When using PolynomialFeatures from Scikit Learn, say you use a degree of 3, when you call .coef_ on the new model using Polynomials you end up with an array with 3 numbers. What does each coefficient mean?
Is it:
First number: change in y for every 1 unit change in x
Second number: change in y for every 1^2(squared) unit change in x
Third number: change in y for every 1^3(cubed) unit change in x
Yes . You could easily check this by substituting the values of x .
y = a0 + a1x + a2x(square) + a3 x(cube)
a0 will be the intercept
Related
I'm attempting to solve the differential equation:
m(t) = M(x)x'' + C(x, x') + B x'
where x and x' are vectors with 2 entries representing the angles and angular velocity in a dynamical system. M(x) is a 2x2 matrix that is a function of the components of theta, C is a 2x1 vector that is a function of theta and theta' and B is a 2x2 matrix of constants. m(t) is a 2*1001 array containing the torques applied to each of the two joints at the 1001 time steps and I would like to calculate the evolution of the angles as a function of those 1001 time steps.
I've transformed it to standard form such that :
x'' = M(x)^-1 (m(t) - C(x, x') - B x')
Then substituting y_1 = x and y_2 = x' gives the first order linear system of equations:
y_2 = y_1'
y_2' = M(y_1)^-1 (m(t) - C(y_1, y_2) - B y_2)
(I've used theta and phi in my code for x and y)
def joint_angles(theta_array, t, torques, B):
phi_1 = np.array([theta_array[0], theta_array[1]])
phi_2 = np.array([theta_array[2], theta_array[3]])
def M_func(phi):
M = np.array([[a_1+2.*a_2*np.cos(phi[1]), a_3+a_2*np.cos(phi[1])],[a_3+a_2*np.cos(phi[1]), a_3]])
return np.linalg.inv(M)
def C_func(phi, phi_dot):
return a_2 * np.sin(phi[1]) * np.array([-phi_dot[1] * (2. * phi_dot[0] + phi_dot[1]), phi_dot[0]**2])
dphi_2dt = M_func(phi_1) # (torques[:, t] - C_func(phi_1, phi_2) - B # phi_2)
return dphi_2dt, phi_2
t = np.linspace(0,1,1001)
initial = theta_init[0], theta_init[1], dtheta_init[0], dtheta_init[1]
x = odeint(joint_angles, initial, t, args = (torque_array, B))
I get the error that I cannot index into torques using the t array, which makes perfect sense, however I am not sure how to have it use the current value of the torques at each time step.
I also tried putting odeint command in a for loop and only evaluating it at one time step at a time, using the solution of the function as the initial conditions for the next loop, however the function simply returned the initial conditions, meaning every loop was identical. This leads me to suspect I've made a mistake in my implementation of the standard form but I can't work out what it is. It would be preferable however to not have to call the odeint solver in a for loop every time, and rather do it all as one.
If helpful, my initial conditions and constant values are:
theta_init = np.array([10*np.pi/180, 143.54*np.pi/180])
dtheta_init = np.array([0, 0])
L_1 = 0.3
L_2 = 0.33
I_1 = 0.025
I_2 = 0.045
M_1 = 1.4
M_2 = 1.0
D_2 = 0.16
a_1 = I_1+I_2+M_2*(L_1**2)
a_2 = M_2*L_1*D_2
a_3 = I_2
Thanks for helping!
The solver uses an internal stepping that is problem adapted. The given time list is a list of points where the internal solution gets interpolated for output samples. The internal and external time lists are in no way related, the internal list only depends on the given tolerances.
There is no actual natural relation between array indices and sample times.
The translation of a given time into an index and construction of a sample value from the surrounding table entries is called interpolation (by a piecewise polynomial function).
Torque as a physical phenomenon is at least continuous, a piecewise linear interpolation is the easiest way to transform the given function value table into an actual continuous function. Of course one also needs the time array.
So use numpy.interp1d or the more advanced routines of scipy.interpolate to define the torque function that can be evaluated at arbitrary times as demanded by the solver and its integration method.
I must solve the Euler Bernoulli differential beam equation which is:
w’’’’(x) = q(x)
and boundary conditions:
w(0) = w(l) = 0
and
w′′(0) = w′′(l) = 0
The beam is as shown on the picture below:
beam
The continious force q is 2N/mm.
I have to use shooting method and scipy.integrate.odeint() func.
I can't even manage to start as i do not understand how to write the differential equation as a system of equation
Can someone who understands solving of differential equations with boundary conditions in python please help!
