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@ -280,7 +280,7 @@ the second vector and sliding it across the first vector. The *dot product* of t
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is calculated at each point as the second vector is slid across the first vector.
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is calculated at each point as the second vector is slid across the first vector.
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The dot products are collected in a *third vector* which is the *convolution* of the two vectors.
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The dot products are collected in a *third vector* which is the *convolution* of the two vectors.
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=== Moving Average
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=== Moving Average function
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Before looking at an example of convolution its useful to review the `movingAvg` function. The moving average
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Before looking at an example of convolution its useful to review the `movingAvg` function. The moving average
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function computes a moving average by sliding a window across a vector and computing
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function computes a moving average by sliding a window across a vector and computing
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@ -56,6 +56,6 @@ record in your Solr Cloud cluster computable.
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== <<curve-fitting.adoc#curve-fitting,Curve Fitting>>
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== <<curve-fitting.adoc#curve-fitting,Curve Fitting>>
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== <<dsp.adoc#digital-signal-processing, Digital Signal Processing>>
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== <<dsp.adoc#dsp,Digital Signal Processing>>
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== <<machine-learning.adoc#machine-learning,Machine Learning>>
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== <<machine-learning.adoc#machine-learning,Machine Learning>>
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