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Consider the data set in Table 1, ID refers to identifier for each data point in a 2-D space (i.e., labelled Xi, and Yi). The last column corresponds to class label that divide this data set into either Good or Bad category. Next, use this data to classify a new/unseen data point, which is (-2, 1), using K-nearest neighbor classification (with K = 3)
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- Design an adjacency Matrix of the alphabets of my name (Isha Tir Razia). In accordance with the following conditions: If name has repeated characters (e.g. character E, 2 times) then you will consider only 1 time. If Name contains both G and S, then there will be an edge between them and one additional edge from W to each if W is also in your name. If N is the alphabet in your name, it will have an edge to A and S if it available in your name. If P is available then it will have an edge with L if it is available. If there is a blank space in full Name then it will be represented by “_”, and it must have an edge with all alphabets. (Note: Place on in 1 for Edge and 0 for No Edge) Sketch an undirected graph of the above designed adjacency matrix.Each person in a group of n persons (n >= 4) has independent information known only to themselves. Whenever a person calls another person in the group, they exchange all the information they know at the time of calling. Design a sequence of calls (specifying the persons involved in each call) that they have to make in order to ensure that everyone knows all the information. How many calls are there in your scheme?Design an adjacency Matrix of the alphabets of your full name. In accordance with the following conditions: Let my name is Muhammad Adnan If your name has repeated characters (e.g. character E, 2 times) then you will consider only 1 time. If your Name contains both G and S, then there will be an edge between them and one additional edge from W to each if W is also in your name. If N is the alphabet in your name, it will have an edge to A and S if it available in your name. If P is available then it will have an edge with L if it is available. If there is a blank space in full Name then it will be represented by “_”, and it must have an edge with all alphabets. (Note: Place on in 1 for Edge and 0 for No Edge) Sketch an undirected graph of the above designed adjacency matrix.
- Use the subset construction algorithm from lecture to produce an equivalent DFA from the following NFA. Please show your work so that we know how each state is generated. Name your DFA states so that the link to the NFA states is clear; i.e. you should have DFA states that look like {90, 91}. You will need to show both the table and the diagram in your solution. For full marks, you will need to show all intermediate steps of the algorithm (in tabular form is fine; the DFA diagram is required only for the final DFA). Old State Symbol New State 91 92 1 93 1 94 q1 91 1 92 42 93 92 1 94 93 93 93 1 94 94 94 94 1 95 95 95 The start state of this NFA is qo; the accepting (final) states are qo, q1, and q5.Consider the below given iris data set. This dataset contains 3 classes of 15 instances each and each class refers to a type of iris plant. The dataset has two features: sepal length, sepal width. The third column is for species, which holds the value for these types of plants. A new plant is identified. You have to classify the Species class of new identified plant with the help of KNN algorithm.1. 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.
- Consider the below given iris data set. This dataset contains 3 classes of 15 instances each and each class refers to a type of iris plant. The dataset has two features: sepal length, sepal width. The third column is for species, which holds the value for these types of plants. A new plant is identified. You have to classify the Species class of new identified plant with the help of KNN algorithm.KNN modify the given data by adding number 12 to special length and special width with everyoneDesign an adjacency Matrix of the alphabets of Uzair Bhatti . In accordance with the following conditions: If your name has repeated characters (e.g. character E, 2 times) then you will consider only 1 time. If your Name contains both G and S, then there will be an edge between them and one additional edge from W to each if W is also in your name. If N is the alphabet in your name, it will have an edge to A and S if it available in your name. If P is available then it will have an edge with L if it is available. If there is a blank space in full Name then it will be represented by “_”, and it must have an edge with all alphabets. (Note: Place on in 1 for Edge and 0 for No Edge) Sketch an undirected graph of the above designed adjacency matrixConsider the elliptic curve group based on the equation where a = 1405, b = 2011, and p = 2531. We will use these values as the parameters for a session of Elliptic Curve Diffie-Hellman Key Exchange. We will use P = (0,98) as a subgroup generator. You may want to use mathematical software to help with the computations, such as the Sage Cell Server (SCS). On the SCS you can construct this group as: G=EllipticCurve(GF(2531),[1405,2011]) Here is a working example. (Note that the output on SCS is in the form of homogeneous coordinates. If you do not care about the details simply ignore the 3rd coordinate of output.) 62 Alice selects the private key 41 and Bob selects the private key 20. What is A, the public key of Alice? y² = x³ + ax+b mod p What is B, the public key of Bob? 2472 After exchanging public keys, Alice and Bob both derive the same secret elliptic curve point TAB. The shared secret will be the x-coordinate of TAB. What is it?
- Instructions: In the next exercise, you will use a known dataset used as an example for classification problems. Refer to: Index of /ml/machine-learning-databases/balance-scale (uci.edu). The problem has 3 classes and 4 input features. The possible classes are (B: balanced, L: left, R: right), and the 4 input features are: (LW: left weight, RW: right weight, LD: left distance, RD: Right distance). The balance shows up the value ‘B’ when LW + LD = RD + RW. Each raw of the file data is constructed as the following: Class, LW, LD, RW, RD. Example: B, 3, 2, 4, 1. In addition, all the input features might have values 1, ..., 5. For more information refer to website given before. Remark: you are free to propose a preprocessing of the input features, also you’re advised to try different values of the learning rate and observe the training curve. Remark: in your code, you are asked to use python, numpy and matplotlib. You shouldn’t use libraries like scikit, tensorflow, pytorch, or any other…We use AdaBoost to perform classification on 5 objects (1) If first classifier f, classifies 2 objects incorrectly (classification error = 0.4). What weight should be assigned to objects that were classified correctly and incorrectly, respectively, such that, under the new weights, the performance of fi would be the same as random (classification error = 0.5). (2) Next, choose a second classifier f2, what is the classification error? What is the new weight? (3) Find a third classifier f3, what is the classification error? What is the new weight? (4) Combine the three classifiers using AdaBoost. What is the final classifier H(x)? What is the final error rate?I want this in C Phase 1 Each person eligible to win is placed in one of G groups. For each group, a process is used to narrow down the possible number of winners. The process is as follows:Let a group initially start with p people. We can number the people within a group from 1 to p and assume they are arranged in a circle(circular linked list), with 1, then 2, ..., then p, followed by 1. Each group has its own skip number, s, and its own threshold number, t, where t< p. To eliminate some people in a group from possibility of winning the lottery, start at the person labeled 1 and skip over speople in line. Then, remove the following person. (Thus the first person removed is always person number s+1.)Then repeat the process. Since the line is circular, once you pass the highest numbered person left in line, you'll continue to the lowest numbered person left in line. Continue eliminating people in this fashion until there are precisely t people left. Phase 2 Of all of the people…