Thanks :)
The shooting method
To solve the fourth order ODE BVP with scipy.integrate.odeint() using the shooting method you need to:
1.) Separate the 4th order ODE into 4 first order ODEs by substituting:
u = w
u1 = u' = w' # 1
u2 = u1' = w'' # 2
u3 = u2' = w''' # 3
u4 = u3' = w'''' = q # 4
2.) Create a function to carry out the derivation logic and connect that function to the integrate.odeint() like this:
function calc(u, x , q)
{
return [u[1], u[2], u[3] , q]
}
w = integrate.odeint(calc, [w(0), guess, w''(0), guess], xList, args=(q,))
Explanation:
We are sending the boundary value conditions to odeint() for x=0 ([w(0), w'(0) ,w''(0), w'''(0)]) which calls the function calc which returns the derivatives to be added to the current state of w. Note that we are guessing the initial boundary conditions for w'(0) and w'''(0) while entering the known w(0)=0 and w''(0)=0.
Addition of derivatives to the current state of w occurs like this:
# the current w(x) value is the previous value plus the current change of w in dx.
w(x) = w(x-dx) + dw/dx
# others are calculated the same
dw(x)/dx = dw(x-dx)/dx + d^2w(x)/dx^2
# etc.
This is why we are returning values [u[1], u[2], u[3] , q] instead of [u[0], u[1], u[2] , u[3]] from the calc function, because u[1] is the first derivative so we add it to w, etc.
3.) Now we are able to set up our shooting method. We will be sending different initial boundary values for w'(0) and w'''(0) to odeint() and then check the end result of the returned w(x) profile to determine how close w(L) and w''(L) got to 0 (the known boundary conditions).
The program for the shooting method:
# a function to return the derivatives of w
def returnDerivatives(u, x, q):
return [u[1], u[2], u[3], q]
# a shooting funtion which takes in two variables and returns a w(x) profile for x=[0,L]
def shoot(u2, u4):
# the number of x points to calculate integration -> determines the size of dx
# bigger number means more x's -> better precision -> longer execution time
xSteps = 1001
# length of the beam
L= 1.0 # 1m
xSpace = np.linspace(0, L, xSteps)
q = 0.02 # constant [N/m]
# integrate and return the profile of w(x) and it's derivatives, from x=0 to x=L
return odeint(returnDerivatives, [ 0, u2, 0, u4] , xSpace, args=(q,))
# the tolerance for our results.
tolerance = 0.01
# how many numbers to consider for u2 and u4 (the guess boundary conditions)
u2_u4_maxNumbers = 1327 # bigger number, better precision, slower program
# you can also divide into separate variables like u2_maxNum and u4_maxNum
# these are already tested numbers (the best results are somewhere in here)
u2Numbers = np.linspace(-0.1, 0.1, u2_u4_maxNumbers)
# the same as above
u4Numbers = np.linspace(-0.5, 0.5, u2_u4_maxNumbers)
# result list for extracted values of each w(x) profile => [u2Best, u4Best, w(L), w''(L)]
# which will help us determine if the w(x) profile is inside tolerance
resultList = []
# result list for each U (or w(x) profile) => [w(x), w'(x), w''(x), w'''(x)]
resultW = []
# start generating numbers for u2 and u4 and send them to odeint()
for u2 in u2Numbers:
for u4 in u4Numbers:
U = []
U = shoot(u2,u4)
# get only the last row of the profile to determine if it passes tolerance check
result = U[len(U)-1]
# only check w(L) == 0 and w''(L) == 0, as those are the known boundary cond.
if (abs(result[0]) < tolerance) and (abs(result[2]) < tolerance):
# if the result passed the tolerance check, extract some values from the
# last row of the w(x) profile which we will need later for comaprisons
resultList.append([u2, u4, result[0], result[2]])
# add the w(x) profile to the list of profiles that passed the tolerance
# Note: the order of resultList is the same as the order of resultW
resultW.append(U)
# go through the resultList (list of extracted values from last row of each w(x) profile)
for i in range(len(resultList)):
x = resultList[i]
# both boundary conditions are 0 for both w(L) and w''(L) so we will simply add
# the two absolute values to determine how much the sum differs from 0
y = abs(x[2]) + abs(x[3])
# if we've just started set the least difference to the current
if i == 0:
minNum = y # remember the smallest difference to 0
index = 0 # remember index of best profile
elif y < minNum:
# current sum of absolute values is smaller
minNum = y
index = i
# print out the integral for w(x) over the beam
sum = 0
for i in resultW[index]:
sum = sum + i[0]
print("The integral of w(x) over the beam is:")
print(sum/1001) # sum/xSteps
This outputs:
The integral of w(x) over the beam is:
0.000135085272117
To print out the best profile for w(x) that we found:
print(resultW[index])
which outputs something like:
# w(x) w'(x) w''(x) w'''(x)
[[ 0.00000000e+00 7.54147813e-04 0.00000000e+00 -9.80392157e-03]
[ 7.54144825e-07 7.54142917e-04 -9.79392157e-06 -9.78392157e-03]
[ 1.50828005e-06 7.54128237e-04 -1.95678431e-05 -9.76392157e-03]
...,
[ -4.48774290e-05 -8.14851572e-04 1.75726275e-04 1.01560784e-02]
[ -4.56921910e-05 -8.14670764e-04 1.85892353e-04 1.01760784e-02]
[ -4.65067671e-05 -8.14479780e-04 1.96078431e-04 1.01960784e-02]]
To double check the results from above we will also solve the ODE using the numerical method.
