Suppose that we have a linear autoassociator that has been de- signed for Q orthogonal prototype vectors of length R using the Hebb rule. The vector elements are either 1 or -1. i. Show that the Q prototype patterns are eigenvectors of the weight matrix.
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- If there is a non-singular matrix P such as P-1AP=D, matrix A is called a diagonalizable matrix. A, n x n square matrix is diagonalizable if and only if matrix A has n linearly independent eigenvectors. In this case, the diagonal elements of the diagonal matrix D are the eigenvalues of the matrix A. A=({{1, -1, -1}, {1, 3, 1}, {-3, 1, -1}}) : 1 -1 -1 1 3 1 -3 1 -1 a)Write a program that calculates the eigenvalues and eigenvectors of matrix A using NumPy. b)Write the program that determines whether the D matrix is diagonal by calculating the D matrix, using NumPy. #UsePython. The determinant of an n X n matrix can be used in solving systems of linear equations, as well as for other purposes. The determinant of A can be defined in terms of minors and cofactors. The minor of element aj is the determinant of the (n – 1) X (n – 1) matrix obtained from A by crossing out the elements in row i and column j; denote this minor by Mj. The cofactor of element aj, denoted by Cj. is defined by Cy = (-1y**Mg The determinant of A is computed by multiplying all the elements in some fixed row of A by their respective cofactors and summing the results. For example, if the first row is used, then the determi- nant of A is given by Σ (α(CI) k=1 Write a program that, when given n and the entries in an n Xn array A as input, computes the deter- minant of A. Use a recursive algorithm.Verify that the full perspective to canonical matrix Mprojection takes (r, t, n) to (1, 1, 1).
- Find the eigenvalues of the matrix and determine whether there is a sufficient number to guarantee that the matrix is diagonalizable. (Recall that the matrix may be diagonalizable even though it is not guaranteed to be diagonalizable by the theorem shown below.) Sufficient Condition for Diagonalization If an n xn matrix A has n distinct eigenvalues, then the corresponding eigenvectors are linearly independent and A is diagonalizable. Find the eigenvalues. (Enter your answers as a comma-separated list.) Is there a sufficient number to guarantee that the matrix is diagonalizable? O Yes O No Need Help? Read it4. Consider the set V of vectors (x, X2, X3, X4) ER such that X1 + x3 = 0 and x, + x4 = 0. a) Prove that V is a subspace of R*. b) Give a basis and the dimension of V.2. For a n-vector x, and X1 + x2 X2 + x3 y = Ax = Xn-1 + Xn a) Find A b) Are the columns of A linearly independent? Justify your answer? c) Are the rows of A linearly independent? Justify your answer?
- Current Attempt in Progress Suppose that a 4x 4 matrix A has eigenvalues A = 1,A2=-7. As-10, and A4=-10. Use the following method to find tr(A). If A is a square matrix and p(A) = det (Al - A) is the characteristic polynomial of A, then the coefficient of in p) is the negative of the trace of A. tr(A) =Assume A is k x n-matrix and P is k × k-invertible matrix. Prove that rank(PA) = rank(A).Suppose we have a matrix A of dimension n x n of rank n - 1. Explain two numeri- cally stable ways to find a nonzero vector z so that Az = 0.
- Using the algorithm for Gauss-Jordan without pivoting, code a function myGaussJordanNoPivotthat takes as input a matrix A and collection of q right-hand sides bl forming the q columns of of a matrix B. Yourfunction shall return the q solution vectors xl as columns of a matrix X. Do not use any Matlab build-in functionsfor solving matrices"onsdensiks Consider the m x n-matrix A and the vector be Rm that are given by A = [aij], 6 = [bi] where aij = (–1)++i(i – j), b; = (-1)' for i = 1,... m and j = 1,..· , n. %3D Note that aij is the entry of A at the i-th row and the j-th column. Consider the following condition on vectors iE R": (Condition1) Aa õ. Write and run a python Jupyter notebook using the PULP package, to check whether there is a vector iE R" that satisfies (Condition1), • when m =n = 10, and if it exists, find one. Submit your screenshots or the pdf file of your Jupyter Notebook; it should show both the codes and the results. [For credits, you must write a python Jupyter notebook to solve this problem.] Python Hint: Start with giving М-10 N=10 You may want to use Python lists combined with 'forloop'. For example, column=[j+1 for j in range(N)] row=[i+1 for i in range(M)] Then, one can define the matrix A and the vector 6, using Python dictionaries combined with 'forloop' as a= {i:{j:(-1)**(i+j)*(i-j) for j in…Prove that in a given vector space V, the zero vector is unique. Suppose, by way of contradiction, that there are two distinct additive identities 0 and u,. Which of the following statements are then true about the vectors 0 and u,? (Select all that apply.) O The vector 0 + u, is not equal to u, + 0. O The vector 0 + u, is equal to un: O The vector 0 + u, is not equal to 0. O The vector 0 + u, does not exist in the vector space V. O The vector 0 + u, is equal to 0. O The vector o + u, is not equal to u: Which of the following is a result of the true statements that were chosen and what contradiction then occurs? O The statement u, + o 0, which contradicts that u, is an additive identity. O The statement u, +0 # 0 + u, which contradicts the commutative property. O The statement u, = 0, which contradicts that there are two distinct additive identities. O The statement u, + 0 U, which contradicts that O is an additive identity. O The statement u, + 0 + 0, which contradicts that u, must…