quadratic

问题 I used the qda{MASS} to find the classfier for my data and it always reported " some group is too small for 'qda' ". Is it due to the size of test data I used for model ? I increased the test sample size from 30 to 100, it reported the same error. Helpppppppp..... set.seed(1345) AllMono <- AllData[AllData$type == "monocot",] MonoSample <- sample (1:nrow(AllMono), size = 100, replace = F) set.seed(1355) AllEudi <- AllData[AllData$type == "eudicot",] EudiSample <- sample (1:nrow(AllEudi), size

问题 I am trying to write a function in C++ that solves for X using the quadratic equation. This is what I have written initially, which seems to work as long as there are no complex numbers for an answer: float solution1 = (float)(-1.0 * b) + (sqrt((b * b) - (4 * a * c))); solution1 = solution1 / (2*a); cout << "Solution 1: " << solution1 << endl; float solution2 = (float)(-b) - (sqrt((b*b) - (4 * a * c))); solution2 = solution2 / (2*a); cout << "Solution 2: " << solution2; If, for example, I use

问题 As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, visit the help center for guidance. Closed 8 years ago . I'm looking for a good easy to use Java based Quadratic Programming (QP) solver. Googling around I came across ojAlgo (http://ojalgo

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 5 years ago . The only Google search result I found is QuadProg++ but it can not solve the quadratic programming problem whose matrix is not applicable for Cholesky decomposition. So can anyone give me some suggestion on other library? Thanks. 回答1: CGAL looks great for quadratic programming. There is even a manual. // by

问题 I'm not sure what I'm doing wrong in my code but I can't get it to run properly...the tester keeps returning NaN instead of what's supposed to be expected. The goal of this exercise is to print all real solutions to the quadratic equation. solution1 should return the smaller solution and solution2 should return the smaller solution. Here's my class. public class QuadraticEquation { private int a; private int b; private int c; private boolean hasSolutions; private double discriminant = (b * b)

问题 Given the following code that computes distances between vectors in list 'vect’: import numpy as np vect=([0.123, 0.345, 0.789], [0.234, 0.456, 0.567],[0.134, 0.246, 0.831]) def kn(): for j in vect: c=np.array(j) for z in vect: p=np.array(z) space = np.linalg.norm(c-p) print space kn() Is there a way to avoid the quadratic complexity that will result from the double ‘for loop’? Computation as a result of the double for loop (quadratic) ==3X3=9 Ideal computation (what I want) should be (3): [0

问题 I have to write a read method for a quadratic class where a quadratic is entered in the form ax^2 + bx + c = 0 I found this way and here's the code: import java.io.BufferedReader; import java.io.IOException; import java.io.InputStreamReader; import java.util.Arrays; import java.util.regex.Matcher; import java.util.regex.Pattern; public class ParseEquation { public static String coeff(String str, String regex) { Pattern patt = Pattern.compile(regex); Matcher match = patt.matcher(str); //

问题 I'm doing some specific signal analysis, and I am in need of a method that would smooth out a given bell-shaped distribution curve. A running average approach isn't producing the results I desire. I want to keep the min/max, and general shape of my fitted curve intact, but resolve the inconsistencies in sampling. In short: if given a set of data that models a simple quadratic curve, what statistical smoothing method would you recommend? If possible, please reference an implementation, library

问题 I have a quadratic curve drawn on html canvas using context.quadraticCurveTo(controlX, controlY, endX, endY); . I have the control-point and the starting and end points, which are not necessarily level with each other horizontally. How can I find the centre point on the curve using these parameters? Actually I want to put a div tag on this center point. Is there any equation solving involved in this process? 回答1: quadraticCurveTo draws a quadratic Bézier curve. The formulas to calculate the

问题 I am trying to use solve.QP to solve a portfolio optimization problem (quadratic problem) Total 3 assets There are 4 constraints: sum of weights equal to 1 portfolio expected return equals to 5.2% each asset weight greater than 0 each asset weight smaller than .5 Dmat is the covariance matrix Dmat <- matrix(c(356.25808, 12.31581, 261.8830, 212.31581, 27.24840, 18.50515, 261.88302, 18.50515,535.45960), nrow=3, ncol=3) dvec is each asset's expected return dvec <- matrix(c(9.33, 3.33, 9.07),