From 34730a26e82ad1ce3df724ca180b49e1542dace1 Mon Sep 17 00:00:00 2001 From: sshivam95 Date: Wed, 20 Mar 2024 19:23:45 +0000 Subject: [PATCH] deploy: 8da02d1831d04bf6f2d528eca7b0a86945f45c84 --- _modules/dicee/models/quaternion.html | 6 ++---- _sources/autoapi/dicee/index.rst.txt | 3 +-- _sources/autoapi/dicee/models/index.rst.txt | 6 ++---- .../dicee/models/quaternion/index.rst.txt | 6 ++---- autoapi/dicee/index.html | 3 +-- autoapi/dicee/models/index.html | 6 ++---- autoapi/dicee/models/quaternion/index.html | 6 ++---- diceembeddings.pdf | Bin 1037823 -> 1037826 bytes 8 files changed, 12 insertions(+), 24 deletions(-) diff --git a/_modules/dicee/models/quaternion.html b/_modules/dicee/models/quaternion.html index 45983a11..ddc1cbec 100644 --- a/_modules/dicee/models/quaternion.html +++ b/_modules/dicee/models/quaternion.html @@ -115,8 +115,7 @@

Source code for dicee.models.quaternion

 
     Notes
     -----
-    The function assumes that the input quaternions have unit norm. 
-    It first normalizes the second quaternion to eliminate the scaling effect, and then performs the Hamilton product of the two quaternions.
+    The function assumes that the input quaternions have unit norm. It first normalizes the second quaternion to eliminate the scaling effect, and then performs the Hamilton product of the two quaternions.
     """
     (
         a_h,
@@ -471,8 +470,7 @@ 
Source code for dicee.models.quaternion
         Computes scores for a batch of triples against a sampled subset of entities in a K-vs-Sample setting.
 
         Given a batch of head entities and relations (h,r), this method computes the scores for all possible triples
-        formed with these head entities and relations against a subset of entities, i.e., 
-        [score(h,r,x)|x \in Entities] => [0.0,0.1,...,0.8], shape=> (1, |Entities|). TODO: Add mathematical format for sphinx.
+        formed with these head entities and relations against a subset of entities, i.e., [score(h,r,x)|x \in Entities] => [0.0,0.1,...,0.8], shape=> (1, |Entities|). TODO: Add mathematical format for sphinx.
         The subset of entities is specified by the `target_entity_idx`, which is an integer index representing a specific entity.
         Given a batch of head entities and relations => shape (size of batch,| Entities|).
 
diff --git a/_sources/autoapi/dicee/index.rst.txt b/_sources/autoapi/dicee/index.rst.txt
index 820b52b1..b6a26ec1 100644
--- a/_sources/autoapi/dicee/index.rst.txt
+++ b/_sources/autoapi/dicee/index.rst.txt
@@ -2358,8 +2358,7 @@ Attributes
       Computes scores for a batch of triples against a sampled subset of entities in a K-vs-Sample setting.
 
       Given a batch of head entities and relations (h,r), this method computes the scores for all possible triples
-      formed with these head entities and relations against a subset of entities, i.e.,
-      [score(h,r,x)|x \in Entities] => [0.0,0.1,...,0.8], shape=> (1, |Entities|). TODO: Add mathematical format for sphinx.
+      formed with these head entities and relations against a subset of entities, i.e., [score(h,r,x)|x \in Entities] => [0.0,0.1,...,0.8], shape=> (1, |Entities|). TODO: Add mathematical format for sphinx.
       The subset of entities is specified by the `target_entity_idx`, which is an integer index representing a specific entity.
       Given a batch of head entities and relations => shape (size of batch,| Entities|).
 
diff --git a/_sources/autoapi/dicee/models/index.rst.txt b/_sources/autoapi/dicee/models/index.rst.txt
index 3846f629..d9c855e8 100644
--- a/_sources/autoapi/dicee/models/index.rst.txt
+++ b/_sources/autoapi/dicee/models/index.rst.txt
@@ -1818,8 +1818,7 @@ Functions
 
    .. rubric:: Notes
 
-   The function assumes that the input quaternions have unit norm.
-   It first normalizes the second quaternion to eliminate the scaling effect, and then performs the Hamilton product of the two quaternions.
+   The function assumes that the input quaternions have unit norm. It first normalizes the second quaternion to eliminate the scaling effect, and then performs the Hamilton product of the two quaternions.
 
