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Desktop video games became an incredible cultural and monetary strength, and a topic of intensive educational curiosity. Up before, even though, machine video games have got particularly little recognition from philosophy. trying to therapy this, the current choice of newly written papers through philosophers and media researchers addresses various philosophical questions relating to 3 problems with an important significance for realizing the phenomenon of desktop video games: the character of gameplay and participant event, the ethical evaluability of participant and avatar activities, and the truth prestige of the gaming surroundings. by way of doing so, the publication goals to set up the philosophy of desktop video games as a major strand of desktop video games learn, and as a separate box of philosophical inquiry.

The booklet is needed examining for an individual with a tutorial or specialist curiosity in desktop video games, and also will be of price to readers taken with the philosophical concerns raised by means of modern electronic tradition.

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Read or Download The Philosophy of Computer Games (Philosophy of Engineering and Technology, Volume 7) PDF

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The Philosophy of Computer Games (Philosophy of Engineering and Technology, Volume 7)

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Create a New NumPy Array in C++ and Return it to Python This code creates a new one-dimensional array of extent n, where n is provided by the caller and assigns it the values 0, . . , n − 1. PyObject* make array(int n) { int dimensions[1]; dimensions[0] = n; PyArrayObject* result = 26 Financial Modelling in Python reinterpret cast( boost::python::expect non null( PyArray FromDims(1, dimensions, PyArray DOUBLE))); double* buffer = reinterpret cast(result->data); for(int i = 1; i < n; ++i) buffer[i] = i; return PyArray Return(result); } Back to the interpreter for one last time: >>> a = make array(6) >>> print a [ 0.

0. 5, -1. 2 ], [ 14. 6 GENERALISED LINEAR LEAST SQUARES Generalised linear least squares is a method for fittin a set of data points (xi , yi )i = 1, linear combination of basis functions. 9) k=1 where f 1 (x), . . , f M (x) are the basis functions. The central idea behind the method is to fin the fittin coefficient a1 , . . , aM by minimising the merit function χ2 = N yi − M k=1 ak f k (xi ) σi i=1 2 . 10) The σ i represent the measurement error, or equivalently the standard deviation, of the ith data point.

5, -1. 2 ], [ 14. 5], [-1. 7 Singular Value Decomposition We show how to calculate the singular value decomposition of an M × N matrix A, with M ≥ N, into the product of a M × N orthogonal matrix U, an N × N diagonal matrix W with positive or zero elements (the singular values), and the transpose of an N × N orthogonal matrix V. svd and the following code snippet illustrates a sample call to svd. ]])) >>> print A [[ 1. ] [ 3. ] [ 5. 52551809 0. ] [ 0. 51430058] [ 0. 0. ]]) Given a singular value decomposition of a matrix A = UWV, we can easily solve the matrix equation Ax = b using back substitution.

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