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  2. Golden-section search - Wikipedia

    en.wikipedia.org/wiki/Golden-section_search

    Specify the function to be minimized, ⁠ ⁠, the interval to be searched as {X 1,X 4}, and their functional values F 1 and F 4. Calculate an interior point and its functional value F 2. The two interval lengths are in the ratio c : r or r : c where r = φ − 1; and c = 1 − r, with φ being the golden ratio.

  3. 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]

  4. Jaccard index - Wikipedia

    en.wikipedia.org/wiki/Jaccard_index

    The Probability Jaccard Index has a geometric interpretation as the area of an intersection of simplices. Every point on a unit -simplex corresponds to a probability distribution on + elements, because the unit -simplex is the set of points in + dimensions that sum to 1. To derive the Probability Jaccard Index geometrically, represent a ...

  5. Z-order curve - Wikipedia

    en.wikipedia.org/wiki/Z-order_curve

    The Z-ordering can be used to efficiently build a quadtree (2D) or octree (3D) for a set of points. [5] [6] The basic idea is to sort the input set according to Z-order.Once sorted, the points can either be stored in a binary search tree and used directly, which is called a linear quadtree, [7] or they can be used to build a pointer based quadtree.

  6. Empirical distribution function - Wikipedia

    en.wikipedia.org/wiki/Empirical_distribution...

    Its value at any specified value of the measured variable is the fraction of observations of the measured variable that are less than or equal to the specified value. The empirical distribution function is an estimate of the cumulative distribution function that generated the points in the sample.

  7. Multidimensional scaling - Wikipedia

    en.wikipedia.org/wiki/Multidimensional_scaling

    An R-square of 0.6 is considered the minimum acceptable level. [citation needed] An R-square of 0.8 is considered good for metric scaling and .9 is considered good for non-metric scaling. Other possible tests are Kruskal’s Stress, split data tests, data stability tests (i.e., eliminating one brand), and test-retest reliability.

  8. Chebyshev nodes - Wikipedia

    en.wikipedia.org/wiki/Chebyshev_nodes

    For both kinds of nodes, we first plot the points equi-distant on the upper half unit circle in blue. Then the blue points are projected down to the x-axis. The projected points, in red, are the Chebyshev nodes. In numerical analysis, Chebyshev nodes are a set of specific real algebraic numbers, used as nodes for polynomial interpolation.

  9. Logistic map - Wikipedia

    en.wikipedia.org/wiki/Logistic_map

    If we restrict the variables to 0 ≤ x ≤ 1, then the range of the parameter r is necessarily restricted to 0 to 4 (0 ≤ r ≤ 4). This is because if is in the range [0, 1], then the maximum value of + is r/4. Thus, when r > 4, the value of + can exceed 1. On the other hand, when r is negative, x can take negative values.