The numerical method
To solve the problem using the numerical method we first need to solve the differential equations. We will get four constants which we need to find with the help of the boundary conditions. The boundary conditions will be used to form a system of equations to help find the necessary constants.
For example:
w’’’’(x) = q(x);
means that we have this:
d^4(w(x))/dx^4 = q(x)
Since q(x) is constant after integrating we have:
d^3(w(x))/dx^3 = q(x)*x + C
After integrating again:
d^2(w(x))/dx^2 = q(x)*0.5*x^2 + C*x + D
After another integration:
dw(x)/dx = q(x)/6*x^3 + C*0.5*x^2 + D*x + E
And finally the last integration yields:
w(x) = q(x)/24*x^4 + C/6*x^3 + D*0.5*x^2 + E*x + F
Then we take a look at the boundary conditions (now we have expressions from above for w''(x) and w(x)) with which we make a system of equations to solve the constants.
w''(0) => 0 = q(x)*0.5*0^2 + C*0 + D
w''(L) => 0 = q(x)*0.5*L^2 + C*L + D
This gives us the constants:
D = 0 # from the first equation
C = - 0.01 * L # from the second (after inserting D=0)
After repeating the same for w(0)=0 and w(L)=0 we obtain:
F = 0 # from first
E = 0.01/12.0 * L^3 # from second
Now, after we have solved the equation and found all of the integration constants we can make the program for the numerical method.
The program for the numerical method
We will make a FOR loop to go through the entire beam for every dx at a time and sum up (integrate) w(x).
L = 1.0 # in meters
step = 1001.0 # how many steps to take (dx)
q = 0.02 # constant [N/m]
integralOfW = 0.0; # instead of w(0) enter the boundary condition value for w(0)
result = []
for i in range(int(L*step)):
x= i/step
w = (q/24.0*pow(x,4) - 0.02/12.0*pow(x,3) + 0.01/12*pow(L,3)*x)/step # current w fragment
# add up fragments of w for integral calculation
integralOfW += w
# add current value of w(x) to result list for plotting
result.append(w*step);
print("The integral of w(x) over the beam is:")
print(integralOfW)
which outputs:
The integral of w(x) over the beam is:
0.00016666652805511192
Now to compare the two methods
Result comparison between the shooting method and the numerical method
The integral of w(x) over the beam:
Shooting method -> 0.000135085272117
Numerical method -> 0.00016666652805511192
That's a pretty good match, now lets see check the plots:
From the plots it's even more obvious that we have a good match and that the results of the shooting method are correct.
To get even better results for the shooting method increase xSteps and u2_u4_maxNumbers to bigger numbers and you can also narrow down the u2Numbers and u4Numbers to the same set size but a smaller interval (around the best results from previous program runs). Keep in mind that setting xSteps and u2_u4_maxNumbers too high will cause your program to run for a very long time.
You need to transform the ODE into a first order system, setting u0=w one possible and usually used system is
u0'=u1,
u1'=u2,
u2'=u3,
u3'=q(x)
This can be implemented as
def ODEfunc(u,x): return [ u[1], u[2], u[3], q(x) ]
Then make a function that shoots with experimental initial conditions and returns the components of the second boundary condition
def shoot(u01, u03): return odeint(ODEfunc, [0, u01, 0, u03], [0, l])[-1,[0,2]]
Now you have a function of two variables with two components and you need to solve this 2x2 system with the usual methods. As the system is linear, the shooting function is linear as well and you only need to find the coefficients and solve the resulting linear system.
I'm writing a program which randomly chooses two integers within a certain interval. I also wrote a class (which I didn't add below) which uses two numbers 'a' and 'b' and creates an elliptical curve of the form:
y^2 = x^3 + ax + b
I've written the following to create the two random numbers.
def numbers():
n = 1
while n>0:
a = random.randint(-100,100)
b = random.randint(-100,100)
if -16 * (4 * a ** 3 + 27 * b ** 2) != 0:
result = [a,b]
return result
n = n+1
Now I would like to generate a random point on this elliptical curve. How do I do that?
