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Haskell data analysis cookbook : explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes / / Nishant Shukla ; cover image by Jarek Blaminsky
| Haskell data analysis cookbook : explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes / / Nishant Shukla ; cover image by Jarek Blaminsky |
| Autore | Shukla Nishant |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham, [England] : , : Packt Publishing, , 2014 |
| Descrizione fisica | 1 online resource (334 p.) |
| Disciplina | 005.133 |
| Collana | Quick answers to common problems |
| Soggetto topico | Haskell (Computer program language) |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-78328-634-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 Introduction |
| Record Nr. | UNINA-9910464631403321 |
Shukla Nishant
|
||
| Birmingham, [England] : , : Packt Publishing, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Haskell data analysis cookbook : explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes / / Nishant Shukla ; cover image by Jarek Blaminsky
| Haskell data analysis cookbook : explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes / / Nishant Shukla ; cover image by Jarek Blaminsky |
| Autore | Shukla Nishant |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham, [England] : , : Packt Publishing, , 2014 |
| Descrizione fisica | 1 online resource (334 p.) |
| Disciplina | 005.133 |
| Collana | Quick answers to common problems |
| Soggetto topico | Haskell (Computer program language) |
| ISBN | 1-78328-634-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 Introduction |
| Record Nr. | UNINA-9910786672203321 |
|
Shukla Nishant
|
||
| Birmingham, [England] : , : Packt Publishing, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Haskell data analysis cookbook : explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes / / Nishant Shukla ; cover image by Jarek Blaminsky
| Haskell data analysis cookbook : explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes / / Nishant Shukla ; cover image by Jarek Blaminsky |
| Autore | Shukla Nishant |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Birmingham, [England] : , : Packt Publishing, , 2014 |
| Descrizione fisica | 1 online resource (334 p.) |
| Disciplina | 005.133 |
| Collana | Quick answers to common problems |
| Soggetto topico | Haskell (Computer program language) |
| ISBN | 1-78328-634-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 Introduction |
| Record Nr. | UNINA-9910828283303321 |
|
Shukla Nishant
|
||
| Birmingham, [England] : , : Packt Publishing, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||