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Posted to commits@lucene.apache.org by ep...@apache.org on 2021/01/07 22:59:01 UTC
[lucene-solr] branch jira/solr-13105-toMerge updated: copy editing
This is an automated email from the ASF dual-hosted git repository.
epugh pushed a commit to branch jira/solr-13105-toMerge
in repository https://gitbox.apache.org/repos/asf/lucene-solr.git
The following commit(s) were added to refs/heads/jira/solr-13105-toMerge by this push:
new 27e7268 copy editing
27e7268 is described below
commit 27e726812977bd44e9bbd774dfb7c8a844f1c8ac
Author: epugh@opensourceconnections.com <>
AuthorDate: Thu Jan 7 17:58:31 2021 -0500
copy editing
---
solr/solr-ref-guide/src/curve-fitting.adoc | 4 ++--
solr/solr-ref-guide/src/logs.adoc | 12 ++++++------
solr/solr-ref-guide/src/machine-learning.adoc | 6 +++---
solr/solr-ref-guide/src/numerical-analysis.adoc | 2 +-
solr/solr-ref-guide/src/regression.adoc | 4 ++--
solr/solr-ref-guide/src/simulations.adoc | 4 ++--
solr/solr-ref-guide/src/statistics.adoc | 6 +++---
solr/solr-ref-guide/src/term-vectors.adoc | 6 +++---
solr/solr-ref-guide/src/time-series.adoc | 10 +++++-----
solr/solr-ref-guide/src/variables.adoc | 4 ++--
10 files changed, 29 insertions(+), 29 deletions(-)
diff --git a/solr/solr-ref-guide/src/curve-fitting.adoc b/solr/solr-ref-guide/src/curve-fitting.adoc
index c2edcec..31009a0 100644
--- a/solr/solr-ref-guide/src/curve-fitting.adoc
+++ b/solr/solr-ref-guide/src/curve-fitting.adoc
@@ -124,12 +124,12 @@ oscillations. This is particularly true if the sine wave has noise. After the cu
extrapolated to any point in time in the past or future.
-In the example below the orginal control points are shown in blue and the fitted curve is shown in yellow.
+In the example below the original control points are shown in blue and the fitted curve is shown in yellow.
image::images/math-expressions/harmfit.png[]
-The output of `harmfit` is a model that can be used by the `predict` function to interpolate and exptrapolate
+The output of `harmfit` is a model that can be used by the `predict` function to interpolate and extrapolate
the sine wave. In the example below the `natural` function creates an *x* axis from 0 to 127
used to predict results for the model. This extrapolates the sine wave out to 128 points, when
the original model curve had only 19 control points.
diff --git a/solr/solr-ref-guide/src/logs.adoc b/solr/solr-ref-guide/src/logs.adoc
index a049844..581a32e 100644
--- a/solr/solr-ref-guide/src/logs.adoc
+++ b/solr/solr-ref-guide/src/logs.adoc
@@ -55,7 +55,7 @@ When working with unfamiliar installations exploration can be used to understand
covered in the logs, what shards and cores are in those collections and the types of operations being
performed on those collections.
-With familiar Solr installations exploration is still extremely
+Even with familiar Solr installations exploration is still extremely
important while trouble shooting because it will often turn up surprises such as unknown errors or
unexpected admin or indexing operations.
@@ -150,7 +150,7 @@ better understanding of what's contained in the logs.
== Query Counting
-Distributed searches produce more then one log record for each query. There will be one *top level* log
+Distributed searches produce more than one log record for each query. There will be one *top level* log
record for
the top level distributed query and a *shard level* log record on one replica from each shard. There may also
be a set of *ids* queries to retrieve fields by id from the shards to complete the page of results.
@@ -186,7 +186,7 @@ image::images/math-expressions/query-ids.png[]
== Query Performance
-One of the important tasks of Solr log analytics is understanding how well a Solr Cluster
+One of the important tasks of Solr log analytics is understanding how well a Solr cluster
is performing.