The curve has an infinite length, as for every y ϵ ℝ there is at least one x ϵ ℝ so that (x, y) is on the curve. So if we speak of a random point on the curve we cannot hope to have a homogeneous distribution of the random point over the whole curve.
But if that is not important, you could take a random value for y within some range, and then calculate the roots of the following function:
f(x) = x3 + ax + b - y2
This will result in three roots, of which possibly two are complex (not real numbers). You can take a random real root from that. This will be the x coordinate for the random point.
With the help of numpy, getting the roots is easy, so this is the function for getting a random point on the curve, given values for a and b:
def randomPoint(a, b):
y = random.randint(-100,100)
# Get roots of: f(x) = x^3 + ax + b - y^2
roots = numpy.roots([1, 0, a, b - y**2])
# 3 roots are returned, but ignore potential complex roots
# At least one will be real
roots = [val.real for val in roots if val.imag == 0]
# Choose a random root among those real root(s)
x = random.choice(roots)
return [x, y]
See it run on repl.it.
I am trying to fit the beneath data to the form - I am most interested in 'c' (I know that c ≈ 1/8, b ≈ 3) but would like to extract all these values from the data.
Formula:
y = a*(x-b)**c
Values.txt:
# "values.txt"
2.000000e+00 6.058411e-04
2.200000e+00 5.335520e-04
2.400000e+00 3.509583e-03
2.600000e+00 1.655943e-03
2.800000e+00 1.995418e-03
3.000000e+00 9.437851e-04
3.200000e+00 5.516159e-04
3.400000e+00 6.765981e-04
3.600000e+00 3.860859e-04
3.800000e+00 2.942881e-04
4.000000e+00 5.039975e-04
4.200000e+00 3.962199e-04
4.400000e+00 4.659717e-04
4.600000e+00 2.892683e-04
4.800000e+00 2.248839e-04
5.000000e+00 2.536980e-04
I have tried using the following commands in gnuplot however I am not meaningful results
f(x) = a*(x-b)**c
b = 3
c = 1/8
fit f(x) "values.txt" via a,b,c
Does anyone know the best way to extract these values? I would rather not provide initial guesses for 'b' & 'c' if possible.
Thanks,
J
The main problem with your fitting function is finding b. You can express your equation as a linear function in log(x-b), after which the fitting is trivial:
b = 3
f(x) = c0 + c1 * x
fit f(x) "values.txt" using (log($1-b)):(log($2)) via c0, c1
a = exp(c0)
c = c1
As you see, you need to provide b but do not need initial guesses for the other parameters because it's a trivial linear fit.
Now, I would suggest that you provide a series of values of b and check how good the fitting is for each value. gnuplot gives you the error in the fitting parameter. Then you can plot the overall error (error_c0 + error_c1) as a function of b and figure out for which b the error is minimum. About the optimum b the curve error_c0 + error_c1 vs b should be quadratic and have the minimum at b_opt. Then run the fitting as in the code above with this b = b_opt and get a and c.
How can I transform the blue curve values into linear (red curve)? I am doing some tests in excel, but basically I have those blue line values inside a 3D App that I want to manipulate with python so I can make those values linear. Is there any mathematical approach that I am missing?
The x axis goes from 0 to 90, and the y axis from 0 to 1.
For example: in the middle of the graph the blue line gives me a value of "0,70711", and I know that in linear it is "0,5". I was wondering if there's an easy formula to transform all the incoming non-linear values into linear.
I have no idea what "formula" is creating that non-linear blue line, also ignore the yellow line since I was just trying to "reverse engineer" to see if would lead me to any conclusion.
Thank you
Find a linear function y = ax + b that for x = 0 gives the value 1 and for x = 90 gives 0, just like the function that is represented by a blue curve.
In that case, your system of equations is the following:
1 = b // for x = 0
0 = a*90 + b // for x = 90
Solution provided by solver is the following : { a = -1/90, b = 1 }, the red linear function will have form y = ax + b, we put the values of a and b we found from the solver and we discover that the linear function you are looking for is y = -x/90 + 1 .
The tool I used to solve the system of equations:
http://wims.unice.fr/wims/en_tool~linear~linsolver.en.html
What exactly do you mean? You can calculate points on the red line like this:
f(x) = 1-x/90
and the point then is (x,f(x)) = (x, 1-x/90). But to be honest, I think your question is still rather unclear.