Python code Archives — MATLAB Number ONE https://matlab1.com/product-category/python-code/ MATLAB Simulink | Tutorial | Code | Project Mon, 15 Jan 2024 15:34:31 +0000 en-GB hourly 1 https://matlab1.com/wp-content/uploads/2018/08/icon1-100x100.png Python code Archives — MATLAB Number ONE https://matlab1.com/product-category/python-code/ 32 32 Find the least squares solution of the inconsistent linear system https://matlab1.com/shop/matlab-code/find-the-least-squares-solution-of-the-inconsistent-linear-system/ https://matlab1.com/shop/matlab-code/find-the-least-squares-solution-of-the-inconsistent-linear-system/#respond Sun, 07 Mar 2021 12:57:31 +0000 https://matlab1.com/?post_type=product&p=7760 Find the least-squares solution of the inconsistent linear system Ax = b, where:     The normal equations are given by where, The least squares solution xˆ is obtained by solving the normal equations. In MATLAB: >> A = [1 2 4; 3 1 5; 1 1 1; 2 2 1; 3 1 3] >> […]

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]]> https://matlab1.com/shop/matlab-code/find-the-least-squares-solution-of-the-inconsistent-linear-system/feed/ 0 Python code for successive over-relaxation (SOR) method https://matlab1.com/shop/python-code/python-code-for-successive-over-relaxation-sor-method/ https://matlab1.com/shop/python-code/python-code-for-successive-over-relaxation-sor-method/#respond Sun, 07 Mar 2021 11:36:23 +0000 https://matlab1.com/?post_type=product&p=7758 This MATLAB function receives a matrix A, a vector b, an initial starting vector x0, a real value ω, and a tolerance ε, and returns an approximate solution of the system Ax = b within the given tolerance together with the number of iterations.   x = [[-1.00000000e+00] [-1.94732314e-10] [ 1.00000000e+00]] Iterations = 16   […]

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]]> https://matlab1.com/shop/python-code/python-code-for-successive-over-relaxation-sor-method/feed/ 0 Python code implements the Gauss-Seidel method in the matrix form https://matlab1.com/shop/python-code/python-code-implements-the-gauss-seidel-method-in-the-matrix-form/ https://matlab1.com/shop/python-code/python-code-implements-the-gauss-seidel-method-in-the-matrix-form/#respond Fri, 19 Feb 2021 07:28:20 +0000 https://matlab1.com/?post_type=product&p=7748    

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]]> https://matlab1.com/shop/python-code/python-code-implements-the-gauss-seidel-method-in-the-matrix-form/feed/ 0 Python code of the Gauss-Seidel method https://matlab1.com/shop/python-code/python-code-of-the-gauss-seidel-method/ https://matlab1.com/shop/python-code/python-code-of-the-gauss-seidel-method/#respond Fri, 19 Feb 2021 07:19:27 +0000 https://matlab1.com/?post_type=product&p=7745     A = np.array([[-5, 1, -2], [1, 6, 3], [2, -1, -4]]) b = np.array([[13], [1], [-1]]) x0 = np.zeros((3, 1), 'float') Eps = 1e-8 Iterations = 16 x = [[-2.] [ 1.] [-1.]]

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]]> https://matlab1.com/shop/python-code/python-code-of-the-gauss-seidel-method/feed/ 0 Python code of the Jacobi method in the matrix form https://matlab1.com/shop/python-code/python-code-of-the-jacobi-method-in-the-matrix-form/ https://matlab1.com/shop/python-code/python-code-of-the-jacobi-method-in-the-matrix-form/#respond Fri, 19 Feb 2021 06:23:13 +0000 https://matlab1.com/?post_type=product&p=7742 Matrix A can be expressed as: A = L+ D +U therefore, the linear system Ax = b can be written as: (L+ D +U)x = b The Jacobi method chooses S = D and T = L+U. It is worthy to notice that no diagonal element in D can be 0. That is , […]

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]]> https://matlab1.com/shop/python-code/python-code-of-the-jacobi-method-in-the-matrix-form/feed/ 0 The Jacobi Method for linear system of equations https://matlab1.com/shop/matlab-code/the-jacobi-method-for-linear-system-of-equations/ https://matlab1.com/shop/matlab-code/the-jacobi-method-for-linear-system-of-equations/#respond Thu, 18 Feb 2021 12:38:40 +0000 https://matlab1.com/?post_type=product&p=7719 MATLAB and Python code for Jacobi method :   Given the linear system of equations: From the above equation, follows that: The Jacobi method is an iterative method, which starts from an initial guess for the solution Then, the solution in iteration k is used to find an approximation for the system solution in iteration […]

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]]> https://matlab1.com/shop/matlab-code/the-jacobi-method-for-linear-system-of-equations/feed/ 0 Python code for backward substitution method for solving the linear system https://matlab1.com/shop/python-code/python-code-for-backward-substitution-method-for-solving-the-linear-system/ https://matlab1.com/shop/python-code/python-code-for-backward-substitution-method-for-solving-the-linear-system/#respond Thu, 18 Feb 2021 08:46:38 +0000 https://matlab1.com/?post_type=product&p=7718 This function uses the backward substitution method for solving the linear system Ax = b, where A is an upper triangular matrix b is a known vector and n is the dimension of the problem. Example :     A = np.diag([2, -1, 3]) b = np.array([4, -1, 3]) Final result of the python code […]

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]]> https://matlab1.com/shop/python-code/python-code-for-backward-substitution-method-for-solving-the-linear-system/feed/ 0 MNE package tutorial https://matlab1.com/shop/python-code/mne-package-tutorial/ https://matlab1.com/shop/python-code/mne-package-tutorial/#respond Wed, 30 Dec 2020 18:52:09 +0000 https://matlab1.com/?post_type=product&p=7084 MSE package is a package that can be used for application related to human neurophysiological data. In this course, we will learn you how to work with this dataset in MATLAB.  

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]]> https://matlab1.com/shop/python-code/mne-package-tutorial/feed/ 0 Denoising using autoencoders in TensorFlow https://matlab1.com/shop/python-code/denoising-using-autoencoders-in-tensorflow/ https://matlab1.com/shop/python-code/denoising-using-autoencoders-in-tensorflow/#respond Sun, 10 Jun 2018 11:03:13 +0000 https://matlab1.com/?post_type=product&p=5590 Autoencoders can also be used for image denoising. Denoising is the process of removing noise from the image. A denoising encoder can be trained in an unsupervised manner. The noise can be introduced in a normal image and the autoencoder is trained against the original images. Later, the full autoencoder can be used to produce […]

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]]> https://matlab1.com/shop/python-code/denoising-using-autoencoders-in-tensorflow/feed/ 0 Extracting bottleneck features for an image in tensorflow https://matlab1.com/shop/python-code/extracting-bottleneck-features-for-an-image-in-tensorflow/ https://matlab1.com/shop/python-code/extracting-bottleneck-features-for-an-image-in-tensorflow/#respond Sun, 10 Jun 2018 10:44:42 +0000 https://matlab1.com/?post_type=product&p=5576 Bottleneck features are the values computed in the pre-classification layer. In this product, we will see how to extract the bottleneck features from a pre-trained model using TensorFlow. Let’s start by importing the required libraries, using the following code: import os import urllib.request from tensorflow.python.platform import gfile import tarfile       Running the above code […]

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