I have a nested for loop and I have already managed to vectorise one dimension, but since the other dimension is also large, I was looking for a way to vectorise this dimension too. Please help.
def g(x,y):
return np.exp((np.linalg.norm(x-y, axis=1)**2))
y[i] = np.sum(a*s*g(np.tile(X[i],(n,1)),s_x).reshape((n,1)))
now a is of dim(n,1), s of dim(n,1), X[i] of dim(1,p), s_x of dim(n,p)
any ideas on this? I have tried a long time but I am not able to get to any conclusion, please help.
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I'm trying to solve the recurrence f(n)=2f(n/2)+logn when f(1)=1 and n is a power of 2. I think that I should be able to do this using the master method. I've seen this before, but never with log. Can I get some help getting started, please?
One trick that’s often useful here is to replace the log n term with something that grows strictly faster or slower and to see what you get. For example, your recurrence is bounded from above and below, respectively, by these recurrences:
A(n) = 2A(n / 2) + √n.
B(n) = 2A(n / 2) + 1.
What do these solve to? What does that tell you about your recurrence?
I need to find the number of times the accelerometer value stream attains a maximum. I made a plot of the accelerometer values obtained from an iPhones against time, using CoreMotion method to obtain the DeviceMotionUpdates. When the data was being recorded, I shook the phone 9 times (where each extremity was one of the highest points of acceleration).
I have marked the 18 (i.e. 9*2) times when acceleration had attained maximum in red boxes on the plot.
But, as you see, there are some local maxima that I do not want to consider. Can someone direct me towards an idea that will help me achieve detecting only the maxima of importance to me?
Edit: I think I have to use a low pass filter. But, how do I implement this in Swift? How do I choose the frequency of cut-off?
Edit 2:
I implemented a low pass filter and passed the raw motion data through it and obtained the graph as shown below. This is a lot better. I still need a way to avoid the insignificant maxima that can be observed. I'll work in depth with the filter and probably fix it.
Instead of trying to find the maximas, I would try to look for cycles. Especially, we note that the (main) minimas seem to be a lot more consistent than the maximas.
I am not familiar with swift, so I'll layout my idea in pseudo code. Suppose we have our values in v[i] and the derivative in dv[i] = v[i] - v[i - 1]. You can use any other differentiation scheme if you get a better result.
I would try something like
cycles = [] // list of pairs
cstart = -1
cend = -1
v_threshold = 1.8 // completely guessing these figures looking at the plot
dv_threshold = 0.01
for i in v:
if cstart < 0 and
v[i] > v_threshold and
dv[i] < dv_threshold then:
// cycle is starting here
cstart = i
else if cstart > 0 and
v[i] < v_threshold and
dv[i] < dv_threshold then:
// cycle ended
cend = i
cycles.add(pair(cstart, cend))
cstart = -1
cend = -1
end if
Now you note in comments that the user should be able to shake with different force and you should be able to recognise the motion. I would start with a simple 'hard-coded' cases as the one above, and see if you can get it to work sufficiently well. There is a lot of things you could try to get a variable threshold, but you will nevertheless always need one. However, from the data you show I strongly suggest at least limiting yourself to looking at the minimas and not the maximas.
Also: the code I suggested is written assuming you have the full data set, however you will want to run this in real time. This will be no problem, and the algorithm will still work (that is, the idea will still work but you'll have to code it somewhat differently).
I have been trying to get my hands dirty with Information Retrieval.My professor gave us this problem to solve, but I can't get my way around it. The matrix given, if it is a distance matrix, the diagonal elements should all be 0. But in the table, they're given as 1. The other entries are also less than 1. How is this possible? Can someone please explain?
Please see question 5.c. I could not enter the table manually and apologize for that.
In every similarity measurement, 1 means totally similar and 0 means there is no similarity between documents.
I'm working with openCV and I'm a newbie in this field. I'm researching about Camshift. I want to extend this method by using multiple histograms. It means when tracking an object has many than one apperance (ex: rubik cube with six apperance), if we use only one histogram, Camshift will most likely fail.
I know calcHist function in openCV (http://docs.opencv.org/modules/imgproc/doc/histograms.html#calchist) has a parameter is "accumulate", but I don't know how to use and when to use (apply for camshiftdemo.cpp in opencv samples folder). This function can help me solve this problem? Or I have to use difference solution?
I have an idea, that is: create an array histogram for object, for every appearance condition that strongly varies in color, we pre-compute and store all to this array. But when we compute new histogram? It means that the pre-condition to start compute new histogram is what?
And what happend if I have to track multiple object has same color?
Everybody please help me. Thank you so much!
In my application I need to determine what the plates a user can load on their barbell to achieve the desired weight.
For example, the user might specify they are using a 45LB bar and have 45,35,25,10,5,2.5 pound plates to use. For a weight like 115, this is an easy problem to solve as the result neatly matches a common plate. 115 - 45 / 2 = 35.
So the objective here is to find the largest to smallest plate(s) (from a selection) the user needs to achieve the weight.
My starter method looks like this...
-(void)imperialNonOlympic:(float)barbellWeight workingWeight:(float)workingWeight {
float realWeight = (workingWeight - barbellWeight);
float perSide = realWeight / 2;
.... // lots of inefficient mod and division ....
}
My thought process is to determine first what the weight per side would be. Total weight - weight of the barbell / 2. Then determine what the largest to smallest plate needed would be (and the number of each, e.g. 325 would be 45 * 3 + 5 or 45,45,45,5.
Messing around with fmodf and a couple of other ideas it occurred to me that there might be an algorithm that solves this problem. I was looking into BFS, and admit that it is above my head but still willing to give it a shot.
Appreciate any tips on where to look in algorithms or code examples.
Your problem is called Knapsack problem. You will find a lot solution for this problem. There are some variant of this problem. It is basically a Dynamic Programming (DP) problem.
One of the common approach is that, you start taking the largest weight (But less than your desired weight) and then take the largest of the remaining weight. It easy. I am adding some more links ( Link 1, Link 2, Link 3 ) so that it becomes clear. But some problems may be hard to understand, skip them and try to focus on basic knapsack problem. Good luck.. :)
Let me know if that helps.. :)