lib/nlp: move vsm.nit to its own package

[nit.git] / lib / vsm / static / gitweb.css

diff --git a/lib/nlp/vsm.nit b/lib/nlp/vsm.nit
deleted file mode 100644 (file)
index 7fe0a84..0000000
--- a/lib/nlp/vsm.nit
+++ /dev/null
@@ -1,92 +0,0 @@
-# This file is part of NIT ( http://www.nitlanguage.org ).
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# NLPVector Space Model.
-#
-# The Vector Space Model (VSM) is used to compare natural language texts.
-# Texts are translated to multidimensionnal vectors then compared by cosine
-# similarity.
-module vsm
-
-import counter
-
-# A multi-dimensional vector.
-class NLPVector
-       super Counter[String]
-
-       # Cosine similarity of `self` and `other`.
-       #
-       # Gives the proximity in the range `[0.0 .. 1.0]` where 0.0 means that the
-       # two vectors are orthogonal and 1.0 means that they are identical.
-       #
-       # ~~~
-       # var v1 = new NLPVector
-       # v1["x"] = 1
-       # v1["y"] = 2
-       # v1["z"] = 3
-       #
-       # var v2 = new NLPVector
-       # v2["x"] = 1
-       # v2["y"] = 2
-       # v2["z"] = 3
-       #
-       # var v3 = new NLPVector
-       # v3["a"] = 1
-       # v3["b"] = 2
-       # v3["c"] = 3
-       #
-       # print v1.cosine_similarity(v2)
-       # #assert v1.cosine_similarity(v2) == 1.0
-       # print v1.cosine_similarity(v3)
-       # assert v1.cosine_similarity(v3) == 0.0
-       # ~~~
-       fun cosine_similarity(other: SELF): Float do
-               # Collect terms
-               var terms = new HashSet[String]
-               for k in self.keys do terms.add k
-               for k in other.keys do terms.add k
-
-               # Get dot product of two verctors
-               var dot = 0
-               for term in terms do
-                       dot += self.get_or_default(term, 0) * other.get_or_default(term, 0)
-               end
-
-               return dot.to_f / (self.norm * other.norm)
-       end
-
-       # The norm of the vector.
-       #
-       # `||x|| = (x1 ** 2 ... + xn ** 2).sqrt`
-       #
-       # ~~~
-       # var v = new NLPVector
-       # v["x"] = 1
-       # v["y"] = 1
-       # v["z"] = 1
-       # v["t"] = 1
-       # assert v.norm.is_approx(2.0, 0.001)
-       #
-       # v["x"] = 1
-       # v["y"] = 2
-       # v["z"] = 3
-       # v["t"] = 0
-       # assert v.norm.is_approx(3.742, 0.001)
-       # ~~~
-       fun norm: Float do
-               var sum = 0
-               for v in self.values do sum += v ** 2
-               return sum.to_f.sqrt
-       end
-end