I have a problem that is equal parts trig and Python. I am plotting a cosine over time interval [0,t] whose frequency changes (slightly) according to another cosine function. So what I'd expect to see is a repeating pattern of higher-to-lower frequency that repeats over the duration of the window [0,t].
Instead what I'm seeing is that over time a low-freq motif emerges in the cosine plot and repeats over time, each time becoming lower and lower in freq until eventually the cosine doesn't even oscillate properly it just "wobbles", for lack of a better term.
I don't understand how this is emerging over the course of the window [0,t] because cosine is (obviously) periodic and the function modulating it is as well. So how can "new" behavior emerge?? The behavior should be identical across all periods of the modulatory cosine that tunes the freq of the base cosine, right?
As a note, I'm technically using a modified cosine, instead of cos(wt) I'm using e^(cos(wt)) [called von mises eq or something similar].
Minimum needed Code:
cos_plot = []
for wind,pos_theta in zip(window,pos_theta_vec): #window is vec of time increments
# for ref: DBFT(pos_theta) = (1/(2*np.pi))*np.cos(np.radians(pos_theta - base_pos))
f = float(baserate+DBFT(pos_theta)) # DBFT() returns a val [-0.15,0.15] periodically depending on val of pos_theta
cos_plot.append(np.exp(np.cos(f*2*np.pi*wind)))
plt.plot(cos_plot)
plt.show()
What you are observing could depend on "aliasing", i.e. the emergence of low-frequency figures because of sampling of an high frequency function with a step that is too big.
(picture taken from the linked Wikipedia page)
If the issue is NOT aliasing consider that any function shape between -1 and 1 can be obtained with cos(f(x)*x) by simply choosing f(x).
For, consider any function -1 <= g(x) <= 1 and set f(x) = arccos(g(x))/x.
To look for the problem try plotting your "frequency" and see if anything really strange is present in it. May be you've a bug in DBFT.
In the interest of posterity, in case anyone ever needs an answer to this question:
I wanted a cosine whose frequency was a time-varying function freq(t). My mistake was simply evaluating this function at each time t like this: Acos(2pifreq(t)t). Instead you need to integrate freq(t) from 0 to t at each time point: y = cos(2%piintegral(f(t)) + 2%pi*f0*t + phase). The term for this procedure is a frequency sweep or chirp (not identical terms, but similar if you need to google/SO answers).
Thanks to those who responded with help :)
SB
Related
I'm currently working on a trajectory optimization problem that involves binary actuators. In order to avoid solving an MINLP I do not simply optimize over the states and control inputs but instead, assume that each of the binary actuators alternates between the states "on" and "off" and optimize over the duration of those intervals. I will call the array containing the decision variable h (an N by 2 matrix in the particular case below).
A minimal example using a double integrator that has two control inputs that enact a positive or negative force on the system respectively would be:
Here I model the state trajectory as some train of 3rd order polynomial.
I particularly do not want to merge these actuators into one with the states -1,0,1 since the more general system I'd like to apply this to also has more binary actuators. I add some additional constraints such as connecting the polynomials continuously and differentiably; enforce that the sum of all intervals is equal to the desired final time; enforce initial and final state constraints and finally enforce the dynamics of the system.
My initial idea was to then enforce the dynamics at constant intervals, i.e.:
However, the issue here is that any of the actuators could be in any arbitrary interval for some time t. Since the intervals can shrink to duration zero one actuator might be in the last interval while the other one remains in the first. I.e. the value of a decision variable (time duration) changes which decision variables are dependent on each other. This is more or less manifested in drake by the fact that I cannot do a comparison such as Tau < t if Tau is a drake expression and t is some number. The code snippet is:
# Enforce dynamics at the end of each control interval
for t in np.arange(0, Tf, dt_dyn):
# Find the index of the interval that is active for each actuator
t_ctrl = np.cumsum(h, axis=0)
intervals = (t_ctrl < t)
idxs = np.sum(intervals, axis=0)
# If the idx is even the actuator is off, otherwise its on
prog.AddConstraint(eq(qdd(q_a, t, dt_state),
continuous_dynamics(q(q_a, t, dt_state),
qd(q_a, t, dt_state),
[idxs[0] % 2, idxs[1] % 2])))
and the resulting error message:
Traceback (most recent call last):
File "test.py", line 92, in <module>
intervals = (t_ctrl < t)
RuntimeError: You should not call `__bool__` / `__nonzero__` on `Formula`. If you are trying to make a map with `Variable`, `Expression`, or `Polynomial` as keys (and then access the map in Python), please use pydrake.common.containers.EqualToDict`.
