Merge pull request #1866 from redis/DOC-5226-enable-testable-csharp-prob

DOC-5226 enabled testable probabilistic examples for C#

Author: andy-stark-redis

content/develop/clients/dotnet/prob.md

@@ -99,49 +99,16 @@ add. The following example adds some names to a Bloom filter representing
 a list of users and checks for the presence or absence of users in the list.
 Note that you must use the `BF()` method to access the Bloom filter commands.
 
-<!--< clients-example home_prob_dts bloom "C#" >}}
-< /clients-example >}}-->
-```cs
-bool[] res1 = db.BF().MAdd(
-    "recorded_users", "andy", "cameron", "david", "michelle"
-);
-Console.WriteLine(string.Join(", ", res1));
-// >>> true, true, true, true
-
-bool res2 = db.BF().Exists("recorded_users", "cameron");
-Console.WriteLine(res2); // >>> true
-
-bool res3 = db.BF().Exists("recorded_users", "kaitlyn");
-Console.WriteLine(res3); // >>> false
-```
+{{< clients-example home_prob_dts bloom "C#" >}}
+{{< /clients-example >}}
 
 A Cuckoo filter has similar features to a Bloom filter, but also supports
 a deletion operation to remove hashes from a set, as shown in the example
 below. Note that you must use the `CF()` method to access the Cuckoo filter
 commands.
 
-<!--< clients-example home_prob_dts cuckoo "C#" >}}
-< /clients-example >}}-->
-```cs
-bool res4 = db.CF().Add("other_users", "paolo");
-Console.WriteLine(res4); // >>> true
-
-bool res5 = db.CF().Add("other_users", "kaitlyn");
-Console.WriteLine(res5); // >>> true
-
-bool res6 = db.CF().Add("other_users", "rachel");
-Console.WriteLine(res6); // >>> true
-
-bool[] res7 = db.CF().MExists("other_users", "paolo", "rachel", "andy");
-Console.WriteLine(string.Join(", ", res7));
-// >>> true, true, false
-
-bool res8 = db.CF().Del("other_users", "paolo");
-Console.WriteLine(res8); // >>> true
-
-bool res9 = db.CF().Exists("other_users", "paolo");
-Console.WriteLine(res9); // >>> false
-```
+{{< clients-example home_prob_dts cuckoo "C#" >}}
+{{< /clients-example >}}
 
 Which of these two data types you choose depends on your use case.
 Bloom filters are generally faster than Cuckoo filters when adding new items,
@@ -161,35 +128,8 @@ You can also merge two or more HyperLogLogs to find the cardinality of the
 [union](https://en.wikipedia.org/wiki/Union_(set_theory)) of the sets they
 represent.
 
-<!--< clients-example home_prob_dts hyperloglog "C#" >}}
-< /clients-example >}}-->
-```cs
-bool res10 = db.HyperLogLogAdd(
-    "group:1",
-    new RedisValue[] { "andy", "cameron", "david" }
-);
-Console.WriteLine(res10); // >>> true
-
-long res11 = db.HyperLogLogLength("group:1");
-Console.WriteLine(res11); // >>> 3
-
-bool res12 = db.HyperLogLogAdd(
-    "group:2",
-    new RedisValue[] { "kaitlyn", "michelle", "paolo", "rachel" }
-);
-Console.WriteLine(res12); // >>> true
-
-long res13 = db.HyperLogLogLength("group:2");
-Console.WriteLine(res13); // >>> 4
-
-db.HyperLogLogMerge(
-    "both_groups",
-    "group:1", "group:2"
-);
-
-long res14 = db.HyperLogLogLength("both_groups");
-Console.WriteLine(res14); // >>> 7
-```
+{{< clients-example home_prob_dts hyperloglog "C#" >}}
+{{< /clients-example >}}
 
 The main benefit that HyperLogLogs offer is their very low
 memory usage. They can count up to 2^64 items with less than
@@ -229,44 +169,8 @@ a Count-min sketch object, add data to it, and then query it.
 Note that you must use the `CMS()` method to access the Count-min
 sketch commands.
 
-<!--< clients-example home_prob_dts cms "C#" >}}
-< /clients-example >}}-->
-```cs
-// Specify that you want to keep the counts within 0.01
-// (1%) of the true value with a 0.005 (0.5%) chance
-// of going outside this limit.
-bool res15 = db.CMS().InitByProb("items_sold", 0.01, 0.005);
-Console.WriteLine(res15); // >>> true
-
-long[] res16 = db.CMS().IncrBy(
-    "items_sold",
-    new Tuple<RedisValue, long>[]{
-        new("bread", 300),
-        new("tea", 200),
-        new("coffee", 200),
-        new("beer", 100)
-    }
-);
-Console.WriteLine(string.Join(", ", res16));
-// >>> 300, 200, 200, 100
-
-long[] res17 = db.CMS().IncrBy(
-    "items_sold",
-    new Tuple<RedisValue, long>[]{
-        new("bread", 100),
-        new("coffee", 150),
-    }
-);
-Console.WriteLine(string.Join(", ", res17));
-// >>> 400, 350
-
-long[] res18 = db.CMS().Query(
-    "items_sold",
-    "bread", "tea", "coffee", "beer"
-);
-Console.WriteLine(string.Join(", ", res18));
-// >>> 400, 200, 350, 100
-```
+{{< clients-example home_prob_dts cms "C#" >}}
+{{< /clients-example >}}
 
