I got a equation in the following form
f(t) = 2*cos(t) + 3*sin(t)
and I want to reduce it to something like
f(t) = (9 + 4)^(1/2) * cos(t - atan(3/2))
None of the trigonometric simplify functions seems to work. Is there a way to do so?
Related
I've got this equation from mathematical model to know the thermal behavior of a battery.
dTsdt = Ts * a+ Ta * b + dTadt * c + d
However, i can't get to solve it due to the nested derivatives.
I need to solve the equation for Ts and Ta.
I tried to define it as follows, but python does not like it and several eŕrors show up.
Im using scipy.integrate and the solver ODEint
Since the model takes data from vectors, it has to be solved for every time step and record the output accordingly.
I also tried assinging the derivatives to a variable v1,v2, and then put everything in an equation without derivatives like the second approach shown as follows.
def Tmodel(z,t,a,b,c,d):
Ts,Ta= z
dTsdt = Ts*a+ Ta*b + dTadt*c+ d
dzdt=[dTsdt]
return dzdt
z0=[0,0]
# solve ODE
for i in range(0,n-1):
tspan = [t[i],t[i+1]]
# solve for next step
z = odeint(Tmodel,z0,tspan,arg=(a[i],b[i],c[i],d[i],))
# store solution for plotting
Ts[i] = z[1][0]
Ta[i] = z[1][1]
# next initial condition
z0 = z[1]
def Tmodel(z,t,a,b,c,d):
Ts,v1,Ta,v2= z
# v1= dTsdt
# v2= dTadt
v1 = Ts*a+ Ta*b + v2*c+ d
dzdt=[v1,v2]
return dzdt
That did not work either.I believe there might be a solver capable of solving that equation or the equation must be decouple in a way and solve accordingly.
Any advice on how to solve such eqtn with python would be appreciate it.
Best regards,
MM
Your difficulty seems to be that you are given Ta in a form with no easy derivative, so you do not know where to take it from. One solution is to avoid this derivative completely and solve the system for y=Ts-c*Ta. Substitute Ts=y+c*Ta in the right side to get
dy/dt = y*a + Ta*(b+c*a) + d
Of course, this requires then a post-processing step Ts=y+c*Ta to get to the requested variable.
If Ta is given as function table, use an interpolation function to get values at any odd time t that is demanded by the ODE solver.
Ta_func = interp1d(Ta_times,Ta_values)
def Tmodel(y,t,a,b,c,d):
Ta= Ta_func(t)
dydt = y*a+ Ta*(b+c*a) + d
return dydt
y[0] = Ts0-c*Ta_func(t[0])
for i in range(len(t)-1):
y[i+1] = odeint(Tmodel,y[i],t[i:i+2],arg=(a[i],b[i],c[i],d[i],))[-1,0]
Ts = y + c*Ta_func(t)
In VAE tutorial, kl-divergence of two Normal Distributions is defined by:
And in many code, such as here, hereand here, the code is implemented as:
KL_loss = -0.5 * torch.sum(1 + logv - mean.pow(2) - logv.exp())
or
def latent_loss(z_mean, z_stddev):
mean_sq = z_mean * z_mean
stddev_sq = z_stddev * z_stddev
return 0.5 * torch.mean(mean_sq + stddev_sq - torch.log(stddev_sq) - 1)
How are they related? why there is not any "tr" or ".transpose()" in code?
The expressions in the code you posted assume X is an uncorrelated multi-variate Gaussian random variable. This is apparent by the lack of cross terms in the determinant of the covariance matrix. Therefore the mean vector and covariance matrix take the forms
Using this we can quickly derive the following equivalent representations for the components of the original expression
Substituting these back into the original expression gives
I came across a problem. One string is taken as input say
input_string = "12345 + x = x * 5 + (1+x)* x + (1+18/100)"
And get output of x using python. I am not able to figure out logic for this.
Here is a complete SymPy example for your input:
from sympy import Symbol, solve, Eq
from sympy.parsing.sympy_parser import parse_expr
input_string = "12345 + x = x * 5 + (1+x)* x + (1+18/100)"
x = Symbol('x', real=True)
lhs = parse_expr(input_string.split('=')[0], local_dict={'x':x})
rhs = parse_expr(input_string.split('=')[1], local_dict={'x':x})
print(lhs, "=", rhs)
sol = solve(Eq(lhs, rhs), x)
print(sol)
print([s.evalf() for s in sol])
This outputs:
x + 12345 = x*(x + 1) + 5*x + 59/50
[-5/2 + 9*sqrt(15247)/10, -9*sqrt(15247)/10 - 5/2]
[108.630868798908, -113.630868798908]
Note that solve() gives a list of solutions. And that SymPy normally does not evaluate fractions and square roots, as it prefers solutions without loss of precision. evalf() evaluates a float value for these expressions.
Well, that example shows a quadratic equation which may have no solutions, one solution, or two solutions. You would have to rearrange the terms symbolically to come to
input_string = "x**2 + 5*x - 12345 + (118/100)"
But that means you need to implement rules for multiplication, addition, subtraction and potentially division. At least for Python there is a library called SymPy which can parse such strings and provide an expression that you can evaluate and even solve.
I have a normalized 2D vector that I am using to rotate other 2D vectors. In one instance it indicates "spin" (or "angular momentum") and is used to rotate the "orientation" of a simple polygon. My vector class contains this method:
rotateByXY(x, y) {
let rotX = x * this.x - y * this.y;
let rotY = y * this.x + x * this.y;
this.x = rotX;
this.y = rotY;
}
So far, this is all efficient and uses no trig whatsoever.
However, I want the "spin" to decay over time. This means that the angle of the spin should tend towards zero. And here I'm at a loss as to how to do this without expensive trig calls like this:
let angle = Math.atan2(spin.y, spin.x);
angle *= SPIN_DECAY;
spin = new Vector2D(Math.cos(angle), Math.sin(angle));
Is there a better/faster way to accomplish this?
If it's really the trigonometric functions what is slowing down your computation, you might try to approximate them with their Taylor expansions.
For x close to zero the following identities hold:
cos(x) = 1 - (x^2)/2! + (x^4)/4! - (x^6)/6! + ...
sin(x) = x - (x^3)/3! + (x^5)/5! - (x^7)/7! + ...
atan(x) = x - (x^3)/3 + (x^5)/5 - (x^7)/7 + ...
Based on the degree of accuracy you need for your application you can trim the series. For instance,
cos(x) = 1 - (x^2)/2
with an error of the order of x^3 (actually, x^4, as the term with x^3 is zero anyway).
However, I don't think that this is going to solve your problem: the actual implementation of atan is likely to be already using the same trick, written by someone with lots of experience of speeding these things up. So this is not really a proper answer but I hope it could still be useful.
I would like to ask how can I set arbitrary base of logarithm in gnuplot (I would need f(x)=x^{1+9log2(x)} function to plot).
It seems like there are only builtin functions for the natural log and log10. But you can easily change the base of the logarithm.
log_b(x) = log_k(x) / log_k(b)
Thus, you can rewrite your formula as
f(x) = x**(1 + 9 * log(x)/log(2))
To expand upon the answer of tobias_k, you can create a user-defined function which takes the base as a secondary argument:
logb(x, base) = log(x)/log(base)
And then rewrite your formulas as:
f(x) = x**(1 + 9 * logb(x, 2))