scipy-optimize

来源： https://stackoverflow.com/questions/64504648/variable-constraint-sci-py-minimize

问题 I am trying to fit a exponential decay to some experimental data and using polyfit in numpy. Also, fit itself is weighted, that is every data point has some weight associated with it and the polyfit function can find that optimal weights ? Referred: Constrained np.polyfit So final output would be coefficient value and the weight vector for every associated data points. Also I want to constrain M parameter between 0.9 to 1 and N to -0.001 to -0.009 Data: t(x) rate(y) 0 0.950 1 0.940 2 0.931 3

问题 Following this post, I now have serious doubts if R-squared or F-test are good indications of a good linear fit into some data with random noise. Hence, I want to develop a custom made regression function so I can both learn how it works and maybe improve upon the existing tools. Consider these randomly generated ndarrays x and y : import numpy as np np.random.seed(42) x = np.random.rand(30) * 10 y = 1.5 * x + 0.3 + (np.random.rand(30) - 0.5) * 3.5 now I can define the average/mean absolute

问题 Following this question, I want to make my question as specific as possible focusing on the part that I can not solve. Consider a very simple function of: def foo(x, y, a, b, c): return a * x**4 + b * y**2 + c now I want to use the scipy.optimize.minimize or any of other existing functions to find the x and y (i.e., parameters) to minimize foo given the constants a , b , and c (i.e., args). If I had only one parameter and multiples arguments then from this page I could do: def foo(x, *args):

问题 Given inputs such as electricity consumption, generation from solar panel, price, (All at a given time t), we have a battery, and we want to evaluate how much it should (dis)/charge at any given time. The Problem can be formulated as follows: Pt = price of electricity at time t Lt = consumption of electricity at time t Zt = charge of battery at time t (how much is in the battery) St = Electricity generated from solar generator at time t Qt = amount the battery (dis)/charges at time t the

问题 Given inputs such as electricity consumption, generation from solar panel, price, (All at a given time t), we have a battery, and we want to evaluate how much it should (dis)/charge at any given time. The Problem can be formulated as follows: Pt = price of electricity at time t Lt = consumption of electricity at time t Zt = charge of battery at time t (how much is in the battery) St = Electricity generated from solar generator at time t Qt = amount the battery (dis)/charges at time t the

问题 I am trying to fit x y data which look something like x = np.linspace(-2, 2, 1000) a = 0.5 yl = np.ones_like(x[x < a]) * -0.4 + np.random.normal(0, 0.05, x[x < a].shape[0]) yr = np.ones_like(x[x >= a]) * 0.4 + np.random.normal(0, 0.05, x[x >= a].shape[0]) y = np.concatenate((yl, yr)) plt.scatter(x, y, s=2, color='k') I'm using a variation of the Heaviside step function def f(x, a, b): return 0.5 * b * (np.sign(x - a)) and fitting with popt, pcov = curve_fit(f, x, y, p0=p) where p is some

问题 I want to solve a systen of non-linear equations created by loops using root from scipy.optimize . I want to create the equations with one method and then solve them with another. I created the equations with sympy and want to solve them with scipy. My real code has too many loops and every time root from scipy iterates those loops. This is a very simplified version of what I tried. I made the equations with sympy symbols and then I used lambdify to take the equations out of sympy but the

问题 I am trying to optimize SSE (sum of squared error) of a function using scipy.optimize . To test with, I created a simple problem as below code. But the optimized parameters output by scipy never makes SSE=0. Can someone help me to understand, where am I going wrong. I tried to cross check with the SSE calculated by my code with the one computed in excel. It matched. Then I used minimize function to minimize that SSE function, the ones computed by Scipy is not matching with the hand calculated

问题 I am using the method dogleg within optimize.minimize tool in Scipy to solve my non-linear 2-equations system. sol = optimize.minimize(self.myF, self.initialWCEC,rZ,jac=self.myJacobian,hess=self.myJHessian,tol=6e-11, method='dogleg',options={'gtol':1e-12}) I have defined my own hessian and Jacobian functions in order to use this method. They look like def myJacobian(self,WCEC): jacF = numpy.array([[- k - (k**2*EC*WC*(Rs - rZ))/50,- (k**2*EC**2*(Rs - rZ))/100 - (Cmax**2*w**2*(Rs - rZ))/100],