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  2. NumPy - Wikipedia

    en.wikipedia.org/wiki/NumPy

    NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3] The predecessor of NumPy, Numeric, was originally created by Jim Hugunin with ...

  3. List of numerical-analysis software - Wikipedia

    en.wikipedia.org/wiki/List_of_numerical-analysis...

    pandas is a BSD-licensed library providing data structures and data analysis tools for the Python programming language. Perl Data Language provides large multidimensional arrays for the Perl programming language, and utilities for image processing and graphical plotting.

  4. Laplacian matrix - Wikipedia

    en.wikipedia.org/wiki/Laplacian_matrix

    Given a simple graph with vertices , …,, its Laplacian matrix is defined element-wise as [1],:= {⁡ = , or equivalently by the matrix =, where D is the degree matrix, and A is the graph's adjacency matrix.

  5. CuPy - Wikipedia

    en.wikipedia.org/wiki/CuPy

    CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.

  6. Covariance matrix - Wikipedia

    en.wikipedia.org/wiki/Covariance_matrix

    Throughout this article, boldfaced unsubscripted and are used to refer to random vectors, and Roman subscripted and are used to refer to scalar random variables.. If the entries in the column vector = (,, …,) are random variables, each with finite variance and expected value, then the covariance matrix is the matrix whose (,) entry is the covariance [1]: 177 ...

  7. Ordinary least squares - Wikipedia

    en.wikipedia.org/wiki/Ordinary_least_squares

    In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values ...

  8. Tab-separated values - Wikipedia

    en.wikipedia.org/wiki/Tab-separated_values

    Records are typically separated by a line feed, as is typical for Unix platforms, or a carriage return and line feed, as is typical for Microsoft platforms. Some programs may expect the latter. The de-facto specification [ 9 ] specifies that records are separated by an EOL , but does not specify any specific newline .

  9. Line integral - Wikipedia

    en.wikipedia.org/wiki/Line_integral

    The function to be integrated may be a scalar field or a vector field. The value of the line integral is the sum of values of the field at all points on the curve, weighted by some scalar function on the curve (commonly arc length or, for a vector field, the scalar product of the vector field with a differential vector in the curve

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