Add the missing images to the similarity algorithm docs (#714)

* add cosine images

* add Jaccard image

* replace euclidean with wiki version

Author: davidoliverSP2

doc/asciidoc/similarity-cosine.adoc

@@ -65,7 +65,9 @@ include::scripts/similarity-cosine.cypher[tag=function]
 These two lists of numbers have a Cosine similarity of 0.863.
 We can see how this result is derived by breaking down the formula:
 
-// Turn this into an image
+image::cosine-similarity2.png[role="middle"]
+
+// This is the raw information for this image:
 // [subs = none]
 // \( similarity(A, B) = \frac{3 \cdot 10 + 8 \cdot 8 + 7 \cdot 6 + 5 \cdot 6 + 2 \cdot 4 + 9 \cdot 5}{\sqrt{3^2 + 8^2 + 7^2 + 5^2 + 2^2 + 9^2} \times \sqrt{10^2 + 8^2 + 6^2 + 6^2 + 4^2 + 5^2}} = \frac{219}{15.2315 \times 16.6433} = 0.8639 \)
 

doc/asciidoc/similarity-euclidean.adoc

@@ -18,7 +18,9 @@ Euclidean distance measures the straight line distance between two points in n-d
 
 Euclidean distance is computed using the following formula:
 
-// // Turn this into an image
+image::euclidean.png[role="middle"]
+
+// This is the raw information for this image:
 // [subs = none]
 // \( similarity(p_1, p_2) = \sqrt{\sum_{i~\in~\textrm{item}} (s_{p_1} - s_{p_2})^2} \)
 

doc/asciidoc/similarity-jaccard.adoc

@@ -19,10 +19,13 @@ It is defined as the size of the intersection divided by the size of the union o
 
 Jaccard similarity is computed using the following formula:
 
-```
-J(A,B) = ∣A ∩ B∣ / ∣(A ∪ B)∣
-       = ∣A ∩ B∣ / ∣A∣ + ∣B∣ - ∣A ∩ B|
-```
+image::jaccard.png[role="middle"]
+
+// This is the raw information for this image:
+// ```
+// J(A,B) = ∣A ∩ B∣ / ∣(A ∪ B)∣
+//        = ∣A ∩ B∣ / ∣A∣ + ∣B∣ - ∣A ∩ B|
+// ```
 
 The library contains both procedures and functions to calculate similarity between sets of data.
 The function is best used when calculating the similarity between small numbers of sets.