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In OCaml, the library function Oo.copy performs shallow copying of an object. In Python, the library's copy module provides shallow copy and deep copy of objects through the copy() and deepcopy() functions, respectively. [13] Programmers may define special methods __copy__() and __deepcopy__() in an object to provide custom copying implementation.
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A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
This could be modeled in an object-oriented implementation by a "Person object" with an attribute/field to hold each data item that the entry comprises: the person's name, a list of phone numbers, and a list of addresses. The list of phone numbers would itself contain "PhoneNumber objects" and so on.
The black set is the set of objects that can be shown to have no outgoing references to objects in the white set, and to be reachable from the roots. Objects in the black set are not candidates for collection. The grey set contains all objects reachable from the roots but yet to be scanned for references to "white" objects.
Computer algorithms for recognizing objects in photos often learn by example. CIFAR-10 is a set of images that can be used to teach a computer how to recognize objects. Since the images in CIFAR-10 are low-resolution (32x32), this dataset can allow researchers to quickly try different algorithms to see what works.
In software development, an object is an entity that has state, behavior, and identity. [ 1 ] : 78 An object can model some part of reality or can be an invention of the design process whose collaborations with other such objects serve as the mechanisms that provide some higher-level behavior.
For many algorithms that solve these tasks, the data in raw representation have to be explicitly transformed into feature vector representations via a user-specified feature map: in contrast, kernel methods require only a user-specified kernel, i.e., a similarity function over all pairs of data points computed using inner products.