In the end, my question is more of a conceptual than technical nature: Does drake support this dependence of the "dependence of decision variables on the values" in some other way? Or is there a different way I can transcribe my problem to avoid the ambiguity in which interval my actuators are for some given time?
I've also linked to the overall script that implements the minimal example here
The immediate problem is that intervals = (t_ctrl < t) is a vector of dtype Formula, not a vector of dtype Variable(type=BINARY), so you can't actually sum it up. To do an arithmetic sum, you'd need to change that line to an np.vectorize-wrapped function that calls something like if_then_else(t_argument < t_constant, 0.0, 1.0) in order to have it be an Expression-valued vector, at which which would be sum-able.
That won't actually help you, though, since you cannot do % (modular) arithmetic on symbolic expressions anyway, so the % 2.0 == 0.0 stuff will raise an exception once you make it that far.
I suspect that you'll need a different approach to encoding the problem into variables, but unfortunately an answer there is beyond my skill level at the moment.
I am interested in learning if there has been published some type of code or package that can help me with the following problem:
An event takes place 30 times.
Each event can return 6 different values (0,1,2,3,4,5), each with their own unique probability.
I would like to estimate the probability of the total values -after all the scenarios have been simulated - is above X (e.g. 24).
The issue I have is that I can't - in a given event where the value is 3- multiply the probability of value 3*3 and add it together with the previous obtained values. Instead I need to simulate every single variation that is possible.
Is there any relatively simple solution to solve this issue?
First of all, what you're describing isn't scenario analysis. That said, Python can be used to estimate complex probabilities where an analytical solution might be hard or impossible to find.
Assuming an event takes place 30 times, with outcomes [0,1,2,3,4,5], and each outcome has a probability of occurring given by the list (for example) p =
[.1,.2,.2,.3,.1,.1], you can approximate the probability that the sum of all 30 events is greater than X with
import numpy as np
X = 80
np.mean([sum(np.random.choice(a=[0,1,2,3,4,5], size= 30,p=[.1,.2,.2,.3,.1,.1])) > X for i in range(10000)])
I am a beginner to Python. Currently I'm writing a code for developing a simple solver for non-linear ODE systems with initial value. The equations of the system are as follow.
The function of myu is evaluated first to get the value of myu, then used in dX/dt, dS/dt, and dDO/dt. At the next step, myu is evaluated again to get its new value based on new value of S and DO.
I am using General Linear Method (GLM), proposed by J. C. Butcher, as my method. This method use a transition matrix, which value and size depends on numerical method that we use. In this case, I use Runge Kutta Cash-Karp.
While you may find in the equation that D is also a function, here I set the value of D as a constant.
In initialization, the value of h is set first, to get the number of step. I create a vector named 'initValue', with 8 columns and 4 rows, consist of values of k for each equations (row 1 to 6), initial value for fourth order of the Runge Kutta (row 7. I set it to 0 since it just act as a 'predictor'), and initial value for each equations (row 8).
Transition matrix is created based on the GLM, which values inside it comes from the constants of stage equations (to find the value of k1 to k6) and step equations (to find the solutions) of Runge Kutta Cash-Karp.
In the looping, at the very first time, I simply add the initial values to an array named 'result'. At the first step, I simply multiple the transition matrix with vector 'initValue'. And at the next until final step, I initialize the 'initValue' based on result from previous step.