The *qtime_i* field contains the query time (QTime) in millis
@@ -218,7 +218,7 @@ from the mean.
=== Highest QTime Scatter Plot
-Its often useful to be able to visualize the highest query times recorded in the log data.
+It's often useful to be able to visualize the highest query times recorded in the log data.
This can be done by using the `search` function and sorting on *qtime_i desc*.
In the example below the `search` function returns the highest 500 query times from the *ptest1*
@@ -252,7 +252,7 @@ This converts the query times from milli-seconds to seconds. The result is set t
*c*.
The `round` function then rounds all elements of the query times vector to the nearest second.
-The means all query times less then 500 millis will round to 0.
+This means all query times less than 500 millis will round to 0.
The `freqTable` function is then applied to the vector of query times rounded to
the nearest second.
@@ -362,7 +362,7 @@ image::images/math-expressions/query-spike.png[]
The next step is to generate a time series that counts commits across the same time intervals.
-The time series below uses the sames *start*, *end* and *gap* as the initial time series. But
+The time series below uses the same *start*, *end* and *gap* as the initial time series. But
this time series is computed for records that have a log type of *commit*. The count for the
commits is calculated and plotted on *y-axis*.
diff --git a/solr/solr-ref-guide/src/machine-learning.adoc b/solr/solr-ref-guide/src/machine-learning.adoc
index 6bf8c63..58801f6 100644
--- a/solr/solr-ref-guide/src/machine-learning.adoc
+++ b/solr/solr-ref-guide/src/machine-learning.adoc
@@ -64,7 +64,7 @@ When this expression is sent to the `/stream` handler it responds with:
}
----
-Below the distance is calculated using *Manahattan* distance.
+Below the distance is calculated using *Manhattan* distance.
[source,text]
----
@@ -314,7 +314,7 @@ r=knnRegress(obs, quality, 5, cosine()),
2) The `robust` named parameter can be used to perform a regression analysis that is robust
to outliers in the outcomes. When the `robust` named parameter is used the median outcome
-of the K nearest neighbors is used rather then then average.
+of the K nearest neighbors is used rather than the average.
Sample syntax:
@@ -488,7 +488,7 @@ field. This analyzer returns bigrams which are then annotated to documents in a
The `termVectors` function then creates TD-IDF term vectors from the bigrams stored in the *terms* field.
The `kmeans` function is then used to cluster the bigram term vectors into 5 clusters.
-Finally the top 5 features are extracted from the centroids an returned. Notice
+Finally the top 5 features are extracted from the centroids and returned. Notice
that the features are all bigram phrases with semantic significance.
[source,text]
diff --git a/solr/solr-ref-guide/src/numerical-analysis.adoc b/solr/solr-ref-guide/src/numerical-analysis.adoc
index 41ee44d..16ec68e 100644
--- a/solr/solr-ref-guide/src/numerical-analysis.adoc
+++ b/solr/solr-ref-guide/src/numerical-analysis.adoc
@@ -279,7 +279,7 @@ the 9th floor was `415000` (row 3, column 3).
The `bicubicSpline` function is then used to
interpolate the grid, and the `predict` function is used to predict a value for year 2003, floor 8.
-Notice that the matrix does not include a data point for year 2003, floor 8. The `bicupicSpline`
+Notice that the matrix does not include a data point for year 2003, floor 8. The `bicubicSpline`
function creates that data point based on the surrounding data in the matrix:
[source,json]
diff --git a/solr/solr-ref-guide/src/regression.adoc b/solr/solr-ref-guide/src/regression.adoc
index 78b5a40..fb29e18 100644
--- a/solr/solr-ref-guide/src/regression.adoc
+++ b/solr/solr-ref-guide/src/regression.adoc
@@ -164,8 +164,8 @@ When this expression is sent to the `/stream` handler it responds with:
=== Regression Plot
Using *zplot* and the Zeppelin-Solr interpreter we can visualize both the observations and the predictions in
-the same scatter plot. In the example below zplot is plotting the filesize_d observations on the
-*x* axis, the response_d observations on the *y* access and the predictions on the *y1* access.
