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A two-dimensional set x = {(6, 3); (12, 4); (18, 10); (24, 11); (30, 12);
(42, 20); (48, 25)}. Please use K Means Clustering to cluster the above data into
two clusters; by assigning each point to the nearest centroid (using
City Block / Manhattan distance formula) with initial centroid C1 = (18, 2); and C2 = (45, 10).
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- Consider a set of data points {(-10, 13), (-6, 9), (-6, 17), (-2, 13), (-3, 0)}. Add last two digits of your registration with each given value such as. If last two digit of your registration number is 14 then First point will be (-10+14, 13+14) = (4, 27). Use the k-means clustering procedure with k = 2 to groups these data points. Start with the three initial cluster centers: (-6,9), (-2,13), (10,15). Show your working for 2 iterations. Use D=√(xi-yi)*2 for distance calculation.Machine Learning and Stats) Show the first two iterations (after the initialization) of the k-means clustering algorithm (show centers and assignments of data points to clusters) for the following 2D data set: (4, 2), (0, 1), (1, 4), (2, 8), (3, 5), (8, 8), (3, 3), (10, 10), (20, 18), and (12, 9). Assume the number of centers is equal to 2 and the centers are initialized to (1, 1) and (7, 8).Given are the points A = (3,4), B = (4,4), C = (4,3), D = (-3,6), E = (-4,-3), F = (-3,-3). Starting from initial clusters Cluster1 = {A} which contains only the point A and Cluster2 = {D} which contains only the point D, run the K-means clustering algorithm and report the final clusters. Use L1 distance as the distance between points which is given by d ((x1, y1), (x2, y2)) = | x1 – x2 | + | y1 – y2 |. Draw the points on a 2-D grid and check if the clusters make sense. You may stop the clustering process if it found that two iterations have the same clusters, otherwise, at least 4 iterations are required. Given are the 1-dimensional points A = 2, B = 3, C = 4, D = 9, E = 10, F = 11. Compute complete-linkage hierarchical clustering using d (x, y) = |x – y| as the distance between points. And also draw a dendrogram of it.
- to write some code: Cluster MNIST with k-means (you know optimal k already). Assign a label to each cluster by the most popular y (classes from 0 to 9) in that cluster. Now we can compute accuracy_score of our cluster labels comparing them with true label of each point. This way we can get an idea of how good our clustering is without looking at it (because you can't really look at 64 dimensional points, right?). q1: What accuracy_score did you get? Hint: you can use np.bincount(x): Count number of occurrences of each value in array of non-negative ints. Each bin gives the number of occurrences of its index value in `x`. Hint: use KMeans(..., random_state=0) for reproducible results. from sklearn.metrics import accuracy_score # YOUR CAN WRITE CODE HERECorrect answer will be upvoted else Multiple Downvoted. Don't submit random answer. Computer science. You are given a cluster an of length 2n. Consider a segment of exhibit an into two aftereffects p and q of length n each (every component of cluster an ought to be in precisely one aftereffect: either in p or in q). We should sort p in non-diminishing request, and q in non-expanding request, we can indicate the arranged adaptations by x and y, individually. Then, at that point, the expense of a segment is characterized as f(p,q)=∑ni=1|xi−yi|. Track down the amount of f(p,q) over all right parcels of cluster a. Since the appropriate response may be too huge, print its remaining portion modulo 998244353. Input The primary line contains a solitary integer n (1≤n≤150000). The subsequent line contains 2n integers a1,a2,… ,a2n (1≤ai≤109) — components of exhibit a. Output Print one integer — the response to the issue, modulo 998244353.Insert integers 11, 22, 15, 44, 6, 9, 3, 33, 17, 5 into a BST in that order== What is the level of node 17, and node 11, respectively? What is the depth of node 3, and node 9, respectively?