What I'm looking for is the solution which should look like this.
My code works if h is less than 1. I compare my result with result from scipy.integrate.odeint. But when I set h bigger than 1, it show different result than the result it should be. For example, in the code, I set h = 100, which means that it will only display the initial value and final value (when time = 100). While X and S should going upward, and DO and Xr going downward, mine was the opposite of them. The result from odeint when h is set to bigger than 1 show the same result with the expected solution.
I need help to fix my code so it can display the expected solution for any value of h.
Thank you.
Why do you expect any type of reasonable result for ridiculously large step sizes?
The most simple demonstration is y'=-y and the explicit Euler method. If you use step sizes smaller 1 you will get qualititively correct solutions. For step sizes smaller 0.1, you will start to get also quantitatively correct step sizes.
However, if you use a step size h=10, then the iteration
y[k+1]= y[k] - h*y[k] = -9*y[k]
will explode. The same also happens for higher order methods, sufficiently small step sizes give quantitatively correct results, medium step sizes can still give a qualitatively correct picture, large step sizes lead to errors that are very quickly larger than the solution values.
I'm constructing a Naive Bayes text classifier from scratch in Python and I am aware that, upon encountering a product of very small probabilities, using a logarithm over the probabilities is a good choice.
The issue now, is that the mathematical function that I'm using has a summation OVER a product of these extremely small probabilities.
To be specific, I'm trying to calculate the total word probabilities given a mixture component (class) over all classes.
Just plainly adding up the logs of these total probabilities is incorrect, since the log of a sum is not equal to the sum of logs.
To give an example, lets say that I have 3 classes, 2000 words and 50 documents.
Then I have a word probability matrix called wordprob with 2000 rows and 3 columns.
The algorithm for the total word probability in this example would look like this:
sum = 0
for j in range(0,3):
prob_product = 1
for i in words: #just the index of words from my vocabulary in this document
prob_product = prob_product*wordprob[i,j]
sum = sum + prob_product
What ends up happening is that prob_product becomes 0 on many iterations due to many small probabilities multiplying with each other.
Since I can't easily solve this with logs (because of the summation in front) I'm totally clueless.
Any help will be much appreciated.
I think you may be best to keep everything in logs. The first part of this, to compute the log of the product is just adding up the log of the terms. The second bit, computing the log of the sum of the exponentials of the logs is a bit trickier.
One way would be to store each of the logs of the products in an array, and then you need a function that, given an array L with n elements, will compute
S = log( sum { i=1..n | exp( L[i])})
One way to do this is to find the maximum, M say, of the L's; a little algebra shows
S = M + log( sum { i=1..n | exp( L[i]-M)})
Each of the terms L[i]-M is non-positive so overflow can't occur. Underflow is not a problem as for them exp will return 0. At least one of them (the one where L[i] is M) will be zero so it's exp will be one and we'll end up with something we can pass to log. In other words the evaluation of the formula will be trouble free.
If you have the function log1p (log1p(x) = log(1+x)) then you could gain some accuracy by omitting the (just one!) i where L[i] == M from the sum, and passing the sum to log1p instead of log.
your question seems on the math side of things rather than the coding of it.
I haven't quite figured out what your issue is but the sum of logs equals the log of the products. Dont know if that helps..
Also, you are calculating one prob_product for every j but you are just using the last one (and you are re-initializing it). you meant to do one of two things: either initialize it before the j-loop or use it before you increment j. Finally, i doesnt look that you need to initialize sum unless this is part of yet another loop you are not showing here.
That's all i have for now.
Sorry for the long post and no code.