+the same scatter plot. In the example below zplot is plotting the *filesize_d* observations on the
+*x* axis, the *response_d* observations on the *y* access and the predictions on the *y1* access.
image::images/math-expressions/linear.png[]
diff --git a/solr/solr-ref-guide/src/simulations.adoc b/solr/solr-ref-guide/src/simulations.adoc
index 35b98d7..7d14f3d 100644
--- a/solr/solr-ref-guide/src/simulations.adoc
+++ b/solr/solr-ref-guide/src/simulations.adoc
@@ -48,7 +48,7 @@ Autocorrelation measures the degree to which a signal is correlated with itself.
if a vector contains a signal or if there is dependency between values in a time series. If there is no
signal and no dependency between values in the time series then the time series is random.
-Its useful to plot the autocorrelation of the *change_d* vector to confirm that it is indeed random.
+It's useful to plot the autocorrelation of the *change_d* vector to confirm that it is indeed random.
In the example below the search results are set to a variable and then the *change_d* field
is vectorized and stored in variable *b*. Then the
@@ -158,7 +158,7 @@ image::images/math-expressions/randomwalk5.1.png[]
The `monteCarlo` function can also be used to model a random walk of
daily stock prices from the `normalDistribution` of daily stock returns.
A random walk is a time series where each step is calculated by adding a random sample to the previous
-step. This creates a time series where each value is dependant on the previous value,
+step. This creates a time series where each value is dependent on the previous value,
which simulates the autocorrelation of stock prices.
In the example below the random walk is achieved by adding a random sample to the
diff --git a/solr/solr-ref-guide/src/statistics.adoc b/solr/solr-ref-guide/src/statistics.adoc
index 24787b5..c2c378a 100644
--- a/solr/solr-ref-guide/src/statistics.adoc
+++ b/solr/solr-ref-guide/src/statistics.adoc
@@ -162,7 +162,7 @@ image::images/math-expressions/cumProb.png[]
Custom histograms can be defined and visualized by combining the output from multiple
`stats` functions into a single histogram. Instead of automatically binning a numeric
-field the custom histogram allows for comparision of bins based on queries.
+field the custom histogram allows for comparison of bins based on queries.
NOTE: The `stats` function is first discussed in the *Searching, Sampling and Aggregation* section of the
user guide.
@@ -194,7 +194,7 @@ the occurrence of each discrete data value and returns a list of tuples with the
frequency statistics for each value.
Below is an example of a frequency table built from a result set
-of rounded *differences* in daily opening stock prises for the stock ticker *amzn*.
+of rounded *differences* in daily opening stock prices for the stock ticker *amzn*.
This example is interesting because it shows a multi-step process to arrive
at the result. The first step is to *search* for records in the the *stocks*
@@ -538,7 +538,7 @@ from the same population.
drawn from the same population.
* `mannWhitney`: The Mann-Whitney test is a non-parametric test that tests if two
-samples of continuous were pulled
+samples of continuous data were pulled
from the same population. The Mann-Whitney test is often used instead of the T-test when the
underlying assumptions of the T-test are not
met.
diff --git a/solr/solr-ref-guide/src/term-vectors.adoc b/solr/solr-ref-guide/src/term-vectors.adoc
index 9736489..85de054 100644
--- a/solr/solr-ref-guide/src/term-vectors.adoc
+++ b/solr/solr-ref-guide/src/term-vectors.adoc
@@ -129,7 +129,7 @@ The phrase query "Man on Fire" is searched for and the top 5000 results, by scor
returned. A single field from the results is return which is the "review_t" field that
contains text of the movie review.
