Utilize the following ordered sets of vectors, each which represents a basis for R³, for this activity.ScriptB =C =1 %Store the ordered basis vectors for the set B in the matrix B.23 %Store the ordered basis vectors for the set C in the matrix C.45 %Store the augmented matrix [C | B] in tempCB.6SaveC ResetMATLAB Documentation7 %Row reduce the augmented matrix tempCB to get [I | P_BtoC]. Store the reduced matrix in temp_reduced.89%Store the change of coordinates matrix from basis B to basis C in P_BtoC.10

Calculating the determinants gives us zero therefore the vector in set B are linearly dependent The…

Answered: Utilize the following ordered sets of…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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def weighted_mse_grad(w, X, y, V):"""Calculate gradient of the weight MSE loss in the given point w Parameters----------w: array_likeCurrent point, in which we want to evaluate gradient (vector with k+1 elements)X: array_likeDesign matrix with N rows and k+1 columnsy: array_likeVector with N observations of the target variableV: array_likeDiagonal matrix N x N, with the weightsReturns----------out: ndarrayvector of gradient, k+1"""# your code here # TEST weighted_mse_grad()w = np.zeros(5)X = np.random.randn(10, 5)y = np.ones(10)V = np.diag(np.ones(10)) L_grad = weighted_mse_grad(w, X, y, V)assert(L_grad.shape == (5,)) w = np.zeros(5)X = np.eye(5)y = np.ones(5)V = np.diag(np.ones(5))L_grad = weighted_mse_grad(w, X, y, V)assert(L_grad.shape == (5,)), 'The shape of the output on the test case is wrong'assert(np.allclose(5 * L_grad,- y * 2)), 'The values of the output on the test case is wrong'
A: If x=[26 12; 15 6 3; 10 11 1], then a) replace the first row elements of matrix x with its average value. b) reshape this matrix into row vector.
In matlap The empty vector operator is used to delete row or column in matrix От OF In matlap A row vector is converted to a column vector using the transpose operator True False In matlap We can view a particular element in a matrix by specifying its location OF
The command (find) is -:used to O O find the maximum number within a matrix find the minimum number within a matrix extract the numbers satisfying a condition evaluate the average of a group of numbers determine the locations of numbers satisfying a condition O O
The empty vector operator is used to delete row or column in matrix In matlab T OF A row vector is converted to a column vector using the transpose operator In matlab True False We can view a particular element in a matrix by specifying its location In matlab T F
Calculate sparsity of a sparse matrix in the Python code by finding the percentage of the number of non-zero elements in the matrix and subtracting this value from 1.which finds the manually calculated sparsity
Create a row vector of 1's that has 4 rows. Now Create a column vector of 1's that has 4 columns. Using matrix multiplication, multiply both of these to get a 4x4 matrix and display the resulting matrix. Using matlab, do this. Please answer it correctly and don’t copy from anywhere otherwise it will be downvoted.
نقطتان (2) * When the command (max) is applied to a two dimensional matrix it evaluates the maximum number of .all matrix elements True O False оо
Checking the overlap status of the courses on the same day in the curriculum in a section will be done. The starting hours and hours of the lessons A[N][2] for N lessons given in the matrix. where each lesson is represented by a node and if two lessons overlap create the neighborhood matrix where the connection between them is 1 and 0 if they do not overlap, and this matrix The code of the program that prints the lessons that conflict with a given lesson using the C Write in your language. Example: Below is the overlap graph for the start time and duration of the 3 courses. Lesson No, Start Time, Time
The formula C=numpy.add(A, B) adds the two matrices A and B and stores the result in C.• To subtract matrix B from matrix A, enter C=numpy.subtract(A, B). The result is stored in C.• C=numpy.divide(A, B): Split matrix A into two equal parts and store the result in C.• C stands for numpy.multiply(A, B): Multiply matrices A and B, then store the result in matrices C.• C=numpy.sum(A): Calculate the sum of each element in the matrix A, then store the result in c R.• C=numpy.sum(A, axis = 0): Create a vector C by adding the columns of the matrix A.• C=numpy.sum(A, axis = 1): Summarize matrix A row-by-row and store the result in the vector C. Create Python code that demonstrates the use of these techniques in a sample matrix.
Linear Equation SystemLU Decomposition Method AlgorithmDecomposition phase The LU Decomposition (Doolittle) method has the following properties: The U matrix is identical to the upper triangular matrix resulting from the Gaussian Elimination;The elements at the bottom below the main diagonal of the matrix L are the multipliers used during the Gaussian Elimination, that is, Li*j is the multiplier that eliminated Ai*j.It is common practice to store the multipliers at the bottom of the coefficient matrix, replacing the coefficients as they are eliminated (Li*j replacing Ai*j) The diagonal elements of L do not need to be stored, as their values are understood to be unitary. The final form of the coefficient matrix would be the mixture of L and U: The initial part of the LU Decomposition Method algorithm is identified with Gaussian Elimination, except that each λ multiplier is now stored in the lower triangular portion of matrix A: solve the system of linear equations followed by the…
To create a matrix that has multiple rows, separate the rows with Semicolons ● O.Space Vector ● • row Other: *

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