 The advantage of using a CMS over keeping an exact count with a
 [sorted set]({{< relref "/develop/data-types/sorted-sets" >}})
@@ -298,53 +202,8 @@ shows how to merge two or more t-digest objects to query the combined
 data set. Note that you must use the `TDIGEST()` method to access the
 t-digest commands.
 
-<!--< clients-example home_prob_dts tdigest "C#" >}}
-< /clients-example >}}-->
-```cs
-bool res19 = db.TDIGEST().Create("male_heights");
-Console.WriteLine(res19); // >>> true
-
-bool res20 = db.TDIGEST().Add(
-    "male_heights",
-    175.5, 181, 160.8, 152, 177, 196, 164
-);
-Console.WriteLine(res20); // >>> true
-
-double res21 = db.TDIGEST().Min("male_heights");
-Console.WriteLine(res21); // >>> 152.0
-
-double res22 = db.TDIGEST().Max("male_heights");
-Console.WriteLine(res22); // >>> 196.0
-
-double[] res23 = db.TDIGEST().Quantile("male_heights", 0.75);
-Console.WriteLine(string.Join(", ", res23)); // >>> 181.0
-
-// Note that the CDF value for 181.0 is not exactly
-// 0.75. Both values are estimates.
-double[] res24 = db.TDIGEST().CDF("male_heights", 181.0);
-Console.WriteLine(string.Join(", ", res24)); // >>> 0.7857142857142857
-
-bool res25 = db.TDIGEST().Create("female_heights");
-Console.WriteLine(res25); // >>> true
-
-bool res26 = db.TDIGEST().Add(
-    "female_heights",
-    155.5, 161, 168.5, 170, 157.5, 163, 171
-);
-Console.WriteLine(res26); // >>> true
-
-double[] res27 = db.TDIGEST().Quantile("female_heights", 0.75);
-Console.WriteLine(string.Join(", ", res27)); // >>> 170.0
-
-// Specify 0 for `compression` and false for `override`.
-bool res28 = db.TDIGEST().Merge(
-    "all_heights", 0, false, "male_heights", "female_heights"
-);
-Console.WriteLine(res28); // >>> true
-
-double[] res29 = db.TDIGEST().Quantile("all_heights", 0.75);
-Console.WriteLine(string.Join(", ", res29)); // >>> 175.5
-```
+{{< clients-example home_prob_dts tdigest "C#" >}}
+{{< /clients-example >}}
 
 A t-digest object also supports several other related commands, such
 as querying by rank. See the
@@ -366,54 +225,5 @@ top *k* items and query whether or not a given item is in the
 list. Note that you must use the `TOPK()` method to access the
 Top-K commands.
 
-<!--< clients-example home_prob_dts topk "C#" >}}
-< /clients-example >}}-->
-```cs
-bool res30 = db.TOPK().Reserve("top_3_songs", 3, 7, 8, 0.9);
-Console.WriteLine(res30); // >>> true
-
-RedisResult[] res31 = db.TOPK().IncrBy(
-    "top_3_songs",
-    new Tuple<RedisValue, long>[] {
-        new("Starfish Trooper", 3000),
-        new("Only one more time", 1850),
-        new("Rock me, Handel", 1325),
-        new("How will anyone know?", 3890),
-        new("Average lover", 4098),
-        new("Road to everywhere", 770)
-    }
-);
-Console.WriteLine(
-    string.Join(
-        ", ",
-        string.Join(
-            ", ",
-            res31.Select(
-                r => $"{(r.IsNull ? "Null" : r)}"
-            )
-        )
-    )
-);
-// >>> Null, Null, Null, Rock me, Handel, Only one more time, Null
-
-RedisResult[] res32 = db.TOPK().List("top_3_songs");
-Console.WriteLine(
-    string.Join(
-        ", ",
-        string.Join(
-            ", ",
-            res32.Select(
-                r => $"{(r.IsNull ? "Null" : r)}"
-            )
-        )
-    )
-);
-// >>> Average lover, How will anyone know?, Starfish Trooper
-
-bool[] res33 = db.TOPK().Query(
-    "top_3_songs",
-    "Starfish Trooper", "Road to everywhere"
-);
-Console.WriteLine(string.Join(", ", res33));
-// >>> true, false
-```
+{{< clients-example home_prob_dts topk "C#" >}}
+{{< /clients-example >}}