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I'm writing a moving average function that uses the convolve function in numpy, which should be equivalent to a (weighted moving average). When my weights are all equal (as in a simple arithmatic average), it works fine:
data = numpy.arange(1,11)
numdays = 5
w = [1.0/numdays]*numdays
numpy.convolve(data,w,'valid')
gives
array([ 3., 4., 5., 6., 7., 8.])
However, when I try to use a weighted average
w = numpy.cumsum(numpy.ones(numdays,dtype=float),axis=0); w = w/numpy.sum(w)
instead of the (for the same data) 3.667,4.667,5.667,6.667,... I expect, I get
array([ 2.33333333, 3.33333333, 4.33333333, 5.33333333, 6.33333333,
7.33333333])
If I remove the 'valid' flag, I don't even see the correct values. I would really like to use convolve for the WMA as well as MA as it makes the code cleaner (same code, different weights) and otherwise I think I'll have to loop through all the data and take slices.
Any ideas about this behavior?
311
Method 1: Using Numpy A moving average can be calculated by finding the sum of elements present in the window and dividing it with window size.
Convolution is an operation that is performed on an image to extract features from it applying a smaller tensor called a kernel like a sliding window over the image. Depending on the values in the convolutional kernel, we can pick up specific patterns from the image.
The np. convolve() is a built-in numpy library method used to return discrete, linear convolution of two one-dimensional vectors. The numpy convolve() method accepts three arguments which are v1, v2, and mode, and returns discrete the linear convolution of v1 and v2 one-dimensional vectors.
Use the pandas Module to Calculate the Moving Average We first convert the numpy array to a time-series object and then use the rolling() function to perform the calculation on the rolling window and calculate the Moving Average using the mean() function.
What you want is np.correlate in a convolution the second argument is inverted basically, so that your expected result would be with np.convolve(data, w[::-1], 'valid').
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