Found 46 Articles for Scipy

Clustering is a technique in machine learning and data science that involves grouping together similar data points or objects into clusters or subsets. The goal of clustering is to find patterns and structures in data that may not be immediately apparent, and to group related data points together which can be used for further analysis. In this article, we are going to see how to implement clustering with the help of the SciPy library. SciPy provides us with various scientific computing tools to perform tasks like numerical integration, optimization, linear algebra, signal processing etc. It's used by researchers, scientists, engineers, ... Read More

What are the uses of gauss pulse? Gaussian pulses are widely used in signal processing, particularly in radar, sonar, and communications. This pulse is a pulse that has a Gaussian shape in the time domain, which makes it useful for detecting small signals that may be obscured by noise. In this tutorial, we will explore how to generate a Gaussian pulse using the scipy.signal.gausspulse function. What is a Gaussian Pulse? Gaussian pulses are a type of function that has a Gaussian-shaped envelope in the time domain. The Gaussian function is a bell-shaped curve that is symmetrical around its peak. It ... Read More

The sqrtm() function of scipy.linalg package can be used to find the square root of an input matrix.Syntaxscipy.linalg.sqrtm(x)Example 1Let us consider the following example −# Importing the required libraries from scipy from scipy import linalg import numpy as np # Define the input array x = np.array([[14 , 2] , [89 , 33]]) print("Input array:", x) # Calculate the square root r = linalg.sqrtm(x) # Display the square root print("Square Root of x: ", r)OutputIt will generate the following output −Input array: [[14 2] [89 33]] Square Root of x: [[3.43430132 0.22262855] [9.90697038 5.54927253]]Example 2Let us take ... Read More

The norm() function of the scipy.linalg package is used to return one of eight different matrix norms or one of an infinite number of vector norms.Syntaxscipy.linalg.norm(x)Where x is an input array or a square matrix.Example 1Let us consider the following example −# Importing the required libraries from scipy from scipy import linalg import numpy as np # Define the input array x = np.array([7 , 4]) print("Input array:", x) # Calculate the L2 norm r = linalg.norm(x) # Calculate the L1 norm s = linalg.norm(x, 3) # Display the norm values print("Norm Value of r :", ... Read More

The scipy.linalg package contains a of different functionalities that are used for Linear Algebra. One of them is the inv() function, which is used to find the inverse of a square matrix.Syntaxscipy.linalg.inv(x)Where x is a square matrix.Example 1Let us consider the following example −# Import the required libraries from scipy import linalg import numpy as np # defines the array a = np.array([[5, 3], [6, 4]]) print("Input matrix :", a) # Finding the inverse of a square matrix x = linalg.inv(a) print(" Inverse of Square Matrix A :", x)OutputThe above program will generate the following output −Input matrix ... Read More

The scipy.linalg package contains a set of different functionalities that are used for Linear Algebra. One of them is the det() function. This function is used to find the determinant of a two-dimensional matrix.Syntaxscipy.linalg.det(x)Where x is a square matrix.Example 1Let us consider the following example −# Importing the required libraries from scipy import linalg import numpy as np # Initialize the matrix A A = np.array([[8, 5], [3, 4]]) print("Input Matrix :", A) # Find the determinant of matrix X x = linalg.det(A) print("Determinant Value of A:", x)OutputIt will generate the following output −Input Matrix : [[8 5] ... Read More

The scipy.special package contains a set of different functionalities that are used for mathematical physics. One of them is the logsumexp() function. This function is used to compute the log of the sum of exponentials of input elements. Let us take a couple of examples and see how to use this function.Syntaxscipy.special.logsumexp(x)where, x is the input value.Example 1Let us consider the following example −# Import logsumexp from scipy.special from scipy.special import logsumexp import numpy as np # Input array a = np.arange(10) print("Input Array:", a) # logsum() function res = logsumexp(a) print("logsumexp of a:", res)OutputIt will produce the ... Read More

The linear function named scipy.linalg.solve_toeplitz is used to solve the Toeplitz matrix equation. The form of this function is as follows −scipy.linalg.solve_toeplitz(c_or_cr, b, check_finite=True)This linear function will solve the equation Tx = b for x where T is the Toeplitz matrix.ParametersBelow are given the parameters of the function scipy.linalg.solve_toeplitz()c_or_cr− array_like or tuple of (array_like, array_like)This parameter is the vector c or tuple of arrays (c, r). Despite the actual shape of c, it will always be converted to a one-dimensional array. If r is not given, the assumption made is r = conjugate(c). Below are given two cases −    ... Read More

The linear function named scipy.linalg.solveh_triangular is used to solve the banded matrix equation. In the below given example we will be solving the triangular system ax = b where −$$\mathrm{a} = \begin{bmatrix} 3 & 0 & 0 & 0\ 2 & 1 & 0 & 0\ 1 &0 &1 &0 \ 1& 1& 1& 1 \end{bmatrix};\; \mathrm{b} =\begin{bmatrix} 1\ 2\ 1\ 2 \end{bmatrix}$$Examplefrom scipy.linalg import solve_triangular import numpy as np a = np.array([[3, 0, 0, 0], [2, 1, 0, 0], [1, 0, 1, 0], [1, 1, 1, 1]]) b = np.array([1, 2, 1, 2]) x = solve_triangular(a, b, lower=True) print (x)Outputarray([ 0.33333333, 1.33333333, 0.66666667, -0.33333333])

The linear function named scipy.linalg.solve_triangular is used to solve the triangular matrix e8quation. The form of this function is as follows −scipy.linalg.solve_triangular(a, b, trans=0, lower=False, unit_diagonal=False, overwrite_b=False, debug=None, check_finite=True)This linear function will solve the equation ax = b for x where a is a triangular matrix.P ParametersBelow are given the parameters of the function scipy.linalg.solve_triangular() −a− (M, M) array_likeThis parameter represents the triangular matrix.b− (M, ) or (M, N)array_likeThis parameter represents the right-hand side matrix in the equation ax = b.lower− bool, optionalBy using this parameter, we will be able to use only the data that is contained in the ... Read More