Chapter 6: Graph Fundamentals