 
 .. py:class:: QMult(args: dict)
@@ -1996,8 +1995,7 @@ Functions
       Computes scores for a batch of triples against a sampled subset of entities in a K-vs-Sample setting.
 
       Given a batch of head entities and relations (h,r), this method computes the scores for all possible triples
-      formed with these head entities and relations against a subset of entities, i.e.,
-      [score(h,r,x)|x \in Entities] => [0.0,0.1,...,0.8], shape=> (1, |Entities|). TODO: Add mathematical format for sphinx.
+      formed with these head entities and relations against a subset of entities, i.e., [score(h,r,x)|x \in Entities] => [0.0,0.1,...,0.8], shape=> (1, |Entities|). TODO: Add mathematical format for sphinx.
       The subset of entities is specified by the `target_entity_idx`, which is an integer index representing a specific entity.
       Given a batch of head entities and relations => shape (size of batch,| Entities|).
 
diff --git a/_sources/autoapi/dicee/models/quaternion/index.rst.txt b/_sources/autoapi/dicee/models/quaternion/index.rst.txt
index c29f3b08..7b67977f 100644
--- a/_sources/autoapi/dicee/models/quaternion/index.rst.txt
+++ b/_sources/autoapi/dicee/models/quaternion/index.rst.txt
@@ -41,8 +41,7 @@ Functions
 
    .. rubric:: Notes
 
-   The function assumes that the input quaternions have unit norm.
-   It first normalizes the second quaternion to eliminate the scaling effect, and then performs the Hamilton product of the two quaternions.
+   The function assumes that the input quaternions have unit norm. It first normalizes the second quaternion to eliminate the scaling effect, and then performs the Hamilton product of the two quaternions.
 
 
 .. py:class:: QMult(args: dict)
@@ -219,8 +218,7 @@ Functions
       Computes scores for a batch of triples against a sampled subset of entities in a K-vs-Sample setting.
 
       Given a batch of head entities and relations (h,r), this method computes the scores for all possible triples
-      formed with these head entities and relations against a subset of entities, i.e.,
-      [score(h,r,x)|x \in Entities] => [0.0,0.1,...,0.8], shape=> (1, |Entities|). TODO: Add mathematical format for sphinx.
+      formed with these head entities and relations against a subset of entities, i.e., [score(h,r,x)|x \in Entities] => [0.0,0.1,...,0.8], shape=> (1, |Entities|). TODO: Add mathematical format for sphinx.
       The subset of entities is specified by the `target_entity_idx`, which is an integer index representing a specific entity.
       Given a batch of head entities and relations => shape (size of batch,| Entities|).
 
diff --git a/autoapi/dicee/index.html b/autoapi/dicee/index.html
index 1b6959ae..ba5b624c 100644
--- a/autoapi/dicee/index.html
+++ b/autoapi/dicee/index.html
@@ -3793,8 +3793,7 @@ 
Parameterforward_k_vs_sample(x: torch.FloatTensor, target_entity_idx: int) → torch.FloatTensor
 
Computes scores for a batch of triples against a sampled subset of entities in a K-vs-Sample setting.

