polynomials

来源： https://stackoverflow.com/questions/41317127/python-equivalent-to-r-poly-function

来源： https://stackoverflow.com/questions/41317127/python-equivalent-to-r-poly-function

问题 SymPy factor function can factorize over symbolic roots, e.g. : In [1]: from sympy import * In [2]: init_printing(use_latex=False) In [3]: z, a, b = symbols('z a b') In [4]: poly = expand((z - sqrt(a))*(z - sqrt(b)), z) In [5]: poly Out[5]: 2 √a⋅√b - √a⋅z - √b⋅z + z In [6]: factor(poly, z) Out[6]: (-√a + z)⋅(-√b + z) but the factorization fails if b = a : In [10]: b = a In [11]: poly = expand((z - sqrt(a))*(z - sqrt(b)), z) In [12]: poly Out[12]: 2 -2⋅√a⋅z + a + z In [13]: factor(poly, z) Out

问题 SymPy factor function can factorize over symbolic roots, e.g. : In [1]: from sympy import * In [2]: init_printing(use_latex=False) In [3]: z, a, b = symbols('z a b') In [4]: poly = expand((z - sqrt(a))*(z - sqrt(b)), z) In [5]: poly Out[5]: 2 √a⋅√b - √a⋅z - √b⋅z + z In [6]: factor(poly, z) Out[6]: (-√a + z)⋅(-√b + z) but the factorization fails if b = a : In [10]: b = a In [11]: poly = expand((z - sqrt(a))*(z - sqrt(b)), z) In [12]: poly Out[12]: 2 -2⋅√a⋅z + a + z In [13]: factor(poly, z) Out

问题 EDIT: I am not asking how to solve an equation in terms of a given variable (as in this supposed duplicated question), but how to represent an expression in terms of an other one, as specified in the question. I believe it is the "duplicated" question to have a misleading title. I am very new with SymPy. I have an expression that, once expressed in terms to an other expression, should become very nice. The problem is that I don't know how to "force" to express the original expression in terms

问题 EDIT: I am not asking how to solve an equation in terms of a given variable (as in this supposed duplicated question), but how to represent an expression in terms of an other one, as specified in the question. I believe it is the "duplicated" question to have a misleading title. I am very new with SymPy. I have an expression that, once expressed in terms to an other expression, should become very nice. The problem is that I don't know how to "force" to express the original expression in terms

问题 I'm new to Machine Learning and currently got stuck with this. First I use linear regression to fit the training set but get very large RMSE. Then I tried using polynomial regression to reduce the bias. import numpy as np from sklearn.linear_model import LinearRegression from sklearn.preprocessing import PolynomialFeatures from sklearn.metrics import mean_squared_error poly_features = PolynomialFeatures(degree=2, include_bias=False) X_poly = poly_features.fit_transform(X) poly_reg =

问题 I have created a class for complex numbers: public class Complex { private double x; //Real part x of the complex number x+iy. private double y; //Imaginary part y of the complex number x+iy. public Complex(double x, double y) { //Constructor: Initializes x, y. this.x=x; this.y=y; } public Complex(double x) { //Real constructor - initialises with a real number. this(x, 0.0); } public Complex() { //Default constructor; initialiase x and y to zero. this(0.0, 0.0); } } What I would like to do is

问题 I have an assignment where I basically need to count the number of floating point operations in a simple program, which involves a loop, a matrix, and operations such as *, + and ^. From my understanding, a floating-point operation is an operation that involves floating-point numbers, and we may be interested in counting these operations because I think they may be more expensive for the computer. If you want to add more details to this part, it would be nice. My problem is that I've no idea

问题 I have fit a second order polynomial to a number of x/y points in the following way: poly = np.polyfit(x, y, 2) How can I invert this function in python, to get the two x-values corresponding to a specific y-value? 回答1: Here's an example that shows how you can combine your poly with my answer at Inverse function of numpy.polyval(). First some data: In [44]: x Out[44]: array([0, 1, 2, 3, 4, 5, 6, 7]) In [45]: y Out[45]: array([ 9, 4, 0, -1, -1, 4, 8, 16]) Fit a polynomial to the data: In [46]: