Search results
Results from the WOW.Com Content Network
Since Python is a dynamically-typed language, Python values, not variables, carry type information. All variables in Python hold references to objects, and these references are passed to functions. Some people (including Guido van Rossum himself) have called this parameter-passing scheme "call by object reference".
Pandas' syntax for mapping index values to relevant data is the same syntax Python uses to map dictionary keys to values. For example, if s is a Series, s['a'] will return the data point at index a. Unlike dictionary keys, index values are not guaranteed to be unique.
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
The syntax :=, called the "walrus operator", was introduced in Python 3.8. It assigns values to variables as part of a larger expression. [105] In Python, == compares by value. Python's is operator may be used to compare object identities (comparison by reference), and comparisons may be chained—for example, a <= b <= c.
In Python, a generator can be thought of as an iterator that contains a frozen stack frame. Whenever next() is called on the iterator, Python resumes the frozen frame, which executes normally until the next yield statement is reached. The generator's frame is then frozen again, and the yielded value is returned to the caller.
A function object solves those problems since the function is really a façade for a full object, carrying its own state. Many modern (and some older) languages, e.g. C++, Eiffel, Groovy, Lisp, Smalltalk, Perl, PHP, Python, Ruby, Scala, and many others, support first-class function objects and may even make significant use of them. [3]
An example of unsupervised dictionary learning is sparse coding, which aims to learn basis functions (dictionary elements) for data representation from unlabeled input data. Sparse coding can be applied to learn overcomplete dictionaries, where the number of dictionary elements is larger than the dimension of the input data. [ 21 ]
Sparse dictionary learning (also known as sparse coding or SDL) is a representation learning method which aims to find a sparse representation of the input data in the form of a linear combination of basic elements as well as those basic elements themselves. These elements are called atoms, and they compose a dictionary.