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Posted to commits@datasketches.apache.org by le...@apache.org on 2022/08/16 22:21:06 UTC
[datasketches-website] branch master updated (b63bfec8 -> 6b86bb8c)
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leerho pushed a change to branch master
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from b63bfec8 Release memory 2.2.0
new 344c2e55 update Tutorial
new 6b86bb8c Merge branch 'master' of https://github.com/apache/datasketches-website
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Summary of changes:
docs/Quantiles/SketchingQuantilesAndRanksTutorial.md | 12 ++++++------
1 file changed, 6 insertions(+), 6 deletions(-)
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[datasketches-website] 01/02: update Tutorial
Posted by le...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
leerho pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/datasketches-website.git
commit 344c2e556d7a81690afe13d64f832c250f40c8bb
Author: Lee Rhodes <le...@users.noreply.github.com>
AuthorDate: Tue Aug 16 15:12:05 2022 -0700
update Tutorial
---
docs/Quantiles/SketchingQuantilesAndRanksTutorial.md | 12 ++++++------
1 file changed, 6 insertions(+), 6 deletions(-)
diff --git a/docs/Quantiles/SketchingQuantilesAndRanksTutorial.md b/docs/Quantiles/SketchingQuantilesAndRanksTutorial.md
index cc515b04..584ec923 100644
--- a/docs/Quantiles/SketchingQuantilesAndRanksTutorial.md
+++ b/docs/Quantiles/SketchingQuantilesAndRanksTutorial.md
@@ -19,7 +19,7 @@ layout: doc_page
specific language governing permissions and limitations
under the License.
-->
-# Sketching Quantiles and Ranks, the Basics
+# Sketching Quantiles and Ranks Tutorial
Streaming quantiles algorithms, or quantiles sketches, enable us to analyze the distributions
of massive data very quickly using only a small amout of space.
They allow us to compute a quantile values given a desired rank, or compute a rank given
@@ -130,7 +130,7 @@ These next examples use a small data set that mimics what could be the result of
## The rank functions with inequalities
-### ***rank(quantile, NON_INCLUSIVE)*** or ***r(q, LT)*** :=<br>Given *q*, return the rank, *r*, of the largest quantile that is strictly *Less Than* *q*.
+### ***rank(quantile, EXCLUSIVE)*** or ***r(q, LT)*** :=<br>Given *q*, return the rank, *r*, of the largest quantile that is strictly *Less Than* *q*.
<b>Implementation:</b>
@@ -184,7 +184,7 @@ Given *q*, search the quantile array until we find the adjacent pair *{q1, q2}*
## The quantile functions with inequalities
-### ***quantile(rank, NON_INCLUSIVE)*** or ***q(r, GT)*** :=<br>Given *r*, return the quantile, *q*, of the smallest rank that is strictly Greater Than *r*.
+### ***quantile(rank, EXCLUSIVE)*** or ***q(r, GT)*** :=<br>Given *r*, return the quantile, *q*, of the smallest rank that is strictly Greater Than *r*.
<b>Implementation:</b>
Given *r*, search the rank array until we find the adjacent pair *{r1, r2}* where *r1 <= r < r2*. Return the quantile associated with *r2*, the second of the pair.
@@ -210,7 +210,7 @@ Given *r*, search the rank array until we find the adjacent pair *{r1, r2}* wher
--------
-### ***quantile(rank, NON_INCLUSIVE_STRICT)*** or ***q(r, GT_STRICT)*** :=<br>Given *r*, return the quantile, *q*, of the smallest rank that is strictly Greater Than *r*.
+### ***quantile(rank, EXCLUSIVE_STRICT)*** or ***q(r, GT_STRICT)*** :=<br>Given *r*, return the quantile, *q*, of the smallest rank that is strictly Greater Than *r*.
In <b>STRICT</b> mode, the only difference is the following:
@@ -246,11 +246,11 @@ For example *q(.786) = 30*
## These inequality functions maintain the 1:1 functional relationship
-### The non inclusive search for q(r) is the inverse of the non inclusive search for r(q).
+### The *exclusive* search for q(r) is the inverse of the *exclusive* search for r(q).
##### Therefore, *q = q(r(q))* and *r = r(q(r))*.
-### The inclusive search for q(r) is the inverse of the inclusive search for r(q).
+### The *inclusive* search for q(r) is the inverse of the *inclusive* search for r(q).
##### Therefore, *q = q(r(q))* and *r = r(q(r))*.
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[datasketches-website] 02/02: Merge branch 'master' of https://github.com/apache/datasketches-website
Posted by le...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
leerho pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/datasketches-website.git
commit 6b86bb8c9cbe5d9c9b1e9b05e01476484d6977b8
Merge: 344c2e55 b63bfec8
Author: Lee Rhodes <le...@users.noreply.github.com>
AuthorDate: Tue Aug 16 15:19:48 2022 -0700
Merge branch 'master' of https://github.com/apache/datasketches-website
_includes/downloadsInclude.txt | 3 +-
.../SketchingQuantilesAndRanksTutorial.md | 98 +++++++++++-----------
docs/Tuple/TupleEngagementExample.md | 2 +-
3 files changed, 52 insertions(+), 51 deletions(-)
diff --cc docs/Quantiles/SketchingQuantilesAndRanksTutorial.md
index 584ec923,e92478f7..b9f54ac8
--- a/docs/Quantiles/SketchingQuantilesAndRanksTutorial.md
+++ b/docs/Quantiles/SketchingQuantilesAndRanksTutorial.md
@@@ -19,13 -19,13 +19,13 @@@ layout: doc_pag
specific language governing permissions and limitations
under the License.
-->
-# Sketching Quantiles and Ranks, the Basics
+# Sketching Quantiles and Ranks Tutorial
Streaming quantiles algorithms, or quantiles sketches, enable us to analyze the distributions
- of massive data very quickly using only a small amout of space.
- They allow us to compute a quantile values given a desired rank, or compute a rank given
+ of massive data very quickly using only a small amount of space.
+ They allow us to compute quantile values given a desired rank, or compute a rank given
a quantile value. Quantile sketches enable us to plot the CDF, PMF or histograms of a distribution.
- The goal of this short tutorial it to introduce to the reader some of the basic concepts
+ The goal of this short tutorial it to introduce the reader to some of the basic concepts
of quantiles, ranks and their functions.
## What is a rank?
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