the R data set “anscombe” to save a subset of all the columns of the data except x4 and y4 Add two new columns to the subset Use set.seed(1)
Q: 0 1 11 7 1 2 2 DO 8 6 -7 2 3 14 5 10
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- Assign letter grades O solutions submitted (max: 1) In the attached spreadsheet, you are given homework grades of 25 students. Sheet 1 (hw1) stores the first homework grades, Sheet 2 (hw2) is the second homework grades and so on. Use either xlsread() or readmatrix() to import the given data into Matlab. Once you have the data in Matlab; 1- Create separate arrays for each homework, name them as hw1, hw2, hw3, hw4, hw5 2- Create an average grade array named hwAvg. Store the average homework grade in hwAvg using the below weights Homework 1, 2: each 15% Homework 3, 4 : 20% Homework 5: 30% 3- Create an array named letterGrade to store letter grades, this will be an array of strings. • 4- Preallocate the letterGrade array. Make sure it has the same length as hwAvg array. 5- Assign letter grades from hwAvg array using the grading policy shown below 100 - 90 : A <90 - 80 : B <80 - 70:C <70 - 60 : D <60 :F It is recommended that you download the file, see what it looks like, figure the steps…Create two column vectors: N with 10 elements (-50, -40, -30, -20, -10, 5, 10, 15, 25, 35). M with 10 elements (12, 14.56, 17.11, 19.67, 22.22, 24.78, 27.33, 29.89, 32.44, 35) Do not type the elements explicitly. Then: A- By only using the name of vectors, create a 10 x 2 matrix Q from vectors N and M. B- Create a 3 x 2 matrix P from the first, second, and fifth rows of matrix Q.All your fractions are belong to base def group_and_skip(n, out, ins): A pile of n identical coins lies on the table. In each move, the coins in the currently remaining pile are arranged into groups of exactly out coins each, where out is a positive integer greater than one. The n%out leftover coins that did not make a complete group of out elements are taken aside and recorded. From each complete group of out coins taken out, exactly ins coins are put back into the pile, and the rest of the coins of that group are discarded aside. Repeat this until the pile becomes empty, which must eventually happen whenever out>ins. Return a list that tells how many coins were taken aside in each step. As you can see in the first three rows, this method produces the digits of n in base out in reverse order. So this entire setup turned out to be the cleverly disguised algorithm to construct the representation of integer n in base out. However, an improvement over the standard algorithm for such…
- Write a r programming code for random forest use mtcars dataset and calculate these classification metrics: contusion matrix, accuracy, precision, recall, sensitivity, specificity, F1 score and matthew correlation coefficient.All your fractions are belong to base def group_and_skip(n, out, ins): A pile of n identical coins lies on the table. In each move, the coins in the currently remaining pile are arranged into groups of exactly out coins each, where out is a positive integer greater than one. The n%out leftover coins that did not make a complete group of out elements are taken aside and recorded. From each complete group of out coins taken out, exactly ins coins are put back into the pile, and the rest of the coins of that group are discarded aside. Repeat this until the pile becomes empty, which must eventually happen whenever out>ins. Return a list that tells how many coins were taken aside in each step. As you can see in the first three rows, this method produces the digits of n in base out in reverse order. So this entire setup turned out to be the cleverly disguised algorithm to construct the representation of integer n in base out. However, an improvement over the standard algorithm for such…All your fractions are belong to base def group_and_skip(n, out, ins): A pile of n identical coins lies on the table. In each move, the coins in the currently remaining pile are arranged into groups of exactly out coins each, where out is a positive integer greater than one. The n%out leftover coins that did not make a complete group of out elements are taken aside and recorded. From each complete group of out coins taken out, exactly ins coins are put back into the pile, and the rest of the coins of that group are discarded aside. Repeat this until the pile becomes empty, which must eventually happen whenever out>ins. Return a list that tells how many coins were taken aside in each step n out ins Expected result 123456789 10 1 [9, 8, 7, 6, 5, 4, 3, 2, 1] 987654321 1000 1 [321, 654, 987] 255 2 1 [1, 1, 1, 1, 1, 1, 1, 1] 81 5 3 [1, 3, 2, 0, 4, 3] 10**9 13 3 [12, 1, 2, 0, 7, 9, 8, 11, 6, 8, 10, 5,8, 3]
- Modify 1. Replace the elements of r_vec with the indices 4, 6, 8, 10 and 15 with 3, 6, 9, 12 and 15, respectively. 2. Replace the elements of mat with the indices (4, 5), (4, 17), (20, 5) and (20,17) with 5, 8, 2 and 9, respectively. 3. Replace the all the elements of the eleventh column of mat with ones. 4. Replace the characters 11 to 19 of chr with the words white cat. 5. Replace the first entry of str with the value of sclr.Using the linear_predict function that you previously wrote, write a function linear_residuals that given the learned weights in the linear_regression function calculates the residuals vector. Your functions takes the following arguments as input: 1. X:A numpy array of the shape (N,d) where N is the number of data points, and d is the data dimension. Do not assume anything about N or d other than being a positive integer. 2. beta : Anumpy array of the shape (d+1,1) where d is the data dimension Bo Ba 3. Y: A numpy array of the shape (N,) where N is the number of data points. Your function should produce the e numpy array with the shape of (N,), whose i" element is defined as el) = yl) – (Bo + Bix? + Bzx + + Bax) In [ ]: def linear_residuals (X,beta,Y): assert X.ndim==2 N = X.shape[0] х.shape[1] assert beta.shape assert Y.shape d = (d+1,1) (N,) == == #3 your code here e e.reshape(-1) %3D assert e.size return e (np.arange ( 35).reshape ( 7,5) ** 13) % 20 2.** (-np.arange (6).reshape…implement the Selection sort on the dataset above using the Python programming language.
- Rewrite the pattern match function so that, instead of finding only the first match, it reports all match locations, including overlapping matches. (It should report a match by printing "match at index k" where k is replaced by the first index of the match.) Assume that the prefix array π has already been computedA dataset in R has a column "icecream" whose responses are a factor with 3 levels: "strawberry," "chocolate," and "vanilla" (1, 2, and 3 respectively). We want to create a new column that simply classifies chocolate or non-chocolate (1 for chocolate, 0 for non-chocolate). How do we code this?Question 14 The domain for variable x is the set 1, 2, 3. The table below gives the values of P(x, y) for every pair of elements from the domain. For example, P(2, 3) = T because the value in row 2, column 3, is T. T T F T 3 F Select the statement that is true. O vxvyP(x.y) O vxvy((xay)vP(x.y) O vxvy((x*y)AP(x.y) O vwvy(xay)→P(x.y) 2.