sliding-window

Problem description: I'm interested in looking at terms in the text window of, say, 3 words to the left and 3 to the right. The base case has the form of w-3 w-2 w-1 term w+1 w+2 w+3. I want to implement a sliding window over my text with which I will be able to record the context words of each term. So, every word is once treated as a term, but when the window moves, it becomes a context word, etc. However, when the term is the 1st word in line, there are no context words on the left (t w+1 w+2 w+3), when it's the 2nd word in line, there's only one context word on the left, and so on. So, I

I have the following function which calculates statistic parameters. I would like to pass this function to nlfilter to do the calculation for a whole image. But the output of nlfilter must be a scalar. How can I convert this to a function handle suitable for use with nlfilter so I can save the output of the function getStatistics2 ? The getStatistics2 function's output is an struct array. function [out] = getStatistics2(D) D = double(D); % out.MAX = max(D);%maximum % out.MIN = min(D);%minimum out.MEA = mean(D);%mean out.MAD = mad(D);% mean absolute deviation y=mean(abs(X-mean(x))) out.MED =

I would like to compute the product of the next n adjacent elements of a matrix. The number n of elements to be multiplied should be given in function's input. For example for this input I should compute the product of every 3 consecutive elements, starting from the first. [p, ind] = max_product([1 2 2 1 3 1],3); This gives [1*2*2, 2*2*1, 2*1*3, 1*3*1] = [4,4,6,3] . Is there any practical way to do it? Now I do this using: for ii = 1:(length(v)-2) p = prod(v(ii:ii+n-1)); end where v is the input vector and n is the number of elements to be multiplied. in this example n=3 but can take any

I have the following numpy arrays: arr_1 = [[1,2],[3,4],[5,6]] # 3 X 2 arr_2 = [[0.5,0.6],[0.7,0.8],[0.9,1.0],[1.1,1.2],[1.3,1.4]] # 5 X 2 arr_1 is clearly a 3 X 2 array, whereas arr_2 is a 5 X 2 array. Now without looping, I want to element-wise multiply arr_1 and arr_2 so that I apply a sliding window technique (window size 3) to arr_2. Example: Multiplication 1: np.multiply(arr_1,arr_2[:3,:]) Multiplication 2: np.multiply(arr_1,arr_2[1:4,:]) Multiplication 3: np.multiply(arr_1,arr_2[2:5,:]) I want to do this in some sort of a matrix multiplication form to make it faster than my current

Sorry for the long post, but I have provided copy & paste sample data and a possible solution approach below. The relevant part of the question is in the upper part of the post (above the horizontal rule). I have the following table Dt customer_id buy_time money_spent ------------------------------------------------- 2000-01-04 100 11:00:00.00 2 2000-01-05 100 16:00:00.00 1 2000-01-10 100 13:00:00.00 4 2000-01-10 100 14:00:00.00 3 2000-01-04 200 09:00:00.00 10 2000-01-06 200 10:00:00.00 11 2000-01-06 200 11:00:00.00 5 2000-01-10 200 08:00:00.00 20 and want a query to get this result set Dt Dt