methods 1 and 2. 1. p → q 2. (р^q) →r pr P wing q→r :. r

Write the polynomial function, which takes a list of tuples and returns the polynomial function corre- sponding to that list. Each tuple in the input list consists of (i) the coefficient, and (ii) the exponent. # (* below is the polynomial function f(x) = 3x^3 2x + 5 *) # let f = polynomial [3, 3; -2, 1; 5, 0];; val f : int -> int = # f 2; ; : int = 25

(V, E) be a connected, undirected graph. Let A = V, B = V, and f(u) = neighbours of u. Select all that are true. Let G = a) f: AB is not a function Ob) f: A B is a function but we cannot always apply the Pigeonhole Principle with this A, B Odf: A B is a function but we cannot always apply the extended Pigeonhole Principle with this A, B d) none of the above

Return co-ordinate 1 from (vector 3 1 4 2). In Racket

Let Σ = {a, b}. Compute L(a + ab∗)

Pls Use Python Using NumPy, write the program that determines whether the A=({{1, 5, -2}, {1, 2, -1}, {3, 6, -3}}) matrix is ​​nilpotent.  Itro: Nilpotent Matrix: A square matrix A is called nilpotent matrix of order k provided it satisfies the relation, Ak = O and Ak-1≠O where k is a positive integer & O is a null matrix of order k and k is the order of the nilpotent matrix A . The following picture is an example of the intro:   Ps: Please also explain step by step with " # "

x Bird(x)=Can- Fly(x) This universal quantifier used here implies that Oa. All reptiles cannot fly Ob All that flies is a Bird All Birds can fly Oc. All chicken are birds Od.

import numpy as npfrom scipy.optimize import minimizeimport matplotlib.pyplot as pltimport pickle #Implement the function calAnaliticalSolution def calAnaliticalSolution(X,y):    # Inputs:                                                             # X = N x d     # y = N x 1                                                                   # Output:     # w = d x 1 (which is the parameter calculated from data X and label y using alalytical solution)​    # IMPLEMENT THIS METHOD - REMOVE THE NEXT LINE    w = np.zeros((X.shape[0],1))​    return w #Implement the function calRegressionError def calRegressionError(w,Xtest,ytest):    # Inputs:    # w = d x 1    # Xtest = N x d    # ytest = N x 1    # Output:    # mse = scalar value (which is the error of the regression model)​    # IMPLEMENT THIS METHOD - REMOVE THE NEXT LINE    mse = 0​    return mse Xtrain,ytrain,Xtest,ytest = pickle.load(open('diabetes.pickle','rb'))  ​x1 = np.ones((len(Xtrain),1))x2 = np.ones((len(Xtest),1))​Xtrain_i =…

Computer Science In C++ no vectors include int main follow all directions