High school algebra tells you this:
log(A*B*....*Z) = log(A) + log(B) + ... + log(Z) != log(A + B + .... + Z)
I have a 1D array of data and wish to extract the spatial variation. The standard way to do this which I wish to pythonize is to perform a moving linear regression to the data and save the gradient...
def nssl_kdp(phidp, distance, fitlen):
kdp=zeros(phidp.shape, dtype=float)
myshape=kdp.shape
for swn in range(myshape[0]):
print "Sweep ", swn+1
for rayn in range(myshape[1]):
print "ray ", rayn+1
small=[polyfit(distance[a:a+2*fitlen], phidp[swn, rayn, a:a+2*fitlen],1)[0] for a in xrange(myshape[2]-2*fitlen)]
kdp[swn, rayn, :]=array((list(itertools.chain(*[fitlen*[small[0]], small, fitlen*[small[-1]]]))))
return kdp
This works well but is SLOW... I need to do this 17*360 times...
I imagine the overhead is in the iterator in the [ for in arange] line... Is there an implimentation of a moving fit in numpy/scipy?
the calculation for linear regression is based on the sum of various values. so you could write a more efficient routine that modifies the sum as the window moves (adding one point and subtracting an earlier one).
this will be much more efficient than repeating the process every time the window shifts, but is open to rounding errors. so you would need to restart occasionally.
you can probably do better than this for equally spaced points by pre-calculating all the x dependencies, but i don't understand your example in detail so am unsure whether it's relevant.
so i guess i'll just assume that it is.
the slope is (NΣXY - (ΣX)(ΣY)) / (NΣX2 - (ΣX)2) where the "2" is "squared" - http://easycalculation.com/statistics/learn-regression.php
for evenly spaced data the denominator is fixed (since you can shift the x axis to the start of the window without changing the gradient). the (ΣX) in the numerator is also fixed (for the same reason). so you only need to be concerned with ΣXY and ΣY. the latter is trivial - just add and subtract a value. the former decreases by ΣY (each X weighting decreases by 1) and increases by (N-1)Y (assuming x_0 is 0 and x_N is N-1) each step.
i suspect that's not clear. what i am saying is that the formula for the slope does not need to be completely recalculated each step. particularly because, at each step, you can rename the X values as 0,1,...N-1 without changing the slope. so almost everything in the formula is the same. all that changes are two terms, which depend on Y as Y_0 "drops out" of the window and Y_N "moves in".
I've used these moving window functions from the somewhat old scikits.timeseries module with some success. They are implemented in C, but I haven't managed to use them in a situation where the moving window varies in size (not sure if you need that functionality).
http://pytseries.sourceforge.net/lib.moving_funcs.html
Head here for downloads (if using Python 2.7+, you'll probably need to compile the extension itself -- I did this for 2.7 and it works fine):
http://sourceforge.net/projects/pytseries/files/scikits.timeseries/0.91.3/
I/we might be able to help you more if you clean up your example code a bit. I'd consider defining some of the arguments/objects in lines 7 and 8 (where you're defining 'small') as variables, so that you don't end row 8 with so many hard-to-follow parentheses.
Ok.. I have what seems to be a solution.. not an answer persay, but a way of doing a moving, multi-point differential... I have tested this and the result looks very very similar to a moving regression... I used a 1D sobel filter (ramp from -1 to 1 convolved with the data):
def KDP(phidp, dx, fitlen):
kdp=np.zeros(phidp.shape, dtype=float)
myshape=kdp.shape
for swn in range(myshape[0]):
#print "Sweep ", swn+1
for rayn in range(myshape[1]):
#print "ray ", rayn+1
kdp[swn, rayn, :]=sobel(phidp[swn, rayn,:], window_len=fitlen)/dx
return kdp
def sobel(x,window_len=11):
"""Sobel differential filter for calculating KDP
output:
differential signal (Unscaled for gate spacing
example:
"""
s=np.r_[x[window_len-1:0:-1],x,x[-1:-window_len:-1]]
#print(len(s))
w=2.0*np.arange(window_len)/(window_len-1.0) -1.0
#print w
w=w/(abs(w).sum())
y=np.convolve(w,s,mode='valid')
return -1.0*y[window_len/2:len(x)+window_len/2]/(window_len/3.0)
this runs QUICK!