-Then `cartesiaProduct` function is run over the search results. The `cartesianProduct`
+Then `cartesianProduct` function is run over the search results. The `cartesianProduct`
function applies the `analyze` function, which takes the *review_t* field and analyzes
it with the Lucene/Solr analyzer attached to the *text_bigrams* schema field. This analyzer
emits the bigrams found in the text field. The `cartesianProduct` function explodes each
@@ -266,7 +266,7 @@ the noisy terms helps keep the term vector matrix small enough to fit comfortabl
There are four parameters designed to filter noisy terms from the term vector matrix:
-`minTermLength`::
+minTermLength::
The minimum term length required to include the term in the matrix.
minDocFreq::
@@ -276,5 +276,5 @@ maxDocFreq::
The maximum percentage, expressed as a number between 0 and 1, of documents the term can appear in to be included in the index.
exclude::
-A comma delimited list of strings used to exclude terms. If a term contains any of the exclude strings that
+A comma delimited list of strings used to exclude terms. If a term contains any of the excluded strings that
term will be excluded from the term vector.
diff --git a/solr/solr-ref-guide/src/time-series.adoc b/solr/solr-ref-guide/src/time-series.adoc
index 2c04899..5797490 100644
--- a/solr/solr-ref-guide/src/time-series.adoc
+++ b/solr/solr-ref-guide/src/time-series.adoc
@@ -112,7 +112,7 @@ functions are often used to confirm the direction of the trend.
=== Moving Average
The `movingAvg` function computes a simple moving average over a sliding window of data.
-The example below generates a time series, vectorizes the avg(close_d) field and computes the
+The example below generates a time series, vectorizes the *avg(close_d)* field and computes the
moving average with a window size of 5.
The moving average function returns an array that is of shorter length
@@ -131,7 +131,7 @@ image::images/math-expressions/movingavg.png[]
The `expMovingAvg` function uses a different formula for computing the moving average that
responds faster to changes in the underlying data. This means that it is
-less of a lagging indicator then the simple moving average.
+less of a lagging indicator than the simple moving average.
Below is an example that computes a moving average and exponential moving average and plots them
along with the original y values. Notice how the exponential moving average is more sensitive
@@ -143,7 +143,7 @@ image::images/math-expressions/expmoving.png[]
=== Moving Median
The `movingMedian` function uses the median of the sliding window rather than the average.
-In many cases the moving median will be more *robust* to outliers then moving averages.
+In many cases the moving median will be more *robust* to outliers than moving averages.
Below is an example computing the moving median:
@@ -218,7 +218,7 @@ The `movingMAD` (moving mean absolute deviation) function can be used to surface
in a time series by measuring dispersion (deviation from the mean) within a sliding window.
The `movingMAD` function operates in a similar manner as a moving average, except it
-measures the mean absolute deviation within the window rather then the average. By
+measures the mean absolute deviation within the window rather than the average. By
looking for unusually high or low dispersion we can find anomalies in the time
series.
@@ -253,7 +253,7 @@ The `outliers` function takes four parameters:
* Probability distribution
* Numeric vector
* Low probability threshold
-* High probablity threshold
+* High probability threshold
* List of results that the numeric vector was selected from.
The `outliers` function iterates the numeric vector and uses the probability
diff --git a/solr/solr-ref-guide/src/variables.adoc b/solr/solr-ref-guide/src/variables.adoc
index ee7109c..ba6c574 100644
--- a/solr/solr-ref-guide/src/variables.adoc
+++ b/solr/solr-ref-guide/src/variables.adoc
@@ -208,8 +208,8 @@ be cached in-memory for future use.
The `putCache` function adds a variable to the cache.
-In the example below an array is cached in the `workspace` "workspace1"
-and bound to the `key` "key1". The workspace allows different users to cache
+In the example below an array is cached in the workspace *workspace1*
+and bound to the key *key1*. The workspace allows different users to cache
objects in their own workspace. The `putCache` function returns
the variable that was added to the cache.