polynomials

问题 I've found polynomial coefficients from my data: R <- c(0.256,0.512,0.768,1.024,1.28,1.437,1.594,1.72,1.846,1.972,2.098,2.4029) Ic <- c(1.78,1.71,1.57,1.44,1.25,1.02,0.87,0.68,0.54,0.38,0.26,0.17) NN <- 3 ft <- lm(Ic ~ poly(R, NN, raw = TRUE)) pc <- coef(ft) So I can create a polynomial function: f1 <- function(x) pc[1] + pc[2] * x + pc[3] * x ^ 2 + pc[4] * x ^ 3 And for example, take a derivative: g1 <- Deriv(f1) How to create a universal function so that it doesn't have to be rewritten for

问题 I'm using R to create a linear regression model having orthogonal polynomial. My model is: fit=lm(log(UFB2_BITRATE_REF3) ~ poly(QPB2_REF3,2) + B2DBSA_REF3,data=UFB) UFB2_FPS_REF1= 29.98 27.65 26.30 25.69 24.68 23.07 22.96 22.16 21.51 20.75 20.75 26.15 24.59 22.91 21.02 19.59 18.80 18.21 17.07 16.74 15.98 15.80 QPB2_REF1 = 36 34 32 30 28 26 24 22 20 18 16 36 34 32 30 28 26 24 22 20 18 16 B2DBSA_REF1 = DOFFSOFF DOFFSOFF DOFFSOFF DOFFSOFF DOFFSOFF DOFFSOFF DOFFSOFF DOFFSOFF DOFFSOFF DOFFSOFF

问题 from numpy import linalg,dot import numpy.polynomial.polynomial as poly x7=poly.Polynomial([1,2]) print x7 according to above code in python it should print 1 + 2x^2, but it is printing poly [1. 2.]. Please help. 回答1: I'd recommend using numpy.poly1d and numpy.polymul , where the coefficients are a0*x2 + a1*x + a2 . For example, to represent 3*x**2 + 2*x + 1 : p1 = numpy.poly1d([3,2,1]) therefor for your problem you could use: p2= numpy.poly1d([2,0,1]) print p2 and printing p2 will represent:

问题 A = [1 2 3; 4 5 6; 7 8 9]; Now reshaping the matrix A to form a row vector gives B. B = [1 4 7 2 5 8 3 6 9]; Evaluating polynomial function f(x) = (7x+6x^2+3x^3)mod 9 by putting values for 'x' ranging from (1,...,9) since there are 9 elements. Ex. For x=1, f(x) = 16 mod 9 = 7 For x=2, f(x) = 62 mod 9 = 8 till x = 9 results in permute. permute = [7 8 3 1 2 6 4 5 9]; permute vector gives positions. Using matrix indexing, the positions of elements in row vector B are arranged according to

问题 I'm struggling to create the __ str __ function (aka pretty print) with polynomials, where dictionaries are used to contain the powers as keys and the elements as coefficients. I have done it with lists but I haven't mastered dictionaries yet. Is there anything to improve? You can see in the second polynomial that if my last constant is not a constant, after arranging the keys with the reverse() function, the plus is always there, what can i do to prevent that? By the way I am trying to

问题 I want to generate random binary polynomials with parameters (n,m) . n is the number of polynomials to be generated and m is the number of elements of each polynomials. At the same time I need it's polynomial to be unique. And I also need to exclude the result with all elements equal to zero. For example for n=3 and m=3 I am looking for something like [1 0 1] [1 0 0] [1 1 1] . Is there any command in mat lab which I can use to have the above results?? I would also like to avoid the for loop

问题 Is there a way to construct a an lmfit Model based on a function with an arbitrary number of dependent variables? For example: from lmfit import Model def my_poly(x, *params): func = 0 for i in range(len(params)): func+= params[i]*z**i return func #note: below does not work my_model = Model(my_poly, independent_vars = ['x'], param_names = ['A','B','C']) Something similar to the above would be wonderful if I am interested in a polynomial series and want to test the performance as the series

问题 currently i'm working on a project (namely, creating a class of a polynomial) and i've already implemented the "add-subtract-divide-and-so-on" methods. But i'm stuck on a method to pass from a string like that 3x^1-2x^4 to a vector of coefficients like 0 3 0 0 4. So here's the code: string s; cin >> s; istringstream iss(s); double coeff; char x, sym; int degree; vector<double> coefficients; int i = 0; while (iss >> coeff >> x >> sym >> degree) { //if (sign == '-') coeff *= -1; if (degree == i

问题 I am trying to write an algorithm which will find a(0),..., a(n-1) , given the values of n, x_1, ..., x_n, a(n) , such that: a(n)*p^n + a(n-1)*p^(n-1) + ... + a(1)*p + a(0) = a(n)(p-x_1)(p-x_2)...(p-x_n) for all real p. After multiplying a(n)(p-x_1)(p-x_2) I've thought of using Viete's formulas to find the coefficients. But it turns out writing the code down isn't as obvious as I expected. I want to use only the basics in my code - that is loops, if-s addition and multiplication - no ready/

问题 I have problems by solving a polynomial function with sympy. The following example shows a case which gives an error message that I cannot manage. If the polynomial gets simpler the solver works properly. Please copy and paste the code to check the error on your system as well. import sympy from sympy import I omega = sympy.symbols('omega') def function(omega): return - 0.34*omega**4 \ + 7.44*omega**3 \ + 4.51*I*omega**3 \ + 87705.64*omega**2 \ - 53.08*I*omega**2 \ - 144140.03*omega \ - 22959