simulation

问题 I can set an initial condition y(0)=5 in Gekko with y = m.Var(5) but how do I set a value that is not the initial condition such as y(3)=6 where the value at time=3 is 6 as shown by the red dot? from gekko import GEKKO import numpy as np import matplotlib.pyplot as plt m = GEKKO(remote=False) m.time = np.linspace(0,10,11) x = m.Var(np.ones(11)*6) m.Equation(5*x.dt() == -x) m.options.IMODE = 4 m.solve() plt.plot(m.time, x.value) plt.plot([3],[6],'ro',MarkerSize=5) plt.show() I have a

问题 I can set an initial condition y(0)=5 in Gekko with y = m.Var(5) but how do I set a value that is not the initial condition such as y(3)=6 where the value at time=3 is 6 as shown by the red dot? from gekko import GEKKO import numpy as np import matplotlib.pyplot as plt m = GEKKO(remote=False) m.time = np.linspace(0,10,11) x = m.Var(np.ones(11)*6) m.Equation(5*x.dt() == -x) m.options.IMODE = 4 m.solve() plt.plot(m.time, x.value) plt.plot([3],[6],'ro',MarkerSize=5) plt.show() I have a

问题 Using numpy I can simulate unconditionally from a multivariate normal distribution by mean = [0, 0] cov = [[1, 0], [0, 100]] # diagonal covariance x, y = np.random.multivariate_normal(mean, cov, 5000).T How do I simulate y from the same distribution, given that I have 5000 realizations of x? I'm looking for a generalized solution that can be scaled to an arbitrary dimension. 回答1: Looking up in Eaton, Morris L. (1983). Multivariate Statistics: a Vector Space Approach, I gathered following

问题 Using numpy I can simulate unconditionally from a multivariate normal distribution by mean = [0, 0] cov = [[1, 0], [0, 100]] # diagonal covariance x, y = np.random.multivariate_normal(mean, cov, 5000).T How do I simulate y from the same distribution, given that I have 5000 realizations of x? I'm looking for a generalized solution that can be scaled to an arbitrary dimension. 回答1: Looking up in Eaton, Morris L. (1983). Multivariate Statistics: a Vector Space Approach, I gathered following

问题 Using numpy I can simulate unconditionally from a multivariate normal distribution by mean = [0, 0] cov = [[1, 0], [0, 100]] # diagonal covariance x, y = np.random.multivariate_normal(mean, cov, 5000).T How do I simulate y from the same distribution, given that I have 5000 realizations of x? I'm looking for a generalized solution that can be scaled to an arbitrary dimension. 回答1: Looking up in Eaton, Morris L. (1983). Multivariate Statistics: a Vector Space Approach, I gathered following

问题 I am a mechanical engineer who has only limited knowledge in C programming. I wrote some code in order to make simulations, and I want to visualize the simulation results. At the moment I am using Dev-C for writing my codes. With fopen and fprintf commands I generate a .dat file which includes the results. Then I open GNUPLOT program and import my .dat file to plot the results. This takes time and I have to wait till the end of the simulation. Is there an easy way to connect my plotter with

问题 I am a mechanical engineer who has only limited knowledge in C programming. I wrote some code in order to make simulations, and I want to visualize the simulation results. At the moment I am using Dev-C for writing my codes. With fopen and fprintf commands I generate a .dat file which includes the results. Then I open GNUPLOT program and import my .dat file to plot the results. This takes time and I have to wait till the end of the simulation. Is there an easy way to connect my plotter with

问题 I want to simulate a network that is under DDoS TCP SYN Flooding attack and evaluate the performance of a new solution and compare it with other solutions. i was planning to do it using NS3 but i read somewhere in the internet that i can't do it well using a descret event network simulator (NS3 is a descret event network simulator), i want to know why ? and what is the best alternative ? 回答1: Unfortunately, DoS attacks cannot be simulated in ns-3 . By extension, DDoS attacks cannot be

问题 I want to simulate left truncated failure time data from Weibull distribution. My objective is to simulate data and retrieve the coefficients(of x1,x2,x3,x4, and x5 which I used for the simulation) by fitting a Weibull regression model. Here the xt=runif(N, 30, 80) denotes the start of the study, Tm <- qweibull(runif(N,pweibull(xt,shape = 7.5, scale = 82*exp(lp)),1), shape=7.5, scale=82*exp(lp)) variable denotes the failure time. But whenever I do the regression I am getting this warning

问题 I am trying to simulate data via bootstrapping to create confidence bands for my real data with a funnel plot. I am building on the strategy of the accepted answer to a previous question. Instead of using a single probability distribution for simulating my data I want to modify it to use different probability distributions depending on the part of the data being simulated. I greatly appreciate anyone who can help answer the question or help me phrase the question more clearly. My problem is