- We are going to use K-means algorithm to cluster 6 data points from dataset D = {0, 1, 2, 3, 4, 2022} in R1 into 2 clusters. Before the first iteration, the cluster centers are randomly initialized at c1 = 0.235 and c2 = 1.984. Next, we simulate the first iteration (for part (a) and (b)) of K-means with manual computation. (a) Compute the cluster assignment for each of the 6 data points given, using the Euclidean distance. [5 pts] (b) Compute the updated cluster center c1 and c2. [5 pts] (c) How many iterations are needed to finish the K-means algorithm for this problem? [10 pts]Q3. Compute the cost of r JOINA=B s using the method of index loops with an index of height 2 on B of s. Do the computation for the clustering index case. Assume that r occupies 4,000 blocks with 20 tuples per block, s occupies 10,000 blocks with 5 tuples per block, and the amount of main memory available is 402 blocks. Assume that at most 5 tuples in s match each tuple in r.Correct answer will be upvoted else Multiple Downvoted. Don't submit random answer. Computer science. each individual has a rating chart portrayed by a variety of integers an of length n. You are currently refreshing the foundation, so you've made a program to pack these diagrams. The program functions as follows. Given an integer boundary k, the program takes the base of each adjoining subarray of length k in a. All the more officially, for a cluster an of length n and an integer k, characterize the k-pressure exhibit of an as a cluster b of length n−k+1, to such an extent that bj=minj≤i≤j+k−1ai For instance, the 3-pressure cluster of [1,3,4,5,2] is [min{1,3,4},min{3,4,5},min{4,5,2}]=[1,3,2]. A stage of length m is an exhibit comprising of m unmistakable integers from 1 to m in subjective request. For instance, [2,3,1,5,4] is a stage, however [1,2,2] isn't a change (2 shows up twice in the exhibit) and [1,3,4] is likewise not a stage (m=3 but rather there is 4 in the…
- Correct answer will be upvoted else Multiple Downvoted. Don't submit random answer. Computer science. Yurii is certain he can do everything. However, would he be able to tackle this assignment? He has a cluster a comprising of n positive integers. How about we call a subarray a[l...r] great if the accompanying conditions are all the while fulfilled: l+1≤r−1, I. e. the subarray has length something like 3; (al⊕ar)=(al+1+al+2+… +ar−2+ar−1), where ⊕ indicates the bitwise XOR activity. All in all, a subarray is acceptable if the bitwise XOR of the two boundary components is equivalent to the amount of the remainder of the components. Yurii needs to compute the absolute number of good subarrays. What is it equivalent to? A cluster c is a subarray of an exhibit d if c can be gotten from d by erasure of a few (conceivably, zero or all) components from the start and a few (perhaps, zero or all) components from the end. Input The principal line contains a solitary…Note: c++ language code Error code eill downvoted. Chanek has a cluster an of n integers. The attractiveness worth of an is meant as: ∑i=1n∑j=1ngcd(ai,aj)⋅gcd(i,j) where gcd(x,y) signifies the best normal divisor (GCD) of integers x and y. At the end of the day, the beauty worth of a cluster an is the all out amount of gcd(ai,aj)⋅gcd(i,j) for all sets (i,j). Help Mr. Chanek discover the attractiveness worth of a, and output the outcome modulo 109+7! Input :the principal line contains an integer n (2≤n≤105). The subsequent line contains n integers a1,a2,… ,an (1≤ai≤105). Output :Output an integer signifying the beauty worth of a modulo 109+7.THIS IS MY CODE HELP ME ACHIEVE POINTS OUTLINED BELOW : #include <stdio.h>#include <stdlib.h>#include <string.h>#include <float.h>#include "graph.h"#include "dijkstra.h" #define INFINITY DBL_MAX /* find shortest paths between source node id and all other nodes in graph. *//* upon success, returns an array containing a table of shortest paths. *//* return NULL if *graph is uninitialised or an error occurs. *//* each entry of the table array should be a Path *//* structure containing the path information for the shortest path between *//* the source node and every node in the graph. If no path exists to a *//* particular desination node, then next should be set to -1 and weight *//* to DBL_MAX in the Path structure for this node */Path *dijkstra(Graph *graph, int id, int *pnEntries){ int n; int i, j; int* nv = get_vertices(graph, &n); int *S = malloc(n * sizeof(int)); double *D = malloc(n * sizeof(double)); int *R = malloc(n…