create a truth table for a. (p^q) V~q b. r =>(p V q)
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- If A={8,12,16,20} and B={12,16,20,24,28}, what is the value of P (A and B)?The following data are given: x= [1,3,5,7]; y=[ln (1), In (3), ln (5), ln (7)] 3. a) Use the interpld function from scipy.interpolate library to interpolate the above data using the spline interpolation method. b) Estimate the logarithm of 2 for linear, quadratic and cubic splines. Print out the true value, interpolating values and the true relative errors in % for linear, quadratic and cubic splines. c) Show on a graph the true function In(x), interpolating linear spline, interpolating quadratic spline, interpolating cubic spline, and data points. Round numbers to four significant digits.Set B = { a,b,c,d} and Set C = {b,d,f,g}. Find B U C.
- Q2: Simplify F(A, B, C, D) = E0,4,5,6,7,8,12)? (= %3DNORMALIZATION PROBLEM: Normalization is one of the most basic preprocessing techniques in data analytics. This involves centering and scaling process. Centering means subtracting the data from the mean and scaling means dividing with its standard deviation. Mathematically, normalization can be expressed as: Z = X-x σ In Python, element-wise mean and element-wise standard deviation can be obtained by using .mean() and .std() calls. In this problem, create a random 5 x 5 ndarray and store it to variable X. Normalize X.Step 1. Intersection over Union # def intersection_over_union(dt_bbox, gt_bbox): ---> return iou Step 2. Evaluate Sample We now have to evaluate the predictions of the model. To do this, we will write a function that will do the following: Take model predictions and ground truth bounding boxes and labels as inputs. For each bounding box from the prediction, find the closest bounding box among the answers. For each found pair of bounding boxes, check whether the IoU is greater than a certain threshold iou_threshold. If the IoU exceeds the threshold, then we consider this answer as True Positive. Remove a matched bounding box from the evaluation. For each predicted bounding box, return the detection score and whether we were able to match it or not. def evaluate_sample(target_pred, target_true, iou_threshold=0.5): # ground truth gt_bboxes = target_true['boxes'].numpy() gt_labels = target_true['labels'].numpy() # predictions dt_bboxes =…
- True or False 1. Matrices are often represented by single small letters a, b, c... etc.2. Two m x n matrices A and B are equal if aij=bij for each i & j. (i.e., the two matrices havesame size, and all the corresponding elements are equal).3. Matrices A & B are said to be conformable in the order AB if, and only if, the number ofrows in A is equal to the number of columns in B.4. Suppose Matrix A is having 4 rows and 3 columns, and Matrix B is having 3 rows and 2columns. The product size of AB is a 4 x 2 matrix.5. Suppose B is the matrix obtained from an n x n matrix A by multiplying the entries in arow/column by a non-zero constant and adding the result to the corresponding entries inanother row/column. Then, det(B) = det(A).Write a code to decide if Gram-Schmidt Algorithm can be applied to columns of a given matrix A through calculation of rank. The code should print appropriate messages indicating whether Gram-Schmidt is applicable on columns of the matrix or not. Deliverable(s) : The code that performs the test.3. Let A = {1, 2, 3} and B = {4,5}. a) Find A x B b) Find B x A