a) Define all boundaries for Z(X, Y) (5%) b) Define the boundary values for each boundary (7%). c) Define the test cases for each boundary. (8%)
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- Now, we'll instead use sklearn's train_test_split() function here to define our train and test set. Store train data (predictors) into MR_train_X and labels (outcomes) into MR_train_Y. Similarly, store test data into MR_test_X and test labels into MR_test_Y. In addition to providing the predictors (MR_X) and outcomes (MR_Y) to the function, we will use the following arguments for this task: test_size: 0.2 random_state: 200Write a user-defined MatLab function I=Simp38(FunName,a,b) using the composite Simpson's 3/8 method. FunName is a handle function that calculates the value of the function to be integrated, a and b are the limits of integration and I is the value of the integral. The function should calculate the value of the integral in iterations. In the first iteration the interval [a,b] is divided in two subintervals and in every iterations that follows the number of subintervals should be doubled. Stop the iterations when the difference in the value between two successive iterations is smaller than 1%.Create a MATLAB code that solves roots of non-linear equations using False-Position Method. The code should have the following features: Nonlinear function can be declared directly on the MLX file Accepts 2 initial limits of interval (bracketing values xu and xl) and the error tolerance Check if the initial limits of the interval has opposing function values. Check if the initial limits of the interval are roots of the inputted function. Displays the iteration table. (For more information, watch the Week 3 Lecture Video: ALTERNATIVE CODING FOR BISECTION METHOD ). Gives the following output values: approximate root, percent approximate relative error, number of iterations required to meet the condition (percent approximate relative error < error tolerance). Gives a non-convergence message when the condition (percent approximate relative error < error tolerance) is not met after 150 iterations.
- Q1: Write a Matlab function which calculates f(x) from the following inequality: - 1- x2, -11 or x <-1 Matlab Then plot f(x) over the range [-10, 10].3. Construct a reduced dfa for the following extended regular expression over {0,1}: T(01*)* n 1*01* | Note: You must first determine nfas for (01*)* and 1*01*, then do the intersection. The answer must then be reduced.The compass gradient operators of size 3x3 are designed to measure gradients of edges oriented in eight directions: E, NE, N, NW, W, SW, S, and SE. i) Give the form of these eight operators using coefficients valued 0, 1 or – 1. ii) Specify the gradient vector direction of each mask, keeping in mind that the gradient direction is orthogonal to the edge direction.
- Exercise. In this exercise, you will examine the changes to the analytic step response of a transfer function with a single zero. Consider the transfer function used in the previous activity: S – Z0 G = s2 + 2s + 2 (a) Compute the symbolic form of the step response of the transfer function, x(t). You can perform the analytic computations by hand or using the Symbolic Math Toolbox. % Use these symbolic variables syms s z_0 t positive % write your solution here X = NaN (b) Plot the analytic step response with zo = -1 on the interval te [0, 10]. If you computed the step response function with the Symbolic Math Toolbox, you can use the matlabFunction command to convert it to a function handle that you can evaluate. % Create your plot here (c) Using the analytic form of the transfer function, compute the limit of the function as t →. % write your solution here tlim = NaN1. In multidimensional data analysis, it is interesting to extract pairs of similar cell characteristics associated with substantial changes in measure in a data cube, where cells are considered similar if they are related by roll-up (ie. Ancestors), drill-down (ie. Descendants), or 1-dimensional mutation (ie, siblings) operations. Such an analysis is called cube gradient analysis. Suppose the measure of the cube is average. A user poses a set of probe cells and would like to find their corresponding sets of gradient cells, each of which satisfies a certain gradient threshold. For example, find the set of corresponding gradient cells whose average sale price is greater than 20% of that of the given probe cells. Develop an algorithm than mines the set of constrained gradient cells efficiently in a large data cube.Develop multiple-condition coverage test cases for statement 21 in Figure 8.8: Until (dayOK && monthOK && yearOK)
- Write down all 16 tests to satisfy Multiple Base Choice coverage (MBCC) for the second categorization of triang()’s inputs in Table 6.2. Use the values in Table 6.3.Question 4 Using train_test_split, split X and y into training and test sets (X_train, x_test, y_train, and y_test). Set the random number generator state to 0 using random_state=0 to make sure your results match the autograder! This function should return a tuple of length 4: (x_train, x_test, y_train, y_test), where x_train has shape (426, 30) x_test has shape (143, 30) y_train has shape (426,) y_test has shape (143,) . . from sklearn.model_selection import train_test_split def answer_four (): # YOUR CODE HERE raise Not ImplementedError()Write Algorithm for Unsupervised Feature Selection with 2,1–2Input: data matrix X, hyper-parameters α and β, penalty parameter ρ