further solr-ref-guide grammar fixes (s/rather then/rather than/g)

solr/solr-ref-guide/src/curve-fitting.adoc

@@ -238,7 +238,7 @@ There are also three helper functions that can be used to retrieve the estimated
 * `getAngularFrequency`: Returns the angular frequency of the sine wave.
 * `getPhase`: Returns the phase of the sine wave.
 
-NOTE: The `harmfit` function works best when run on a single oscillation rather then a long sequence of
+NOTE: The `harmfit` function works best when run on a single oscillation rather than a long sequence of
 oscillations. This is particularly true if the sine wave has noise. After the curve has been fit it can be
 extrapolated to any point in time in the past or future.
 

solr/solr-ref-guide/src/dsp.adoc

@@ -231,7 +231,7 @@ When this expression is sent to the `/stream` handler it responds with:
 ----
 
 One approach to scaling the dot product is to first scale the vectors so that both vectors have a magnitude of 1. Vectors with a
-magnitude of 1, also called unit vectors, are used when comparing only the angle between vectors rather then the magnitude.
+magnitude of 1, also called unit vectors, are used when comparing only the angle between vectors rather than the magnitude.
 The `unitize` function can be used to unitize the vectors before calculating the dot product.
 
 Notice in the example below the dot product result, set to variable *`e`*, is effectively 1. When applied to unit vectors the dot product
@@ -463,7 +463,7 @@ difference in the formula when representing correlation is that the second vecto
 
 Notice in the example below that the second vector is reversed by the `rev` function before it is operated on by the `conv` function.
 The `conv` function reverses the second vector so it will be flipped back to its original order to perform the correlation calculation
-rather then the convolution calculation.
+rather than the convolution calculation.
 
 Notice in the result the highest value is 217. This is the point where the two vectors have the highest correlation.
 
@@ -734,7 +734,7 @@ the matrix which is a vector containing the real values of the fft response.
 
 The plot of the real values of the `fft` response is shown below. Notice there are two
 peaks on opposite sides of the plot. The plot is actually showing a mirrored response. The right side
-of the plot is an exact mirror of the left side. This is expected when the `fft` is run on real rather then
+of the plot is an exact mirror of the left side. This is expected when the `fft` is run on real rather than
 complex data.
 
 Also notice that the `fft` has accumulated significant power in a single peak. This is the power associated with

solr/solr-ref-guide/src/machine-learning.adoc

@@ -386,7 +386,7 @@ This expression returns the following response:
 === Cluster Features
 
 The example below examines the top features of a specific cluster. This example uses the same techniques
-as the centroids example but the top features are extracted from a cluster rather then the centroids.
+as the centroids example but the top features are extracted from a cluster rather than the centroids.
 
 [source,text]
 ----
@@ -490,7 +490,7 @@ The `multiKmeans` function runs the k-means clustering algorithm for a given num
 best result based on which trial produces the lowest intra-cluster variance.
 
 The example below is identical to centroids example except that it uses `multiKmeans` with 100 trials,
-rather then a single trial of the `kmeans` function.
+rather than a single trial of the `kmeans` function.
 
 [source,text]
 ----

solr/solr-ref-guide/src/time-series.adoc

@@ -329,7 +329,7 @@ trend or seasonality from a time series. This is known as making a time series
 
 === First Difference
 
-The actual technique of differencing is to use the difference between values rather then the
+The actual technique of differencing is to use the difference between values rather than the
 original values. The *first difference* takes the difference between a value and the value
 that came directly before it. The first difference is often used to remove the trend
 from a time series.