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5

As of version 10.0 there is a built in implementation of Random Forests which is accessible through the Classify function. trainingset = {1 -> "A", 2 -> "A", 3.5 -> "B", 4 -> "B"}; classifier = Classify[trainingset, Method->"RandomForest"];


5

I post this for illustrative purposes. You can access values. I suggest looking at the properties of your model, e.g. if your model is nlm then nlm["Properties"]. Some data and model: wd = WeatherData["Brisbane", "Temperature", {{2004, 1, 1}, {2013, 12, 31}, "Day"}]; vl = QuantityMagnitude /@ wd["Values"]; bnl = ...


5

(* Your equations *) eq = D[c[x, t], t] - d*D[c[x, t], {x, 2}] == 0; ibc = {c[x, 0] == KroneckerDelta[x, 0.], c[0, t] == 1, Derivative[1, 0][c][0.3, t] == 0}; (* a sample data set with d->1/100 *) sol = NDSolve[{eq /. d -> 1/2, ibc}, c, {x, 0, 0.3}, {t, 0, 3.5}]; pts = Flatten[Table[{x, t, c[x, t] /. sol[[1]]}, {x, 0, 0.3, .03}, {t, 0, 3.5, .35}], ...


4

Your set up: c = 116*10^(-6); U = 16000; data = {{0, 0}, {0.25*10^(-6), 132000}, {0.5*10^(-6), 330000}, {1*10^(-6), 462000}, {2*10^(-6), 600000}, {3*10^(-6), 462000}, {4*10^(-6), 330000}, {5*10^(-6), 66000}, {6*10^(-6), -198000}, {7*10^(-6), -264000}, {8*10^(-6), \ -198000}, {9*10^(-6), -132000}}; Use analytic not numerical form DSolve: ...


4

I have changed the name of some of the parameters (B->d,C->v,[Theta]->u). Using mydata (and changing the model to $\rho(T)=\rho(0)+(T/\theta)^n\int_0^{\theta/T}x^5/(e^x-1)(1-e^{-x})dx$ where $n=5$. f[a_, b_, c_] := c b^5/a^5 Integrate[x^5/(Exp[x] - 1) (1 - Exp[-x]), {x, 0, a/b}] nlm = NonlinearModelFit[mydata, d + f[u, t, v], {{u, 100}, {v, 0.2}, {d, ...


3

Assume that you have assigned the variable "model" as your LinearModelFit result. Then you can get the F statistic and its p-value with: model[{"ANOVATableFStatistics", "ANOVATablePValues"}] For interactions, you can include them when you build your data. For example, assume that you have two independent variables. Build your data list as: data = ...


2

I have rewritten your code to remove function definitions from the first argument of Manipulate. It is always a bad idea to define functions in the first argument of Manipulate -- such functions get redefined every time the front end refreshes the visible contents of the Manipulate. I have also put some effort on reducing the amount of evaluation done when a ...


1

Using only the first cbMax amount of $c_b$s, we get: getmodel[a_, cbMax_] := With[{cb = Table[FindRoot[cb Cot[cb] + a L, {cb, Pi (n + 1/2), n Pi, (n + 1) Pi}][[1, 2]], {n, 0, cbMax}]}, 1 - Sum[2 cb[[i]] Sin[cb[[i]] x/L] E^(a x), {i, cbMax + 1}]] And now notice that for the rest of the $c_b$s, the difference becomes constant (Pi) very fast: ...



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