The advantage of using the SciPy library in Python when building ML models is that it also makes a powerful programming language available for use in developing less complex programs and applications.
Output: 2.999999999999997li >
| tr > |
Output: array ([[0., 1., 0.], [0., 0., 1.], [1., 0., 0.]]) array ([[1., 0., 0.], [0.14285714, 1., 0.], [0.57142857, 0.5, 1. ]]) array ([[7., 8., 8.], [0., 0.85714286, 1.85714286], [0., 0., 0.5]]) array ([[7., 8., 8.] , [1., 2., 3 .], [4., 5., 6.]])
Output : array ([15.55528261 + 0.j, -1.41940876 + 0.j, -0.13587385 + 0.j]) array ([[- 0.24043423, -0.67468642, 0.51853459], [-0.54694322, -0.23391616, - 0.78895962], [-0.80190056, 0.70005819, 0.32964312]])
Output: array ([, , ]) array ([ [-2.33333333], [3.66666667], [-1. ]])
SciPy has several routines for computing sparse and potentially very large matrices. The required tools are in the scipy.sparse submodule.
Let`s see how to build a large sparse matrix:
Output: & lt; 1000x1000 sparse matrix of type `` with 0 stored elements in LInked List format & gt; & lt; 1000x1000 sparse matrix of type `` with 1199 stored elements in LInked List format & gt;
Output: array ([- 2.53380006e + 03, -1.25513773e + 03, 9.14885544e-01, 2.74521543e + 00, 5.99942835e-01, 4.57778093e-01, 1.87104209e-01, 2.15228367e + 00, 8.78588432e-01, 1.85105721e + 03, 1.00842538e + 00, 4.33970632e + 00, 5.26601699e + 00, 2.17572231e-01, 1.79869079e + 00, 3.83800946e-01, 2.57817130e-01, 5.18025462e-01, 1.68672669e + 00, 3.07971950e + 00, 6.20604437e-01, 1.41365890e-01, 3.18167429e-01, 2.06457302e-01, 8.94813817e-01, 5.06084834e + 00, 5.00913942e-01, 1.37391305e + 00, 2.32081425e + 00, 4.98093749e + 00, 1.75492222e + 00, 3.17278127e-01, 8.50013844e-01, 1.17524493e + 00, 1.70173722e + 00, .............))
When a function is very difficult to integrate analytically, you can simply find a solution using numerical integration methods. SciPy can do numeric integration as well. Scipi has integration methods in the scipy.integrate module.
The quad function returns two values, where the first number is the integral value and the second — probable error in the value of the integral.
Output: (0.6666666666666667, 7.401486830834377e-15)
SciPy can do a lot do for example Fourier transforms, Bessel functions, etc.
You can refer to the documentation for more details!
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