 
Given a batch of head entities and relations (h,r), this method computes the scores for all possible triples
-formed with these head entities and relations against a subset of entities, i.e.,
-[score(h,r,x)|x in Entities] => [0.0,0.1,…,0.8], shape=> (1, |Entities|). TODO: Add mathematical format for sphinx.
+formed with these head entities and relations against a subset of entities, i.e., [score(h,r,x)|x in Entities] => [0.0,0.1,…,0.8], shape=> (1, |Entities|). TODO: Add mathematical format for sphinx.
 The subset of entities is specified by the target_entity_idx, which is an integer index representing a specific entity.
 Given a batch of head entities and relations => shape (size of batch,| Entities|).

 

diff --git a/autoapi/dicee/models/index.html b/autoapi/dicee/models/index.html
index 4e85af6c..dd37ef33 100644
--- a/autoapi/dicee/models/index.html
+++ b/autoapi/dicee/models/index.html
@@ -2536,8 +2536,7 @@ 
FunctionsNotes

-
The function assumes that the input quaternions have unit norm.
-It first normalizes the second quaternion to eliminate the scaling effect, and then performs the Hamilton product of the two quaternions.

+
The function assumes that the input quaternions have unit norm. It first normalizes the second quaternion to eliminate the scaling effect, and then performs the Hamilton product of the two quaternions.

 

 
 

@@ -2743,8 +2742,7 @@ 
Functionsforward_k_vs_sample(x: torch.FloatTensor, target_entity_idx: int) → torch.FloatTensor[source]
 
Computes scores for a batch of triples against a sampled subset of entities in a K-vs-Sample setting.

 
Given a batch of head entities and relations (h,r), this method computes the scores for all possible triples
-formed with these head entities and relations against a subset of entities, i.e.,
-[score(h,r,x)|x in Entities] => [0.0,0.1,…,0.8], shape=> (1, |Entities|). TODO: Add mathematical format for sphinx.
+formed with these head entities and relations against a subset of entities, i.e., [score(h,r,x)|x in Entities] => [0.0,0.1,…,0.8], shape=> (1, |Entities|). TODO: Add mathematical format for sphinx.
 The subset of entities is specified by the target_entity_idx, which is an integer index representing a specific entity.
 Given a batch of head entities and relations => shape (size of batch,| Entities|).

 

diff --git a/autoapi/dicee/models/quaternion/index.html b/autoapi/dicee/models/quaternion/index.html
index 10848d63..6f9dd6e3 100644
--- a/autoapi/dicee/models/quaternion/index.html
+++ b/autoapi/dicee/models/quaternion/index.html
@@ -169,8 +169,7 @@ 
FunctionsNotes

-
The function assumes that the input quaternions have unit norm.
-It first normalizes the second quaternion to eliminate the scaling effect, and then performs the Hamilton product of the two quaternions.

+
The function assumes that the input quaternions have unit norm. It first normalizes the second quaternion to eliminate the scaling effect, and then performs the Hamilton product of the two quaternions.

 

 
 

@@ -376,8 +375,7 @@ 
Functionsforward_k_vs_sample(x: torch.FloatTensor, target_entity_idx: int) → torch.FloatTensor[source]
 
Computes scores for a batch of triples against a sampled subset of entities in a K-vs-Sample setting.

 
Given a batch of head entities and relations (h,r), this method computes the scores for all possible triples
-formed with these head entities and relations against a subset of entities, i.e.,
-[score(h,r,x)|x in Entities] => [0.0,0.1,…,0.8], shape=> (1, |Entities|). TODO: Add mathematical format for sphinx.
+formed with these head entities and relations against a subset of entities, i.e., [score(h,r,x)|x in Entities] => [0.0,0.1,…,0.8], shape=> (1, |Entities|). TODO: Add mathematical format for sphinx.
 The subset of entities is specified by the target_entity_idx, which is an integer index representing a specific entity.
 Given a batch of head entities and relations => shape (size of batch,| Entities|).

 

diff --git a/diceembeddings.pdf b/diceembeddings.pdf
index beb673e8d6503a91742a58895ef6de20de818aea..e506ea1a71623f4c5d3e621ce95758c5be0b9101 100644
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